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WHAT'S NEW

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05/27/21

Now available in PowerStats and TrendStats: the 2019 National Household Education Survey (NHES) for Early Childhood Program Participation (ECPP) and Parent and Family Involvement in Education (PFI). Learn more about ECPP and PFI.

05/06/21

Now available in PowerStats and QuickStats: the Private School Universe Survey (PSS) for 2015-16. Learn more about PSS.

02/19/21

Tables for Baccalaureate and Beyond (B&B:08/18): First Look at the 2018 Employment and Educational Experiences of 2007–08 College Graduates are now available in the DataLab Tables Library.

01/26/21

Now available in PowerStats: the Adult Education Survey of the 2005 National Household Education Surveys Program (AE-NHES:2005) Learn more about the Early Childhood Longitudinal Study.

01/26/21

Now available in PowerStats: the Early Childhood Longitudinal Study: Birth Cohort. Learn more about the Early Childhood Longitudinal Study.

01/15/21

Tables for A 2017 Follow-up: A Look at 2011–12 First-time Postsecondary Students Six Years Later are now available in the DataLab Tables Library.

01/15/21

Tables for A 2017 Follow-up: Six-Year Persistence and Attainment at First Institution for 2011–12 First-time Postsecondary Students are now available in the DataLab Tables Library.

01/15/21

Tables for A 2017 Follow-up: Six-Year Persistence and Attainment at Any Institution for 2011–12 First-time Postsecondary Students are now available in the DataLab Tables Library.

01/14/21

Now available in PowerStats and QuickStats: the Private School Universe Survey (PSS) for 2013-14. Learn more about PSS.

12/03/20

Newly released data are now available in PowerStats and QuickStats. The 2011-12 Beginning Postsecondary Students Longitudinal Study (BPS:12/17) now includes data from the Postsecondary Education Transcript Studies (PETS). Learn more about the 2011-12 Beginning Postsecondary Students Longitudinal Study (BPS:12/17).

10/23/20

Now available in PowerStats: the 1988 National Study of Postsecondary Faculty (NSOPF:88). Learn more about the National Study of Postsecondary Faculty (NSOPF).

08/26/20

Now available in PowerStats: the 1999 National Study of Postsecondary Faculty (NSOPF:99). Learn more about the National Study of Postsecondary Faculty (NSOPF).

08/26/20

Now available in PowerStats: the 1993 National Study of Postsecondary Faculty (NSOPF:93). Learn more about the National Study of Postsecondary Faculty (NSOPF).

08/11/20

Tables for One Year After a Bachelor’s Degree: A Profile of 2015-16 Graduates are now available in the DataLab Tables Library.

08/11/20

Tables for First-Time Subbaccalaureate Students: An Overview of Their Institutions, Programs, Completion, and Labor Market Outcomes After 3 Years are now available in the DataLab Tables Library.

08/04/20

Now available in PowerStats: the 1992-93 National Postsecondary Student Aid Study (NPSAS:93). Learn more about the Postsecondary Student Aid Study (NPSAS).

07/30/20

Now available in PowerStats: the 1986-87 National Postsecondary Student Aid Study (NPSAS:87). Learn more about the Postsecondary Student Aid Study (NPSAS).

07/29/20

Now available in PowerStats: the 1989-90 National Postsecondary Student Aid Study (NPSAS:90). Learn more about the Postsecondary Student Aid Study (NPSAS).

05/21/20

Tables for What Is the Price of College? Total, Net, and Out-of-Pocket Prices in 2015-16 are now available in the DataLab Tables Library.

02/05/20

Tables for High School Longitudinal Study of 2009 (HSLS:09): A First Look at the Postsecondary Transcripts and Student Financial Aid Records of Fall 2009 Ninth-Graders are now available in the DataLab Tables Library.

02/04/20

Now available in PowerStats and QuickStats: the 1999-2000 School Survey on Crime and Safety (SSOCS). Learn more about SSOCS.

01/23/20

Download the Creating a TrendStats Chart (PDF, 3.04 MB) tutorial to learn more about creating a custom chart in TrendStats.

12/09/19

Now available in PowerStats and QuickStats: 2016/2017 Baccalaureate and Beyond (B&B:16/17). Learn more about Baccalaureate and Beyond.

11/21/19

Now available in PowerStats and QuickStats: the 2003-04 School Survey on Crime and Safety (SSOCS). Learn more about SSOCS.

11/21/19

Tables for Trends in Graduate Student Financing: Selected Years, 2003–04 to 2015–16 are now available in the DataLab Tables Library.

11/21/19

Tables for Trends in Undergraduate Nonfederal Grant and Scholarship Aid by Demographic and Enrollment Characteristics: Selected Years, 2003–04 to 2015–16 are now available in the DataLab Tables Library.

11/21/19

Tables for Trends in Pell Grant Receipt and the Characteristics of Pell Grant Recipients: Selected Years, 2003–04 to 2015–16 are now available in the DataLab Tables Library.

11/21/19

Tables for Trends in Ratio of Pell Grant to Total Price of Attendance and Federal Loan Receipt are now available in the DataLab Tables Library.

11/07/19

Newly released data are now available in PowerStats and QuickStats. The 2011-12 Beginning Postsecondary Students Longitudinal Study (BPS:12/17) now includes data from the second follow-up in 2017. Learn more about the 2011-12 Beginning Postsecondary Students Longitudinal Study (BPS:12/17).

10/31/19

Tables for 	 Baccalaureate and Beyond (B&B:16/17): A First Look at the Employment and Educational Experiences of College Graduates, 1 Year Later are now available in the DataLab Tables Library.

10/25/19

Tables for Profile of Very Low- and Low-Income Undergraduates in 2015–16 are now available in the DataLab Tables Library.

10/25/19

Tables for Considerations for Using the School Courses for the Exchange of Data (SCED) Classification System in High School Transcript Studies: Applications for Converting Course Codes from the Classification of Secondary School Courses (CSSC) are now available in the DataLab Tables Library.

08/23/19

Tables for Changes in Undergraduate Program Completers’ Borrowing Rates and Loan Amounts by Age: 1995–96 Through 2015–16  are now available in the DataLab Tables Library.

08/14/19

Tables for Advanced Placement, International Baccalaureate, and Dual-Enrollment Courses: Availability, Participation, and Related Outcomes for 2009 Ninth-Graders: 2013 are now available in the DataLab Tables Library.

06/26/19

TrendStas Charts
Visit TrendStats to create custom bar charts, stacked bar charts, pie charts, and line graphs from your TrendStats analysis.

06/25/19

Tables for Going Back to College: Undergraduates Who Already Held a Postsecondary Credential are now available in the DataLab Tables Library.

6/20/19

Now available in PowerStats and QuickStats: the High School and Beyond Longitudinal Study and the National Education Longitudinal Study of 1988. Learn more about HS&B and NELS.

06/18/19

Tables for Student Financing of Undergraduate Education in 2015–16: Students’ Net Price, Expected Family Contribution, and Financial Need are now available in the DataLab Tables Library

06/07/19

Tables for Student Financing of Undergraduate Education in 2015–16: Income, Tuition, and Total Price are now available in the DataLab Tables Library

05/28/19

Tables for Student Financing of Undergraduate Education in 2015–16: Financial Aid by Type and Source are now available in the DataLab Tables Library.

05/28/19

Tables for Profile and Financial Aid Estimates of Graduate Students: 2015–16 are now available in the DataLab Tables Library.

05/28/19

Tables for Profile of Undergraduate Students: Attendance, Distance and Remedial Education, Degree Program and Field of Study, Demographics, Financial Aid, Financial Literacy, Employment, and Military Status: 2015–16 are now available in the DataLab Tables Library.

03/26/19

Tables for Persistence, Retention, and Attainment of 2011–12 First-Time Beginning Postsecondary Students as of Spring 2017 are now available in the DataLab Tables Library.

12/31/18

Tables for 	Military Service and Educational Attainment of High School Sophomores After 9/11: Experiences of 2002 High School Sophomores as of 2012 are now available in the DataLab Tables Library.

12/6/18

New variables released in Baccalaureate and Beyond: 2008/2012 PowerStats. See a list of variables or learn more.

      
B&B 2012 Students  
          

New variables now available in B&B 2012 PowerStats. The following variables have been added:

         

              
Variable Name  Variable Label              
B2DISTINSTE  Distance between primary job in 2012 and bachelor's degree institution 
B2DISTINSTR  Distance between residence in 2012 and bachelor's degree institution 
B2SMSTE  Primary job in 2012 is in same state as bachelor's degree institution state             
B2SMSTER  Primary job and residence in 2012 are in same state as bachelor's degree institution state              
B2SMSTR  Residence in 2012 is in same state as bachelor's degree institution state             
B2STCDE  State of primary job: 2012            
B2STCDR  State of residence: 2012

Variable Name	Variable Label
B2DISTINSTE	Distance between primary job in 2012 and bachelor's degree institution
B2DISTINSTR	Distance between residence in 2012 and bachelor's degree institution
B2SMSTE	Primary job in 2012 is in same state as bachelor's degree institution state
B2SMSTER	Primary job and residence in 2012 are in same state as bachelor's degree institution state
B2SMSTR	Residence in 2012 is in same state as bachelor's degree institution state
B2STCDE	State of primary job: 2012
B2STCDR	State of residence: 2012

12/4/18

Now available in PowerStats, QuickStats, and TrendStats: the National Household Education Survey (NHES) for 2012 and 2016 Parent and Family Involvement in Education (PFI). Learn more about PFI.

10/31/18

Tables for  What High Schoolers and Their Parents Know About Public 4-Year Tuition and Fees in Their State are now available in the DataLab Tables Library.

10/31/18

New IPEDS Tables are now available in the DataLab Tables Library.

10/30/18

Now available in PowerStats and QuickStats: the National Household Education Survey (NHES) for 2016 Adult Training and Education (ATES). Learn more about ATES.

9/24/18

Now available in PowerStats, QuickStats, and TrendStats: the National Household Education Survey (NHES) for 2012 and 2016 Early Childhood Program Participation (ECPP). Learn more about ECPP.

10/19/18

QuickStats Charts
Visit QuickStats to view the latest enhancements to charting options, including new chart types, color switching, and more.

9/24/18

Introducing the DataLab Tables Library
The College & Career Tables Library is now the DataLab Tables Library. Visit the DataLab Tables Library.

9/6/18

Tables for Trends in Free Application for Federal Student Aid (FAFSA) Submissions are now available in the DataLab Tables Library.

8/28/18

Tables for Four Years Later: 2007–08 College Graduates' Employment, Debt, and Enrollment in 2012 (2018435) are now available in the DataLab Tables Library.

8/24/18

Tables for Working Before, During, and After Beginning at a Public 2-Year Institution: Labor Market Experiences of Community College Students (2018428) are now available in the DataLab Tables Library.

8/17/18

Tables for First-Generation Students: College Access, Persistence, and Postbachelor’s Outcomes (2018401) are now available in the DataLab Tables Library.

7/9/18

Now available in PowerStats and QuickStats: the 2015-16 School Survey on Crime and Safety (SSOCS). Learn more about SSOCS.

6/21/18

Now available in PowerStats and QuickStats: the High School Longitudinal Study of 2009 (HSLS:09) Second Follow-Up. Learn more about HSLS.

6/21/18

Tables for High School Longitudinal Study of 2009 (HSLS:09) Second Follow Up: A First Look at Fall 2009 Ninth–Graders in 2016 (2018139) are now available in the DataLab Tables Library.

6/20/18

Now available in PowerStats and QuickStats: the 2015-16  National Teacher and Principal Survey (NTPS). Learn more about NTPS.

5/15/18

Now available in PowerStats and QuickStats: the 2015-16 National Postsecondary Student Aid Study, Undergraduate Students (NPSAS:2016 UG). Learn more about NPSAS.

5/15/18

Now available in PowerStats and QuickStats: the 2015-16 National Postsecondary Student Aid Study, Graduate Students (NPSAS:2016 GR). Learn more about NPSAS.

5/15/18

New variables released in NPSAS 1995-96, 1999-2000, 2003-04, 2007-08, and 2011-12 Undergraduate PowerStats. See a list of variables or learn more.


    NPSAS Undergraduate Students

    
    
        New variables now available in NPSAS Undergraduates PowerStats. The following variables have been added:


        
            
                Variable Name
                Variable Label
            
            INGRTAMT2 Institutional grants total (updated) 
            INSTAMT2 Institutional aid total (updated) 
            INSTNEED2 Institutional need-based grants (updated)
            STATNEED2 State need-based grants (updated) 
            STGTAMT2 State grants total (updated)
            STATEAMT2 State aid total (updated)

Variable Name	Variable Label
INGRTAMT2	Institutional grants total (updated)
INSTAMT2	Institutional aid total (updated)
INSTNEED2	Institutional need-based grants (updated)
STATNEED2	State need-based grants (updated)
STGTAMT2	State grants total (updated)
STATEAMT2	State aid total (updated)

5/15/18

Tables for National Postsecondary Student Aid Study (NPSAS:16): Student Financial Aid Estimates for 2015–16 First Look (2018466) are now available in the DataLab Tables Library.

3/14/18

Tables for First-Generation Students: College Access, Persistence, and Postbachelor's Outcomes (2018421) are now available in the DataLab Tables Library.

3/13/18

Now available in PowerStats and QuickStats: the 1999-00, 2003-04, and 2007-08 Schools and Staffing District Survey (SASS). Learn more about SASS.

3/13/18

Now available in PowerStats and QuickStats: the 1999-00, 2003-04, and 2007-08 Schools and Staffing Library Media Center Survey (SASS). Learn more about SASS.

1/29/18

Tables for Characteristics and Outcomes of Undergraduates with Disabilities (2018432) are now available in the DataLab Tables Library.

1/29/18

Tables for Science, Technology, Engineering, and Mathematics (STEM) Majors: Where Are They 4 Years After Receiving a Bachelor's Degree? (2018423) are now available in the DataLab Tables Library.

12/20/17

Now available in PowerStats and QuickStats: the 1999-00, 2003-04, and 2007-08 Schools and Staffing School Survey (SASS). Analyze these datasets by public, private, or combined public and private schools. Learn more about SASS.

12/13/17

Tables for Beginning College Students Who Change Their Majors Within 3 Years of Enrollment (2018434) are now available in the DataLab Tables Library.

10/5/17

2015 Federal Student Aid Supplements added to BPS:1996/2001 and BPS:2004/2009 PowerStats. Additional variables related to borrowing, repayment, and student characteristics are now available.

10/5/17

Tables for Repayment of Student Loans as of 2015 Among 1995-96 and 2003-04 First-Time Beginning Students (2018410) are now available in the DataLab Tables Library.

7/13/17

Now available in PowerStats and QuickStats: the 1999-00, 2003-04, and 2007-08 Schools and Staffing Principal Survey (SASS). Analyze these datasets by public, private, or combined public and private school principals. Learn more about SASS.

7/12/17

Now available in PowerStats and QuickStats: the 1999-00, 2003-04, and 2007-08 Schools and Staffing Teacher Survey (SASS). Analyze these datasets by public, private, or combined public and private school teachers. Learn more about SASS.

7/6/17

Tables for Four Years After a Bachelor’s Degree: Employment, Enrollment, and Debt Among College Graduates (2017438) are now available in the DataLab Tables Library.

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7/6/17

Tables for The Debt Burden of Bachelor’s Degree Recipients (2017436) are now available in the DataLab Tables Library.

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7/6/17

Tables for A Profile of the Enrollment Patterns and Demographic Characteristics of Undergraduates at For-Profit Institutions (2017416) are now available in the DataLab Tables Library.

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4/24/17

Interested in trends related to crime and safety in US public schools? Identify trends over time in school crime with the newest addition to TrendStats, the School Survey on Crime and Safety.

4/10/17

Now available in PowerStats and QuickStats: the 2011-12 Private School Universe Survey (PSS). Learn more about PSS.

2/28/17

IPEDS Fall 2013 State/Compendium Tables are now available in the DataLab Tables Library.

2/28/17

IPEDS Spring 2013 State/Compendium Tables are now available in the DataLab Tables Library.

2/2/17

Tables for New American Undergraduates Enrollment Trends and Age at Arrival of Immigrant and Second-Generation Students (NCES 2017414) are now available in the DataLab Tables Library.

2/2/17

Tables for Use of Private Loans by Postsecondary Students: Selected Years 2003-04 Through 2011-12 (2017420) tables are now available in the DataLab Tables Library.

2/2/17

Tables for Employment and Enrollment Status of Baccalaureate Degree Recipients 1 Year After Graduation: 1994, 2001, and 2009 (2017407) are now available in the DataLab Tables Library.

10/7/16

Tables for First-time Postsecondary Students in 2011-12: Three-year Withdrawal, Stopout, and Transfer Rates (2016139) are now available in the DataLab Tables Library.

10/7/16

Tables for First-time Postsecondary Students in 2011-12: Three-year Persistence and Attainment at Any Institution (2016138) are now available in the DataLab Tables Library.

10/7/16

Tables for First-time Postsecondary Students in 2011-12: Three-year Retention and Attainment at First Institution (2016137) are now available in the DataLab Tables Library.

10/7/16

Tables for First-time Postsecondary Students in 2011-12: A Profile (2016136)  are now available in the DataLab Tables Library.

10/3/16

Tables for Reaching the Limit: Undergraduates Who Borrow the Maximum Amount in Federal Direct Loans: 2011-12  (2016408) are now available in the DataLab Tables Library.

9/22/16

Tables for Bachelor's degree recipients 1 year after graduation: employment and enrollment in 1994, 2001, and 2009 (2016435) are now available in the DataLab Tables Library.

9/22/16

Tables for Changes in Pell Grant Participation and Median Income of Recipients (2016407) are now available in the DataLab Tables Library.

8/11/16

Tables for A Profile of Military Undergraduates: 2011-12 (2016415) are now available in the DataLab Tables Library.

8/3/16

Tables for Undergraduates Who Do Not Apply for Financial Aid (2016406) are now available in the DataLab Tables Library.

8/3/16

Additional 2011-12 National Postsecondary Student Aid Study (NPSAS:12) variables released. See list.


     New Variables in NPSAS:2011-12 PowerStats

    
    
        The following NPSAS Undergraduate variables are now available in PowerStats.


						
						  
						Name    Label
							AIDAPP2 Applied for any aid (including nonfederal only)
							AIDCST3 Ratio of aid (excluding private loans and Direct PLUS loans to parents) to student budget

Name		Label
AIDAPP2		Applied for any aid (including nonfederal only)
AIDCST3		Ratio of aid (excluding private loans and Direct PLUS loans to parents) to student budget

6/9/16

New variables added to High School Longitudinal Study of 2009 PowerStats. See list.


     New Variables added to High School Longitudinal Study of 2009 PowerStats

    
    
        The following HSLS:09 variables are now available in PowerStats.


						
						  
						Name    Label
							P1DISAB P1 Doctor/school has told parent 9th grader has a disability
							P1EAREYE P1 D03D Doctor/school has told parent 9th grader has hearing/vision problem
							P1JOINT P1 D03E Doctor/school has told parent 9th grader has bone/joint/muscle problem
							S3CLGSECTOR U13 Postsecondary institutional sector

Name		Label
P1DISAB		P1 Doctor/school has told parent 9th grader has a disability
P1EAREYE		P1 D03D Doctor/school has told parent 9th grader has hearing/vision problem
P1JOINT		P1 D03E Doctor/school has told parent 9th grader has bone/joint/muscle problem
S3CLGSECTOR		U13 Postsecondary institutional sector

5/26/16

Newly released data are now available in PowerStats and QuickStats. The 2011-12 Beginning Postsecondary Students Longitudinal Study, First Follow-up (BPS:12/14) now includes over 250 new variables focusing on students’ borrowing while enrolled and their labor market outcomes 3 years after beginning postsecondary education. Learn more about the 2011-12 Beginning Postsecondary Students Longitudinal Study, First Follow-up (BPS:12/14).

5/23/16

Additional 2008-12 Baccalaureate and Beyond (B&B:08/12) variables released. See list.


    New Variables in 2008-12 Baccalaureate and Beyond (B&B:08/12) PowerStats

    
    
        The following B&B:08/12 variables are now available in PowerStats.


        
						  
						Name    Label
							B2CMRJST Primary job: Employed in primary job in 2012
							B2EMPTYP Primary job: Employer type, 2012
							BA_ENR Months between bachelor's degree award date and first post-bachelor's enrollment
							BA_JOB1 Months between bachelor's degree award date and first job
							JOB1GT3 Held first job longer than 3 months

Name		Label
B2CMRJST		Primary job: Employed in primary job in 2012
B2EMPTYP		Primary job: Employer type, 2012
BA_ENR		Months between bachelor's degree award date and first post-bachelor's enrollment
BA_JOB1		Months between bachelor's degree award date and first job
JOB1GT3		Held first job longer than 3 months

5/11/16

Tables from K-12 Teaching Experience Among 2007-08 College Graduates: 2012 (2016641) are now available in the DataLab Tables Library.

5/5/16

Now available in PowerStats: the School Survey on Crime and Safety 2007-08 (SSOCS:2008) and School Survey on Crime and Safety 2005-06 (SSOCS:2006).

4/13/16

Now available in PowerStats and QuickStats: High School Longitudinal Study of 2009 (HSLS:2009).

3/16/16

Additional NPSAS Undergraduate variables added to TrendStats. See list.

NPSAS Undergraduate Variables Added to TrendStats

The following NPSAS Undergraduate variables are now available in TrendStats.

Name Description
AIDCST3 Total aid received excluding private loans and Direct PLUS loans to parents as a percentage of the total student budget.
AIDSNEED The amount of financial aid exceeding federal need.
AIDSRC Aid package by source of aid received.
ATTEND2 Student's attendance status during the fall term.
ATTENDMR The student's main reason for enrolling at NPSAS.
ATTNPT Student's attendance intensity at all institutions in the given survey year.
CALSYS The NPSAS institution's academic calendar system.
CLOCK Denotes if the NPSAS institution is on a clock or credit hour system.
CNTLAFFI The NPSAS institution's control or affiliation.
CRNUMCRD The number of credit cards a student has in his or her own name.
CUMLNTP1 Indicates the source of the student's loans during undergraduate education.
DEGPR Indicates whether the student has earned degrees or certificates since high school.
DEGPRAA Indicates whether the student has already earned an associate's degree since high school.
DEGPRCRT Indicates whether the student has already earned an undergraduate certificate/diploma since high school.
DEGPRMS Indicates whether the student has already earned a master's degree since high school.
DEGPRPTB Indicates whether the student has already earned a post-baccalaureate certificate since high school.
DEGPRPTM Indicates whether the student has already earned a post-master's certificate since high school.
DEPEND4 Student's dependency status including dependents and marital status.
DEPNUM Number of student's dependents (children and others).
DEPOTHER Indicates whether the student had dependents other than children.
DEPTYPE Type of student's dependents.
DEPYNG The age of the student's youngest child.
DSTUINC Dependent student's own income in the year two years prior to the survey year, excluding the income of the parents.
EFCAID The total amount of financial aid that is subject to federal EFC limitations if the student received need-based federal aid.
EFFORT18 Net price after all aid except work-study as a percentage of total income.
EFFORT20 Net price after grants and loans as a percentage of total income.
EMPLWAIV Tuition waivers for staff and families of staff at the institution.
EMPLYAM3 Indicates the amount of tuition aid received from the student's or the parents' employers.
EMPLYAMT Total amount of aid received from employers.
ENLEN Number of months enrolled.
ENR01 Monthly enrollment status for July of the academic year.
ENR02 Monthly enrollment status for August of the academic year.
ENR03 Monthly enrollment status for September of the academic year.
ENR04 Monthly enrollment status for October of the academic year.
ENR05 Monthly enrollment status for November of the academic year.
ENR06 Monthly enrollment status for December of the academic year.
ENR07 Monthly enrollment status for January of the academic year.
ENR08 Monthly enrollment status for February of the academic year.
ENR09 Monthly enrollment status for March of the academic year.
ENR10 Monthly enrollment status for April of the academic year.
ENR11 Monthly enrollment status for May of the academic year.
ENR12 Monthly enrollment status for June of the academic year.
ENRFALL Student was enrolled during the July through December term.
ENRSPR Student was enrolled during the January through June term.
ENRSTAT Student's enrollment pattern during the academic year.
ESUBMX2 Indicates whether undergraduates who took out subsidized Stafford loans took out their individual maximum subsidized amount, that is, the maximum they could borrow that was allowed for the program (based on their class level) and also taking into account their financial need after all aid except for any subsidized Stafford loans.
ETOTMX2 Indicates whether undergraduates who took out Stafford loans in took out their individual maximum total amount (sum of subsidized and unsubsidized), that is, the maximum they could borrow that was allowed for the program (based on their class level and dependency) and also taking into account their total price of attendance reduced by all financial aid except for any loans.
EVER2PUB The student has ever taken classes for credit offered through a community college.
EVER4YR The student has ever attended a 4-year college.
FEDBEN This variable indicates whether any member of the student's household received any of the following federal benefits during the academic year: - Food Stamps Benefits - Free/Reduced Price School Lunch Benefits - Supplemental Security Income Benefits - TANF Benefits - WIC Benefits.
FEDBENA This variable indicates whether any member of the student's household received the following federal benefit during the academic year: Food Stamp Benefit.
FEDBENB This variable indicates whether any member of the student's household received the following federal benefit during the academic year: Free or Reduced Price School Lunch Benefits.
FEDBENC This variable indicates whether any member of the student's household received the following federal benefit during the academic year: Supplemental Security Income Benefits.
FEDBEND This variable indicates whether any member of the student's household received the following federal benefit during the academic year: TANF Benefits.
FEDBENE This variable indicates whether any member of the student's household received the following federal benefit during the academic year: WIC Benefits.
FEDGRPCT Ratio of total federal grants to total aid received during the academic year.
FEDLNPAK Federal loan package by type of loan received during the academic year.
FEDNEED Total amount of federal need-based aid received during the academic year.
FEDPCT Ratio of total federal aid to total aid received during the academic year.
FLNPCT6 Ratio of federal loans to federal aid (excluding PLUS loans and Veterans' benefit) received during the academic year.
GRNTSRC Grant package by source of grant received during the academic year.
GRTCST Total grants received as a percentage of the total student budget.
GRTLOAN Ratio of total grants to total loans received during the academic year.
GRTPCTTN Total grants received as a percentage of tuition and fees at NPSAS.
GRTRATIO Ratio of total grants to total grants and loans received during the academic year.
GRTSNEED Grant amount exceeding federal need.
HCHONORS Count of high school subject areas (English, math, foreign languages, science, and social studies) in which the student has taken an advanced placement, accelerated or honors course, according to self-report on standardized test questionnaire.
HCMATHHI Highest level of math completed or planned to take, according to self-report on standardized test questionnaire and the student interview.
HCSCINUM Number of science courses the student took or planned to take, according to self-report on standardized test questionnaire.
HCTKBIOL Student took or planned to take to take Biology, according to self-report on standardized test questionnaire.
HCTKCHEM Student took or planned to take to take Chemistry, according to self-report on standardized test questionnaire.
HCTKPHYS Student took or planned to take to take Physics, according to self-report on standardized test questionnaire.
HCYSENGL Years of high school coursework completed or planned in English, according to self-report on standardized test questionnaire.
HCYSLANG Years of high school coursework completed or planned in foreign languages, according to self-report on standardized test questionnaire.
HCYSMATH Years of high school coursework completed or planned in math, according to self-report on standardized test questionnaire and the student interview.
HCYSSCIE Years of high school coursework completed or planned in science, according to self-report on standardized test questionnaire.
HCYSSOCI Years of high school coursework completed or planned in social studies, according to self-report on standardized test questionnaire.
HIGHLVEX The highest level of education that the student ever expects to complete.
HLOFFER Highest level of postsecondary degree or award offered at the NPSAS sample institution attended during the academic year.
HOMESTUD The student owns a home or pays a mortgage on a home.
HSCRDANY Indicates whether or not student earned any college credits in high school. College credits can be college credits earned at a college or Advanced Placement credits earned in high school.
HSCRDAP Indicates whether or not student earned Advanced Placement credits in high school.
HSCRDCOL Indicates whether or not student earned college credits at a college during high school.
HSGPA High school grade point average on the standardized test date, according to self-report on test questionnaire.
HSGRADYY The calendar year the student graduated from high school.
HSIZE Student's family size during the academic year.
INCPCT2 The tuition charged at the NPSAS institution as a percentage of total income.
INJURIS Indicates whether the tuition charged at public NPSAS institutions was in or out of jurisdiction.
INSTCAT Institutional category was derived using the level of offerings reported on the Institutional Characteristics (IC) component and the number and level of awards that were reported on the Completions (C) component.
INSTGPCT Ratio of total institutional grants to total aid received during the academic year.
INSTPACK Aid package with institutional aid received during the academic year. For years 1996 and 2000 the categories for "No institutional" and "No aid received" are combined to be comparable to "No Institutional Aid" in subsequent years.
INSTPCT Ratio of total institutional aid to total aid received during the academic year.
INSWAIV Total amount of institutional tuition waivers received during the academic year.
JOBEARN The student's total amount earned from work (excluding work-study, assistantship, and traineeship) during the academic year.
JOBONOFF The location of the student's job.
JOBROLE The student's primary role while enrolled at NPSAS and also working.
JOBTYPE2 Indicates the student has a work-study/assistantship job, a regular job, or both.
LNREPAY The student expects help with repaying their student loans, from an individual other than their spouse.
LOANCST Total student loans (excluding Parent PLUS) received as a percentage of the total student budget.
LOANCST2 Total student loans (including Parent PLUS loans) received as a percentage of the total student budget.
LOANPCT Ratio of total loans (excluding Parent PLUS loans) to total aid received during the academic year.
LOANPCT2 Ratio of total loans (including Parent PLUS loans) to total aid received during the academic year.
LOCALE The degree of urbanization in which the NPSAS institution is located.
LOCALEST The degree of urbanization in which the students home is located.
MAJCHGFQ The frequency with which the student formally changed his or her major.
MAJORS23 The student's undergraduate major or field of study during the academic year.
MAJORS2Y The student's undergraduate major or field of study during the academic year.
MAJORS4Y The student's undergraduate major or field of study during the academic year.
MFT Number of months enrolled full-time between July and June of the academic year.
MHT Number of months enrolled half-time between July and June of the academic year.
MLT Number of months enrolled less than half-time between July and June of the academic year.
MNTRENT The average monthly rent or mortgage the student paid during the academic year.
MPT Number of months enrolled part time between July and June of the academic year.
NEEDAID1 Indicates the amount of need-based aid received.
NETCST10 Tuition and fees minus federal grants for the academic year.
NETCST12 Tuition and fees minus state grants for the academic year.
NETCST13 Tuition and fees minus institutional grants from the NPSAS institution for the academic year.
NETCST14 Tuition and fees minus non-federal grants for the academic year.
NETCST15 Tuition and fees minus all state and institutional grants for the academic year.
NETCST16 Total net price after all federal and state grants for the academic year.
NETCST17 Total net price after all grants and loans for the academic year.
NETCST18 Total net price after all financial aid except work-study for the academic year.
NETCST4 Total net price after all grants and one-half of all loans for the academic year.
NETCST41 Net total price after all financial aid except private loans for the academic year.
OTHFDGRT Total amount of grants from various small federal programs.
OTHRSCR Total aid from outside sources received.
OTHTYPE2 Total amount of aid received during the academic year that was not classified by type as grants and loans to students, but included work-study.
PARBORN Student's parent(s) were born in the United States.
PDADED Father's highest level of education.
PELLRAT1 Ratio of Pell grants to total aid received during the academic year.
PELLRAT2 Ratio of Pell grants to total grants received during the academic year.
PELLYRS Number of years that a Pell grant was received.
PERKCUM1 Indicates the cumulative amount of Perkins loans ever borrowed for undergraduate education through July 1 of the survey year.
PFAMNUM Family size of dependent student during the academic year.
PFEDTAX The amount of federal income tax paid by the parents of dependent student in the year two years prior to the survey year.
PINCOL Number of parent's dependent children in college during the academic year.
PLUSPCT Ratio of Parent PLUS loans to total aid received during the academic year.
PMARITAL Parent's marital status during the academic year.
PMOMED Mother's highest level of education.
PRIVAMT Total private source grants and loans for the academic year.
PRIVCST Private loans received as a percentage of the total student budget.
PRIVLRAT Ratio of private loans to total loans received during the academic year.
PRIVPCT Ratio of private loans to total aid received during the academic year.
PTAXFILE Whether or not parents of dependent student filed federal income tax in the year two years prior to the survey year.
REANOAPA The student did not apply for financial aid because he/she did not want to take on debt.
REANOAPB The student did not apply for financial aid because the application forms were too much work or too time consuming.
REANOAPC The student did not apply for financial aid he/she did not have enough information about how to apply for financial aid.
REANOAPD The student did not apply for financial aid because he/she did not need financial aid.
REANOAPE The student did not apply for financial aid because he/she thought he/she would be ineligible.
SECTOR1 Sector of the NPSAS sample institution attended during the academic year.
SFAMNUM Indicates the number of persons in the independent student's own family (including the student).
SFEDTAX The amount of federal income tax the independent student paid for the year two years prior to the survey.
SIBINCOL Indicates whether the student has siblings who attended college or graduate school during the academic year.
SINCOL Number of persons in the independent student's household who attended college during the academic year.
SJHOURS The average number of hours the student worked per week during the academic year.
SJMAJOR The student's work-study job was related to his/her major or field of study.
SJONOFF The location of the student's work-study job.
SJSCHOOL The student's work study job was for NPSAS or for another institution or organization.
SNEED3 The remaining need after all federal grants, calculated as the total student budget minus the Expected Family Contribution and minus federal grants.
SNEED4 The remaining need after all grants and federal need-based aid, calculated as the total student budget minus the Expected Family Contribution and minus aid subject to the federal EFC limitation.
SNEED7 The remaining need after federal and state grants, calculated as the total student budget minus the Expected Family Contribution and minus federal and state grants.
SNEED8 The remaining need after all federal, state, and other grants, calculated as the total student budget minus the Expected Family Contribution and minus federal state, and outside grants.
SPINCOL The student's spouse attended college or graduate school during the academic year.
SPSINC Spouse's earned income in the year two years prior to the survey year, if married.
STAFFRAT Ratio of Stafford loans to total loans received during the academic year.
STAFFST This variable indicates the first year that a Direct Subsidized or Unsubsidized Loan (also known as subsidized and unsubsidized Stafford Loan) was received.
STAFLST This variable indicates the last year that a Direct Subsidized or Unsubsidized Loan (also known as subsidized and unsubsidized Stafford Loan) was received.
STAFYRS The number of years that the student received Direct Subsidized Loans or Direct Unsubsidized Loans (also known as subsidized and unsubsidized Stafford Loans).
STAPCT Ratio of total state aid to total aid received during the academic year.
STAXFILE Whether or not independent student filed federal income tax for the year two years prior to the survey year.
STGRPCT Ratio of total state grants to total aid received during the academic year.
STNOND1 Indicates the amount of state grants received in the academic year that were based neither on need nor merit.
STSBCUM1 Cumulative subsidized Stafford loan amounts borrowed for undergraduate education through July 1 of the survey year.
STSUBMX Indicates whether undergraduates who took out subsidized Stafford loans took out the maximum subsidized amount that was allowed for the program, based on their class level.
STTOTMX Indicates whether undergraduates who took out Stafford loans in took out the maximum total amount (sum of subsidized and unsubsidized) that was allowed for the program, based on their class level and dependency status.
SUBCUM1 Cumulative federal subsidized loan amounts borrowed for undergraduate education through July 1 of the survey year.
T4LNAMT2 Total amount of federal Title IV loans (including Parent PLUS loans) received during the academic year.
TEACTDER ACT composite score, derived from either a reported ACT score or the SAT I combined score converted to an estimated ACT composite score.
TESATCP1 The average percentile rank of reported SAT verbal and math scores, among all test takers.
TESATCRE The sum of reported SAT verbal and math scores.
TESATMDE SAT I math score, derived as either the actual SAT I math score or the ACT math score converted to an estimated SAT I math score.
TESATMP1 Percentile rank of reported SAT math score (recentered), among all test takers.
TESATMRE The reported SAT math score (recentered).
TESATNP1 Percentile rank of derived SAT math score, among all test takers (nationwide).
TESATVDE SAT I verbal score derived as either the actual SAT I verbal score or the ACT English and reading score converted to an estimated SAT I verbal score.
TESATVP1 Percentile rank of reported SAT verbal score (recentered), among all test takers.
TESATVRE The reported SAT verbal score (recentered).
TETOOK Indicates whether the student took the SAT I or ACT college entrance exam.
TFEDAID2 Total amount of federal aid (including Veterans' benefit & DOD) received during the academic year.
TFEDAID6 Total amount of federal aid excluding Parent PLUS loans and Veterans' benefit received during the academic year.
TNFEDAID Total amount of non-federal financial aid received during the academic year.
TOTAID2 Total amount of federal Title IV, state, and institutional aid received during the academic year.
TOTAID4 Total amount of all financial aid received except Parent PLUS loans during the academic year.
TOTAID6 Total amount of all financial aid received except Parent PLUS loans and federal Veterans' benefit during the academic year.
TOTAID7 Total amount of all financial aid received during the academic year except for federal Veterans' benefit.
TOTAID8 Total amount of all financial aid received during the academic year except for private loans.
TOTGRT4 Total amount of state and institutional grants received in the academic year.
TOTNOND3 Indicates non-need based aid received from both institutional non-need aid and state non-need aid programs during the academic year.
UNSBLOAN Total amount of all unsubsidized loans from any source received during the academic year.
UNTAXBFC Indicates if the student received worker's compensation during the academic year.
USBORN The student was born in the United States.
VETBEN Indicates the total amount of federal veterans' benefit received in the academic year.
VOCHELP Total amount of vocational rehabilitation and job training grants received during the academic year.
WORKPCT Ratio of total work-study to total aid received during the academic year.

Name		Description
AIDCST3		Total aid received excluding private loans and Direct PLUS loans to parents as a percentage of the total student budget.
AIDSNEED		The amount of financial aid exceeding federal need.
AIDSRC		Aid package by source of aid received.
ATTEND2		Student's attendance status during the fall term.
ATTENDMR		The student's main reason for enrolling at NPSAS.
ATTNPT		Student's attendance intensity at all institutions in the given survey year.
CALSYS		The NPSAS institution's academic calendar system.
CLOCK		Denotes if the NPSAS institution is on a clock or credit hour system.
CNTLAFFI		The NPSAS institution's control or affiliation.
CRNUMCRD		The number of credit cards a student has in his or her own name.
CUMLNTP1		Indicates the source of the student's loans during undergraduate education.
DEGPR		Indicates whether the student has earned degrees or certificates since high school.
DEGPRAA		Indicates whether the student has already earned an associate's degree since high school.
DEGPRCRT		Indicates whether the student has already earned an undergraduate certificate/diploma since high school.
DEGPRMS		Indicates whether the student has already earned a master's degree since high school.
DEGPRPTB		Indicates whether the student has already earned a post-baccalaureate certificate since high school.
DEGPRPTM		Indicates whether the student has already earned a post-master's certificate since high school.
DEPEND4		Student's dependency status including dependents and marital status.
DEPNUM		Number of student's dependents (children and others).
DEPOTHER		Indicates whether the student had dependents other than children.
DEPTYPE		Type of student's dependents.
DEPYNG		The age of the student's youngest child.
DSTUINC		Dependent student's own income in the year two years prior to the survey year, excluding the income of the parents.
EFCAID		The total amount of financial aid that is subject to federal EFC limitations if the student received need-based federal aid.
EFFORT18		Net price after all aid except work-study as a percentage of total income.
EFFORT20		Net price after grants and loans as a percentage of total income.
EMPLWAIV		Tuition waivers for staff and families of staff at the institution.
EMPLYAM3		Indicates the amount of tuition aid received from the student's or the parents' employers.
EMPLYAMT		Total amount of aid received from employers.
ENLEN		Number of months enrolled.
ENR01		Monthly enrollment status for July of the academic year.
ENR02		Monthly enrollment status for August of the academic year.
ENR03		Monthly enrollment status for September of the academic year.
ENR04		Monthly enrollment status for October of the academic year.
ENR05		Monthly enrollment status for November of the academic year.
ENR06		Monthly enrollment status for December of the academic year.
ENR07		Monthly enrollment status for January of the academic year.
ENR08		Monthly enrollment status for February of the academic year.
ENR09		Monthly enrollment status for March of the academic year.
ENR10		Monthly enrollment status for April of the academic year.
ENR11		Monthly enrollment status for May of the academic year.
ENR12		Monthly enrollment status for June of the academic year.
ENRFALL		Student was enrolled during the July through December term.
ENRSPR		Student was enrolled during the January through June term.
ENRSTAT		Student's enrollment pattern during the academic year.
ESUBMX2		Indicates whether undergraduates who took out subsidized Stafford loans took out their individual maximum subsidized amount, that is, the maximum they could borrow that was allowed for the program (based on their class level) and also taking into account their financial need after all aid except for any subsidized Stafford loans.
ETOTMX2		Indicates whether undergraduates who took out Stafford loans in took out their individual maximum total amount (sum of subsidized and unsubsidized), that is, the maximum they could borrow that was allowed for the program (based on their class level and dependency) and also taking into account their total price of attendance reduced by all financial aid except for any loans.
EVER2PUB		The student has ever taken classes for credit offered through a community college.
EVER4YR		The student has ever attended a 4-year college.
FEDBEN		This variable indicates whether any member of the student's household received any of the following federal benefits during the academic year: - Food Stamps Benefits - Free/Reduced Price School Lunch Benefits - Supplemental Security Income Benefits - TANF Benefits - WIC Benefits.
FEDBENA		This variable indicates whether any member of the student's household received the following federal benefit during the academic year: Food Stamp Benefit.
FEDBENB		This variable indicates whether any member of the student's household received the following federal benefit during the academic year: Free or Reduced Price School Lunch Benefits.
FEDBENC		This variable indicates whether any member of the student's household received the following federal benefit during the academic year: Supplemental Security Income Benefits.
FEDBEND		This variable indicates whether any member of the student's household received the following federal benefit during the academic year: TANF Benefits.
FEDBENE		This variable indicates whether any member of the student's household received the following federal benefit during the academic year: WIC Benefits.
FEDGRPCT		Ratio of total federal grants to total aid received during the academic year.
FEDLNPAK		Federal loan package by type of loan received during the academic year.
FEDNEED		Total amount of federal need-based aid received during the academic year.
FEDPCT		Ratio of total federal aid to total aid received during the academic year.
FLNPCT6		Ratio of federal loans to federal aid (excluding PLUS loans and Veterans' benefit) received during the academic year.
GRNTSRC		Grant package by source of grant received during the academic year.
GRTCST		Total grants received as a percentage of the total student budget.
GRTLOAN		Ratio of total grants to total loans received during the academic year.
GRTPCTTN		Total grants received as a percentage of tuition and fees at NPSAS.
GRTRATIO		Ratio of total grants to total grants and loans received during the academic year.
GRTSNEED		Grant amount exceeding federal need.
HCHONORS		Count of high school subject areas (English, math, foreign languages, science, and social studies) in which the student has taken an advanced placement, accelerated or honors course, according to self-report on standardized test questionnaire.
HCMATHHI		Highest level of math completed or planned to take, according to self-report on standardized test questionnaire and the student interview.
HCSCINUM		Number of science courses the student took or planned to take, according to self-report on standardized test questionnaire.
HCTKBIOL		Student took or planned to take to take Biology, according to self-report on standardized test questionnaire.
HCTKCHEM		Student took or planned to take to take Chemistry, according to self-report on standardized test questionnaire.
HCTKPHYS		Student took or planned to take to take Physics, according to self-report on standardized test questionnaire.
HCYSENGL		Years of high school coursework completed or planned in English, according to self-report on standardized test questionnaire.
HCYSLANG		Years of high school coursework completed or planned in foreign languages, according to self-report on standardized test questionnaire.
HCYSMATH		Years of high school coursework completed or planned in math, according to self-report on standardized test questionnaire and the student interview.
HCYSSCIE		Years of high school coursework completed or planned in science, according to self-report on standardized test questionnaire.
HCYSSOCI		Years of high school coursework completed or planned in social studies, according to self-report on standardized test questionnaire.
HIGHLVEX		The highest level of education that the student ever expects to complete.
HLOFFER		Highest level of postsecondary degree or award offered at the NPSAS sample institution attended during the academic year.
HOMESTUD		The student owns a home or pays a mortgage on a home.
HSCRDANY		Indicates whether or not student earned any college credits in high school. College credits can be college credits earned at a college or Advanced Placement credits earned in high school.
HSCRDAP		Indicates whether or not student earned Advanced Placement credits in high school.
HSCRDCOL		Indicates whether or not student earned college credits at a college during high school.
HSGPA		High school grade point average on the standardized test date, according to self-report on test questionnaire.
HSGRADYY		The calendar year the student graduated from high school.
HSIZE		Student's family size during the academic year.
INCPCT2		The tuition charged at the NPSAS institution as a percentage of total income.
INJURIS		Indicates whether the tuition charged at public NPSAS institutions was in or out of jurisdiction.
INSTCAT		Institutional category was derived using the level of offerings reported on the Institutional Characteristics (IC) component and the number and level of awards that were reported on the Completions (C) component.
INSTGPCT		Ratio of total institutional grants to total aid received during the academic year.
INSTPACK		Aid package with institutional aid received during the academic year. For years 1996 and 2000 the categories for "No institutional" and "No aid received" are combined to be comparable to "No Institutional Aid" in subsequent years.
INSTPCT		Ratio of total institutional aid to total aid received during the academic year.
INSWAIV		Total amount of institutional tuition waivers received during the academic year.
JOBEARN		The student's total amount earned from work (excluding work-study, assistantship, and traineeship) during the academic year.
JOBONOFF		The location of the student's job.
JOBROLE		The student's primary role while enrolled at NPSAS and also working.
JOBTYPE2		Indicates the student has a work-study/assistantship job, a regular job, or both.
LNREPAY		The student expects help with repaying their student loans, from an individual other than their spouse.
LOANCST		Total student loans (excluding Parent PLUS) received as a percentage of the total student budget.
LOANCST2		Total student loans (including Parent PLUS loans) received as a percentage of the total student budget.
LOANPCT		Ratio of total loans (excluding Parent PLUS loans) to total aid received during the academic year.
LOANPCT2		Ratio of total loans (including Parent PLUS loans) to total aid received during the academic year.
LOCALE		The degree of urbanization in which the NPSAS institution is located.
LOCALEST		The degree of urbanization in which the students home is located.
MAJCHGFQ		The frequency with which the student formally changed his or her major.
MAJORS23		The student's undergraduate major or field of study during the academic year.
MAJORS2Y		The student's undergraduate major or field of study during the academic year.
MAJORS4Y		The student's undergraduate major or field of study during the academic year.
MFT		Number of months enrolled full-time between July and June of the academic year.
MHT		Number of months enrolled half-time between July and June of the academic year.
MLT		Number of months enrolled less than half-time between July and June of the academic year.
MNTRENT		The average monthly rent or mortgage the student paid during the academic year.
MPT		Number of months enrolled part time between July and June of the academic year.
NEEDAID1		Indicates the amount of need-based aid received.
NETCST10		Tuition and fees minus federal grants for the academic year.
NETCST12		Tuition and fees minus state grants for the academic year.
NETCST13		Tuition and fees minus institutional grants from the NPSAS institution for the academic year.
NETCST14		Tuition and fees minus non-federal grants for the academic year.
NETCST15		Tuition and fees minus all state and institutional grants for the academic year.
NETCST16		Total net price after all federal and state grants for the academic year.
NETCST17		Total net price after all grants and loans for the academic year.
NETCST18		Total net price after all financial aid except work-study for the academic year.
NETCST4		Total net price after all grants and one-half of all loans for the academic year.
NETCST41		Net total price after all financial aid except private loans for the academic year.
OTHFDGRT		Total amount of grants from various small federal programs.
OTHRSCR		Total aid from outside sources received.
OTHTYPE2		Total amount of aid received during the academic year that was not classified by type as grants and loans to students, but included work-study.
PARBORN		Student's parent(s) were born in the United States.
PDADED		Father's highest level of education.
PELLRAT1		Ratio of Pell grants to total aid received during the academic year.
PELLRAT2		Ratio of Pell grants to total grants received during the academic year.
PELLYRS		Number of years that a Pell grant was received.
PERKCUM1		Indicates the cumulative amount of Perkins loans ever borrowed for undergraduate education through July 1 of the survey year.
PFAMNUM		Family size of dependent student during the academic year.
PFEDTAX		The amount of federal income tax paid by the parents of dependent student in the year two years prior to the survey year.
PINCOL		Number of parent's dependent children in college during the academic year.
PLUSPCT		Ratio of Parent PLUS loans to total aid received during the academic year.
PMARITAL		Parent's marital status during the academic year.
PMOMED		Mother's highest level of education.
PRIVAMT		Total private source grants and loans for the academic year.
PRIVCST		Private loans received as a percentage of the total student budget.
PRIVLRAT		Ratio of private loans to total loans received during the academic year.
PRIVPCT		Ratio of private loans to total aid received during the academic year.
PTAXFILE		Whether or not parents of dependent student filed federal income tax in the year two years prior to the survey year.
REANOAPA		The student did not apply for financial aid because he/she did not want to take on debt.
REANOAPB		The student did not apply for financial aid because the application forms were too much work or too time consuming.
REANOAPC		The student did not apply for financial aid he/she did not have enough information about how to apply for financial aid.
REANOAPD		The student did not apply for financial aid because he/she did not need financial aid.
REANOAPE		The student did not apply for financial aid because he/she thought he/she would be ineligible.
SECTOR1		Sector of the NPSAS sample institution attended during the academic year.
SFAMNUM		Indicates the number of persons in the independent student's own family (including the student).
SFEDTAX		The amount of federal income tax the independent student paid for the year two years prior to the survey.
SIBINCOL		Indicates whether the student has siblings who attended college or graduate school during the academic year.
SINCOL		Number of persons in the independent student's household who attended college during the academic year.
SJHOURS		The average number of hours the student worked per week during the academic year.
SJMAJOR		The student's work-study job was related to his/her major or field of study.
SJONOFF		The location of the student's work-study job.
SJSCHOOL		The student's work study job was for NPSAS or for another institution or organization.
SNEED3		The remaining need after all federal grants, calculated as the total student budget minus the Expected Family Contribution and minus federal grants.
SNEED4		The remaining need after all grants and federal need-based aid, calculated as the total student budget minus the Expected Family Contribution and minus aid subject to the federal EFC limitation.
SNEED7		The remaining need after federal and state grants, calculated as the total student budget minus the Expected Family Contribution and minus federal and state grants.
SNEED8		The remaining need after all federal, state, and other grants, calculated as the total student budget minus the Expected Family Contribution and minus federal state, and outside grants.
SPINCOL		The student's spouse attended college or graduate school during the academic year.
SPSINC		Spouse's earned income in the year two years prior to the survey year, if married.
STAFFRAT		Ratio of Stafford loans to total loans received during the academic year.
STAFFST		This variable indicates the first year that a Direct Subsidized or Unsubsidized Loan (also known as subsidized and unsubsidized Stafford Loan) was received.
STAFLST		This variable indicates the last year that a Direct Subsidized or Unsubsidized Loan (also known as subsidized and unsubsidized Stafford Loan) was received.
STAFYRS		The number of years that the student received Direct Subsidized Loans or Direct Unsubsidized Loans (also known as subsidized and unsubsidized Stafford Loans).
STAPCT		Ratio of total state aid to total aid received during the academic year.
STAXFILE		Whether or not independent student filed federal income tax for the year two years prior to the survey year.
STGRPCT		Ratio of total state grants to total aid received during the academic year.
STNOND1		Indicates the amount of state grants received in the academic year that were based neither on need nor merit.
STSBCUM1		Cumulative subsidized Stafford loan amounts borrowed for undergraduate education through July 1 of the survey year.
STSUBMX		Indicates whether undergraduates who took out subsidized Stafford loans took out the maximum subsidized amount that was allowed for the program, based on their class level.
STTOTMX		Indicates whether undergraduates who took out Stafford loans in took out the maximum total amount (sum of subsidized and unsubsidized) that was allowed for the program, based on their class level and dependency status.
SUBCUM1		Cumulative federal subsidized loan amounts borrowed for undergraduate education through July 1 of the survey year.
T4LNAMT2		Total amount of federal Title IV loans (including Parent PLUS loans) received during the academic year.
TEACTDER		ACT composite score, derived from either a reported ACT score or the SAT I combined score converted to an estimated ACT composite score.
TESATCP1		The average percentile rank of reported SAT verbal and math scores, among all test takers.
TESATCRE		The sum of reported SAT verbal and math scores.
TESATMDE		SAT I math score, derived as either the actual SAT I math score or the ACT math score converted to an estimated SAT I math score.
TESATMP1		Percentile rank of reported SAT math score (recentered), among all test takers.
TESATMRE		The reported SAT math score (recentered).
TESATNP1		Percentile rank of derived SAT math score, among all test takers (nationwide).
TESATVDE		SAT I verbal score derived as either the actual SAT I verbal score or the ACT English and reading score converted to an estimated SAT I verbal score.
TESATVP1		Percentile rank of reported SAT verbal score (recentered), among all test takers.
TESATVRE		The reported SAT verbal score (recentered).
TETOOK		Indicates whether the student took the SAT I or ACT college entrance exam.
TFEDAID2		Total amount of federal aid (including Veterans' benefit & DOD) received during the academic year.
TFEDAID6		Total amount of federal aid excluding Parent PLUS loans and Veterans' benefit received during the academic year.
TNFEDAID		Total amount of non-federal financial aid received during the academic year.
TOTAID2		Total amount of federal Title IV, state, and institutional aid received during the academic year.
TOTAID4		Total amount of all financial aid received except Parent PLUS loans during the academic year.
TOTAID6		Total amount of all financial aid received except Parent PLUS loans and federal Veterans' benefit during the academic year.
TOTAID7		Total amount of all financial aid received during the academic year except for federal Veterans' benefit.
TOTAID8		Total amount of all financial aid received during the academic year except for private loans.
TOTGRT4		Total amount of state and institutional grants received in the academic year.
TOTNOND3		Indicates non-need based aid received from both institutional non-need aid and state non-need aid programs during the academic year.
UNSBLOAN		Total amount of all unsubsidized loans from any source received during the academic year.
UNTAXBFC		Indicates if the student received worker's compensation during the academic year.
USBORN		The student was born in the United States.
VETBEN		Indicates the total amount of federal veterans' benefit received in the academic year.
VOCHELP		Total amount of vocational rehabilitation and job training grants received during the academic year.
WORKPCT		Ratio of total work-study to total aid received during the academic year.

3/15/18

Additional NPSAS Graduate variables added to TrendStats. See list.

NPSAS Graduate Variables Added to TrendStats

The following NPSAS Graduate variables are now available in TrendStats.

Name Description
AIDCST Total aid received as a percentage of the total student budget.
AIDSNEED The amount of financial aid exceeding federal need.
ATTEND2 Student's attendance status during the fall term.
ATTNPT Student's attendance intensity at all institutions attended in the academic year.
CALSYS The NPSAS institution's academic calendar system.
CLOCK Denotes if the NPSAS institution is on a clock or credit hour system.
CNTLAFFI The NPSAS institution's control or affiliation.
CUMLNTP1 Indicates the source of the student's loans during undergraduate education.
DEGPR The student has earned degrees or certificates since high school.
DEGPRAA The student has already earned an associate's degree since high school.
DEGPRCRT The student has already earned an undergraduate certificate/diploma since high school.
DEGPRMS The student has already earned a master's degree since high school.
DEGPRPTB The student has already earned a post-baccalaureate certificate since high school.
DEGPRPTM The student has already earned a post-master's certificate since high school.
DEPCARE The student has dependent children in daycare during the academic year.
DEPCOST The student's monthly daycare costs for dependent children during the academic year.
DEPEND5A Student's dependency status including dependents and marital status during the academic year.
DEPNUM Number of student's dependents (children and others) during the academic year.
DEPOTHER Indicates whether the student had dependents other than children.
DEPTYPE Indicates whether the student has dependents who are their children, not their children, or both.
DEPYNG The age of the student's youngest child during the academic year.
EFCAID The total amount of financial aid that is subject to federal EFC limitations if the student received need-based federal aid.
EFFORT18 Net price after all aid except work-study as a percentage of total income.
EFFORT3 Net price after grants as a percentage of total income in the year two years prior to the survey year.
EFFORT9 Net tuition after all grants as a percentage of total income in the year two years prior to the survey year.
EMPLWAIV Tuition waivers for staff and families of staff at the institution attended during the academic year.
EMPLYAM3 Indicates the amount of tuition aid received from the student's or the parents' employers in the academic year.
ENLEN Number of months enrolled between July and June of the academic year.
ENR01 Monthly enrollment status for July of the academic year.
ENR02 Monthly enrollment status for August of the academic year.
ENR03 Monthly enrollment status for September of the academic year.
ENR04 Monthly enrollment status for October of the academic year.
ENR05 Monthly enrollment status for November of the academic year.
ENR06 Monthly enrollment status for December of the academic year.
ENR07 Monthly enrollment status for January of the academic year.
ENR08 Monthly enrollment status for February of the academic year.
ENR09 Monthly enrollment status for March of the academic year.
ENR10 Monthly enrollment status for April of the academic year.
ENR11 Monthly enrollment status for May of the academic year.
ENR12 Monthly enrollment status for June of the academic year.
ENRFALL Student was enrolled during the July through December term.
ENRLSIZE Fall enrollment.
ENRSPR Student was enrolled during the January through June term.
ENRSTAT Student's enrollment pattern during the academic year.
FEDBEN This variable indicates whether any member of the student's household received any of the following federal benefits during the academic year: food Stamps Benefits, free/Reduced Price School Lunch Benefits, supplemental Security Income Benefits, TANF Benefits, or WIC Benefits
FEDBENA This variable indicates whether any member of the student's household received the following federal benefit during the academic year: Food Stamp Benefit.
FEDBENB This variable indicates whether any member of the student's household received the following federal benefit during the academic year: Free or Reduced Price School Lunch Benefits.
FEDBENC This variable indicates whether any member of the student's household received the following federal benefit during the academic year: Supplemental Security Income Benefits.
FEDBEND This variable indicates whether any member of the student's household received the following federal benefit during the academic year: TANF Benefits.
FEDBENE This variable indicates whether any member of the student's household received the following federal benefit during the academic year: WIC Benefits.
FEDCUM1 Cumulative federal loan amounts borrowed for undergraduate education through July 1 of the survey year.
FEDOWE1 Indicates total amount owed on all federal loans for undergraduate education as of late in the survey year.
FGRTLN Total amount of federal student loans and federal grants received during the academic year.
FLNPCT6 Ratio of federal loans to federal aid (excluding veterans benefits) received during the academic year.
GRNTSRC Grant package by source of grant received during the academic year.
GRTCST Total grants received as a percentage of the total student budget.
GRTLOAN Ratio of total grants to total loans received during the academic year.
GRTRATIO Ratio of total grants to total grants and loans received during the academic year.
GRTSNEED Grant amount exceeding federal need.
HIGHLVEX The highest level of education that the student ever expects to complete.
HISPANIC Student is of Hispanic or Latino origin.
HISPTYPE Type of Hispanic or Latino origin.
HLOFFER Highest level of postsecondary degree or award offered at the NPSAS sample institution attended during the academic year.
HOMESTUD The student owns a home or pays a mortgage on a home.
IMMIGEN Number of generations the student's family has been in the U.S.
IMMIGRA Student's immigrant status.
INCPCT1 The adjusted cost of attendance at the NPSAS institution as a percentage of total income in the year two years prior to the survey year.
INCPCT2 The tuition charged at the NPSAS institution as a percentage of total income.
INJURIS Indicates whether the tuition charged at public NPSAS institutions was in or out of jurisdiction.
JOBEARN The student's total amount earned from work (excluding work-study, assistantship, and traineeship) during the academic year.
JOBENR The student's intensity of work (excluding work-study/assistantship/traineeship) while enrolled during the academic year.
JOBHOUR The average number of hours worked per week during the academic year (excluding work-study, fellowships, assistantships, and traineeships).
JOBONOFF The location of the student's job.
JOBROLE The student's primary role while enrolled at NPSAS and also working.
JOBTYPE2 Indicates the student has a work-study/assistantship job, a regular job, or both.
LNREPAY The student expects help with repaying their student loans, from an individual other than their spouse.
LOANCST Total student loans (excluding Parent PLUS) received as a percentage of the total student budget.
LOCALE The degree of Urbanization in which the NPSAS institution is located.
LOCALEST Input to this derived variable was the best-known current address after data collection.
MFT Number of months enrolled full time between July and June of the academic year.
MHT Number of months enrolled half-time between July and June of the academic year.
MLT Number of months enrolled less than half-time between July and June of the academic year.
MNTRENT The average monthly rent or mortgage the student paid during the academic year.
MPT Number of months enrolled part-time between July and June of the academic year.
NETCST10 Tuition and fees minus federal grants for the academic year.
NETCST12 Tuition and fees minus state grants for the academic year.
NETCST14 Tuition and fees minus non-federal grants for the academic year.
NETCST15 Tuition and fees minus all state and institutional grants for the academic year.
NETCST16 Net total price after all federal and state grants for the academic year.
NETCST17 Net total price after all grants and loans for the academic year.
NETCST18 Net total price after all financial aid except work-study for the academic year.
NETCST2 Net total price after all federal grants for the academic year.
NETCST4 Net total price after all grants and one-half of all loans for the academic year.
NETCST41 Net total price after all financial aid except private loans for the academic year four years prior to the survey year.
NETCST9 Tuition and fees minus all grants for the academic year.
NFEDCUM1 Cumulative non-federal loan amounts borrowed for undergraduate education through July 1 of the survey year.
OBEREG Region where NPSAS sample institution is located.
OTHTYPE2 Total amount of aid received during the academic year that was not classified by type as grants and loans to students, but included work-study.
PCTPOV Indicates total income as a percentage of the federal poverty level thresholds for the year two years prior to the survey year.
PDADED Father's highest level of education.
PELLFST The first year that a Pell grant was received during the years covered by the survey.
PELLLST The last year that a Pell grant was received during the years covered by the survey.
PELLYRS Number of years that a Pell grant was received.
PERKCUM1 Indicates the cumulative amount of Perkins loans ever borrowed for undergraduate education through July 1 of the survey year.
PMOMED Mother's highest level of education.
PRIVCST Private loans received as a percentage of the total student budget.
PRIVLRAT Ratio of private loans to total loans received during the academic year.
PRIVPACK Loan package by whether the loan received was private (alternative) or not during the academic year.
PRIVPCT Ratio of private loans to total aid received during the academic year.
REANOAPA The student did not apply for financial aid because he/she did not want to take on debt.
REANOAPB The student did not apply for financial aid because the application forms were too much work or too time consuming.
REANOAPC The student did not apply for financial aid he/she did not have enough information about how to apply for financial aid.
REANOAPD The student did not apply for financial aid because he/she did not need financial aid.
REANOAPE The student did not apply for financial aid because he/she thought he/she would be ineligible.
SECTOR1 Sector of the NPSAS sample institution attended during the academic year.
SECTOR4 Sector of the NPSAS sample institution attended during the academic year, for students who attended only one institution.
SFEDTAX The amount of federal income tax paid by the independent student in the year two years prior to the survey year.
SINCOL Number of persons in the independent student's household who attended college during the academic year.
SJEARN The total amount the student earned from his or her assistantship/fellowship/traineeship/work-study job during the academic year.
SJHOURS The average number of hours the student worked per week during the academic year.
SNEED1 The student's total need for need-based financial aid.
SNEED2 The remaining need after all financial aid (need-based and non-need-based) received.
SNEED3 The remaining need after all federal grants. Equal to the total student budget minus Expected Family Contribution, and minus federal grants.
SNEED4 The remaining need after all grants and federal need-based aid. Equal to the total student budget minus Expected Family Contribution, and minus aid subject to the federal EFC limitation.
SNEED7 The remaining need after federal and state grant aid. Equal to the total student budget minus expected family contribution, and minus federal and state grants.
SNEED8 The remaining need after all federal, state, and other grants. Equal to the total student budget minus the Expected Family Contribution, minus federal, state, and outside grants.
SNEED9 The remaining need after all financial aid received except private loans.
SPINCOL The student's spouse attended college or graduate school during the academic year.
STAFFRAT Ratio of Stafford loans to total loans received during the academic year.
STAFFST This variable indicates the first year that a Direct Subsidized or Unsubsidized Loan (also known as subsidized and unsubsidized Stafford Loan) was received.
STAFLST This variable indicates the last year that a Direct Subsidized or Unsubsidized Loan (also known as subsidized and unsubsidized Stafford Loan) was received.
STAFTYPE This variable indicates the combination of subsidized and unsubsidized Stafford loans received at all institutions attended during the academic year.
STAFYRS The number of years that the student received Direct Subsidized Loans or Direct Unsubsidized Loans (also known as subsidized and unsubsidized Stafford Loans).
STAPCT Ratio of total state aid to total aid received during the academic year.
STAXFILE Whether or not independent student filed federal income tax for the year two years prior to the survey year.
STEMMAJ This variable indicates the student's major field of study during the academic year with a focus on science, technology, engineering, and mathematics (STEM) fields.
STGRPCT Ratio of total state grants to total aid received during the academic year.
STSBCUM1 Cumulative subsidized Stafford loan amounts borrowed for undergraduate education through July 1 of the survey year.
STUDMULT Number of institutions attended during the academic year.
STYPELST Student type at the NPSAS sample institution during the academic year.
SUBCUM1 Cumulative federal subsidized loan amounts borrowed for undergraduate education through July 1 of the survey year.
SUBLOAN Total amount of federal Title IV subsidized loans received during the academic year.
TFEDGRT2 Total amount of all federal grants, veterans benefits and Department of Defense aid received during the academic year.
TGRTLN Total amount of loans and grants received during the academic year.
TITIVAMT Total amount of federal Title IV financial aid received during the academic year.
TNFEDAID Total amount of non-federal financial aid received during the academic year.
TNFEDGRT Total amount of non-federal grants received during the academic year.
TNFEDLN Total amount of non-federal loans received during the academic year.
TOTAID5 Total amount of all financial aid received except for work-study during the academic year.
TOTAID7 Total amount of all financial aid received during the academic year except for federal veterans benefits.
TOTGRT2 Total amount of all grants, veteran's benefits and Department of Defense aid received during the academic year.
TOTGRT4 Total amount of state and institutional grants received in the survey year.
TOTLOAN3 Total amount of all loans excluding private loans received during the academic year.
UNSBLOAN Total amount of all unsubsidized loans from any source received during the academic year.
USBORN The student was born in the United States.
VETBEN Indicates the total amount of federal veterans' benefit received in the academic year.
WORKPCT Ratio of total work-study to total aid received during the academic year.

Name		Description
AIDCST		Total aid received as a percentage of the total student budget.
AIDSNEED		The amount of financial aid exceeding federal need.
ATTEND2		Student's attendance status during the fall term.
ATTNPT		Student's attendance intensity at all institutions attended in the academic year.
CALSYS		The NPSAS institution's academic calendar system.
CLOCK		Denotes if the NPSAS institution is on a clock or credit hour system.
CNTLAFFI		The NPSAS institution's control or affiliation.
CUMLNTP1		Indicates the source of the student's loans during undergraduate education.
DEGPR		The student has earned degrees or certificates since high school.
DEGPRAA		The student has already earned an associate's degree since high school.
DEGPRCRT		The student has already earned an undergraduate certificate/diploma since high school.
DEGPRMS		The student has already earned a master's degree since high school.
DEGPRPTB		The student has already earned a post-baccalaureate certificate since high school.
DEGPRPTM		The student has already earned a post-master's certificate since high school.
DEPCARE		The student has dependent children in daycare during the academic year.
DEPCOST		The student's monthly daycare costs for dependent children during the academic year.
DEPEND5A		Student's dependency status including dependents and marital status during the academic year.
DEPNUM		Number of student's dependents (children and others) during the academic year.
DEPOTHER		Indicates whether the student had dependents other than children.
DEPTYPE		Indicates whether the student has dependents who are their children, not their children, or both.
DEPYNG		The age of the student's youngest child during the academic year.
EFCAID		The total amount of financial aid that is subject to federal EFC limitations if the student received need-based federal aid.
EFFORT18		Net price after all aid except work-study as a percentage of total income.
EFFORT3		Net price after grants as a percentage of total income in the year two years prior to the survey year.
EFFORT9		Net tuition after all grants as a percentage of total income in the year two years prior to the survey year.
EMPLWAIV		Tuition waivers for staff and families of staff at the institution attended during the academic year.
EMPLYAM3		Indicates the amount of tuition aid received from the student's or the parents' employers in the academic year.
ENLEN		Number of months enrolled between July and June of the academic year.
ENR01		Monthly enrollment status for July of the academic year.
ENR02		Monthly enrollment status for August of the academic year.
ENR03		Monthly enrollment status for September of the academic year.
ENR04		Monthly enrollment status for October of the academic year.
ENR05		Monthly enrollment status for November of the academic year.
ENR06		Monthly enrollment status for December of the academic year.
ENR07		Monthly enrollment status for January of the academic year.
ENR08		Monthly enrollment status for February of the academic year.
ENR09		Monthly enrollment status for March of the academic year.
ENR10		Monthly enrollment status for April of the academic year.
ENR11		Monthly enrollment status for May of the academic year.
ENR12		Monthly enrollment status for June of the academic year.
ENRFALL		Student was enrolled during the July through December term.
ENRLSIZE		Fall enrollment.
ENRSPR		Student was enrolled during the January through June term.
ENRSTAT		Student's enrollment pattern during the academic year.
FEDBEN		This variable indicates whether any member of the student's household received any of the following federal benefits during the academic year: food Stamps Benefits, free/Reduced Price School Lunch Benefits, supplemental Security Income Benefits, TANF Benefits, or WIC Benefits
FEDBENA		This variable indicates whether any member of the student's household received the following federal benefit during the academic year: Food Stamp Benefit.
FEDBENB		This variable indicates whether any member of the student's household received the following federal benefit during the academic year: Free or Reduced Price School Lunch Benefits.
FEDBENC		This variable indicates whether any member of the student's household received the following federal benefit during the academic year: Supplemental Security Income Benefits.
FEDBEND		This variable indicates whether any member of the student's household received the following federal benefit during the academic year: TANF Benefits.
FEDBENE		This variable indicates whether any member of the student's household received the following federal benefit during the academic year: WIC Benefits.
FEDCUM1		Cumulative federal loan amounts borrowed for undergraduate education through July 1 of the survey year.
FEDOWE1		Indicates total amount owed on all federal loans for undergraduate education as of late in the survey year.
FGRTLN		Total amount of federal student loans and federal grants received during the academic year.
FLNPCT6		Ratio of federal loans to federal aid (excluding veterans benefits) received during the academic year.
GRNTSRC		Grant package by source of grant received during the academic year.
GRTCST		Total grants received as a percentage of the total student budget.
GRTLOAN		Ratio of total grants to total loans received during the academic year.
GRTRATIO		Ratio of total grants to total grants and loans received during the academic year.
GRTSNEED		Grant amount exceeding federal need.
HIGHLVEX		The highest level of education that the student ever expects to complete.
HISPANIC		Student is of Hispanic or Latino origin.
HISPTYPE		Type of Hispanic or Latino origin.
HLOFFER		Highest level of postsecondary degree or award offered at the NPSAS sample institution attended during the academic year.
HOMESTUD		The student owns a home or pays a mortgage on a home.
IMMIGEN		Number of generations the student's family has been in the U.S.
IMMIGRA		Student's immigrant status.
INCPCT1		The adjusted cost of attendance at the NPSAS institution as a percentage of total income in the year two years prior to the survey year.
INCPCT2		The tuition charged at the NPSAS institution as a percentage of total income.
INJURIS		Indicates whether the tuition charged at public NPSAS institutions was in or out of jurisdiction.
JOBEARN		The student's total amount earned from work (excluding work-study, assistantship, and traineeship) during the academic year.
JOBENR		The student's intensity of work (excluding work-study/assistantship/traineeship) while enrolled during the academic year.
JOBHOUR		The average number of hours worked per week during the academic year (excluding work-study, fellowships, assistantships, and traineeships).
JOBONOFF		The location of the student's job.
JOBROLE		The student's primary role while enrolled at NPSAS and also working.
JOBTYPE2		Indicates the student has a work-study/assistantship job, a regular job, or both.
LNREPAY		The student expects help with repaying their student loans, from an individual other than their spouse.
LOANCST		Total student loans (excluding Parent PLUS) received as a percentage of the total student budget.
LOCALE		The degree of Urbanization in which the NPSAS institution is located.
LOCALEST		Input to this derived variable was the best-known current address after data collection.
MFT		Number of months enrolled full time between July and June of the academic year.
MHT		Number of months enrolled half-time between July and June of the academic year.
MLT		Number of months enrolled less than half-time between July and June of the academic year.
MNTRENT		The average monthly rent or mortgage the student paid during the academic year.
MPT		Number of months enrolled part-time between July and June of the academic year.
NETCST10		Tuition and fees minus federal grants for the academic year.
NETCST12		Tuition and fees minus state grants for the academic year.
NETCST14		Tuition and fees minus non-federal grants for the academic year.
NETCST15		Tuition and fees minus all state and institutional grants for the academic year.
NETCST16		Net total price after all federal and state grants for the academic year.
NETCST17		Net total price after all grants and loans for the academic year.
NETCST18		Net total price after all financial aid except work-study for the academic year.
NETCST2		Net total price after all federal grants for the academic year.
NETCST4		Net total price after all grants and one-half of all loans for the academic year.
NETCST41		Net total price after all financial aid except private loans for the academic year four years prior to the survey year.
NETCST9		Tuition and fees minus all grants for the academic year.
NFEDCUM1		Cumulative non-federal loan amounts borrowed for undergraduate education through July 1 of the survey year.
OBEREG		Region where NPSAS sample institution is located.
OTHTYPE2		Total amount of aid received during the academic year that was not classified by type as grants and loans to students, but included work-study.
PCTPOV		Indicates total income as a percentage of the federal poverty level thresholds for the year two years prior to the survey year.
PDADED		Father's highest level of education.
PELLFST		The first year that a Pell grant was received during the years covered by the survey.
PELLLST		The last year that a Pell grant was received during the years covered by the survey.
PELLYRS		Number of years that a Pell grant was received.
PERKCUM1		Indicates the cumulative amount of Perkins loans ever borrowed for undergraduate education through July 1 of the survey year.
PMOMED		Mother's highest level of education.
PRIVCST		Private loans received as a percentage of the total student budget.
PRIVLRAT		Ratio of private loans to total loans received during the academic year.
PRIVPACK		Loan package by whether the loan received was private (alternative) or not during the academic year.
PRIVPCT		Ratio of private loans to total aid received during the academic year.
REANOAPA		The student did not apply for financial aid because he/she did not want to take on debt.
REANOAPB		The student did not apply for financial aid because the application forms were too much work or too time consuming.
REANOAPC		The student did not apply for financial aid he/she did not have enough information about how to apply for financial aid.
REANOAPD		The student did not apply for financial aid because he/she did not need financial aid.
REANOAPE		The student did not apply for financial aid because he/she thought he/she would be ineligible.
SECTOR1		Sector of the NPSAS sample institution attended during the academic year.
SECTOR4		Sector of the NPSAS sample institution attended during the academic year, for students who attended only one institution.
SFEDTAX		The amount of federal income tax paid by the independent student in the year two years prior to the survey year.
SINCOL		Number of persons in the independent student's household who attended college during the academic year.
SJEARN		The total amount the student earned from his or her assistantship/fellowship/traineeship/work-study job during the academic year.
SJHOURS		The average number of hours the student worked per week during the academic year.
SNEED1		The student's total need for need-based financial aid.
SNEED2		The remaining need after all financial aid (need-based and non-need-based) received.
SNEED3		The remaining need after all federal grants. Equal to the total student budget minus Expected Family Contribution, and minus federal grants.
SNEED4		The remaining need after all grants and federal need-based aid. Equal to the total student budget minus Expected Family Contribution, and minus aid subject to the federal EFC limitation.
SNEED7		The remaining need after federal and state grant aid. Equal to the total student budget minus expected family contribution, and minus federal and state grants.
SNEED8		The remaining need after all federal, state, and other grants. Equal to the total student budget minus the Expected Family Contribution, minus federal, state, and outside grants.
SNEED9		The remaining need after all financial aid received except private loans.
SPINCOL		The student's spouse attended college or graduate school during the academic year.
STAFFRAT		Ratio of Stafford loans to total loans received during the academic year.
STAFFST		This variable indicates the first year that a Direct Subsidized or Unsubsidized Loan (also known as subsidized and unsubsidized Stafford Loan) was received.
STAFLST		This variable indicates the last year that a Direct Subsidized or Unsubsidized Loan (also known as subsidized and unsubsidized Stafford Loan) was received.
STAFTYPE		This variable indicates the combination of subsidized and unsubsidized Stafford loans received at all institutions attended during the academic year.
STAFYRS		The number of years that the student received Direct Subsidized Loans or Direct Unsubsidized Loans (also known as subsidized and unsubsidized Stafford Loans).
STAPCT		Ratio of total state aid to total aid received during the academic year.
STAXFILE		Whether or not independent student filed federal income tax for the year two years prior to the survey year.
STEMMAJ		This variable indicates the student's major field of study during the academic year with a focus on science, technology, engineering, and mathematics (STEM) fields.
STGRPCT		Ratio of total state grants to total aid received during the academic year.
STSBCUM1		Cumulative subsidized Stafford loan amounts borrowed for undergraduate education through July 1 of the survey year.
STUDMULT		Number of institutions attended during the academic year.
STYPELST		Student type at the NPSAS sample institution during the academic year.
SUBCUM1		Cumulative federal subsidized loan amounts borrowed for undergraduate education through July 1 of the survey year.
SUBLOAN		Total amount of federal Title IV subsidized loans received during the academic year.
TFEDGRT2		Total amount of all federal grants, veterans benefits and Department of Defense aid received during the academic year.
TGRTLN		Total amount of loans and grants received during the academic year.
TITIVAMT		Total amount of federal Title IV financial aid received during the academic year.
TNFEDAID		Total amount of non-federal financial aid received during the academic year.
TNFEDGRT		Total amount of non-federal grants received during the academic year.
TNFEDLN		Total amount of non-federal loans received during the academic year.
TOTAID5		Total amount of all financial aid received except for work-study during the academic year.
TOTAID7		Total amount of all financial aid received during the academic year except for federal veterans benefits.
TOTGRT2		Total amount of all grants, veteran's benefits and Department of Defense aid received during the academic year.
TOTGRT4		Total amount of state and institutional grants received in the survey year.
TOTLOAN3		Total amount of all loans excluding private loans received during the academic year.
UNSBLOAN		Total amount of all unsubsidized loans from any source received during the academic year.
USBORN		The student was born in the United States.
VETBEN		Indicates the total amount of federal veterans' benefit received in the academic year.
WORKPCT		Ratio of total work-study to total aid received during the academic year.

1/12/16

Tables from Persistence and Attainment of 2011–12 First-Time Postsecondary Students After 3 Years (BPS:12/14 First Look) are now available in the DataLab Tables Library.

1/12/16

Newly released study is now available in PowerStats and QuickStats. Learn more about the 2011-12 Beginning Postsecondary Students Longitudinal Study, First Follow-up (BPS:12/14).

1/12/16

Newly released study is now available in PowerStats and QuickStats. Learn more about the 2011-12 Beginning Postsecondary Students Longitudinal Study, First Follow-up (BPS:12/14).

12/1/15

Important update to financial aid variable documentation. Learn More.


    Financial Aid Variables
    
    
        Unless otherwise specified, variables based on NSLDS data were derived with a filter that removed loans borrowed prior to July 1995.  This loan-level filter was applied to balance the goal of including as many loans as possible while minimizing the use of partial loan information. However, since consolidated loans contain multiple loans, each with potentially different dates of origination, pre-1995 loans may be included in the total consolidated amount.  See codebook entries for NSLDS derived variables for more information. Respondents who had taken out a loan before July 1995 can be identified with variable LOANBF071995.

12/1/15

Additional 2008-12 Baccalaureate and Beyond (B&B:08/12) variables released. See list.


    2008-12 Baccalaureate and Beyond (B&B:08/12)

    
    
        New variables now available in the 2008-12 Baccalaureate and Beyond PowerStats. The following variables have been added:


        
            
                Variable Name
                Variable Label
            
            B2DFROCRECON Number of separate deferments granted for economic difficulty as of 2012
            B2DFROCRENR Number of separate deferments granted for student enrollment as of 2012
            B2DFROCRFAM Number of separate deferments granted for family or disability as of 2012
            B2DFROCRGOV Number of separate deferments granted for government program (Action, Peace Corps, Head Start, NOAA) as of 2012
            B2DFROCRMIL Number of separate deferments granted for military or law enforcement as of 2012
            B2DFROCRNUM Total number of separate deferment incidents as of 2012
            B2DFROCRREAS Reason for most frequently-granted deferment as of 2012
            B2DFROCRTEA Number of separate deferments granted for teacher, medical, or non-profit as of 2012
            LOANBF071995 Borrowed federal loans before July 1995

Variable Name	Variable Label
B2DFROCRECON	Number of separate deferments granted for economic difficulty as of 2012
B2DFROCRENR	Number of separate deferments granted for student enrollment as of 2012
B2DFROCRFAM	Number of separate deferments granted for family or disability as of 2012
B2DFROCRGOV	Number of separate deferments granted for government program (Action, Peace Corps, Head Start, NOAA) as of 2012
B2DFROCRMIL	Number of separate deferments granted for military or law enforcement as of 2012
B2DFROCRNUM	Total number of separate deferment incidents as of 2012
B2DFROCRREAS	Reason for most frequently-granted deferment as of 2012
B2DFROCRTEA	Number of separate deferments granted for teacher, medical, or non-profit as of 2012
LOANBF071995	Borrowed federal loans before July 1995

10/10/15

2008-12 Baccalaureate and Beyond (B&B:08/12) variables revised. Learn More.


    Variables With Revised Data in B&B:2012

    
    
        Newly revised variables now available in the 2008-12 Baccalaureate and Beyond PowerStats and QuickStats. The following variables have been revised:
						
						  
								
Name      Label      How variable was revised      # of cases changed
								B2FDDUE1 Cumulative federal amount owed (principal and interest) for undergraduate as of 2012 Cumulative amount was revised due to re-allocation of consolidated amounts to graduate level (B2FDCUM2) 1,320
								B2FDDUE2 Cumulative federal amount owed (principal and interest) for graduate as of 2012 Cumulative amount was revised due to re-allocation of consolidated amounts to graduate level (B2FDCUM2) 1,310
								B2DFR_AVG Average number of deferments per loan as of 2012 Consolidated and cancelled loan amounts were removed from the denominator 50
								B2DLQ_AVG Average number of delinquencies per federal loan as of 2012 Consolidated and cancelled loan amounts were removed from the denominator 30
								B2FBPERLN Average number of forbearances per loan as of 2012 Consolidated and cancelled loan amounts were removed from the denominator 40
								B2CNSCUM Amount of federal loans consolidated as of 2012 Parent Plus loans and duplicate loans were removed from the total consolidated amount 600
						
						

						

						The values for the following variables have changed as a result of the changes to the variables above:
						
						  
								
Name      Label      # of cases changed
									B2BORAT Cumulative amount borrowed for education as of 2012 70
									B2DATFB Latest forbearance date for borrower as of 2012 10
									B2DEBTRT Ratio of federal loans to annualized salary as of 2012 20
									B2DEFER Ever had a deferment on a loan as of 2012 10
									B2DFR_ECON Number of deferments for economic difficulty for all loans as of 2012 10
									B2DFR_ENR Number of deferments for student enrollment for all loans as of 2012 20
									B2DFR_FAM Number of deferments for family or disability for all loans as of 2012 <10
									B2DFR_GOV Number of deferments for government program (Action, Peace Corps, Head Start, NOAA) for all loans as of 2012 <10
									B2DFR_MIL Number of deferments for military or law enforcement for all loans as of 2012 <10
									B2DFR_NUM Total number of deferments for all loans as of 2012 20
									B2DFR_REAS Most common deferment reason for borrower for all loans as of 2012 10
									B2DFR_TEA Number of deferments for teacher, medical, or non-profit for all loans as of 2012 <10
									B2DLQ_NOW Currently in delinquent status - has a federal loan in delinquency in the 2011-12 academic year <10
									B2EVERDAFB Ever had loans in deferment or forbearance as of 2012 10
									B2EVERPIF Ever had a loan paid in full as of 2012 <10
									B2FDDUE3 Cumulative federal amount owed (principal and interest) for all education as of 2012 10
									B2FDOWE1 Cumulative federal amount owed (principal) for undergraduate as of 2012                                            1,320 
									B2FEDCUM1 Cumulative amount borrowed in federal loans as of 2012 - undergraduate level 70
									B2FEDCUM3 Cumulative total amount borrowed in federal loans as of 2012 70
									B2FEDFYEAR First year borrowed federal loans as of 2012 <10
									B2FEDLYEAR Last year borrowed federal loans as of 2012 <10
									B2FORBAR Ever had any loans in forbearance as of 2012 10
									B2GP_USE Borrower consolidated loans as of 2012 <10
									B2LASTLEV Grade level when last federal loan was received as of 2012 <10
									B2LASTSTDT Date of status of latest federal loan as of 2012 20
									B2LNSTAT Status of latest federal loan as of 2012 10
									B2LOANPAID All federal loans were paid in full as of 2012 <10
									B2OWELRP Total amount owed at time last entered repayment 40
									B2OWEPNLRP Outstanding principle amount at date last entered repayment 40
									B2OWEPRIN Latest federal amount owed - principal as of 2012 50
									B2PAYSTAT Repayment status for any loans in 2012 (federal and private) <10
									B2REPLN Repayment plan of latest federal loan in 2012 10
									B2T4XDUE Cumulative Stafford and Perkins loan amount owed (principal and interest) as of 2012 <10
									B2TOTDUE3 Cumulative amount owed for education loans as of 2012 (federal and private, principal and interest) 10

Name	Label	How variable was revised	# of cases changed
B2FDDUE1	Cumulative federal amount owed (principal and interest) for undergraduate as of 2012	Cumulative amount was revised due to re-allocation of consolidated amounts to graduate level (B2FDCUM2)	1,320
B2FDDUE2	Cumulative federal amount owed (principal and interest) for graduate as of 2012	Cumulative amount was revised due to re-allocation of consolidated amounts to graduate level (B2FDCUM2)	1,310
B2DFR_AVG	Average number of deferments per loan as of 2012	Consolidated and cancelled loan amounts were removed from the denominator	50
B2DLQ_AVG	Average number of delinquencies per federal loan as of 2012	Consolidated and cancelled loan amounts were removed from the denominator	30
B2FBPERLN	Average number of forbearances per loan as of 2012	Consolidated and cancelled loan amounts were removed from the denominator	40
B2CNSCUM	Amount of federal loans consolidated as of 2012	Parent Plus loans and duplicate loans were removed from the total consolidated amount	600

Name	Label	# of cases changed
B2BORAT	Cumulative amount borrowed for education as of 2012	70
B2DATFB	Latest forbearance date for borrower as of 2012	10
B2DEBTRT	Ratio of federal loans to annualized salary as of 2012	20
B2DEFER	Ever had a deferment on a loan as of 2012	10
B2DFR_ECON	Number of deferments for economic difficulty for all loans as of 2012	10
B2DFR_ENR	Number of deferments for student enrollment for all loans as of 2012	20
B2DFR_FAM	Number of deferments for family or disability for all loans as of 2012	<10
B2DFR_GOV	Number of deferments for government program (Action, Peace Corps, Head Start, NOAA) for all loans as of 2012	<10
B2DFR_MIL	Number of deferments for military or law enforcement for all loans as of 2012	<10
B2DFR_NUM	Total number of deferments for all loans as of 2012	20
B2DFR_REAS	Most common deferment reason for borrower for all loans as of 2012	10
B2DFR_TEA	Number of deferments for teacher, medical, or non-profit for all loans as of 2012	<10
B2DLQ_NOW	Currently in delinquent status - has a federal loan in delinquency in the 2011-12 academic year	<10
B2EVERDAFB	Ever had loans in deferment or forbearance as of 2012	10
B2EVERPIF	Ever had a loan paid in full as of 2012	<10
B2FDDUE3	Cumulative federal amount owed (principal and interest) for all education as of 2012	10
B2FDOWE1	Cumulative federal amount owed (principal) for undergraduate as of 2012	1,320
B2FEDCUM1	Cumulative amount borrowed in federal loans as of 2012 - undergraduate level	70
B2FEDCUM3	Cumulative total amount borrowed in federal loans as of 2012	70
B2FEDFYEAR	First year borrowed federal loans as of 2012	<10
B2FEDLYEAR	Last year borrowed federal loans as of 2012	<10
B2FORBAR	Ever had any loans in forbearance as of 2012	10
B2GP_USE	Borrower consolidated loans as of 2012	<10
B2LASTLEV	Grade level when last federal loan was received as of 2012	<10
B2LASTSTDT	Date of status of latest federal loan as of 2012	20
B2LNSTAT	Status of latest federal loan as of 2012	10
B2LOANPAID	All federal loans were paid in full as of 2012	<10
B2OWELRP	Total amount owed at time last entered repayment	40
B2OWEPNLRP	Outstanding principle amount at date last entered repayment	40
B2OWEPRIN	Latest federal amount owed - principal as of 2012	50
B2PAYSTAT	Repayment status for any loans in 2012 (federal and private)	<10
B2REPLN	Repayment plan of latest federal loan in 2012	10
B2T4XDUE	Cumulative Stafford and Perkins loan amount owed (principal and interest) as of 2012	<10
B2TOTDUE3	Cumulative amount owed for education loans as of 2012 (federal and private, principal and interest)	10

10/10/15

Additional 2008-12 Baccalaureate and Beyond (B&B:08/12) variables released.  See list.


    Now available in the 2008-12 Baccalaureate and Beyond PowerStats

    
    
        New variables now available in the 2008-12 Baccalaureate and Beyond PowerStats. The following variables have been added:


        
            
                Name
                    
                Label
            
            B2DFROCRECON Number of separate deferments granted for economic difficulty as of 2012
            B2DFROCRENR Number of separate deferments granted for student enrollment as of 2012
            B2DFROCRFAM Number of separate deferments granted for family or disability as of 2012
            B2DFROCRGOV Number of separate deferments granted for government program (Action, Peace Corps, Head Start, NOAA) as of 2012
            B2DFROCRMIL Number of separate deferments granted for military or law enforcement as of 2012
            B2DFROCRNUM Total number of separate deferment incidents as of 2012
            B2DFROCRREAS Reason for most frequently-granted deferment as of 2012
            B2DFROCRTEA Number of separate deferments granted for teacher, medical, or non-profit as of 2012
            LOANBF071995 Borrowed federal loans before July 1995

Name		Label
B2DFROCRECON		Number of separate deferments granted for economic difficulty as of 2012
B2DFROCRENR		Number of separate deferments granted for student enrollment as of 2012
B2DFROCRFAM		Number of separate deferments granted for family or disability as of 2012
B2DFROCRGOV		Number of separate deferments granted for government program (Action, Peace Corps, Head Start, NOAA) as of 2012
B2DFROCRMIL		Number of separate deferments granted for military or law enforcement as of 2012
B2DFROCRNUM		Total number of separate deferment incidents as of 2012
B2DFROCRREAS		Reason for most frequently-granted deferment as of 2012
B2DFROCRTEA		Number of separate deferments granted for teacher, medical, or non-profit as of 2012
LOANBF071995		Borrowed federal loans before July 1995

10/10/15

Important update to financial aid variable documentation. Learn More.


    Important update to financial aid variable documentation
    
    
        Unless otherwise specified, variables based on NSLDS data were derived with a filter that removed loans borrowed prior to July 1995.  This loan-level filter was applied to balance the goal of including as many loans as possible while minimizing the use of partial loan information. However, since consolidated loans contain multiple loans, each with potentially different dates of origination, pre-1995 loans may be included in the total consolidated amount.  See codebook entries for NSLDS derived variables for more information. Respondents who had taken out a loan before July 1995 can be identified with variable LOANBF071995.

10/8/15

Tables from Trends in Undergraduate Nonfederal Grant and Scholarship Aid by Demographic and Enrollment Characteristics, Selected Years: 1999-2000 to 2011-12 (NPSAS:96, NPSAS:2000, NPSAS:04, NPSAS:08 and NPSAS:12) are now available in the DataLab Tables Library.

10/8/15

Tables from Demographic and Enrollment Characteristics of Nontraditional Undergraduates: 2011-12 (NPSAS:12) are now available in the DataLab Tables Library.

10/8/15

Tables from Trends in Pell Grant Receipt and the Characteristics of Pell Grant Recipients: Selected Years, 1999-2000 to 2011-12 (NPSAS:2000, NPSAS:04, NPSAS:08 and NPSAS:12) are now available in the DataLab Tables Library.

9/16/15

Now available in PowerStats: the School Survey on Crime and Safety 2009-10 (SSOCS:2010).

7/29/15

Now available in PowerStats: Education Longitudinal Study of 2002 (ELS:2002).

7/15/15

Now available in DataLab: TrendStats.
TrendStats allows users to compare estimates for like variables across multiple dataset years within a single table. Users can create percentage distribution tables or average, median, and percent greater than tables for both Undergraduate and Graduate respondents of the 1996, 2000, 2004, 2008, and 2012 National Postsecondary Student Aid Surveys (NPSAS).
Visit the Learning Center to learn more.

5/29/15

2008-2012 Baccalaureate and Beyond (B&B:08/12) PowerStats updated. This update adds many new variables to the 2008-2012 Baccalaureate and Beyond PowerStats, including approximately 70 variables on teaching.

5/25/15

Tables from High School Dropouts and Stopouts: Demographic Backgrounds, Academic Experiences, Engagement, and School Characteristics (HSLS:09) are now available in the DataLab Tables Library.

4/2/15

Tables for Trends in Graduate Student Financing: Selected Years, 1995–96 to 2011–12 (NPSAS:96, NPSAS:2000, NPSAS:04, NPSAS:08 and NPSAS:12) are now available in the DataLab Tables Library.

4/2/15

Tables from What Is the Price of College? Total, Net, and Out-of-Pocket Prices by Type of Institution in 2011-12 (NPSAS:12)  are now available in the DataLab Tables Library.

3/17/15

Tables from Baccalaureate Degree Recipients' Early Labor Market and Education Outcomes: 1994, 2001, and 2009 (B&B:93/94, B&B:2000/01, and B&B:08/09) are now available in the DataLab Tables Library.

3/17/15

Tables from Student Financing of Undergraduate Education Web Table: 2011-12 (NPSAS:12) are now available in the DataLab Tables Library.

2/11/15

New variables added to 2011-12 National Postsecondary Student Aid Study, Undergraduate Students  and Graduate Students (NPSAS:12) PowerStats: DECMAJ, BUDGETBK, and MAJORCTE added to Undergraduate Students and BUDGETBK added to Graduate Students.

12/29/14

Tables for Profile and Financial Aid Estimates of Graduate Students: 2011-12 (NPSAS:12) are now available in the DataLab Tables Library.

12/29/14

Tables for Profile of Undergraduate Students: 2011-12 (Web Tables) (NPSAS:12) are now available in the DataLab Tables Library.

12/11/14

Tables for Transferability of Postsecondary Credit Following Student Transfer or Coenrollment (BPS:04/09) are now available in the DataLab Tables Library.

9/15/14

New variables added to 2007-08 National Postsecondary Student Aid Study, Undergraduate Students (NPSAS:2007-08). INSTCAT, DISTLOC2, TCHPLN, and TCHCRS are now available on PowerStats.

8/21/14

Now available in PowerStats: 2011-12 Schools and Staffing Survey (SASS). Data from the following questionnaires are included: Public and Private Schools; Public and Private School Teachers; Public and Private School Principals; Public School Districts; and Public School Library Media Centers.

7/8/14

Tables for Baccalaureate and Beyond: A First Look at the Employment Experiences and Lives of College Graduates, 4 Years On (B&B:08/12) are now available in the DataLab Tables Library.

7/8/14

Now available in PowerStats and QuickStats: 2008-2012 Baccalaureate and Beyond (B&B:08/12).

ABOUT QUICKSTATS

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datasets

100

selected variables per study

Allows users to create simple tables and charts

ABOUT TRENDSTATS

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datasets

Allows users to create complex tables spanning multiple data collection years

Adjusts dollar variables for inflation

ABOUT POWERSTATS

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RESEARCH QUESTIONS

View All

Has the net cost of college attendance changed over the last 12 years?

Use NPSAS and TrendStats to answer this question

What are the persistence and attainment rates among first time college students?

Use BPS and PowerStats to answer this question

What is the average number of hours teachers are required to receive their base pay versus the average number of hours spent on all teaching?

Use SASS and PowerStats to answer this question

What percentage of high school students enroll in postsecondary education and what types of institutions do they attend?

Use ELS and PowerStats to answer this question

How often does cyberbullying occur among America’s public schools?

Use SSOCS and QuickStats to answer this question

How has the percentage of autistic children among children with disabilities changed over time?

Use PEELS and PowerStats to answer this question

HANDOUTS

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ABOUT DATALAB

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FINDINGS

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Among college graduates, 13% go into teaching within four years of graduation (Source: B&B:08/12)

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Among first-generation college students, 40% were no longer enrolled 3 years after beginning their postsecondary education (Source: BPS:12/14)

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As of 2012, the highest levels of education completed by 2002 High School Sophomores were: 33% bachelor’s degree or higher; 9% associate’s degree; 10% undergraduate certificate; 32% postsecondary attendance but no postsecondary credential; 13% high school diploma or equivalent; and 3% less than high school completion (Source: ELS:2002)

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On average, fall 2009 ninth-graders had earned 3.6 credits in math and 3.3 credits in science by 2013. On average, students had earned 7.6 credits in STEM courses (Source: HSLS:09)

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From 1996 to 2012, the percentage of undergraduates that applied for federal financial aid rose from 44.5% to 70.1% (Source: NPSAS:1995-96 & NPSAS:2011-12)

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Part-time instructional faculty and staff averaged $44,800 in outside income other than consulting income in 2003 compared with $12,400 earned by full-time faculty (Source: NSOPF:2004)

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Preschoolers with disabilities were disproportionately male, 70 percent versus 30 percent female (Source: PEELS)

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The majority of private elementary schools in 2011-12 (13,459 of 30,861 total schools) enrolled less than 50 students (Source: PSS:2011-12)

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More private school students in 2013–14 were enrolled in kindergarten (463,067) than in any other grade level (Source: PSS:2011-12)

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Among Public School Teachers surveyed in 2011-12, 39.9% reported having a Bachelor’s Degree, 47.7% a Master’s Degree, and 8.7% reported having higher than a Master’s degree (Source: SASS:2011-12)

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All Topics

Topics

Attendance and Enrollment
- Enrollment Intensity
- Patterns
Early Childhood
- Early Childhood
Education History
- Academic Experiences
- Academic Performance
- Admissions
- Assessments
- Field of Study
- Outcomes
- Persistence and Attainment
- Programs and Courses
- STEM
- Transcripts
- Transfer
Educational Transitions
- High school to college
- Preschool to elementary school
Employment
- Employment characteristics
- History
- Status
- While enrolled
Faculty and Staff
- Compensation and Benefits
- Education and Training
- Experiences and Attitudes
- Faculty Characteristics
- Institutional Characteristics
- Tenure
Finances
- Application
- Borrowing and Debt
- Cost and Net Price
- Debt
- Expenses
- Federal Aid
- Grants
- Income
- Loans
- Non-Federal Aid
- Support
- Work study
Parents and Family
- Dependency and Marital Status
- Parent Expectations, Attitudes, and Beliefs
- Parental Involvement
- Student's Parents
- Student's Spouse and Dependents
Pre-K and K-12 Staff
- K-12 Staff
- Pre-K Staff
- School Principals
- Security Staff
School and Institutional Characteristics
- Admissions and Tuition
- Attendance and Enrollment
- Classroom Settings, Sizes, and Organization
- Crime and Safety
- Demographics
- Facilities
- Institution/School Type, Level, and Sector
- School Practices and Programs
- Technology Use
School Districts
- District Characteristics
- Hiring and Compensation
Special Education
- Programs and Services
- Teachers and Staffing
Staffing
- Number of Teachers and Staff
- Vacancies
Student Characteristics
- Demographics
- Disabilities
- Military or Public Service
- Residence and Migration
Teachers and Teaching
- Compensation and Benefits
- Credentials
- Demographics
- Education and Training
- Experiences, Performance, and Attitudes
- Professional Development

68,69

134,54,31,20

71,53,1,32

121,82,51,24,35,36,22,14,17

122,83,52,12,37,38,13,18,16

151,130,129

154,132,131

135

136

68,69

134,54,31,20

71,53,1,32

121,82,51,24,35,36,22,14,17

122,83,52,12,37,38,18,16,13

151,130,129

154,132,131

135

136

151,130,129

68,69

134,54,31,20

71,53,1,32

121,82,51,24,35,36,14,17,22

122,83,52,12,37,38,18,16,13

133

154,132,131

135

136

68,69

134,54,31,20

71,53,1,32

121,82,51,24,35,36,22,14,17

122,83,52,12,37,38,13,18,16

133

154,132,131

135

136

59,60,61,117,118,119,114,115,116,111,112,113

134,54,31,20

71,53,1,32

121,82,51,24,35,36,22,14,17

122,83,52,12,37,38,18,16,13

127

135

68,69

134,54,31,20

71,53,1,32

121,82,51,24,35,36,22,14,17

122,83,52,12,37,38,13,18,16

135

136

134,54,31,20

71,53,1,32

121,82,51,24,35,36,14,17,22

122,83,52,12,37,38,18,16,13

133

135

68,69

134,54,31,20

71,53,1,32

133

135

136

68,69

134,54,31,20

71,53,1,32

121,82,51,24,35,36,14,17,22

122,83,52,12,37,38,18,16,13

133

135

136

68,69

134,54,31,20

71,53,1,32

121,82,51,24,35,36,14,17,22

122,83,52,12,37,38,18,16,13

133

135

136

68,69

134,54,31,20

71,53,1,32

121,82,51,24,35,36,14,17,22

122,83,52,12,37,38,18,16,13

135

136

68,69

134,54,31,20

71,53,1,32

135

136

68,69

134,54,31,20

71,53,1,32

121,82,51,24,35,22,14,17

135

136

68,69

71,53,1,32

121,82,51,24,35,36,14,17,22

135

136

68,69

134,54,31,20

71,53,1,32

121,82,51,24,35,36,22,17,14

122,83,52,12,37,38,16,18,13

133

154,132,131

135

136

134,54,31,20

122,83,52,12,37,38,13,16,18

133

154,132,131

68,69

134,54,31,20

71,53,1,32

121,82,51,24,35,36,14,17,22

122,83,52,12,37,38,18,16,13

133

154,132,131

135

136

134,54,31,20

71,53,1,32

121,82,51,24,35,36,14,17,22

122,83,52,12,37,38,18,16,13

133

28,23,25,26

134,54,31,20

71,53,1,32

121,82,51,24,35,36,22,17,14

122,83,52,12,37,38,18,13,16

134,54,31,20

71,53,1,32

121,82,51,24,35,36,22,17,14

122,83,52,12,37,38,18,13,16

134,54,31,20

71,53,1,32

121,82,51,24,35,36,22,17,14

122,83,52,12,37,38,18,13,16

134,54,31,20

71,53,1,32

134,54,31,20

71,53,1,32

121,82,51,24,35,36,14,17,22

122,83,52,12,37,38,18,16,13

134,54,31,20

71,53,1,32

121,82,51,24,35,36,22,17,14

122,83,52,12,37,38,18,13,16

134,54,31,20

71,53,1,32

121,82,51,24,35,36,22,17,14

122,83,52,12,37,38,18,13,16

68,69

134,54,31,20

71,53,1,32

121,82,51,24,35,36,14,17,22

122,83,52,12,37,38,18,16,13

133

135

136

134,54,31,20

71,53,1,32

121,82,51,24,35,36,22,17,14

122,83,52,12,37,38,18,13,16

134,54,31,20

71,53,1,32

121,82,51,24,35,36,22,17,14

122,83,52,12,37,38,18,13,16

134,54,31,20

71,53,1,32

121,82,51,24,35,36,14,17,22

122,83,52,12,37,38,18,16,13

134,54,31,20

71,53,1,32

121,82,51,24,35,36,22,17,14

122,83,52,12,37,38,18,13,16

133

68,69

134,54,31,20

71,53,1,32

121,82,51,24,35,36,14,17,22

122,83,52,12,37,38,13,16,18

151,130,129

133

154,132,131

135

136

68,69

151,130,129

133

154,132,131

135

136

62,63,64,90,91,92,87,88,89,93,94,95

65,66,67,102,103,104,99,100,101,96,97,98

128,70,74,73,138,139

126

133

154,132,131

68,69

134,54,31,20

71,53,1,32

121,82,51,24,35,36,22,17,14

122,83,52,12,37,38,18,13,16

151,130,129

133

154,132,131

135

136

68,69

134,54,31,20

71,53,1,32

121,82,51,24,35,36,22,17,14

122,83,52,12,37,38,18,13,16

151,130,129

133

154,132,131

136

150,149,75

59,60,61,117,118,119,114,115,116,111,112,113

58,110,109,108

57,107,106,105

127

151,130,129

65,66,67,102,103,104,99,100,101,96,97,98

126

128,70,74,73,138,139

59,60,61,117,118,119,114,115,116,111,112,113

134,54,31,20

71,53,1,32

121,82,51,24,35,36,22,17,14

122,83,52,12,37,38,18,13,16

127

150,149,75

59,60,61,117,118,119,114,115,116,111,112,113

128,70,74,73,138,139

127

151,130,129

154,132,131

150,149,75

65,66,67,102,103,104,99,100,101,96,97,98

59,60,61,117,118,119,114,115,116,111,112,113

58,110,109,108

126

127

62,63,64,90,91,92,87,88,89,93,94,95

65,66,67,102,103,104,99,100,101,96,97,98

128,70,74,73,138,139

126

59,60,61,117,118,119,114,115,116,111,112,113

128,70,74,73,138,139

68,69

134,54,31,20

71,53,1,32

121,82,51,24,35,36,22,17,14

122,83,52,12,37,38,18,13,16

127

135

136

57,107,106,105

128,70,74,73,138,139

68,69

134,54,31,20

71,53,1,32

121,82,51,24,35,36,22,17,14

122,83,52,12,37,38,18,13,16

28,23,25,26

135

136

150,149,75

65,66,67,102,103,104,99,100,101,96,97,98

59,60,61,117,118,119,114,115,116,111,112,113

58,110,109,108

126

127

57,107,106,105

154,132,131

58,110,109,108

59,60,61,117,118,119,114,115,116,111,112,113

128,70,74,73,138,139

127

59,60,117,118,119,114,115,116,111,112,113

58,110,109,108

134,54,31,20

127

59,60,61,117,118,119,114,115,116,111,112,113

58,110,109,108

57,107,106,105

127

59,60,61,117,118,119,114,115,116,111,112,113

58,110,109,108

57,107,106,105

127

68,69

134,54,31,20

71,53,1,32

121,82,51,24,35,36,22,14,17

122,83,52,12,37,38,13,18,16

151,130,129

154,132,131

135

136

134,54,31,20

71,53,1,32

121,82,51,24,35,36,22,14,17

122,83,52,12,37,38,13,18,16

151,130,129

154,132,131

135

68,69

134,54,31,20

71,53,1,32

121,82,51,24,35,36,22,14,17

122,83,52,12,37,38,13,18,16

135

136

68,69

134,54,31,20

71,53,1,32

121,82,51,24,35,36,22,14,17

122,83,52,12,37,38,13,18,16

151,130,129

154,132,131

136

62,63,64,90,91,92,87,88,89,93,94,95

65,66,67,102,103,104,99,100,101,96,97,98

128,70,74,73,138,139

134,54,31,20

126

134,54,31,20

62,63,64,90,91,92,87,88,89,93,94,95

65,66,67,102,103,104,99,100,101,96,97,98

128,70,74,73,138,139

134,54,31

126

62,63,64,90,91,92,87,88,89,93,94,95

65,66,67,102,103,104,99,100,101,96,97,98

58,110,109,108

134,54,31,20

126

62,63,64,90,91,92,87,88,89,93,94,95

65,66,67,102,103,104,99,100,101,96,97,98

134,54,31,20

126

135

62,63,64,90,91,92,87,88,89,93,94,95

65,66,67,102,103,104,99,100,101,96,97,98

58,110,109,108

134,54,31,20

126

Matching Datasets

Dataset Available In:
QuickStats
PowerStats
TrendStats

All Datasets

Dataset Available In:
QuickStats
PowerStats
TrendStats

DATASET	YEAR	GROUP	LAUNCH TOOLS

Title

Enter all the data here.

Dataset Available In:
QuickStats
PowerStats
TrendStats

                    Early Childhood Program Participation
National Postsecondary Student Aid Study, Undergraduate
National Postsecondary Student Aid Study, Graduate
Parent and Family Involvement in Education
School Survey on Crime and Safety 
                    
                    
                    
                    
                    
                    
                    
                    
                    
                    
                    
                    
                    
                    
                    
                    Pre-Elementary Education Longitudinal Study
PEELS
Pre-elementary students who received preschool special education services, as they progressed through the early elementary years
Preschool special education, Programs and services received, Transitions between preschool and elementary school,  Function and performance in preschool, kindergarten, and elementary school
https://ies.ed.gov/ncser/projects/peels
482003/2008qsOnpsOntsOff3,000
Imputation
Imputation was conducted for selected items on the teacher questionnaire and parent interview data. In general, the item missing rate was low. The risk of imputation-related bias was judged to be minimal. The variance inflation due to imputation was also low due to the low imputation rate of 10 percent. Imputation for the supplemental sample increased the amount of data usable for analysis, offsetting the potential risk of bias.
The methods of imputation included: hot-deck imputation, regression, external data source, and a derivation method, based on the internal consistency of inter-related variables.
View methodology reportpeels_subject.pdf6.71 MBpeels_varname.pdf6.63 MB00148
Private School Universe Survey
PSS
Private schools
School Affiliation/Associations, Enrollment, Grades Taught, Staffing, General Information
https://nces.ed.gov/surveys/pss/
1502015-2016qsOnpsOntsOff22,428Weighting
The final weight for PSS data items is the product of the Base Weight and the Nonresponse Adjustment Factor, where:
Base Weight is the inverse of the probability of selection of the school. The base weight is equal to one for all list frame schools. For area frame schools, the base weight is equal to the inverse of the probability of selecting the PSU in which the school resides.
Nonresponse Adjustment Factor is an adjustment that accounts for school nonresponse. It is the weighted (base weight) ratio of the total eligible in-scope schools (interviewed schools plus noninterviewed schools) to the total responding in-scope schools (interviewed schools) within cells. Noninterviewed and out-of-scope cases are assigned a nonresponse adjustment factor of zero.
pss2016_subject.pdfpss2016_varname.pdf00260
1492013-2014qsOnpsOntsOff24,566Weighting
The final weight for PSS data items is the product of the Base Weight and the Nonresponse Adjustment Factor, where:
Base Weight is the inverse of the probability of selection of the school. The base weight is equal to one for all list frame schools. For area frame schools, the base weight is equal to the inverse of the probability of selecting the PSU in which the school resides.
Nonresponse Adjustment Factor is an adjustment that accounts for school nonresponse. It is the weighted (base weight) ratio of the total eligible in-scope schools (interviewed schools plus noninterviewed schools) to the total responding in-scope schools (interviewed schools) within cells. Noninterviewed and out-of-scope cases are assigned a nonresponse adjustment factor of zero.
pss2014_subject.pdfpss2014_varname.pdf00260
752011-2012qsOnpsOntsOff26,983Weighting
The final weight for PSS data items is the product of the Base Weight and the Nonresponse Adjustment Factor, where:
Base Weight is the inverse of the probability of selection of the school. The base weight is equal to one for all list frame schools. For area frame schools, the base weight is equal to the inverse of the probability of selecting the PSU in which the school resides.
Nonresponse Adjustment Factor is an adjustment that accounts for school nonresponse. It is the weighted (base weight) ratio of the total eligible in-scope schools (interviewed schools plus noninterviewed schools) to the total responding in-scope schools (interviewed schools) within cells. Noninterviewed and out-of-scope cases are assigned a nonresponse adjustment factor of zero.
pss2012_subject.pdf377 KBpss2012_varname.pdf368 KB00260
Schools and Staffing Survey, Teachers
SASS
Public and private school teachers
Class Organization, Education and Training, Certification, Professional Development, Working Conditions, School Climate and Teacher Attitudes, Employment and Background Information
https://nces.ed.gov/surveys/sass
622011-2012qsOnpsOntsOff42,000Perturbation
To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.


Imputation
Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.

After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records¹,” and the method of imputation that involves imputing data from donor records is called “hot-deck²” imputation. 


Skips and Missing Values
Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information.
 ¹Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables. 
²Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.
View methodology informationsass12teachpub_subject.pdf5.60 MBsass12teachpub_varname.pdf5.50 MB1332
632011-2012qsOnpsOntsOff42,000Perturbation
To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.


Imputation
Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.

After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records¹,” and the method of imputation that involves imputing data from donor records is called “hot-deck²” imputation. 


Skips and Missing Values
Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information.
 ¹Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables. 
²Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.
View methodology informationsass12teachpriv_subject.pdf4.90 MBsass12teachpriv_varname.pdf4.90 MB2332
642011-2012qsOnpsOntsOff42,000Perturbation
To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.


Imputation
Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.

After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records¹,” and the method of imputation that involves imputing data from donor records is called “hot-deck²” imputation. 


Skips and Missing Values
Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information.
 ¹Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables. 
²Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.
View methodology informationsass12teachcombined_subject.pdf5.60 MBsass12teachcombined_varname.pdf5.55 MB3332
902007-2008qsOnpsOntsOff38,200Perturbation
To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.

Imputation
Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.
After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records¹,” and the method of imputation that involves imputing data from donor records is called “hot-deck²” imputation. 

Skips and Missing Values
Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information.
 ¹Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables.
²Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.
View methodology informationsass08teachpub_subject.pdf4.39 MBsass08teachpub_varname.pdf7.01 MB1337
912007-2008qsOnpsOntsOff6,000Perturbation
To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.

Imputation
Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.
After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records¹,” and the method of imputation that involves imputing data from donor records is called “hot-deck²” imputation. 

Skips and Missing Values
Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information.
 ¹Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables.
²Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.
View methodology informationsass08teachpriv_subject.pdf3.27 MBsass08teachpriv_varname.pdf5.11 MB2337
922007-2008qsOnpsOntsOff44,200Perturbation
To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.

Imputation
Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.
After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records¹,” and the method of imputation that involves imputing data from donor records is called “hot-deck²” imputation. 

Skips and Missing Values
Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information.
 ¹Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables.
²Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.
View methodology informationsass08teachcombined_subject.pdf3.25 MBsass08teachcombined_varname.pdf1.09 MB3337
872003-2004qsOnpsOntsOff43,200Perturbation
To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.

Imputation
Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.
After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records¹,” and the method of imputation that involves imputing data from donor records is called “hot-deck²” imputation. 

Skips and Missing Values
Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information.
 ¹Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables.
²Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.
View methodology informationsass04teachpub_subject.pdf4.39 MBsass04teachpub_varname.pdf4.40 MB13312
882003-2004qsOnpsOntsOff8,000Perturbation
To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.

Imputation
Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.
After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records¹,” and the method of imputation that involves imputing data from donor records is called “hot-deck²” imputation. 

Skips and Missing Values
Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information.
 ¹Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables.
²Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.
View methodology informationsass04teachpriv_subject.pdf6.73 MBsass04teachpriv_varname.pdf3.98 MB23312
892003-2004qsOnpsOntsOff51,200Perturbation
To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.

Imputation
Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.
After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records¹,” and the method of imputation that involves imputing data from donor records is called “hot-deck²” imputation. 

Skips and Missing Values
Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information.
 ¹Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables.
²Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.
View methodology informationsass04teachcombined_subject.pdf1.15 MBsass04teachcombined_varname.pdf1.17 MB33312
931999-2000qsOffpsOntsOff52,000Perturbation
To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.

Imputation
Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.
After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records¹,” and the method of imputation that involves imputing data from donor records is called “hot-deck²” imputation. 

Skips and Missing Values
Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information.
 ¹Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables.
²Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.
View methodology informationsass00teachpub_subject.pdf7.91 MBsass00teachpub_varname.pdf1.39 MB13316
941999-2000qsOffpsOntsOff52,000Perturbation
To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.

Imputation
Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.
After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records¹,” and the method of imputation that involves imputing data from donor records is called “hot-deck²” imputation. 

Skips and Missing Values
Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information.
 ¹Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables.
²Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.
View methodology informationsass00teachpriv_subject.pdf6.34 MBsass00teachpriv_varname.pdf1.38 MB23316
951999-2000qsOffpsOntsOff52,000Perturbation
To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.

Imputation
Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.
After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records¹,” and the method of imputation that involves imputing data from donor records is called “hot-deck²” imputation. 

Skips and Missing Values
Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information.
 ¹Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables.
²Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.
View methodology informationsass00teachcombined_subject.pdf6.60 MBsass00teachcombined_varname.pdf1.91 MB33316
Schools and Staffing Survey, Principals
SASS
Public and private school principals
Experience, Training, Education, and Professional Development, Goals and Decision Making, Teacher and Aide Professional Development, School Climate and Safety, Instructional Time, Working Conditions and Principal Perceptions, Teacher and School Performance
https://nces.ed.gov/surveys/sass
652011-2012qsOnpsOntsOff9,000Perturbation
To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.


Imputation
Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.

After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records¹,” and the method of imputation that involves imputing data from donor records is called “hot-deck²” imputation. 


Skips and Missing Values
Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information.
 ¹Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables. 
²Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.
View methodology informationsass12prinpub_subject.pdf1.99 MBsass12prinpub_varname.pdf1.97 MB1343
662011-2012qsOnpsOntsOff9,000Perturbation
To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.


Imputation
Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.

After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records¹,” and the method of imputation that involves imputing data from donor records is called “hot-deck²” imputation. 


Skips and Missing Values
Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information.
 ¹Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables. 
²Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.
View methodology informationsass12prinpriv_subject.pdf1.98 MBsass12prinpriv_varname.pdf1.90 MB2343
672011-2012qsOnpsOntsOff9,000Perturbation
To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.


Imputation
Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.

After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records¹,” and the method of imputation that involves imputing data from donor records is called “hot-deck²” imputation. 


Skips and Missing Values
Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information.
 ¹Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables. 
²Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.
View methodology informationsass12princombined_subject.pdf1.92 MBsass12princombined_varname.pdf2.05 MB3343
1022007-2008qsOnpsOntsOff7,500Perturbation
To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.

Imputation
Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.
After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records¹,” and the method of imputation that involves imputing data from donor records is called “hot-deck²” imputation. 

Skips and Missing Values
Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information.
 ¹Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables.
²Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.
View methodology informationsass08prinpub_subject.pdf2.00 MBsass08prinpub_varname.pdf1.83 MB1348
1032007-2008qsOnpsOntsOff1,900Perturbation
To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.

Imputation
Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.
After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records¹,” and the method of imputation that involves imputing data from donor records is called “hot-deck²” imputation. 

Skips and Missing Values
Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information.
 ¹Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables.
²Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.
View methodology informationsass08prinpriv_subject.pdf1.80 MBsass08prinpriv_varname.pdf1.61 MB2348
1042007-2008qsOnpsOntsOff9,400Perturbation
To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.

Imputation
Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.
After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records¹,” and the method of imputation that involves imputing data from donor records is called “hot-deck²” imputation. 

Skips and Missing Values
Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information.
 ¹Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables.
²Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.
View methodology informationsass08princombined_subject.pdf1.86 MBsass08princombined_varname.pdf1.61 MB3348
992003-2004qsOnpsOntsOff8,100Perturbation
To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.

Imputation
Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.
After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records¹,” and the method of imputation that involves imputing data from donor records is called “hot-deck²” imputation. 

Skips and Missing Values
Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information.
 ¹Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables.
²Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.
View methodology informationsass04prinpub_subject.pdf553 KBsass04prinpub_varname.pdf2.20 MB13413
1002003-2004qsOnpsOntsOff2,400Perturbation
To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.

Imputation
Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.
After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records¹,” and the method of imputation that involves imputing data from donor records is called “hot-deck²” imputation. 

Skips and Missing Values
Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information.
 ¹Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables.
²Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.
View methodology informationsass04prinpriv_subject.pdf466 KBsass04prinpriv_varname.pdf445 KB23413
1012003-2004qsOnpsOntsOff10,500Perturbation
To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.

Imputation
Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.
After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records¹,” and the method of imputation that involves imputing data from donor records is called “hot-deck²” imputation. 

Skips and Missing Values
Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information.
 ¹Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables.
²Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.
View methodology informationsass04princombined_subject.pdf449 KBsass04princombined_varname.pdf433 KB33413
961999-2000qsOffpsOntsOff12,000Perturbation
To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.

Imputation
Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.
After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records¹,” and the method of imputation that involves imputing data from donor records is called “hot-deck²” imputation. 

Skips and Missing Values
Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information.
 ¹Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables.
²Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.
View methodology informationsass00prinpub_subject.pdf1.90 MBsass00prinpub_varname.pdf1.61 MB13417
971999-2000qsOffpsOntsOff12,000Perturbation
To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.

Imputation
Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.
After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records¹,” and the method of imputation that involves imputing data from donor records is called “hot-deck²” imputation. 

Skips and Missing Values
Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information.
 ¹Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables.
²Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.
View methodology informationsass00prinpriv_subject.pdf1.58 MBsass00prinpriv_varname.pdf1.25 MB23417
981999-2000qsOffpsOntsOff12,000Perturbation
To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.

Imputation
Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.
After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records¹,” and the method of imputation that involves imputing data from donor records is called “hot-deck²” imputation. 

Skips and Missing Values
Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information.
 ¹Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables.
²Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.
View methodology informationsass00princombined_subject.pdf1.48 MBsass00princombined_varname.pdf1.26 MB33417
Schools and Staffing Survey, Schools
SASS
Public and private schools
Teacher demand, teacher and principal characteristics, general conditions in schools, principals' and teachers' perceptions of school climate and problems in their schools, teacher compensation, district hiring and retention practices, basic characteristics of the student population
https://nces.ed.gov/surveys/sass
592011-2012qsOnpsOntsOff9,000Perturbation
To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.


Imputation
Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.

After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records¹,” and the method of imputation that involves imputing data from donor records is called “hot-deck²” imputation. 


Skips and Missing Values
Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information.
 ¹Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables. 
²Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.
View methodology informationsass12schoolpub_subject.pdf520 KBsass12schoolpub_varname.pdf530 KB1351
602011-2012qsOnpsOntsOff9,000Perturbation
To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.


Imputation
Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.

After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records¹,” and the method of imputation that involves imputing data from donor records is called “hot-deck²” imputation. 


Skips and Missing Values
Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information.
 ¹Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables. 
²Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.
View methodology informationsass12schoolpriv_subject.pdf720 KBsass12schoolpriv_varname.pdf675 KB2351
612011-2012qsOnpsOntsOff9,000Perturbation
To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.


Imputation
Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.

After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records¹,” and the method of imputation that involves imputing data from donor records is called “hot-deck²” imputation. 


Skips and Missing Values
Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information.
 ¹Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables. 
²Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.
View methodology informationsass12schoolcombined_subject.pdf1.60 MBsass12schoolcombined_varname.pdf1.55 MB3351
1172007-2008qsOnpsOntsOff7,600Perturbation
To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.


Imputation
Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.

After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records¹,” and the method of imputation that involves imputing data from donor records is called “hot-deck²” imputation. 


Skips and Missing Values
Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information.
 ¹Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables. 
²Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.
View methodology informationsass08schoolspub_subject.pdf2.23 MBsass08schoolspub_varname.pdf2.52 MB13520
1182007-2008qsOnpsOntsOff2,000Perturbation
To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.


Imputation
Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.

After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records¹,” and the method of imputation that involves imputing data from donor records is called “hot-deck²” imputation. 


Skips and Missing Values
Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information.
 ¹Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables. 
²Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.
View methodology informationsass08schoolspriv_subject.pdf2.51 MBsass08schoolspriv_varname.pdf3.07 MB23520
1192007-2008qsOnpsOntsOff9,500Perturbation
To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.


Imputation
Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.

After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records¹,” and the method of imputation that involves imputing data from donor records is called “hot-deck²” imputation. 


Skips and Missing Values
Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information.
 ¹Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables. 
²Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.
View methodology informationsass08schoolscombined_subject.pdf1.92 MBsass08schoolscombined_varname.pdf2.27 MB33520
1142003-2004qsOnpsOntsOff8,000Perturbation
To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.


Imputation
Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.

After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records¹,” and the method of imputation that involves imputing data from donor records is called “hot-deck²” imputation. 


Skips and Missing Values
Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information.
 ¹Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables. 
²Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.
View methodology informationsass04schoolspublic_subject.pdf2.27 MBsass04schoolspublic_varname.pdf2.30 MB13519
1152003-2004qsOnpsOntsOff2,500Perturbation
To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.


Imputation
Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.

After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records¹,” and the method of imputation that involves imputing data from donor records is called “hot-deck²” imputation. 


Skips and Missing Values
Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information.
 ¹Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables. 
²Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.
View methodology informationsass04schoolsprivate_subject.pdf3.26 MBsass04schoolsprivate_varname.pdf1.55 MB23519
1162003-2004qsOnpsOntsOff10,400Perturbation
To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.


Imputation
Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.

After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records¹,” and the method of imputation that involves imputing data from donor records is called “hot-deck²” imputation. 


Skips and Missing Values
Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information.
 ¹Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables. 
²Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.
View methodology informationsass04schoolscombined_subject.pdf1.90 MBsass04schoolscombined_varname.pdf2.00 MB33519
1111999-2000qsOffpsOntsOff9,300Perturbation
To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.


Imputation
Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.

After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records¹,” and the method of imputation that involves imputing data from donor records is called “hot-deck²” imputation. 


Skips and Missing Values
Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information.
 ¹Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables. 
²Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.
View methodology informationsass00schoolspublic_subject.pdf2.00 MBsass00schoolspublic_varname.pdf2.07 MB13518
1121999-2000qsOffpsOntsOff2,600Perturbation
To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.


Imputation
Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.

After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records¹,” and the method of imputation that involves imputing data from donor records is called “hot-deck²” imputation. 


Skips and Missing Values
Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information.
 ¹Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables. 
²Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.
View methodology informationsass00schoolsprivate_subject.pdf2.89 MBsass00schoolsprivate_varname.pdf3.18 MB23518
1131999-2000qsOffpsOntsOff11,900Perturbation
To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.


Imputation
Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.

After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records¹,” and the method of imputation that involves imputing data from donor records is called “hot-deck²” imputation. 


Skips and Missing Values
Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information.
 ¹Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables. 
²Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.
View methodology informationsass00schoolscombined_subject.pdf2.20 MBsass00schoolscombined_varname.pdf2.50 MB33518
Schools and Staffing Survey, Districts
SASS
Public school districts
Recruitment and Hiring of Staff, Principal and Teacher Compensation, Student Assignment, Graduation Requirements, Migrant Education, District Performance
https://nces.ed.gov/surveys/sass
582011-2012qsOnpsOntsOff4,500Perturbation
To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.


Imputation
Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.

After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records¹,” and the method of imputation that involves imputing data from donor records is called “hot-deck²” imputation. 


Skips and Missing Values
Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information.
 ¹Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables. 
²Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.
View methodology informationsass12district_subject.pdf1.15 MBsass12district_varname.pdf1.10 MB3164
1102007-2008qsOnpsOntsOff4,600Perturbation
To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.

Imputation
Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.
After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records¹,” and the method of imputation that involves imputing data from donor records is called “hot-deck²” imputation. 

Skips and Missing Values
Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information.
 ¹Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables.
²Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.
View methodology informationsass08district_subject.pdf0.51 MBsass08district_varname.pdf0.53 MB31626
1092003-2004qsOnpsOntsOff4,400Perturbation
To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.

Imputation
Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.
After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records¹,” and the method of imputation that involves imputing data from donor records is called “hot-deck²” imputation. 

Skips and Missing Values
Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information.
 ¹Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables.
²Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.
View methodology informationsass04district_subject.pdf0.88 MBsass04district_varname.pdf0.93 MB31625
1081999-2000qsOffpsOntsOff4,700Perturbation
To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.

Imputation
Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.
After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records¹,” and the method of imputation that involves imputing data from donor records is called “hot-deck²” imputation. 

Skips and Missing Values
Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information.
 ¹Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables.
²Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.
View methodology informationsass00district_subject.pdf1.10 MBsass00district_varname.pdf0.68 MB31624
Schools and Staffing Survey, Library Media Centers
SASS
Library media centers
School information, Facilities, services, and policies, Staffing information, Technology and information literacy, Collections and expenditures
https://nces.ed.gov/surveys/sass
572011-2012qsOnpsOntsOff7,000Perturbation
To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.


Imputation
Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.

After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records¹,” and the method of imputation that involves imputing data from donor records is called “hot-deck²” imputation. 


Skips and Missing Values
Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information.
 ¹Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables. 
²Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.
View methodology informationsass12LMC_subject.pdf675 KBsass12LMC_varname.pdf695 KB3175
1072007-2008qsOnpsOntsOff7,300Perturbation
To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.

Imputation
Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.
After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records¹,” and the method of imputation that involves imputing data from donor records is called “hot-deck²” imputation. 

Skips and Missing Values
Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information.
 ¹Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables.
²Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.
View methodology informationsass08LMC_subject.pdf0.59 MBsass08LMC_varname.pdf0.61 MB31723
1062003-2004qsOnpsOntsOff7,200Perturbation
To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.

Imputation
Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.
After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records¹,” and the method of imputation that involves imputing data from donor records is called “hot-deck²” imputation. 

Skips and Missing Values
Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information.
 ¹Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables.
²Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.
View methodology informationsass04LMC_subject.pdf0.80 MBsass04LMC_varname.pdf0.81 MB31722
1051999-2000qsOffpsOntsOff7,700Perturbation
To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.

Imputation
Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.
After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records¹,” and the method of imputation that involves imputing data from donor records is called “hot-deck²” imputation. 

Skips and Missing Values
Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information.
 ¹Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables.
²Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.
View methodology informationsass00LMC_subject.pdf1.16 MBsass00LMC_varname.pdf1.18 MB31721
School Survey on Crime and Safety
SSOCS
Elementary and secondary schools
School Practices and Programs, Parent and Community Involvement at School, School Security, Staff Training, Limitations on Crime Prevention, Frequency of Crime and Violence, Frequency of hate and gang-related crimes, Disciplinary problems and actions
https://nces.ed.gov/surveys/ssocs
12832015-2016qsOnpsOntsOn3,500Imputation
Completed SSOCS surveys contain some level of item nonresponse after the conclusion of the data collection phase. Imputation procedures were used to impute missing values of key items in SSOCS:2000 and missing values of all items in each subsequent SSOCS. All imputed values are flagged as such.
SSOCS:2004 and Beyond: In subsequent collections, imputation procedures were used to create values for all questionnaire items with missing data. This procedural change from SSOCS:2000 was implemented because the analysis of incomplete datasets may cause different users to arrive at different conclusions, depending on how the missing data are treated. The imputation methods used in SSOCS:2004 and later surveys were tailored to the nature of each survey item. Four methods were used: aggregate proportions, logical, best match, and clerical.    

    Weighting
Data are weighted to compensate for differential probabilities of selection and to adjust for the effects of nonresponse.
Sample weights allow inferences to be made about the population from which the sample units are drawn. Because of the complex nature of the SSOCS sample design, these weights are necessary to obtain population-based estimates, to minimize bias arising from differences between responding and nonresponding schools, and to calibrate the data to known population characteristics in a way that reduces sampling error.    

    An initial (base) weight was first determined within each stratum by calculating the ratio of the number of schools available in the sampling frame to the number of schools selected. Due to nonresponse, the responding schools did not necessarily constitute a random sample from the schools in the stratum. In order to reduce the potential of bias due to nonresponse, weighting classes were determined by using a statistical algorithm similar to CHAID (chi-square automatic interaction detector) to partition the sample such that schools within a weighting class were homogenous with respect to their probability of responding. The same predictor variables from the SSOCS:2004 CHAID analysis were used for SSOCS:2006: instructional level, region, enrollment size, percent minority, student-to-FTE teaching staff ratio, percentage of students eligible for free or reduced-price lunch, and number of full-time equivalent (FTE) teachers. When the number of responding schools in a class was sufficiently small, the weighting class was combined with another to avoid the possibility of large weights. After combining the necessary classes, the base weights were adjusted so that the weighted distribution of the responding schools resembled the initial distribution of the total sample.      

    The nonresponse-adjusted weights were then poststratified to calibrate the sample to known population totals. Two dimension margins were set up for the poststratification—(1) instructional level and school enrollment size; and (2) instructional level and locale—and an iterative process known as the raking ratio adjustment brought the weights into agreement with known control totals. Poststratification works well when the population not covered by the survey is similar to the covered population within each poststratum. Thus, to be effective, the variables that define the poststrata must be correlated with the variables of interest, they must be well measured in the survey, and control totals must be available for the population as a whole. All three requirements were satisfied by the aforementioned poststratification margins. Instructional level, school enrollment, and locale have been shown to be correlated with crime (Miller 2004).    

    Miller, A.K. (2004). Violence in U.S. Public Schools: 2000 School Survey on Crime and Safety (NCES 2004-314R). National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education. Washington, DC.

ssocs2016_subject.pdf375 KBssocs2016_varname.pdf375 KB00867
7032009-2010qsOnpsOntsOn2,600Imputation
Completed SSOCS surveys contain some level of item nonresponse after the conclusion of the data collection phase. Imputation procedures were used to impute missing values of key items in SSOCS:2000 and missing values of all items in each subsequent SSOCS. All imputed values are flagged as such.
SSOCS:2004 and Beyond: In subsequent collections, imputation procedures were used to create values for all questionnaire items with missing data. This procedural change from SSOCS:2000 was implemented because the analysis of incomplete datasets may cause different users to arrive at different conclusions, depending on how the missing data are treated. The imputation methods used in SSOCS:2004 and later surveys were tailored to the nature of each survey item. Four methods were used: aggregate proportions, logical, best match, and clerical.




Weighting
Data are weighted to compensate for differential probabilities of selection and to adjust for the effects of nonresponse.
Sample weights allow inferences to be made about the population from which the sample units are drawn. Because of the complex nature of the SSOCS sample design, these weights are necessary to obtain population-based estimates, to minimize bias arising from differences between responding and nonresponding schools, and to calibrate the data to known population characteristics in a way that reduces sampling error.





An initial (base) weight was first determined within each stratum by calculating the ratio of the number of schools available in the sampling frame to the number of schools selected. Due to nonresponse, the responding schools did not necessarily constitute a random sample from the schools in the stratum. In order to reduce the potential of bias due to nonresponse, weighting classes were determined by using a statistical algorithm similar to CHAID (chi-square automatic interaction detector) to partition the sample such that schools within a weighting class were homogenous with respect to their probability of responding. The same predictor variables from the SSOCS:2004 CHAID analysis were used for SSOCS:2006: instructional level, region, enrollment size, percent minority, student-to-FTE teaching staff ratio, percentage of students eligible for free or reduced-price lunch, and number of full-time equivalent (FTE) teachers. When the number of responding schools in a class was sufficiently small, the weighting class was combined with another to avoid the possibility of large weights. After combining the necessary classes, the base weights were adjusted so that the weighted distribution of the responding schools resembled the initial distribution of the total sample.  





The nonresponse-adjusted weights were then poststratified to calibrate the sample to known population totals. Two dimension margins were set up for the poststratification—(1) instructional level and school enrollment size; and (2) instructional level and locale—and an iterative process known as the raking ratio adjustment brought the weights into agreement with known control totals. Poststratification works well when the population not covered by the survey is similar to the covered population within each poststratum. Thus, to be effective, the variables that define the poststrata must be correlated with the variables of interest, they must be well measured in the survey, and control totals must be available for the population as a whole. All three requirements were satisfied by the aforementioned poststratification margins. Instructional level, school enrollment, and locale have been shown to be correlated with crime (Miller 2004).





Miller, A.K. (2004). Violence in U.S. Public Schools: 2000 School Survey on Crime and Safety (NCES 2004-314R). National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education. Washington, DC.

ssocs2010_subject.pdf565 KBssocs2010_varname.pdf365 KB00857
7432007-2008qsOnpsOntsOn2,560Imputation
Completed SSOCS surveys contain some level of item nonresponse after the conclusion of the data collection phase. Imputation procedures were used to impute missing values of key items in SSOCS:2000 and missing values of all items in each subsequent SSOCS. All imputed values are flagged as such.
SSOCS:2004 and Beyond: In subsequent collections, imputation procedures were used to create values for all questionnaire items with missing data. This procedural change from SSOCS:2000 was implemented because the analysis of incomplete datasets may cause different users to arrive at different conclusions, depending on how the missing data are treated. The imputation methods used in SSOCS:2004 and later surveys were tailored to the nature of each survey item. Four methods were used: aggregate proportions, logical, best match, and clerical.




Weighting
Data are weighted to compensate for differential probabilities of selection and to adjust for the effects of nonresponse.
Sample weights allow inferences to be made about the population from which the sample units are drawn. Because of the complex nature of the SSOCS sample design, these weights are necessary to obtain population-based estimates, to minimize bias arising from differences between responding and nonresponding schools, and to calibrate the data to known population characteristics in a way that reduces sampling error.





An initial (base) weight was first determined within each stratum by calculating the ratio of the number of schools available in the sampling frame to the number of schools selected. Due to nonresponse, the responding schools did not necessarily constitute a random sample from the schools in the stratum. In order to reduce the potential of bias due to nonresponse, weighting classes were determined by using a statistical algorithm similar to CHAID (chi-square automatic interaction detector) to partition the sample such that schools within a weighting class were homogenous with respect to their probability of responding. The same predictor variables from the SSOCS:2004 CHAID analysis were used for SSOCS:2006: instructional level, region, enrollment size, percent minority, student-to-FTE teaching staff ratio, percentage of students eligible for free or reduced-price lunch, and number of full-time equivalent (FTE) teachers. When the number of responding schools in a class was sufficiently small, the weighting class was combined with another to avoid the possibility of large weights. After combining the necessary classes, the base weights were adjusted so that the weighted distribution of the responding schools resembled the initial distribution of the total sample.  





The nonresponse-adjusted weights were then poststratified to calibrate the sample to known population totals. Two dimension margins were set up for the poststratification—(1) instructional level and school enrollment size; and (2) instructional level and locale—and an iterative process known as the raking ratio adjustment brought the weights into agreement with known control totals. Poststratification works well when the population not covered by the survey is similar to the covered population within each poststratum. Thus, to be effective, the variables that define the poststrata must be correlated with the variables of interest, they must be well measured in the survey, and control totals must be available for the population as a whole. All three requirements were satisfied by the aforementioned poststratification margins. Instructional level, school enrollment, and locale have been shown to be correlated with crime (Miller 2004).





Miller, A.K. (2004). Violence in U.S. Public Schools: 2000 School Survey on Crime and Safety (NCES 2004-314R). National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education. Washington, DC.

ssocs2008_subject.pdf1.96 MBssocs2008_varname.pdf912 KB00858
7332005-2006qsOnpsOntsOn2,720Imputation
Completed SSOCS surveys contain some level of item nonresponse after the conclusion of the data collection phase. Imputation procedures were used to impute missing values of key items in SSOCS:2000 and missing values of all items in each subsequent SSOCS. All imputed values are flagged as such.
SSOCS:2004 and Beyond: In subsequent collections, imputation procedures were used to create values for all questionnaire items with missing data. This procedural change from SSOCS:2000 was implemented because the analysis of incomplete datasets may cause different users to arrive at different conclusions, depending on how the missing data are treated. The imputation methods used in SSOCS:2004 and later surveys were tailored to the nature of each survey item. Four methods were used: aggregate proportions, logical, best match, and clerical.




Weighting
Data are weighted to compensate for differential probabilities of selection and to adjust for the effects of nonresponse.
Sample weights allow inferences to be made about the population from which the sample units are drawn. Because of the complex nature of the SSOCS sample design, these weights are necessary to obtain population-based estimates, to minimize bias arising from differences between responding and nonresponding schools, and to calibrate the data to known population characteristics in a way that reduces sampling error.





An initial (base) weight was first determined within each stratum by calculating the ratio of the number of schools available in the sampling frame to the number of schools selected. Due to nonresponse, the responding schools did not necessarily constitute a random sample from the schools in the stratum. In order to reduce the potential of bias due to nonresponse, weighting classes were determined by using a statistical algorithm similar to CHAID (chi-square automatic interaction detector) to partition the sample such that schools within a weighting class were homogenous with respect to their probability of responding. The same predictor variables from the SSOCS:2004 CHAID analysis were used for SSOCS:2006: instructional level, region, enrollment size, percent minority, student-to-FTE teaching staff ratio, percentage of students eligible for free or reduced-price lunch, and number of full-time equivalent (FTE) teachers. When the number of responding schools in a class was sufficiently small, the weighting class was combined with another to avoid the possibility of large weights. After combining the necessary classes, the base weights were adjusted so that the weighted distribution of the responding schools resembled the initial distribution of the total sample.  





The nonresponse-adjusted weights were then poststratified to calibrate the sample to known population totals. Two dimension margins were set up for the poststratification—(1) instructional level and school enrollment size; and (2) instructional level and locale—and an iterative process known as the raking ratio adjustment brought the weights into agreement with known control totals. Poststratification works well when the population not covered by the survey is similar to the covered population within each poststratum. Thus, to be effective, the variables that define the poststrata must be correlated with the variables of interest, they must be well measured in the survey, and control totals must be available for the population as a whole. All three requirements were satisfied by the aforementioned poststratification margins. Instructional level, school enrollment, and locale have been shown to be correlated with crime (Miller 2004).





Miller, A.K. (2004). Violence in U.S. Public Schools: 2000 School Survey on Crime and Safety (NCES 2004-314R). National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education. Washington, DC.

ssocs2006_subject.pdf8.82 MBssocs2006_varname.pdf3.58 MB00859
1382003-2004qsOnpsOntsOff2,800Imputation
Completed SSOCS surveys contain some level of item nonresponse after the conclusion of the data collection phase. Imputation procedures were used to impute missing values of key items in SSOCS:2000 and missing values of all items in each subsequent SSOCS. All imputed values are flagged as such.

SSOCS:2004 and Beyond. In subsequent collections, imputation procedures were used to create values for all questionnaire items with missing data. This procedural change from SSOCS:2000 was implemented because the analysis of incomplete datasets may cause different users to arrive at different conclusions, depending on how the missing data are treated. The imputation methods used in SSOCS:2004 and later surveys were tailored to the nature of each survey item. Four methods were used: aggregate proportions, logical, best match, and clerical.

Weighting
Sample weights allow inferences to be made about the population from which the sample units were drawn. Because of the complex nature of the SSOCS:2004 sample design, these weights are necessary to obtain population-based estimates, to minimize bias arising from differences between responding and nonresponding schools, and to calibrate the data to known population characteristics in a way that reduces sampling error. The procedures used to create the SSOCS sampling weights are described below.

An initial (base) weight was first determined within each stratum by calculating the ratio of the number of schools available in the sampling frame to the number of schools selected. Because some schools refused to participate, the responding schools did not necessarily constitute a random sample from the schools in the stratum. In order to reduce the potential of bias from nonresponse, weighting classes were determined by using a statistical algorithm similar to CHAID (i.e., chi-square automatic interaction detector) to partition the sample such that schools within a weighting class were homogenous with respect to their probability of responding. The predictor variables for the analysis were instructional level, region, enrollment size, percent minority, student-to-teacher ratio, percentage of students eligible for free or reduced-price lunch, and number of full-time-equivalent teachers. When the number of responding schools in a class was sufficiently small, the weighting class was combined with another to avoid the possibility of large weights. After combining the necessary classes, the base weights were adjusted by dividing the base weight by the response rate in each class, so that the weighted distribution of the responding schools resembled the initial distribution of the total sample.

The non-response-adjusted weights were then poststratified to calibrate the sample to known population totals. For SSOCS:2004, two dimension margins were set up for the poststratification: (1) instructional level and school enrollment size, and (2) instructional level and locale. An iterative process known as the raking ratio adjustment brought the weights into agreement with the known control totals. Poststratification works well when the population not covered by the survey is similar to the covered population within each poststratum. Thus, to be effective, the variables that define the poststrata must be correlated with the variables of interest, they must be well measured in the survey, and control totals must be available for the population as a whole. Similar to SSOCS:2000, all three requirements were satisfied by the aforementioned poststratification margins. Instructional level, school enrollment, and locale have been shown to be correlated with crime (Miller 2003).

Miller, A.K. (2003). Violence in U.S. Public Schools: 2000 School Survey on Crime and Safety (NCES 2004-314R). National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education. Washington, DC.
View methodology reportssocs2004_subject.pdf1.2 MBssocs2004_varname.pdf2.1 MB00874
1391999-2000qsOnpsOntsOff2,300Imputation
All key data items with missing values were imputed using well-known procedures. Depending on the type of data to be imputed and the extent of missing values, logical imputation, poststratum means, or "hot-deck" imputation methods were employed. For three data items, imputation was done using information from the 1998-99 CCD file. Logical imputation is the assignment of data values based on other information in the data record. In the poststratum means method, a record with missing data was assigned the mean value of those cases in the same "poststratum" for which information on the item was available. The poststrata or "imputation classes" were defined on the basis of variables that were correlated with the item being imputed. Preliminary exploratory analyses (e.g., using chi-square tests of association, correlation analysis, and regression analysis) were carried out to identify the relevant classification variables. The strength of association of the variables in combination with subjective assessment was used to prioritize the importance of the variables in forming the imputation classes. In the "hot-deck" technique, cases with missing items were assigned the corresponding value of a "similar" respondent in the same "poststratum". Similar to the poststratum means approach, preliminary exploratory analyses were carried out to identify the relevant classification variables to be used to define the poststrata. The classification variables were separated into two groups -- "hard" and "soft" boundary variables. The hard boundary variables were considered to be so important that the imputation classes were always formed within those boundaries. The boundaries formed by the soft boundary variables were crossed, if necessary, to form the imputation class.

Weighting
A stratified random sample design was used to select schools for the SSOCS:2000. Over 3,000 schools were selected at rates that varied by sampling stratum; i.e., the classes formed by crossing instructional level (elementary, middle, secondary, combined), type of locale (city, urban fringe, town, rural), and enrollment size class (less than 300, 300-499, 500-999, 1,000+). Since the schools were selected with unequal probabilities, sampling weights are required for analysis to inflate the survey responses to population levels. Weighting is also used to reduce the potential bias resulting from nonresponse and possible undercoverage of the sampling frame.

One method of computing sampling errors to reflect various aspects of the sample design and estimation procedures is the replication method. Under replication methods, a specified number of subsamples of the full sample (called "replicates") are created. The survey estimates can then be computed for each of the replicates by creating replicate weights that mimic the actual sample design and estimation procedures used in the full sample. The variability of the estimates computed from the replicate weights is then used to estimate the sampling errors of the estimates from the full sample. An important advantage of the replication methods is that they preclude the need to specify cumbersome variance formulas that are typically needed for complex sample designs (McCarthy, 1966).¹ Another advantage is that they can readily be adapted to reflect the variance resulting from nonresponse (and other weight) adjustment procedures. The two most prevalent replication methods are balanced repeated replication (BRR) and jackknife replication. The two methods differ in the manner in which the replicates are constructed. For the SSOCS:2000, a variant of jackknife replication was used to develop replicate weights for variance estimation because the jackknife method is believed to perform somewhat better than BRR for estimates of moderately rare events (e.g., number of schools in which a serious crime was committed). Under the jackknife method, the replicates are formed by deleting specified subsets of units from the full sample. The jackknife method provides a relatively simple way of creating the replicates for variance estimation and has been used extensively in NCES surveys.


1. McCarthy, P. (1966). Replication: An Approach to the Analysis of Data from Complex Surveys. Vital and Health Statistics, Series 2, No. 14. Washington, DC: U.S. Department of Health, Education and Welfare.
View methodology reportssocs2000_subject.pdfX KBssocs2000_varname.pdfX KB00875
Education Longitudinal Study
ELS
Students who were high school sophomores in 2001-02 or high school seniors in 2003-04
Student and Family Background, School and Classroom Characteristics, High School Completion and Dropout Status, Postsecondary Education Choice and Enrollment, Postsecondary Attainment, Employment, Transition to Adult Roles
https://nces.ed.gov/surveys/els2002
682002qsOnpsOntsOff14,000 to 16,000Imputation
Stochastic methods were used to impute the missing values for the ELS:2002 third follow-up data. Specifically, a weighted sequential hot-deck (WSHD) statistical imputation procedure (Cox 1980; Iannacchione 1982) using the final analysis weight (F3QWT) was applied to the missing values for the variables in table 12 in the order in which they are listed. The WSHD procedure replaces missing data with valid data from a donor record within an imputation class. In general, variables with lower item nonresponse rates were imputed earlier in the process.

View methodology reportels2002sophomores_subject.pdf7.58 MBels2002sophomores_varname.pdf7.49 MB32954
692002qsOffpsOntsOff14,000 to 16,000Imputation
Stochastic methods were used to impute the missing values for the ELS:2002 third follow-up data. Specifically, a weighted sequential hot-deck (WSHD) statistical imputation procedure (Cox 1980; Iannacchione 1982) using the final analysis weight (F3QWT) was applied to the missing values for the variables in table 12 in the order in which they are listed. The WSHD procedure replaces missing data with valid data from a donor record within an imputation class. In general, variables with lower item nonresponse rates were imputed earlier in the process.

View methodology reportels2002seniors_subject.pdf6.22 MBels2002seniors_varname.pdf6.16 MB42954
High School Longitudinal Study
HSLS
Students who were high school freshmen in the fall of 2009
Student Background, Math and Science Education, Classroom Characteristics, The Changing Environment of High School, Postsecondary Education Choice and Enrollment, Transition to Adult Roles
https://nces.ed.gov/surveys/hsls09
722009qsOnpsOntsOff23,000Perturbation
 To protect the confidentiality of NCES data that contain information about specific individuals, HSLS:09 data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in nonsampling errors. Data swapping and other forms of perturbation, implemented to protect respondent confidentiality, can also lead to inconsistencies.

 
Imputation
Stochastic methods were used to impute the missing values. Specifically, a weighted sequential hot-deck (WSHD; statistical) imputation procedure (Cox 1980; Iannacchione 1982) using the final student analysis weight (W2STUDENT) was applied to the missing values for variables. The WSHD procedure replaces missing data with valid data from a donor record (i.e., first follow-up student [item] respondent) within an imputation class. In general, variables with lower item nonresponse rates were imputed earlier in the process.


Skips and Missing Values
 The HSLS:09 data were edited using procedures developed and implemented for previous studies sponsored by NCES Following data collection, the information collected in the student instrument was subjected to various quality control checks and examinations. These checks were to confirm that the collected data reflected appropriate skip patterns. Another evaluation examined all variables with missing data and substituted specific values to indicate the reason for the missing data. A variety of explanations are possible for missing data.

 The table below shows codes for missing values used in HSLS:09. Please consult the methodology report (coming soon) for more information.

 Description of missing data codes
     Missing data code  Description   
  -1  Don't know   
  -4  Item not administered: abbreviated interview   
  -5  Suppressed   
  -6  Component not applicable   
  -7  Item legitimate skip/NA   
  -8  Unit nonresponse   
  -9  Missing  
 
hsls2009_subject.pdf5.34 MBhsls2009_varname.pdf8.91 MB001056
Baccalaureate and Beyond
B&B
Bachelor degree recipients who were surveyed at the time of graduation, one year after graduation, four years after graduation, and ten years after graduation
Outcomes for bachelor's degree recipients, Graduate and professional program access, Labor market experiences, Rates of return on investment in education, Post-baccalaureate education, Teacher preparation, Certifications and licenses, Enrollment while employed
https://nces.ed.gov/surveys/b&b
1342016/2017qsOnpsOntsOff29,000Perturbation
 To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.

 Imputation
 All variables with missing data were imputed. The imputation procedures employed a two-step process. The first step is a logical imputation¹. If the imputed value could be deduced from the logical relationships with other variables, then that information was used to impute the value for the recipient. The second step is weighted hot-deck imputation.² This imputation procedure involves identifying a relatively homogenous group of observations, and, from within the group, selecting a random donor’s value to impute a value for the recipient.

   Skips and Missing Values 
   During and following data collection, the data were reviewed to confirm that the data collected reflected the intended skip-pattern relationships. At the conclusion of data collection, special codes were inserted in the database to reflect the different types of missing data. There are a number of explanations for missing data; for example, the item may not have been applicable to certain respondents or a respondent may not have known the answer to the question. With the exception of the not applicable codes, missing data were stochastically imputed. Moreover, for hierarchical analyses and developing survey estimates for faculty members corresponding to sample institutions that provided faculty lists and responded to the institution survey, contextual weights were produced for such subsets of the responding faculty members. 
  The table below shows codes for missing values used. Please consult the methodology report for more information.
 
   Description of missing data codes
                                         Missing data code   Description   
 -3  Legitimate skip
 -7  Not reached
 -9  Missing
 
  ¹Logical imputation is a process that aims to infer or deduce the missing values from answers to other questions. 
 ²Sequential hot-deck imputation involves defining imputation classes, which generally consist of a cross-classification of covariates, and then replacing missing values sequentially from a single pass through the survey data within the imputation classes. When this form of imputation is performed using the sampling weights, the procedure is called weighted sequential hot-deck imputation. This procedure takes into account the unequal probabilities of selection in the original sample to specify the expected number of times a particular respondent’s answer will be used as a donor. These expected selection frequencies are specified so that, over repeated applications of the algorithm, the weighted distribution of all values for that variable imputed and observed will resemble that of the target universe. While each respondent record may be selected for use as a hot-deck donor, the number of times a respondent record is used for imputation is controlled. To implement the weighted sequential hot-deck procedure, imputation classes and sorting variables that are relevant (strong predictors) for each item being imputed are defined. Imputation classes are developed by using a Chi-squared Automatic Interaction.
 
Methodology report coming soonbb2017_subject.pdf608 KBbb2017_varname.pdf420 KB001173
542008/2012qsOnpsOntsOff15,500Perturbation
To protect the confidentiality of NCES data that contain information about specific individuals, B&B:08/12 data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in nonsampling errors. B&B:08/12 has multiple sources of data for some variables (CPS, NLSDS, student interview, etc.), and reporting differences can occur in each. Data swapping and other forms of perturbation, implemented to protect respondent confidentiality, can also lead to inconsistencies.


Imputation
Variables with missing data were imputed for graduates who were respondents in a study wave . The imputation procedures employed a two-step process. The first step is a logical imputation . If the imputed value could be deduced from the logical relationships with other variables, then that information was used to impute the value for the recipient. The second step is weighted hot-deck imputation.  This imputation procedure involves identifying a relatively homogenous group of observations, and, from within the group, selecting a random donor’s value to impute a value for the recipient.


Skips and Missing Values 
The B&B: 08/12 data were edited using procedures developed and implemented for previous studies sponsored by NCES, including the base-year study, NPSAS:08. Following data collection, the information collected in the student instrument was subjected to various quality control checks and examinations. These checks were to confirm that the collected data reflected appropriate skip patterns. Another evaluation examined all variables with missing data and substituted specific values to indicate the reason for the missing data. A variety of explanations are possible for missing data.


The table below shows codes for missing values used in B&B:08/12. Please consult the First Look for more information.


Description of missing value codes

Missing data code Description
-1 Don’t know
-2 Independent student
 -3 Skipped
-9 Missing



 
¹In other words, if a graduate was a respondent in B&B:09, he or she will have no missing data for variables created as part of the B&B:09 wave. Similarly, if a graduate was a respondent in B&B:12, he or she will have no missing data for variables created as part of the B&B:12 wave, but may have missing data for variables created as part of the B&B:09 wave if he or she was not a respondent in B&B:09.
²Logical imputation is a process that aims to infer or deduce the missing values from answers to other questions.
³Sequential hot deck imputation involves defining imputation classes, which generally consist of a cross-classification of covariates, and then replacing missing values sequentially from a single pass through the survey data within the imputation classes. When this form of imputation is performed using the sampling weights, the procedure is called weighted sequential hot-deck imputation. This procedure takes into account the unequal probabilities of selection in the original sample to specify the expected number of times a particular respondent’s answer will be used as a donor. These expected selection frequencies are specified so that, over repeated applications of the algorithm, the weighted distribution of all values for that variable—imputed and observed—will resemble that of the target universe. While each respondent record may be selected for use as a hot-deck donor, the number of times a respondent record is used for imputation is controlled. To implement the weighted sequential hot-deck procedure, imputation classes and sorting variables that are relevant (strong predictors) for each item being imputed are defined. Imputation classes are developed by using a Chi-squared Automatic Interaction. 
View methodology reportbb12_subject.pdf26.6 MBbb12_varname.pdf15.6 MB001149
311993/2003qsOnpsOntsOff11,200Imputation
Variables used in cross-sectional estimates in the Baccalaureate and Beyond descriptive reports were imputed. The variables identified for imputation were used in the two B&B:93/03 descriptive reports (Bradburn, Nevill, and Forrest Cataldi 2006; Alt and Henke 2007). The imputations were performed in three steps. First, the interview variables were imputed using the sequential hot deck imputation method.¹ This imputation procedure involves identifying a relatively homogenous group of observations, and within the group selecting a random donor’s value to impute a value for the recipient. Second, using the interview variables, including the newly imputed variable values, derived variables were constructed.


Skips and Missing Values
Both during and upon completion of data collection, edit checks were performed on the B&B:93/03 data file to confirm that the intended skip patterns were implemented during the interview. At the conclusion of data collection, special codes were added as needed to indicate the reason for missing data. Missing data within individual data elements can occur for a variety of reasons.


The Table below lists each missing value code and its associated meaning in the B&B:93/03 interview. For more information, see the Baccalaureate and Beyond Longitudinal Study (B&B:93/03) methodology report.


Description of missing data codes
 	 	Missing data code  Description  
  	-1  Missing  
  	-2  Not applicable  
  	-3  Skipped  
  	-4  B&B:97 nonrespondent not sampled  
  	-6  Uncodeable, out of range  
  	-7  Not reached  
  	-8  Item was not reached due to an error  
  	-9  Missing, blank  
 


¹Sequential hot-deck imputation involves defining imputation classes, which generally consist of a cross-classification of covariates, and then replacing missing values sequentially from a single pass through the survey data within the imputation classes. When this form of imputation is performed using the sampling weights, the procedure is called weighted sequential hot-deck imputation. This procedure takes into account the unequal probabilities of selection in the original sample to specify the expected number of times a particular respondent’s answer will be used as a donor. These expected selection frequencies are specified so that, over repeated applications of the algorithm, the weighted distribution of all values for that variable—imputed and observed—will resemble that of the target universe. Under this methodology, while each respondent record has a chance to be selected for use as a hot-deck donor, the number of times a respondent record can be used for imputation will be controlled. To implement the weighted sequential hot-deck procedure, imputation classes and sorting variables that are relevant (strong predictor) for each item being imputed were defined. Imputation classes were developed by using a Chi-squared Automatic Interaction.
View methodology reportbb03_subject.pdf4.56 MBbb03_varname.pdf3.98 MB001151
202000/2001qsOffpsOntsOff10,000Perturbation
 To protect the confidentiality of NCES data that contain information about specific individuals, B&B:01 data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in nonsampling errors. 

 Imputation
 All variables with missing data were imputed. The imputation procedures employed a two-step process. The first step is a logical imputation¹. If the imputed value could be deduced from the logical relationships with other variables, then that information was used to impute the value for the recipient. The second step is weighted hot-deck imputation.² This imputation procedure involves identifying a relatively homogenous group of observations, and, from within the group, selecting a random donor’s value to impute a value for the recipient.

  Skips and Missing Values 
   Both during and upon completion of data collection, edit checks were performed on the B&B:00/01 data file to confirm that the intended skip patterns were implemented during the interview. Following data collection, the information collected in CATI was subjected to various checks and examinations. These checks were intended to confirm that the database reflected appropriate skip-pattern relationships and different types of missing data by inserting special codes.
 
  
 The Table below lists each missing value code and its associated meaning in the B&B:00/01 interview. For more information, see the Baccalaureate and Beyond Longitudinal Study (B&B:00/01)   methodology report  .
 
   Description of missing data codes
          Missing data code     Description   
      -1   Don’t know (CATI variables), Data not available (CADE variables)   
      -2   Refused (CATI variables only)   
      -3   Not applicable (CADE and CATI variables only)   
      -4    B&B:97 nonrespondent not sampled   
      -6   Bad data, out of range   
      -7    Item was not reached (abbreviated and partial CATI interviews)   
      -8   Item was not reached due to a CATI error   
      -9   Data missing, reason unknown (CATI variables)   
  
View methodology reportbb01_subject.pdf3.44 MBbb01_varname.pdf3.38 MB001132
Baccalaureate and Beyond, Graduate Students
B&B:GR
Bachelor degree recipients who were surveyed at the time of graduation, one year after graduation, four years after graduation, and ten years after graduation
Outcomes for bachelor's degree recipients, Graduate and professional program access, Labor market experiences, Rates of return on investment in education, Post-baccalaureate education, Teacher preparation, Certifications and licenses, Enrollment while employed
https://nces.ed.gov/surveys/b&b
561993/2003qsOffpsOntsOff4,000Imputation
 Variables used in cross-sectional estimates in the Baccalaureate and Beyond descriptive reports were imputed. The variables identified for imputation were used in the two B&B:93/03 descriptive reports (Bradburn, Nevill, and Forrest Cataldi 2006; Alt and Henke 2007). The imputations were performed in three steps. First, the interview variables were imputed using the sequential hot deck imputation method.¹ This imputation procedure involves identifying a relatively homogenous group of observations, and within the group selecting a random donor’s value to impute a value for the recipient. Second, using the interview variables, including the newly imputed variable values, derived variables were constructed. 

 Skips and Missing Values
 Both during and upon completion of data collection, edit checks were performed on the B&B:93/03 data file to confirm that the intended skip patterns were implemented during the interview. At the conclusion of data collection, special codes were added as needed to indicate the reason for missing data. Missing data within individual data elements can occur for a variety of reasons.

 The Table below lists each missing value code and its associated meaning in the B&B:93/03 interview. For more information, see the Baccalaureate and Beyond Longitudinal Study (B&B:93/03) methodology report.

 Description of missing data codes
  	 	Missing data code  Description  
  	-1  Missing  
  	-2  Not applicable  
  	-3  Skipped  
  	-4  B&B:97 nonrespondent not sampled  
  	-6  Uncodeable, out of range  
  	-7  Not reached  
  	-8  Item was not reached due to an error  
  	-9  Missing, blank  
 

  ¹Sequential hot-deck imputation involves defining imputation classes, which generally consist of a cross-classification of covariates, and then replacing missing values sequentially from a single pass through the survey data within the imputation classes. When this form of imputation is performed using the sampling weights, the procedure is called weighted sequential hot-deck imputation. This procedure takes into account the unequal probabilities of selection in the original sample to specify the expected number of times a particular respondent’s answer will be used as a donor. These expected selection frequencies are specified so that, over repeated applications of the algorithm, the weighted distribution of all values for that variable—imputed and observed—will resemble that of the target universe. Under this methodology, while each respondent record has a chance to be selected for use as a hot-deck donor, the number of times a respondent record can be used for imputation will be controlled. To implement the weighted sequential hot-deck procedure, imputation classes and sorting variables that are relevant (strong predictor) for each item being imputed were defined. Imputation classes were developed by using a Chi-squared Automatic Interaction.
 
View methodology reportbb03_subject_students.pdf9.75 MBbb03_varname_students.pdf8.81 MB001252
Beginning Postsecondary Students
BPS
Beginning students who were surveyed at the end of their first year, and then three and six years after first starting in postsecondary education.
Students’ persistence, progress and attainment of a degree, Labor force experiences
https://nces.ed.gov/surveys/bps/
712012/2017qsOnpsOntsOff22,500Perturbation
 To protect the confidentiality of NCES data that contain information about specific individuals, BPS:12/17 data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in nonsampling errors. BPS:12/17 has multiple sources of data for some variables (CPS, NLSDS, student interview, etc.), and reporting differences can occur in each. Data swapping and other forms of perturbation, implemented to protect respondent confidentiality, can also lead to inconsistencies.

 Imputation
 All variables with missing data were imputed. The imputation procedures employed a two-step process. The first step is a logical imputation¹. If the imputed value could be deduced from the logical relationships with other variables, then that information was used to impute the value for the recipient. The second step is weighted hot-deck imputation.² This imputation procedure involves identifying a relatively homogenous group of observations, and, from within the group, selecting a random donor’s value to impute a value for the recipient.

 Skips and Missing Values
 The BPS:12/17 data were edited using procedures developed and implemented for previous studies sponsored by NCES, including the base-year study, NPSAS:12. Following data collection, the information collected in the student instrument was subjected to various quality control checks and examinations. These checks were to confirm that the collected data reflected appropriate skip patterns. Another evaluation examined all variables with missing data and substituted specific values to indicate the reason for the missing data. A variety of explanations are possible for missing data.

 The table below shows codes for missing values used in BPS:12/17. Please consult the methodology report (coming soon) for more information.

 Description of missing data codes
    Missing data code  Description  
   -1  Not classified   
 -2  Not applicable  
   -3  Skipped  
   -8  Double non-respondent  
   -9  Data missing  
 

  ¹Logical imputation is a process that aims to infer or deduce the missing values from answers to other questions. 
 ²Sequential hot-deck imputation involves defining imputation classes, which generally consist of a cross-classification of covariates, and then replacing missing values sequentially from a single pass through the survey data within the imputation classes. When this form of imputation is performed using the sampling weights, the procedure is called weighted sequential hot-deck imputation. This procedure takes into account the unequal probabilities of selection in the original sample to specify the expected number of times a particular respondent’s answer will be used as a donor. These expected selection frequencies are specified so that, over repeated applications of the algorithm, the weighted distribution of all values for that variable—imputed and observed—will resemble that of the target universe. While each respondent record may be selected for use as a hot-deck donor, the number of times a respondent record is used for imputation is controlled. To implement the weighted sequential hot-deck procedure, imputation classes and sorting variables that are relevant (strong predictors) for each item being imputed are defined. Imputation classes are developed by using a Chi-squared Automatic Interaction. 
View methodology reportbps2017_subject.html9.99 MBbps2017_varname.html9.99 MB001353
532004/2009qsOnpsOntsOff16,500Perturbation
 To protect the confidentiality of NCES data that contain information about specific individuals, BPS:04/09 data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in nonsampling errors. BPS:04/09 has multiple sources of data for some variables (CPS, NLSDS, student interview, etc.), and reporting differences can occur in each. Data swapping and other forms of perturbation, implemented to protect respondent confidentiality, can also lead to inconsistencies.

 Imputation
 All variables with missing data were imputed. The imputation procedures employed a two-step process. The first step is a logical imputation¹. If the imputed value could be deduced from the logical relationships with other variables, then that information was used to impute the value for the recipient. The second step is weighted hot-deck imputation.² This imputation procedure involves identifying a relatively homogenous group of observations, and, from within the group, selecting a random donor’s value to impute a value for the recipient.

 Skips and Missing Values
 The BPS:04/09 data were edited using procedures developed and implemented for previous studies sponsored by NCES, including the base-year study, NPSAS:04. Following data collection, the information collected in the student instrument was subjected to various quality control checks and examinations. These checks were to confirm that the collected data reflected appropriate skip patterns. Another evaluation examined all variables with missing data and substituted specific values to indicate the reason for the missing data. A variety of explanations are possible for missing data.

 The table below shows codes for missing values used in BPS:04/09. Please consult the methodology report (coming soon) for more information.

 Description of missing data codes
     Missing data code  Description  
   -2  Independent student  
   -3  Skipped  
   -9  Data missing  
 

  ¹Logical imputation is a process that aims to infer or deduce the missing values from answers to other questions. 
 ²Sequential hot-deck imputation involves defining imputation classes, which generally consist of a cross-classification of covariates, and then replacing missing values sequentially from a single pass through the survey data within the imputation classes. When this form of imputation is performed using the sampling weights, the procedure is called weighted sequential hot-deck imputation. This procedure takes into account the unequal probabilities of selection in the original sample to specify the expected number of times a particular respondent’s answer will be used as a donor. These expected selection frequencies are specified so that, over repeated applications of the algorithm, the weighted distribution of all values for that variable—imputed and observed—will resemble that of the target universe. While each respondent record may be selected for use as a hot-deck donor, the number of times a respondent record is used for imputation is controlled. To implement the weighted sequential hot-deck procedure, imputation classes and sorting variables that are relevant (strong predictors) for each item being imputed are defined. Imputation classes are developed by using a Chi-squared Automatic Interaction. 
View methodology reportbps2009_subject.pdf7.50 MBbps2009_varname.pdf6.20 MB001333
11996/2001qsOnpsOntsOff12,000Imputation
 Logical imputations were performed where items were missing but their values could be implicitly determined.

 Skips and Missing Values
 During and following data collection, the CATI/CAPI data were reviewed to confirm that the data collected reflected the intended skip-pattern relationships. At the conclusion of data collection, special codes were inserted in the database to reflect the different types of missing data. There are a variety of explanations for missing data within individual data elements. 

 The table below shows codes for missing values used in BPS:01. Please consult the methodology report for more information.

 Description of missing data codes
  	 	Missing data code  Description  
  	-1  Don’t know  
  	-2  Refused  
  	-3  Legitimate skip (item was intentionally not collected because variable was not applicable to this student)  
  	-6  Bad data, out of range, uncodeable userexit string  
  	-7  Not reached  
  	-8  Missing, CATI error  
 -9  Missing

 
View methodology reportbps2001_subject.pdf9.20 MBbps2001_varname.pdf7.10 MB001334
321990/1994qsOnpsOntsOff6,600Perturbation
 To protect the confidentiality of NCES data that contain information about specific individuals, BPS:94 data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in nonsampling errors. BPS:94 has multiple sources of data for some variables (CPS, NLSDS, student interview, etc.), and reporting differences can occur in each. Data swapping and other forms of perturbation, implemented to protect respondent confidentiality, can also lead to inconsistencies.

 Imputation
 All variables with missing data were imputed. The imputation procedures employed a two-step process. The first step is a logical imputation¹. If the imputed value could be deduced from the logical relationships with other variables, then that information was used to impute the value for the recipient. The second step is weighted hot-deck imputation.² This imputation procedure involves identifying a relatively homogenous group of observations, and, from within the group, selecting a random donor’s value to impute a value for the recipient.

  Skips and Missing Values 
The BPS:94 data were edited using procedures developed and implemented for previous studies sponsored by NCES. Following data collection, the information collected in the student instrument was subjected to various quality control checks and examinations. These checks were to confirm that the collected data reflected appropriate skip patterns. Another evaluation examined all variables with missing data and substituted specific values to indicate the reason for the missing data.
A variety of explanations are possible for missing data.


The table below shows codes for missing values used in BPS:94. Please consult the   methodology report   for more information.
 
Description of missing data codes
          Missing data code  Description  
 -2  Independent student  
 -3  Skipped  
 -9  Data missing  
 
View methodology reportbps1994_subject.pdf4.34 MBbps1994_varname.pdf4.17 MB001335
National Postsecondary Student Aid Study, Undergraduate
NPSAS:UG
Students who were undergraduates at the time of interview
General demographics, Types of aid and amounts received, Cost of attending college, Combinations of work, study, and borrowing, Enrollment patterns
https://nces.ed.gov/surveys/npsas
12112016qsOnpsOntsOn89,000Perturbation
 To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.

 Imputation
 All variables with missing data were imputed. The imputation procedures employed a two-step process. The first step is a logical imputation¹. If the imputed value could be deduced from the logical relationships with other variables, then that information was used to impute the value for the recipient. The second step is weighted hot-deck imputation.² This imputation procedure involves identifying a relatively homogenous group of observations, and, from within the group, selecting a random donor’s value to impute a value for the recipient.

 Skips and Missing Values
 Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. 

 The table below shows the set of reserve codes for missing values used in NPSAS 2016. Please consult the data file documentation report for more information.

 Description of missing data codes
   Missing data code  Description  
 -3  Skipped  
  -9  Missing  
 

  ¹Logical imputation is a process that aims to infer or deduce the missing values from answers to other questions. 
 ²Sequential hot-deck imputation involves defining imputation classes, which generally consist of a cross-classification of covariates, and then replacing missing values sequentially from a single pass through the survey data within the imputation classes. When this form of imputation is performed using the sampling weights, the procedure is called weighted sequential hot-deck imputation. This procedure takes into account the unequal probabilities of selection in the original sample to specify the expected number of times a particular respondent’s answer will be used as a donor. These expected selection frequencies are specified so that, over repeated applications of the algorithm, the weighted distribution of all values for that variableimputed and observedwill resemble that of the target universe. While each respondent record may be selected for use as a hot-deck donor, the number of times a respondent record is used for imputation is controlled. To implement the weighted sequential hot-deck procedure, imputation classes and sorting variables that are relevant (strong predictors) for each item being imputed are defined. Imputation classes are developed by using a Chi-squared Automatic Interaction.
 
View methodology reportnpsas2016ug_subject.pdf8.7 MBnpsas2016ug_varname.pdf6.7 MB001462
8212012qsOnpsOntsOn95,000Perturbation
To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in nonsampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.


Missing Values and Imputation
Following data collection, the data are subjected to various consistency and quality control checks before release. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely.


Except for data that were missing for cases to which they did not apply (e.g., whether a spouse is enrolled in college for unmarried students) and in a small number of items describing institutional characteristics, missing data were imputed using a two-step process. The first step is a logical imputation.¹ If a value could be calculated from the logical relationships with other variables, then that information was used to impute the value for the observation with a missing value. The second step is weighted hot deck imputation.² This procedure involves identifying a relatively homogenous group of observations, and, from within the group, selecting a random donor's value to impute a value for the observation with a missing value.


The table below shows the set of missing value codes for missing values that were not imputed in NPSAS:12. More information is available from the NPSAS:12 Data File Documentation (http://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2014182).


Description of missing value codes

Missing data code Description
-1 Not classified
-2 Not applicable
 -3 Skipped
-9 Missing



 
¹Logical imputation is a process that aims to infer or deduce the missing values from values for other items.
²Sequential hot deck imputation involves defining imputation classes, which generally consist of a cross-classification of covariates, and then replacing missing values sequentially from a single pass through the survey data within the imputation classes. When this form of imputation is performed using the sampling weights, the procedure is called weighted sequential hot deck imputation. This procedure takes into account the unequal probabilities of selection in the original sample to specify the expected number of times a particular respondent's answer will be used as a donor. These expected selection frequencies are specified so that, over repeated applications of the algorithm, the weighted distribution of all values for that variable—imputed and observed—will resemble that of the target universe. While each respondent record may be selected for use as a hot deck donor, the number of times a respondent record is used for imputation is controlled. To implement the weighted sequential hot deck procedure, imputation classes and sorting variables that are relevant (strong predictors) for each item being imputed are defined. Imputation classes are developed by using the chi-square automatic interaction detection algorithm.
View methodology reportnpsas2012ug_subject.pdf6.90 MBnpsas2012ug_varname.pdf5.45 MB001436
5112008qsOnpsOntsOn113,500Perturbation
 To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.

 Imputation
 All variables with missing data were imputed. The imputation procedures employed a two-step process. The first step is a logical imputation¹. If the imputed value could be deduced from the logical relationships with other variables, then that information was used to impute the value for the recipient. The second step is weighted hot-deck imputation.² This imputation procedure involves identifying a relatively homogenous group of observations, and, from within the group, selecting a random donor’s value to impute a value for the recipient.

 Skips and Missing Values
 Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. 

 The table below shows the set of reserve codes for missing values used in NPSAS 2008. Please consult the methodology report for more information.

 Description of missing data codes
  	 	Missing data code  Description  
  	-1  Not classified  
  	-2  Not applicable  
  	-6  Out of range  
  	-8  Item was not reached due to an error  
  	-9  Missing  
 

  ¹Logical imputation is a process that aims to infer or deduce the missing values from answers to other questions. 
 ²Sequential hot-deck imputation involves defining imputation classes, which generally consist of a cross-classification of covariates, and then replacing missing values sequentially from a single pass through the survey data within the imputation classes. When this form of imputation is performed using the sampling weights, the procedure is called weighted sequential hot-deck imputation. This procedure takes into account the unequal probabilities of selection in the original sample to specify the expected number of times a particular respondent’s answer will be used as a donor. These expected selection frequencies are specified so that, over repeated applications of the algorithm, the weighted distribution of all values for that variable—imputed and observed—will resemble that of the target universe. While each respondent record may be selected for use as a hot-deck donor, the number of times a respondent record is used for imputation is controlled. To implement the weighted sequential hot-deck procedure, imputation classes and sorting variables that are relevant (strong predictors) for each item being imputed are defined. Imputation classes are developed by using a Chi-squared Automatic Interaction.
 
View methodology reportnpsas2008ug_subject.pdf8.10 MBnpsas2008ug_varname.pdf6.40 MB001437
2412004qsOnpsOntsOn79,900 Perturbation
 To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.

 Imputation
 The imputation procedures employed a two-step process. In the first step, the matching criteria and imputation classes that were used to stratify the dataset were identified such that all imputation was processed independently within each class. In the second step, the weighted sequential hot deck process¹ was implemented, whereby missing data were replaced with valid data from donor records that match the recipients with respect to the matching criteria. Variables requiring imputation were not imputed simultaneously. However, some variables that were related substantively were grouped together into blocks, and the variables within a block were imputed simultaneously. Basic demographic variables were imputed first using variables with full information to determine the matching criteria. The order in which variables were imputed was also determined to some extent by the substantive nature of the variables. For example, basic demographics (such as age) were imputed first and these were used to process education variables (such as student level and enrollment intensity) which in turn were used to impute the financial aid variables (such as aid receipt and loan amounts). 

 Skips and Missing Values
 Edit checks were performed on the NPSAS:04 student interview data and CADE data, both during and upon completion of data collection, to confirm that the intended skip patterns were implemented in both instruments. At the conclusion of data collection, special codes were added as needed to indicate the reason for missing data. Missing data within individual data elements can occur for a variety of reasons. 

 The table below shows the set of reserve codes for missing values used in NPSAS 2004. Please consult the methodology report for more information.

 Description of missing data codes
  	 	Missing data code  Description  
  	-1  Not classified  
  	-3  Legitimate skip  
  	-9  Missing  
 

  ¹Sequential hot-deck imputation involves defining imputation classes, which generally consist of a cross-classification of covariates, and then replacing missing values sequentially from a single pass through the survey data within the imputation classes. When this form of imputation is performed using the sampling weights, the procedure is called weighted sequential hot-deck imputation. This procedure takes into account the unequal probabilities of selection in the original sample to specify the expected number of times a particular respondent’s answer will be used as a donor. These expected selection frequencies are specified so that, over repeated applications of the algorithm, the weighted distribution of all values for that variable—imputed and observed—will resemble that of the target universe. While each respondent record may be selected for use as a hot-deck donor, the number of times a respondent record is used for imputation is controlled. To implement the weighted sequential hot-deck procedure, imputation classes and sorting variables that are relevant (strong predictors) for each item being imputed are defined. Imputation classes are developed by using a Chi-squared Automatic Interaction.
View methodology reportnpsas2004ug_subject.pdf7.75 MBnpsas2004ug_varname.pdf6.00 MB001438
3512000qsOffpsOntsOn50,000Perturbation
 To protect the confidentiality of NCES data that contain information about specific individuals, NPSAS:00 data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in nonsampling errors. 

 Imputation
 All variables with missing data were imputed. The imputation procedures employed a two-step process. The first step is a logical imputation¹. If the imputed value could be deduced from the logical relationships with other variables, then that information was used to impute the value for the recipient. The second step is weighted hot-deck imputation.² This imputation procedure involves identifying a relatively homogenous group of observations, and, from within the group, selecting a random donor’s value to impute a value for the recipient.

  Skips and Missing Values
The NPSAS:00 data were edited using procedures developed and implemented for previous studies sponsored by NCES. Following data collection, the information collected in the student instrument was subjected to various quality control checks and examinations. These checks were to confirm that the collected data reflected appropriate skip patterns. Another evaluation examined all variables with missing data and substituted specific values to indicate the reason for the missing data. A variety of explanations are possible for missing data.
 
   The table below shows codes for missing values used in NPSAS:00 Please consult the   methodology report   for more information.
 
Description of missing data codes
          Missing data code  Description  
 -2  Independent student  
 -3  Skipped  
 -9  Data missing  
 
View methodology reportnpsas2000ug_subject.pdf8.68 MBnpsas2000ug_varname.pdf7.25 MB001439
3611996qsOffpsOntsOn41,500Imputation
 Values for 22 analysis variables were imputed. The variables were imputed using a weighted hot deck procedure, with the exception of estimated family contribution (EFC), which was imputed through a multiple regression approach.The weighed hot deck imputation procedure involves identifying a relatively homogenous group of observations, and, from within the group, selecting a random donor’s value to impute a value for the recipient.

  Skips and Missing Values
The NPSAS:96 data were edited using procedures developed and implemented for previous studies sponsored by NCES. Following data collection, the information collected in the student instrument was subjected to various quality control checks and examinations. These checks were to confirm that the collected data reflected appropriate skip patterns. Another evaluation examined all variables with missing data and substituted specific values to indicate the reason for the missing data. A variety of explanations are possible for missing data.
 
   The table below shows codes for missing values used in NPSAS:96 Please consult the   methodology report   for more information.
 
Description of missing data codes
              Missing data code  Description  
 -1  Don't know  
 -2  Refused  
-3  Skipped  
-8  Data source not available  
 -9  Data missing  
 
View methodology reportnpsas1996ug_subject.pdf3.47 MBnpsas1996ug_varname.pdf3.09 MB001440
221993qsOffpsOntsOff52,700Derived Variables and Imputed Values
 Approximately 800 variables have been constructed based on data collected in the NPSAS:93. As a general rule, the constructions of derive variables that concern financial aid and other financial descriptors depend first on record abstract data from the CADE system. These data are supplemented in many cases with information collected in the telephone interviews with parents and students. As between parent and student data, precedence was generally given to parent data for variables concerning family income and assets. Imputations were performed on seven variables that contained missing values. 

 Skips and Missing Values
 Both the student and parent CATI programs were designed to accommodate responses of "refusal" and "don't know" to any single question. Typically, refusal responses are given for items considered too sensitive by the respondent. "Don't know" responses may be given for any one of several reasons: (1) the respondent misunderstands the question wording, and is not offered subsequent explanation by the interviewer; (2) the respondent is hesitant to provide "best guess" responses, with insufficient prompting from the interviewer; (3) the respondent truly does not know the answer; or (4) the respondent chooses to respond with "don't know" as an implicit refusal to answer the question. Whenever they occur, indeterminate responses in the data set must be resolved by imputation or otherwise dealt with during analysis.

 The table below shows the set of reserve codes for missing values used in NPSAS 1993. Please consult the data file documentation report for more information.

 Description of missing data codes
   Missing
data code  Description
 -1  Legitimate skip  
 -7  Missing, refused  
 -8  Missing, don't know  
 -9  Missing, blank  
View methodology reportnpsas93ug_subject.pdf500 KBnpsas93ug_varname.pdf500 KB001440
171987qsOffpsOntsOff34,500Derived Variables and Imputed Values
 Approximately 800 variables have been constructed based on data collected in the NPSAS:87. As a general rule, the constructions of derive variables that concern financial aid and other financial descriptors depend first on record abstract data from the CADE system. These data are supplemented in many cases with information collected in the telephone interviews with parents and students. As between parent and student data, precedence was generally given to parent data for variables concerning family income and assets. Imputations were performed on seven variables that contained missing values. 

 Skips and Missing Values
 Both the student and parent CATI programs were designed to accommodate responses of "refusal" and "don't know" to any single question. Typically, refusal responses are given for items considered too sensitive by the respondent. "Don't know" responses may be given for any one of several reasons: (1) the respondent misunderstands the question wording, and is not offered subsequent explanation by the interviewer; (2) the respondent is hesitant to provide "best guess" responses, with insufficient prompting from the interviewer; (3) the respondent truly does not know the answer; or (4) the respondent chooses to respond with "don't know" as an implicit refusal to answer the question. Whenever they occur, indeterminate responses in the data set must be resolved by imputation or otherwise dealt with during analysis.

 The table below shows the set of reserve codes for missing values used in NPSAS 1993. Please consult the data file documentation report for more information.

 Description of missing data codes
   Missing
data code  Description
 -1  Legitimate skip  
 -7  Missing, refused  
 -8  Missing, don't know  
 -9  Missing, blank  
View methodology reportnpsas87ug_subject.pdf500 KBnpsas87ug_varname.pdf500 KB001440
141990qsOffpsOntsOff46,800Imputation
 Variables with more than 5 percent missing cases were imputed. After using information from all appropriate secondary sources, there remained eight variables which required some statistical imputation. Two methods of statistical imputation were used, regression-based or hot deck.

 Skips and Missing Values
 Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely.

 The table below shows the set of reserve codes for missing values used in NPSAS 1990. Please consult the data file documentation report for more information.

 Description of missing data codes
 Missing
data code Description
-1  Legitimate skip
 -9  Missing, blank
View methodology reportnpsas90ug_subject.pdf500 KBnpsas90ug_varname.pdf500 KB001440
National Postsecondary Student Aid Study, Graduate
NPSAS:GR
Students who were graduate and first-professional students at the time of interview
General demographics, Types of aid and amounts received, Cost of attending college, Combinations of work, study, and borrowing, Enrollment patterns
https://nces.ed.gov/surveys/npsas
12222016qsOnpsOntsOn24,000Perturbation
 To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.

 Imputation
 All variables with missing data were imputed. The imputation procedures employed a two-step process. The first step is a logical imputation¹. If the imputed value could be deduced from the logical relationships with other variables, then that information was used to impute the value for the recipient. The second step is weighted hot-deck imputation.² This imputation procedure involves identifying a relatively homogenous group of observations, and, from within the group, selecting a random donor’s value to impute a value for the recipient.

 Skips and Missing Values
 Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. 

 The table below shows the set of reserve codes for missing values used in NPSAS 2016. Please consult the data file documentation report for more information.

 Description of missing data codes
   Missing data code  Description  
 -3  Skipped  
  -9  Missing  
 

  ¹Logical imputation is a process that aims to infer or deduce the missing values from answers to other questions. 
 ²Sequential hot-deck imputation involves defining imputation classes, which generally consist of a cross-classification of covariates, and then replacing missing values sequentially from a single pass through the survey data within the imputation classes. When this form of imputation is performed using the sampling weights, the procedure is called weighted sequential hot-deck imputation. This procedure takes into account the unequal probabilities of selection in the original sample to specify the expected number of times a particular respondent’s answer will be used as a donor. These expected selection frequencies are specified so that, over repeated applications of the algorithm, the weighted distribution of all values for that variableimputed and observedwill resemble that of the target universe. While each respondent record may be selected for use as a hot-deck donor, the number of times a respondent record is used for imputation is controlled. To implement the weighted sequential hot-deck procedure, imputation classes and sorting variables that are relevant (strong predictors) for each item being imputed are defined. Imputation classes are developed by using a Chi-squared Automatic Interaction.
 
View methodology reportnpsas2016gr_subject.pdf6.6 MBnpsas2016gr_varname.pdf5.4 MB001563
8322012qsOnpsOntsOn16,000Perturbation
To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in nonsampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.


Missing Values and Imputation
Following data collection, the data are subjected to various consistency and quality control checks before release. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely.


Except for data that were missing for cases to which they did not apply (e.g., whether a spouse is enrolled in college for unmarried students) and in a small number of items describing institutional characteristics, missing data were imputed using a two-step process. The first step is a logical imputation.¹ If a value could be calculated from the logical relationships with other variables, then that information was used to impute the value for the observation with a missing value. The second step is weighted hot deck imputation.² This procedure involves identifying a relatively homogenous group of observations, and, from within the group, selecting a random donor's value to impute a value for the observation with a missing value.


The table below shows the set of missing value codes for missing values that were not imputed in NPSAS:12. More information is available from the NPSAS:12 Data File Documentation (http://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2014182).


Description of missing value codes

Missing data code Description
-1 Not classified
-2 Not applicable
 -3 Skipped
-9 Missing



 
¹Logical imputation is a process that aims to infer or deduce the missing values from values for other items.
²Sequential hot deck imputation involves defining imputation classes, which generally consist of a cross-classification of covariates, and then replacing missing values sequentially from a single pass through the survey data within the imputation classes. When this form of imputation is performed using the sampling weights, the procedure is called weighted sequential hot deck imputation. This procedure takes into account the unequal probabilities of selection in the original sample to specify the expected number of times a particular respondent's answer will be used as a donor. These expected selection frequencies are specified so that, over repeated applications of the algorithm, the weighted distribution of all values for that variable—imputed and observed—will resemble that of the target universe. While each respondent record may be selected for use as a hot deck donor, the number of times a respondent record is used for imputation is controlled. To implement the weighted sequential hot deck procedure, imputation classes and sorting variables that are relevant (strong predictors) for each item being imputed are defined. Imputation classes are developed by using the chi-square automatic interaction detection algorithm.
View methodology reportnpsas2012gr_subject.pdf1.47 MBnpsas2012gr_varname.pdf4.20 MB001541
5222008qsOnpsOntsOn14,200Perturbation
 To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.

 Imputation
 All variables with missing data were imputed. The imputation procedures employed a two-step process. The first step is a logical imputation.¹ If the imputed value could be deduced from the logical relationships with other variables, then that information was used to impute the value for the recipient. The second step is weighted hot-deck imputation.² This imputation procedure involves identifying a relatively homogenous group of observations, and, from within the group, selecting a random donor’s value to impute a value for the recipient.

 Skips and Missing Values
 Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely.

 The table below shows the set of reserve codes for missing values used in NPSAS 2008. Please consult the methodology report for more information.

 Description of missing data codes
  	 	Missing data code  Description  
  	-1  Not classified  
  	-3  Not applicable  
  	-6  Out of range  
  	-8  Item was not reached due to an error  
  	-9  Missing  
 

  ¹Logical imputation is a process that aims to infer or deduce the missing values from answers to other questions. 
 ²Sequential hot-deck imputation involves defining imputation classes, which generally consist of a cross-classification of covariates, and then replacing missing values sequentially from a single pass through the survey data within the imputation classes. When this form of imputation is performed using the sampling weights, the procedure is called weighted sequential hot-deck imputation. This procedure takes into account the unequal probabilities of selection in the original sample to specify the expected number of times a particular respondent’s answer will be used as a donor. These expected selection frequencies are specified so that, over repeated applications of the algorithm, the weighted distribution of all values for that variable—imputed and observed—will resemble that of the target universe. While each respondent record may be selected for use as a hot-deck donor, the number of times a respondent record is used for imputation is controlled. To implement the weighted sequential hot-deck procedure, imputation classes and sorting variables that are relevant (strong predictors) for each item being imputed are defined. Imputation classes are developed by using a Chi-squared Automatic Interaction.
View methodology reportnpsas2008gr_subject.pdf1.02 MBnpsas2008gr_varname.pdf748 KB001542
1222004qsOnpsOntsOn10,900 Perturbation
 To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.

 Imputation
 The imputation procedures employed a two-step process. In the first step, the matching criteria and imputation classes that were used to stratify the dataset were identified such that all imputation was processed independently within each class. In the second step, the weighted sequential hot deck process¹ was implemented, whereby missing data were replaced with valid data from donor records that match the recipients with respect to the matching criteria. Variables requiring imputation were not imputed simultaneously. However, some variables that were related substantively were grouped together into blocks, and the variables within a block were imputed simultaneously. Basic demographic variables were imputed first using variables with full information to determine the matching criteria. The order in which variables were imputed was also determined to some extent by the substantive nature of the variables. For example, basic demographics (such as age) were imputed first and these were used to process education variables (such as student level and enrollment intensity) which in turn were used to impute the financial aid variables (such as aid receipt and loan amounts). 

 Skips and Missing Values
 Edit checks were performed on the NPSAS:04 student interview data and CADE data, both during and upon completion of data collection, to confirm that the intended skip patterns were implemented in both instruments. At the conclusion of data collection, special codes were added as needed to indicate the reason for missing data. Missing data within individual data elements can occur for a variety of reasons. 

 The table below shows the set of reserve codes for missing values used in NPSAS 2004. Please consult the methodology report for more information.

 Description of missing data codes
  	 	Missing data code  Description  
  	-1  Not classified  
  	-3  Legitimate skip  
  	-9  Missing  
 

  ¹Sequential hot-deck imputation involves defining imputation classes, which generally consist of a cross-classification of covariates, and then replacing missing values sequentially from a single pass through the survey data within the imputation classes. When this form of imputation is performed using the sampling weights, the procedure is called weighted sequential hot-deck imputation. This procedure takes into account the unequal probabilities of selection in the original sample to specify the expected number of times a particular respondent’s answer will be used as a donor. These expected selection frequencies are specified so that, over repeated applications of the algorithm, the weighted distribution of all values for that variable—imputed and observed—will resemble that of the target universe. While each respondent record may be selected for use as a hot-deck donor, the number of times a respondent record is used for imputation is controlled. To implement the weighted sequential hot-deck procedure, imputation classes and sorting variables that are relevant (strong predictors) for each item being imputed are defined. Imputation classes are developed by using a Chi-squared Automatic Interaction.
View methodology reportnpsas2004gr_subject.pdf1.06 MBnpsas2004gr_varname.pdf787 KB001543
3722000qsOffpsOntsOn12,000Perturbation
 To protect the confidentiality of NCES data that contain information about specific individuals, NPSAS:00 data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in nonsampling errors. 

 Imputation
 All variables with missing data were imputed. The imputation procedures employed a two-step process. The first step is a logical imputation¹. If the imputed value could be deduced from the logical relationships with other variables, then that information was used to impute the value for the recipient. The second step is weighted hot-deck imputation.² This imputation procedure involves identifying a relatively homogenous group of observations, and, from within the group, selecting a random donor’s value to impute a value for the recipient.

  Skips and Missing Values 
The NPSAS:00 data were edited using procedures developed and implemented for previous studies sponsored by NCES. Following data collection, the information collected in the student instrument was subjected to various quality control checks and examinations. These checks were to confirm that the collected data reflected appropriate skip patterns. Another evaluation examined all variables with missing data and substituted specific values to indicate the reason for the missing data. A variety of explanations are possible for missing data.
 
   The table below shows codes for missing values used in NPSAS:00 Please consult the   methodology report   for more information.
 
Description of missing data codes
          Missing data code  Description  
 -2  Independent student  
 -3  Skipped  
 -9  Data missing  
 
View methodology reportnpsas2000gr_subject.pdf1.71 MBnpsas2000gr_varname.pdf1.43 MB001544
3821996qsOffpsOntsOn7,000Imputation
 Values for 22 analysis variables were imputed. The variables were imputed using a weighted hot deck procedure, with the exception of estimated family contribution (EFC), which was imputed through a multiple regression approach.The weighed hot deck imputation procedure involves identifying a relatively homogenous group of observations, and, from within the group, selecting a random donor’s value to impute a value for the recipient.

  Skips and Missing Values
The NPSAS:96 data were edited using procedures developed and implemented for previous studies sponsored by NCES. Following data collection, the information collected in the student instrument was subjected to various quality control checks and examinations. These checks were to confirm that the collected data reflected appropriate skip patterns. Another evaluation examined all variables with missing data and substituted specific values to indicate the reason for the missing data. A variety of explanations are possible for missing data.
 
   The table below shows codes for missing values used in NPSAS:96 Please consult the   methodology report   for more information.
 
Description of missing data codes
              Missing data code  Description  
 -1  Don''t know  
 -2  Refused  
-3  Skipped  
-8  Data source not available  
 -9  Data missing  
 
View methodology reportnpsas1996gr_subject.pdf2.53 MBnpsas1996gr_varname.pdf2.13 MB001545
131993qsOffpsOntsOff13,400Derived Variables and Imputed Values
 Approximately 800 variables have been constructed based on data collected in the NPSAS:93. As a general rule, the constructions of derive variables that concern financial aid and other financial descriptors depend first on record abstract data from the CADE system. These data are supplemented in many cases with information collected in the telephone interviews with parents and students. As between parent and student data, precedence was generally given to parent data for variables concerning family income and assets. Imputations were performed on seven variables that contained missing values. 

 Skips and Missing Values
 Both the student and parent CATI programs were designed to accommodate responses of "refusal" and "don't know" to any single question. Typically, refusal responses are given for items considered too sensitive by the respondent. "Don't know" responses may be given for any one of several reasons: (1) the respondent misunderstands the question wording, and is not offered subsequent explanation by the interviewer; (2) the respondent is hesitant to provide "best guess" responses, with insufficient prompting from the interviewer; (3) the respondent truly does not know the answer; or (4) the respondent chooses to respond with "don't know" as an implicit refusal to answer the question. Whenever they occur, indeterminate responses in the data set must be resolved by imputation or otherwise dealt with during analysis.

 The table below shows the set of reserve codes for missing values used in NPSAS 1993. Please consult the data file documentation report for more information.

 Description of missing data codes
   Missing
data code  Description
 -1  Legitimate skip  
 -7  Missing, refused  
 -8  Missing, don't know  
 -9  Missing, blank  
View methodology reportnpsas93gr_subject.pdf500 KBnpsas93gr_varname.pdf500 KB001540
161990qsOffpsOntsOff14,300Imputation
 Variables with more than 5 percent missing cases were imputed. After using information from all appropriate secondary sources, there remained eight variables which required some statistical imputation. Two methods of statistical imputation were used, regression-based or hot deck.

 Skips and Missing Values
 Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely.

 The table below shows the set of reserve codes for missing values used in NPSAS 1990. Please consult the data file documentation report for more information.

 Description of missing data codes
 Missing
data code Description
-1  Legitimate skip
 -9  Missing, blank
View methodology reportnpsas90gr_subject.pdf500 KBnpsas90gr_varname.pdf500 KB001540
181987qsOffpsOntsOff8,600Derived Variables and Imputed Values
 Approximately 800 variables have been constructed based on data collected in the NPSAS:93. As a general rule, the constructions of derive variables that concern financial aid and other financial descriptors depend first on record abstract data from the CADE system. These data are supplemented in many cases with information collected in the telephone interviews with parents and students. As between parent and student data, precedence was generally given to parent data for variables concerning family income and assets. Imputations were performed on seven variables that contained missing values. 

 Skips and Missing Values
 Both the student and parent CATI programs were designed to accommodate responses of "refusal" and "don't know" to any single question. Typically, refusal responses are given for items considered too sensitive by the respondent. "Don't know" responses may be given for any one of several reasons: (1) the respondent misunderstands the question wording, and is not offered subsequent explanation by the interviewer; (2) the respondent is hesitant to provide "best guess" responses, with insufficient prompting from the interviewer; (3) the respondent truly does not know the answer; or (4) the respondent chooses to respond with "don't know" as an implicit refusal to answer the question. Whenever they occur, indeterminate responses in the data set must be resolved by imputation or otherwise dealt with during analysis.

 The table below shows the set of reserve codes for missing values used in NPSAS 1993. Please consult the data file documentation report for more information.

 Description of missing data codes
   Missing
data code  Description
 -1  Legitimate skip  
 -7  Missing, refused  
 -8  Missing, don't know  
 -9  Missing, blank  
View methodology reportnpsas87gr_subject.pdf500 KBnpsas87gr_varname.pdf500 KB001540
National Study of Postsecondary Faculty
NSOPF
Postsecondary faculty
Workload, Equity issues, Involvement in undergraduate teaching, Relationship between teaching and research
https://nces.ed.gov/surveys/nsopf
282004qsOnpsOntsOff26,100Perturbation
 A restricted faculty-level data file was created for release to individuals who apply for and meet standards for such data releases. While this file does not include personally identifying information (i.e., name and Social Security number), other data (i.e., institution, Integrated Postsecondary Education Data System [IPEDS] ID, demographic information, and salary data) may be manipulated in such a way to seem to identify data records corresponding to a particular faculty member. To protect further against such situations, some of the variable values were swapped between faculty respondents. This procedure perturbed and added additional uncertainty to the data. Thus, associations made among variable values to identify a faculty respondent may be based on the original or edited, imputed and/or swapped data. For the same reasons, the data from the institution questionnaire were also swapped to avoid data disclosure.

 Imputation
 Item imputation for the faculty questionnaire was performed in several steps. In the first step, the missing values of gender, race, and ethnicity were filled—using cold-deck imputation¹— based on the sampling frame information or institution record data. These three key demographic variables were imputed prior to any other variables since they were used as key predictors for all other variables on the data file. After all logical² and cold-deck imputation procedures were performed, the remaining variables were imputed using the weighted sequential hot-deck method.³ Initially, variables were separated into two groups: unconditional and conditional variables. The first group (unconditional) consisted of variables that applied to all respondents, while the second group (conditional) consisted of variables that applied to only a subset of the respondents. That is, conditional variables were subject to “gate” questions. After this initial grouping, these groups were divided into finer subgroups. After all variables were imputed, consistency checks were applied to the entire faculty data file to ensure that the imputed values did not conflict with other questionnaire items, observed or imputed. This process involved reviewing all of the logical imputation and editing rules as well.

 Skips and Missing Values
 During and following data collection, the data were reviewed to confirm that the data collected reflected the intended skip-pattern relationships. At the conclusion of data collection, special codes were inserted in the database to reflect the different types of missing data. There are a number of explanations for missing data; for example, the item may not have been applicable to certain respondents or a respondent may not have known the answer to the question. With the exception of the not applicable codes, missing data were stochastically imputed. Moreover, for hierarchical analyses and developing survey estimates for faculty members corresponding to sample institutions that provided faculty lists and responded to the institution survey, contextual weights were produced for such subsets of the responding faculty members. 

 The table below shows codes for missing values used in NSOPF:04. Please consult the methodology report for more information.

 Description of missing data codes
  	 	Missing data code  Description 
  	-3  Legitimate skip  
  	-7  Not reached  
  	-9  Missing  
 

  ¹Cold-deck imputation involves replacing the missing values with data from sources such as data used for sampling frame construction. While resource intensive, these methods often obtain the actual value that is missing. Stochastic imputation methods, such as sequential hot-deck imputation, rely on the observed data to provide replacing values (donors) for records with missing values.
 ²Logical imputation is a process that aims to infer or deduce the missing values from answers to other questions.
 ³Sequential hot-deck imputation involves defining imputation classes, which generally consist of a cross-classification of covariates, and then replacing missing values sequentially from a single pass through the survey data within the imputation classes. When this form of imputation is performed using the sampling weights, the procedure is called weighted sequential hot-deck imputation. This procedure takes into account the unequal probabilities of selection in the original sample to specify the expected number of times a particular respondent’s answer will be used as a donor. These expected selection frequencies are specified so that, over repeated applications of the algorithm, the weighted distribution of all values for that variable—imputed and observed—will resemble that of the target universe. Under this methodology, while each respondent record has a chance to be selected for use as a hot-deck donor, the number of times a respondent record can be used for imputation will be controlled. To implement the weighted sequential hot-deck procedure, imputation classes and sorting variables that are relevant (strong predictor) for each item being imputed were defined. Imputation classes were developed by using a Chi-squared Automatic Interaction.
View methodology reportnsopf04_subject.pdf1.16 MBnsopf04_varname.pdf926 KB001646
261999qsOffpsOntsOff18,000Both the faculty and institution questionnaire data were edited using seven principles designed to improve data quality and consistency.

Menu items. For many questions there were several sub-items listed where the respondent was asked to give a response for each sub-item. These questions were cleaned with several procedures. First if the main question had an “NA” (Not Applicable) check box and that box was checked, all of the sub-items were set to a value of “no” or “zero” depending on the wording of the question. Second, if the respondent had filled out one or more of the sub-items with a “yes” response or a positive number but had left other sub-items blank, the missing sub-items were set to “no,” “zero,” or “don’t know” depending on the question wording. If all sub-items were missing and there was no “NA” box, or the “NA” box was not checked, the case was flagged and the data values were imputed for that question. Examples of these types of questions are Question 21 in the institution questionnaire and Question 29 in the faculty questionnaire.
Inter-item consistency checks. Many types of inter-item consistency checks were performed on the data. One procedure was to check groups of related items for internal consistency and to make adjustments to make them consistent. For example, in questions that asked about a spouse in the faculty questionnaire (Questions 66i, Q76i, and 77a) if respondents indicated that they did not have a spouse in one or more of the questions, the other questions were checked for consistency and corrected as necessary. Another procedure checked “NA” boxes. If the respondent had checked the “NA” box for a question but had filled in any of the sub-items for that question the “NA” box was set to blank. For example, this procedure was used with Question 21 in the institution questionnaire and Question 16 in the faculty questionnaire. A third procedure was to check filter items for which more detail was sought in a follow-up open-ended or closed-ended question. If detail was provided, then the filter question was checked to make sure the appropriate response was recorded. For example, this procedure was used with Question 11 in the institution questionnaire and Question 12E in the faculty questionnaire.
Percent items. All items where respondents were asked to give a percentage were checked to make sure they summed to 100 percent. The editing program also looked for any numbers between 0 and 1 to make sure that respondents did not fill in the question with a decimal rather than a percentage. All fractions of a percent were rounded to the nearest whole percent. An example of this type of item is Question 31 in the faculty questionnaire.


Data imputation for the faculty questionnaire was performed in four steps. The imputation method for each variable is specified in the labels for the imputation flags in the faculty dataset.

Logical imputation.  The logical imputation was conducted during the data cleaning steps as explained in the immediately preceding section.
 Cold deck. Missing responses were filled in with data from the sample frame whenever the relevant data were available. Examples include gender, race, and employment status.
Hot deck. This procedure selected non-missing values from “sequential nearest neighbors” within the imputation class. All questions that were categorical and had more than 16 categories were imputed with this method. An example is Question Q14 – principal field of teaching. The imputation class for this question was created using faculty stratum and instructional duty status (Q1).
 Regression type. This procedure employed SAS PROC IMPUTE21. All items that were still missing after the logical, cold deck, and hot deck imputation procedures were imputed with this method. Project staff selected the independent variables by first looking through the questionnaire for logically related items and then by conducting a correlation analysis of the questions against each other to find the top correlates for each item.
View methodology reportnsopf99_subject.pdf500 KBnsopf99_varname.pdf500 KB001646
251993qsOffpsOntsOff31,000Depending on the scale of the variable being imputed, one of two methods were used:
1) Regression imputation was used for continuous and dichotomous variables; and
2) Hotdeck imputation was used for unordered polytomous variables.

The regression method incorporated in NCES’s PROC IMPUTE was used to impute missing values for approximately 90 percent of the 395 items on the faculty questionnaire.
Of the total of 395 items, 353 were imputed using the regression-based imputation procedures only.
View methodology reportnsopf93_subject.pdf500 KBnsopf93_varname.pdf500 KB001646
231988qsOffpsOntsOff25,000NSOPF:88 was conducted with a sample of 480 institutions (including 2-year, 4-year, doctoral-granting, and other colleges and universities), some 11,010 faculty, and more than 3,000 department chairpersons. Institutions were sampled from the 1987 IPEDS universe and were stratified by modified Carnegie Classifications and size (faculty counts). These strata were (1) public, research; (2) private, research; (3) public, other Ph.D. institution (not defined in any other stratum); (4) private, other Ph.D. institution (not defined in any other stratum); (5) public, comprehensive; (6) private, comprehensive; (7) liberal arts; (8) public, 2-year; (9) private, 2-year; (10) religious; (11) medical; and (12) “other” schools (not defined in any other stratum). Within each stratum, institutions were randomly selected. Of the 480 institutions selected, 450 (94 percent) agreed to participate and provided lists of their faculty and department chairpersons. Within 4-year institutions, faculty and department chairpersons were stratified by program area and randomly sampled within each stratum; within 2-year institutions, simple random samples of faculty and department chairpersons were selected; and within specialized institutions (religious, medical, etc.), faculty samples were randomly selected (department chairpersons were not sampled). At all institutions, faculty were also stratified on the basis of employment status—full-time and part-time. Note that teaching assistants and teaching fellows were excluded in NSOPF:88.
Although NSOPF:88 consisted of three questionnaires, imputations were only performed for faculty item nonresponse. The within-cell random imputation method was used to fill in most Faculty Questionnaire items that had missing data.
nsopf88_subject.pdf500 KBnsopf88_varname.pdf500 KB001646
National Study of Postsecondary Faculty, Institutions
NSOPF
Postsecondary institutions
Faculty tenure policies, Union representation, and Faculty attrition
https://nces.ed.gov/surveys/nsopf
292004qsOnpsOntsOff900 Imputation
 The imputation process for the missing data from the institution questionnaire involved similar steps to those used for imputation of the faculty data. The missing data for variables were imputed using the weighted sequential hot-deck method.¹ Analogous to the imputation process for the faculty data, the variables were partitioned into conditional and unconditional groups. The unconditional variables were sorted by percent missing and then imputed in the order from the lowest percent missing to the highest. The conditional group was partitioned into three subgroups based on the level of conditionality for each variable, and then imputed in that order. The imputation class for both unconditional and conditional variables consisted of the institution sampling stratum, and the sorting variables included the number of full-time and part-time faculty members.

 Skips and Missing Values
 During and following data collection, the data were reviewed to confirm that the data collected reflected the intended skip-pattern relationships. At the conclusion of data collection, special codes were inserted in the database to reflect the different types of missing data. There are a number of explanations for missing data; for example, the item may not have been applicable to certain respondents or a respondent may not have known the answer to the question. With the exception of the not applicable codes, missing data were stochastically imputed. Moreover, for hierarchical analyses and developing survey estimates for faculty members corresponding to sample institutions that provided faculty lists and responded to the institution survey, contextual weights were produced for such subsets of the responding faculty members. 

 The table below shows codes for missing values used in NSOPF:04. Please consult the methodology report for more information.

 Description of missing data codes
  	 	Missing data code  Description 
  	-3  Legitimate skip  
  	-7  Not reached  
  	-9  Missing  
 

  ¹Sequential hot-deck imputation involves defining imputation classes, which generally consist of a cross-classification of covariates, and then replacing missing values sequentially from a single pass through the survey data within the imputation classes. When this form of imputation is performed using the sampling weights, the procedure is called weighted sequential hot-deck imputation. This procedure takes into account the unequal probabilities of selection in the original sample to specify the expected number of times a particular respondent’s answer will be used as a donor. These expected selection frequencies are specified so that, over repeated applications of the algorithm, the weighted distribution of all values for that variable—imputed and observed—will resemble that of the target universe. Under this methodology, while each respondent record has a chance to be selected for use as a hot-deck donor, the number of times a respondent record can be used for imputation will be controlled. To implement the weighted sequential hot-deck procedure, imputation classes and sorting variables that are relevant (strong predictor) for each item being imputed were defined. Imputation classes were developed by using a Chi-squared Automatic Interaction. 
View methodology reportnsopf04inst_subject.pdf543 KBnsopf04inst_varname.pdf471 KB001747
National Teacher and Principal Survey, Public School Principals
NTPS
Public school principals
Experience, Training, Education, and Professional Development, Goals and Decision Making, Teacher and Aide Professional Development, School Climate and Safety, Instructional Time, Working Conditions and Principal Perceptions, Teacher and School Performance
https://nces.ed.gov/surveys/ntps
1262015-2016qsOnpsOntsOff8,300Imputation
The NTPS used two main approaches to impute data. First, donor respondent methods, such as hot-deck imputation, were used. Second, if no suitable donor case could be matched, the few remaining items were imputed using mean or mode from groups of similar cases to impute a value to the item with missing data. Finally, in rare cases for which imputed values were inconsistent with existing questionnaire data or out of the range of acceptable values, Census Bureau analysts looked at the items and tried to determine an appropriate value.
Weighting
Weighting of the sample units was carried out to produce national estimates for public schools, principals, and teachers. The weighting procedures used in NTPS had three purposes: to take into account the school's selection probability; to reduce biases that may result from unit nonresponse; and to make use of available information from external sources to improve the precision of sample estimates.
ntps2016principals_subject.pdf1.53 MBntps2016principals_varname.pdf1.70 MB001965
National Teacher and Principal Survey, Public Schools
NTPS
Public schools
Teacher demand, teacher and principal characteristics, general conditions in schools, principals' and teachers' perceptions of school climate and problems in their schools, teacher compensation, district hiring and retention practices, basic characteristics of the student population
https://nces.ed.gov/surveys/ntps
1272015-2016qsOnpsOntsOff8,300Imputation
The NTPS used two main approaches to impute data. First, donor respondent methods, such as hot-deck imputation, were used. Second, if no suitable donor case could be matched, the few remaining items were imputed using mean or mode from groups of similar cases to impute a value to the item with missing data. Finally, in rare cases for which imputed values were inconsistent with existing questionnaire data or out of the range of acceptable values, Census Bureau analysts looked at the items and tried to determine an appropriate value.
Weighting
Weighting of the sample units was carried out to produce national estimates for public schools, principals, and teachers. The weighting procedures used in NTPS had three purposes: to take into account the school's selection probability; to reduce biases that may result from unit nonresponse; and to make use of available information from external sources to improve the precision of sample estimates.
ntps2016schools_subject.pdf2.59 MBntps2016schools_varname.pdf3.35 MB002066
Early Childhood Program Participation
ECPP
Children who were enrolled in some type of childcare program
Children's participation, Relative care, Nonrelative care, Center-based care, Head Start and Early Head start programs, time spent in care, number of children and care providers
https://nces.ed.gov/nhes
15162019qsOffpsOntsOn7,100Imputation
Four approaches to imputation were used in the NHES:2019: logic-based imputation, which was used whenever possible; unweighted sequential hot deck imputation, which was used for the majority of the missing data (i.e., for all variables that were not boundary and sort variables—described below); weighted random imputation, which was used for a small number of variables including boundary and sort variables; and manual imputation, which was used in a very small number of cases for a small number of variables.

For more information about these approaches, please see the NHES: 2019 Data File User's Manual. 
ecpp2019_subject.pdf3.63 MBecpp2019_varname.pdf2.51 MB002169
13062016qsOnpsOntsOn5,800Imputation
Four approaches to imputation were used in the NHES:2016: logic-based imputation, which was used whenever possible; unweighted sequential hot deck imputation, which was used for the majority of the missing data (i.e., for all variables that were not boundary and sort variables—described below); weighted random imputation, which was used for a small number of variables including boundary and sort variables; and manual imputation, which was used in a very small number of cases for a small number of variables.

For more information about these approaches, please see the NHES: 2016 Data File User's Manual. 
ecpp2016_subject.pdfecpp2016_varname.pdf002169
12962012qsOnpsOntsOn7,900Imputation
Three approaches to imputation were used in the NHES:2012: unweighted sequential hot deck imputation, which was used for the majority of the missing data, that is, for all variables that were not required for Interview Status Recode (ISR) classification, as described in chapter 4; weighted random imputation, which was used for a small number of variables; and manual imputation, which was used in a very small number of cases for most variables.

For more information about these approaches, please see the NHES: 2012 Data File User's Manual. 
ecpp2012_subject.pdfecpp2012_varname.pdf002168
Adult Training and Education Survey
ATES
Adults who were enrolled in a training or literacy program
Education, Certifications and Licenses, Certificates, Work Experience Programs, Employment, Background
https://nces.ed.gov/nhes
1332016qsOnpsOntsOff47,700Imputation
Four approaches to imputation were used in the NHES:2016: logic-based imputation, which was used whenever possible; unweighted sequential hot deck imputation, which was used for the majority of the missing data (i.e., for all variables that were not boundary and sort variables—described below); weighted random imputation, which was used for a small number of variables including boundary and sort variables; and manual imputation, which was used in a very small number of cases for a small number of variables.

For more information about these approaches, please see the NHES: 2016 Data File User's Manual. 
ates2016_subject.pdf2.84 MBates2016_varname.pdf2.90 MB002270
Parent and Family Involvement in Education
PFI
Parents and families who were involved in their child's education
Children's schooling, Families and schools, Homework, Family activities, Health, Background, Household
https://nces.ed.gov/nhes
15472019qsOffpsOntsOn16,400Imputation
Four approaches to imputation were used in the NHES:2019: logic-based imputation, which was used whenever possible; unweighted sequential hot deck imputation, which was used for the majority of the missing data (i.e., for all variables that were not boundary and sort variables—described below); weighted random imputation, which was used for a small number of variables including boundary and sort variables; and manual imputation, which was used in a very small number of cases for a small number of variables.

For more information about these approaches, please see the NHES: 2019 Data File User's Manual. 
pfi2019_subject.pdf2.5 MBpfi2019_varname.pdf2.1 MB002372
13272016qsOnpsOntsOn13,500Imputation
Four approaches to imputation were used in the NHES:2016: logic-based imputation, which was used whenever possible; unweighted sequential hot deck imputation, which was used for the majority of the missing data (i.e., for all variables that were not boundary and sort variables—described below); weighted random imputation, which was used for a small number of variables including boundary and sort variables; and manual imputation, which was used in a very small number of cases for a small number of variables.

For more information about these approaches, please see the NHES: 2016 Data File User's Manual. 
pfi2016_subject.pdf2.5 MBpfi2016_varname.pdf2.1 MB002372
13172012qsOnpsOntsOn17,200Imputation
Three approaches to imputation were used in the NHES:2012: unweighted sequential hot deck imputation, which was used for the majority of the missing data, that is, for all variables that were not required for Interview Status Recode (ISR) classification, as described in chapter 4; weighted random imputation, which was used for a small number of variables; and manual imputation, which was used in a very small number of cases for most variables.

For more information about these approaches, please see the NHES: 2012 Data File User's Manual. 
pfi2012_subject.pdfpfi2012_varname.pdf002371
High School and Beyond
HSB
Students who were high school sophomores in 1980
Social background, Test battery and school record, Home educational support system, Postsecondary education choice and enrollment, Employment, Outcomes
https://nces.ed.gov/surveys/hsb/index.asp
1351980qsOnpsOntsOff14,800Nonresponse
 Nonresponse inevitably introduces some degree of error into survey results. In examining the impact of nonresponse, it is useful to think of the survey population as including two strata--a respondent stratum that consists of all units that would have provided data had they been selected for the survey, and a nonrespondent stratum that consists of all units that would not have provided data had they been selected. The actual sample of respondents necessarily consists entirely of units from the respondent stratum. Thus, sample statistics can serve as unbiased estimates only for the respondent stratum; as estimates for the entire population, the sample statistics will be biased to the extent that the characteristics of the respondents differ from those of the entire population.
In the High School and Beyond study, there were two stages of sample selection and therefore two stages of nonresponse. During the base year survey, sample schools were asked to permit the selection of individual sophomores and seniors from school rosters and to designate "survey days" for the collection of student questionnaire and test data. Schools that refused to cooperate in either of these activities were dropped from the sample. Individual students at cooperating schools could also fail to take part in the base year survey. Unlike "refusal" schools, nonparticipating students were not dropped from the sample; they remained eligible for selection into the follow-up samples.
Estimates based on student data from the base year surveys include two components of nonresponse bias: bias introduced by nonresponse at the school level, and bias introduced by nonresponse on the part of students attending cooperating schools. Each component of the overall bias depends on two factors--the level of nonresponse and the difference between respondents and nonrespondents:
	Bias = P1(Y1R - Y1NR) + P2(Y2R - Y2NR)
in which
	P1 = the proportion of the population of students attending schools that would have been nonrespondents,
YlNR = the parameter describing the population of students attending nonrespondent schools,
	P2 = the proportion of students attending respondent schools who would have been nonrespondents, and
	Y2NR = the parameter describing this group of students.
Nonresponse bias will be small if the nonrespondent strata constitute only a small portion of the survey population or if the differences between respondents and nonrespondents are small. The proportions P1 and P2 can generally be estimated from survey data using appropriately weighted nonresponse rates. 
The implications of the equation can be easily seen in terms of a particular base year estimate. On the average, sophomores got 10.9 items right on a standardized vocabulary test. This figure is an estimate of Y2R, the population mean for all participating students at cooperating schools. Now, suppose that sophomores at cooperating schools average two more correct than sophomores attending refusal schools (Y1R - Y1NR = 2), and suppose further that among sophomores attending cooperating schools, student respondents average one more correct answer than student nonrespondents (Y2R - Y2NR = 1). Noting that the base year school nonresponse rate was about .30 and the student nonresponse rate for sophomores was about .12, we can use these figures as estimates of P1 and P2 and we can use this equation to calculate the bias as:
	Bias = .30(2) + .12(1) = .72
	That is, the sample estimate is biased by about .7 of a test score point.
This example assumes knowledge of the relevant population means; in practice, of course, they are not known and, although Pl and P2 can generally be estimated from the nonresponse rates, the lack of survey data for nonrespondents prevents the estimation of the nonresponse bias. The High School and Beyond study is an exception to this general rule: during the first follow-up, school questionnaire data were obtained from most of the base year refusal schools, and student data were obtained from most of the base year student nonrespondents selected for the first follow-up sample. These data provide a basis for assessing the magnitude of nonresponse bias in base year estimates.
The bias introduced by base year school-level refusals is of particular concern since it carries over into successive rounds of the survey. Students attending refusal schools were not sampled during the base year and have no chance for selection into subsequent rounds of observation. To the extent that these students differ from students from cooperating schools during later waves of the study, the bias introduced by base year school nonresponse will persist. Student nonresponse is not carried over in this way since student nonrespondents remain eligible for sampling in later waves of the study.
The results of three types of analyses concerning nonresponse are described in an earlier report. Based on school questionnaire data, schools that participated during the base year were compared with all eligible schools. Based on the first follow-up student data, base year student respondents were compared with nonrespondents. Finally, student nonresponse during the first follow-up survey was analyzed. Taken together, these earlier analyses indicated that nonresponse had little effect on base year and first follow-up estimates. The results presented there suggest that the school-level component of the bias affected base year estimates by 2 percent or less and that the student-level component had even less impact.
hsb1980_subject.pdf17.9 MBhsb1980_varname.pdf13.4 MB002556
National Education Longitudinal Study of 1988
NELS:88
Students who were eighth graders in 1988
School, work, home experiences, educational resources and support, the role in education of parents and peers, neighborhood characteristics, educational and occupational aspirations, other student perceptions
https://nces.ed.gov/surveys/nels88/
1361988qsOnpsOntsOff15,000Nonresponse
School-level nonresponse is a serious concern because it carries over into successive rounds of NELS:88. Students attending schools that did not cooperate in the base year were not sampled and had little or no chance of selection into the follow-up samples. To the extent that students at noncooperating schools differ from students at cooperating schools, the student-level bias introduced by base-year school noncooperation persists during subsequent waves. Nonresponse adjustments to weights are an attempt to compensate for bias in the estimate for a particular subgroup; they do not adjust for nonresponse bias within subgroups.
In the base year, nonresponding schools were asked to supply information about key school questionnaire variables, and virtually all did so. Based on these data, analysis of school-level nonresponse suggests that, to the extent that schools can be characterized by size, control, organizational structure, student composition, and other characteristics, the impact of nonresponding schools on school level estimates is small.²⁵ Readers interested in more information about the analyses of school nonresponse rates and bias for the NELS:88 base year should refer to the NELS:88 Base-Year Sample Design Report (Spencer et al. 1990). School nonresponse was not assessed in the first or second follow-ups for two reasons. First, there was practically no school-level nonresponse; institutional cooperation levels approached 99 percent in both rounds. Second, the first and second follow-up samples were student-driven, unlike the two-stage initial sample design in the base year. Hence, even if a school refused in either the first or second follow-ups, the individual student was pursued outside of school.
25. The use of school questionnaire variables to assess bias in estimates concerning characteristics of the student population is not entirely straightforward. Still, to the extent that school characteristics are closely related to the characteristics of the students attending them, estimates based on school questionnaire data can serve as reasonable proxies for more direct estimates of student-level unit nonresponse bias.
nels1988_subject.pdf6.9 MBnels1988_varname.pdf15.1 MB002657
Early Childhood Longitudinal Study: Birth Cohort
ECLS-B
Children born in 2001
Children's health, development, child care, education during formative years, birth through kindergarten entry.
https://nces.ed.gov/ecls/
302001-02qsOffpsOntsOff10,700Skips and Missing Values
Most variables in the ECLS-B data use a standard scheme for missing values. Codes are used to indicate item nonresponse, legitimate skips, and unit nonresponse 

The table below shows the set of reserve codes for missing values used in ECLS-B.
Code       Description
 -1  Not applicable
 -7  Refused
 -8  Don't Know
 -9  Missing


Please consult the User's Manual for the ECLS-B Longitudinal 9-Month-Preschool Restricted-Use Data File and Electronic Codebook for more information.
View methodology reportecls_subject.pdf500 KBecls_varname.pdf500 KB002779
Adult Education Survey
AE-NHES
Adults who were enrolled in a training or literacy program
Education, Certifications and Licenses, Certificates, Work Experience Programs, Employment, Background
https://nces.ed.gov/nhes
472005qsOffpsOntsOff9,800Imputation
Item nonresponse occurred when some, but not all, of the responses were missing from an otherwise cooperating respondent. To help users of the NHES:2005 data, the missing data were imputed, that is, obtained from a donor case using statistical procedures. For each variable on the AE public-use file with imputed data, an imputation flag variable was created. This flag can be used to identify imputed values. If there is no imputation flag, then no imputation was performed on that variable.
View methodology reportaenhes_subject.pdf500 KBae-nhes_varname.pdf500 KB003170
Adult Education Survey: 2005 2005 Adult Education qsOffpsOntsOff
Adult Training and Education Survey: 2016 2016 Adult Education qsOnpsOntsOff
Baccalaureate and Beyond: 1993/2003 1993/2003 Postsecondary qsOnpsOntsOff
Baccalaureate and Beyond: 1993/2003 1993/2003 Adult Education qsOnpsOntsOff
Baccalaureate and Beyond: 1993/2003 Graduate students 1993/2003 Postsecondary qsOffpsOntsOff
Baccalaureate and Beyond: 1993/2003 Graduate students 1993/2003 Adult Education qsOffpsOntsOff
Baccalaureate and Beyond: 2000/2001 2000/2001 Postsecondary qsOffpsOntsOff
Baccalaureate and Beyond: 2000/2001 2000/2001 Adult Education qsOffpsOntsOff
Baccalaureate and Beyond: 2008/2012 2008/2012 Postsecondary qsOnpsOntsOff
Baccalaureate and Beyond: 2008/2012 2008/2012 Adult Education qsOnpsOntsOff
Baccalaureate and Beyond: 2016/2017 2016/2017 Postsecondary qsOnpsOntsOff
Baccalaureate and Beyond: 2016/2017 2016/2017 Adult Education qsOnpsOntsOff
Beginning Postsecondary Students: 1990/1994 1990/1994 Postsecondary qsOnpsOntsOff
Beginning Postsecondary Students: 1996/2001 1996/2001 Postsecondary qsOnpsOntsOff
Beginning Postsecondary Students: 2004/2009 2004/2009 Postsecondary qsOnpsOntsOff
Beginning Postsecondary Students: 2012/2017 2012/2017 Postsecondary qsOnpsOntsOff
Early Childhood Longitudinal Study: Birth Cohort 2001-02 P-12 qsOffpsOntsOff
Early Childhood Program Participation: 2012 2012 P-12 qsOnpsOntsOn6
Early Childhood Program Participation: 2016 2016 P-12 qsOnpsOntsOn6
Early Childhood Program Participation: 2019 2019 P-12 qsOffpsOntsOn6
Education Longitudinal Study of 2002 2002 Postsecondary qsOnpsOntsOff
Education Longitudinal Study of 2002 2002 P-12 qsOnpsOntsOff
High School and Beyond 1980 Postsecondary qsOnpsOntsOff
High School and Beyond 1980 P-12 qsOnpsOntsOff
High School Longitudinal Study of 2009 2009 Postsecondary qsOnpsOntsOff
High School Longitudinal Study of 2009 2009 P-12 qsOnpsOntsOff
National Education Longitudinal Study of 1988 1988 Postsecondary qsOnpsOntsOff
National Education Longitudinal Study of 1988 1988 P-12 qsOnpsOntsOff
National Postsecondary Student Aid Study: 1987 Graduate Students 1987 Postsecondary qsOffpsOntsOff
National Postsecondary Student Aid Study: 1987 Undergraduates 1987 Postsecondary qsOffpsOntsOff
National Postsecondary Student Aid Study: 1990 Graduate Students 1990 Postsecondary qsOffpsOntsOff
National Postsecondary Student Aid Study: 1990 Undergraduates 1990 Postsecondary qsOffpsOntsOff
National Postsecondary Student Aid Study: 1993 Graduate Students 1993 Postsecondary qsOffpsOntsOff
National Postsecondary Student Aid Study: 1993 Undergraduates 1993 Postsecondary qsOffpsOntsOff
National Postsecondary Student Aid Study: 1996 Graduate Students 1996 Postsecondary qsOffpsOntsOn2
National Postsecondary Student Aid Study: 1996 Undergraduates 1996 Postsecondary qsOffpsOntsOn1
National Postsecondary Student Aid Study: 2000 Graduate Students 2000 Postsecondary qsOffpsOntsOn2
National Postsecondary Student Aid Study: 2000 Undergraduates 2000 Postsecondary qsOffpsOntsOn1
National Postsecondary Student Aid Study: 2004 Graduate Students 2004 Postsecondary qsOnpsOntsOn2
National Postsecondary Student Aid Study: 2004 Undergraduates 2004 Postsecondary qsOnpsOntsOn1
National Postsecondary Student Aid Study: 2008 Graduate Students 2008 Postsecondary qsOnpsOntsOn2
National Postsecondary Student Aid Study: 2008 Undergraduates 2008 Postsecondary qsOnpsOntsOn1
National Postsecondary Student Aid Study: 2012 Graduate Students 2012 Postsecondary qsOnpsOntsOn2
National Postsecondary Student Aid Study: 2012 Undergraduates 2012 Postsecondary qsOnpsOntsOn1
National Postsecondary Student Aid Study: 2016 Graduate Students 2016 Postsecondary qsOnpsOntsOn2
National Postsecondary Student Aid Study: 2016 Undergraduates 2016 Postsecondary qsOnpsOntsOn1
National Study of Postsecondary Faculty: 1988 Faculty 1988 Postsecondary qsOffpsOntsOff
National Study of Postsecondary Faculty: 1993 Faculty 1993 Postsecondary qsOffpsOntsOff
National Study of Postsecondary Faculty: 1999 Faculty 1999 Postsecondary qsOffpsOntsOff
National Study of Postsecondary Faculty: 2004 Faculty 2004 Postsecondary qsOnpsOntsOff
National Study of Postsecondary Faculty: 2004 Institution 2004 Postsecondary qsOnpsOntsOff
National Teacher and Principal Survey, 2015-16 Public School Principals 2015-2016 P-12 qsOnpsOntsOff
National Teacher and Principal Survey, 2015-16 Public Schools 2015-2016 P-12 qsOnpsOntsOff
Parent and Family Involvement in Education: 2012 2012 P-12 qsOnpsOntsOn7
Parent and Family Involvement in Education: 2016 2016 P-12 qsOnpsOntsOn7
Parent and Family Involvement in Education: 2019 2019 P-12 qsOffpsOntsOn7
Pre-Elementary Education Longitudinal Study, Waves 1-5 2003/2008 P-12 qsOnpsOntsOff
Private School Universe Survey: 2011-12 2011-2012 P-12 qsOnpsOntsOff
Private School Universe Survey: 2013-14 2013-2014 P-12 qsOnpsOntsOff
Private School Universe Survey: 2015-16 2015-2016 P-12 qsOnpsOntsOff
School Survey on Crime and Safety: 1999-2000 1999-2000 P-12 qsOnpsOntsOff
School Survey on Crime and Safety: 2003-04 2003-2004 P-12 qsOnpsOntsOff
School Survey on Crime and Safety: 2005-06 2005-2006 P-12 qsOnpsOntsOn3
School Survey on Crime and Safety: 2007-08 2007-2008 P-12 qsOnpsOntsOn3
School Survey on Crime and Safety: 2009-10 2009-2010 P-12 qsOnpsOntsOn3
School Survey on Crime and Safety: 2015-16 2015-2016 P-12 qsOnpsOntsOn3
Schools and Staffing Survey, Districts: 1999-00 1999-2000 P-12 qsOffpsOntsOff
Schools and Staffing Survey, Districts: 2003-04 2003-2004 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Districts: 2007-08 2007-2008 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Districts: 2011-12 2011-2012 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Library Media Centers: 1999-00 1999-2000 P-12 qsOffpsOntsOff
Schools and Staffing Survey, Library Media Centers: 2003-04 2003-2004 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Library Media Centers: 2007-08 2007-2008 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Library Media Centers: 2011-12 2011-2012 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Public and Private School Principals: 1999-00 1999-2000 P-12 qsOffpsOntsOff
Schools and Staffing Survey, Public and Private School Principals: 2003-04 2003-2004 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Public and Private School Principals: 2007-08 2007-2008 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Public and Private School Principals: 2011-12 2011-2012 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Public and Private Schools: 1999-00 1999-2000 P-12 qsOffpsOntsOff
Schools and Staffing Survey, Public and Private Schools: 2003-04 2003-2004 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Public and Private Schools: 2007-08 2007-2008 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Public and Private Schools: 2011-12 2011-2012 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Public and Private Teachers: 1999-00 1999-2000 P-12 qsOffpsOntsOff
Schools and Staffing Survey, Public and Private Teachers: 2003-04 2003-2004 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Public and Private Teachers: 2007-08 2007-2008 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Public and Private Teachers: 2011-12 2011-2012 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Public and Private Teachers: 2011-12 2011-2012 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Public and Private Teachers: 2007-08 2007-2008 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Public and Private Teachers: 2003-04 2003-2004 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Public and Private Teachers: 1999-00 1999-2000 P-12 qsOffpsOntsOff
Schools and Staffing Survey, Public and Private Schools: 2011-12 2011-2012 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Public and Private Schools: 2007-08 2007-2008 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Public and Private Schools: 2003-04 2003-2004 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Public and Private Schools: 1999-00 1999-2000 P-12 qsOffpsOntsOff
Schools and Staffing Survey, Public and Private School Principals: 2011-12 2011-2012 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Public and Private School Principals: 2007-08 2007-2008 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Public and Private School Principals: 2003-04 2003-2004 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Public and Private School Principals: 1999-00 1999-2000 P-12 qsOffpsOntsOff
Schools and Staffing Survey, Library Media Centers: 2011-12 2011-2012 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Library Media Centers: 2007-08 2007-2008 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Library Media Centers: 2003-04 2003-2004 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Library Media Centers: 1999-00 1999-2000 P-12 qsOffpsOntsOff
Schools and Staffing Survey, Districts: 2011-12 2011-2012 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Districts: 2007-08 2007-2008 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Districts: 2003-04 2003-2004 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Districts: 1999-00 1999-2000 P-12 qsOffpsOntsOff
School Survey on Crime and Safety: 2015-16 2015-2016 P-12 qsOnpsOntsOn3
School Survey on Crime and Safety: 2009-10 2009-2010 P-12 qsOnpsOntsOn3
School Survey on Crime and Safety: 2007-08 2007-2008 P-12 qsOnpsOntsOn3
School Survey on Crime and Safety: 2005-06 2005-2006 P-12 qsOnpsOntsOn3
School Survey on Crime and Safety: 2003-04 2003-2004 P-12 qsOnpsOntsOff
School Survey on Crime and Safety: 1999-2000 1999-2000 P-12 qsOnpsOntsOff
Private School Universe Survey: 2015-16 2015-2016 P-12 qsOnpsOntsOff
Private School Universe Survey: 2013-14 2013-2014 P-12 qsOnpsOntsOff
Private School Universe Survey: 2011-12 2011-2012 P-12 qsOnpsOntsOff
Pre-Elementary Education Longitudinal Study, Waves 1-5 2003/2008 P-12 qsOnpsOntsOff
Parent and Family Involvement in Education: 2019 2019 P-12 qsOffpsOntsOn7
Parent and Family Involvement in Education: 2016 2016 P-12 qsOnpsOntsOn7
Parent and Family Involvement in Education: 2012 2012 P-12 qsOnpsOntsOn7
National Teacher and Principal Survey, 2015-16 Public Schools 2015-2016 P-12 qsOnpsOntsOff
National Teacher and Principal Survey, 2015-16 Public School Principals 2015-2016 P-12 qsOnpsOntsOff
National Study of Postsecondary Faculty: 2004 Institution 2004 Postsecondary qsOnpsOntsOff
National Study of Postsecondary Faculty: 2004 Faculty 2004 Postsecondary qsOnpsOntsOff
National Study of Postsecondary Faculty: 1999 Faculty 1999 Postsecondary qsOffpsOntsOff
National Study of Postsecondary Faculty: 1993 Faculty 1993 Postsecondary qsOffpsOntsOff
National Study of Postsecondary Faculty: 1988 Faculty 1988 Postsecondary qsOffpsOntsOff
National Postsecondary Student Aid Study: 2016 Undergraduates 2016 Postsecondary qsOnpsOntsOn1
National Postsecondary Student Aid Study: 2016 Graduate Students 2016 Postsecondary qsOnpsOntsOn2
National Postsecondary Student Aid Study: 2012 Undergraduates 2012 Postsecondary qsOnpsOntsOn1
National Postsecondary Student Aid Study: 2012 Graduate Students 2012 Postsecondary qsOnpsOntsOn2
National Postsecondary Student Aid Study: 2008 Undergraduates 2008 Postsecondary qsOnpsOntsOn1
National Postsecondary Student Aid Study: 2008 Graduate Students 2008 Postsecondary qsOnpsOntsOn2
National Postsecondary Student Aid Study: 2004 Undergraduates 2004 Postsecondary qsOnpsOntsOn1
National Postsecondary Student Aid Study: 2004 Graduate Students 2004 Postsecondary qsOnpsOntsOn2
National Postsecondary Student Aid Study: 2000 Undergraduates 2000 Postsecondary qsOffpsOntsOn1
National Postsecondary Student Aid Study: 2000 Graduate Students 2000 Postsecondary qsOffpsOntsOn2
National Postsecondary Student Aid Study: 1996 Undergraduates 1996 Postsecondary qsOffpsOntsOn1
National Postsecondary Student Aid Study: 1996 Graduate Students 1996 Postsecondary qsOffpsOntsOn2
National Postsecondary Student Aid Study: 1993 Undergraduates 1993 Postsecondary qsOffpsOntsOff
National Postsecondary Student Aid Study: 1993 Graduate Students 1993 Postsecondary qsOffpsOntsOff
National Postsecondary Student Aid Study: 1990 Undergraduates 1990 Postsecondary qsOffpsOntsOff
National Postsecondary Student Aid Study: 1990 Graduate Students 1990 Postsecondary qsOffpsOntsOff
National Postsecondary Student Aid Study: 1987 Undergraduates 1987 Postsecondary qsOffpsOntsOff
National Postsecondary Student Aid Study: 1987 Graduate Students 1987 Postsecondary qsOffpsOntsOff
National Education Longitudinal Study of 1988 1988 Postsecondary qsOnpsOntsOff
National Education Longitudinal Study of 1988 1988 P-12 qsOnpsOntsOff
High School Longitudinal Study of 2009 2009 Postsecondary qsOnpsOntsOff
High School Longitudinal Study of 2009 2009 P-12 qsOnpsOntsOff
High School and Beyond 1980 Postsecondary qsOnpsOntsOff
High School and Beyond 1980 P-12 qsOnpsOntsOff
Education Longitudinal Study of 2002 2002 Postsecondary qsOnpsOntsOff
Education Longitudinal Study of 2002 2002 P-12 qsOnpsOntsOff
Early Childhood Program Participation: 2019 2019 P-12 qsOffpsOntsOn6
Early Childhood Program Participation: 2016 2016 P-12 qsOnpsOntsOn6
Early Childhood Program Participation: 2012 2012 P-12 qsOnpsOntsOn6
Early Childhood Longitudinal Study: Birth Cohort 2001-02 P-12 qsOffpsOntsOff
Beginning Postsecondary Students: 2012/2017 2012/2017 Postsecondary qsOnpsOntsOff
Beginning Postsecondary Students: 2004/2009 2004/2009 Postsecondary qsOnpsOntsOff
Beginning Postsecondary Students: 1996/2001 1996/2001 Postsecondary qsOnpsOntsOff
Beginning Postsecondary Students: 1990/1994 1990/1994 Postsecondary qsOnpsOntsOff
Baccalaureate and Beyond: 2016/2017 2016/2017 Postsecondary qsOnpsOntsOff
Baccalaureate and Beyond: 2016/2017 2016/2017 Adult Education qsOnpsOntsOff
Baccalaureate and Beyond: 2008/2012 2008/2012 Postsecondary qsOnpsOntsOff
Baccalaureate and Beyond: 2008/2012 2008/2012 Adult Education qsOnpsOntsOff
Baccalaureate and Beyond: 2000/2001 2000/2001 Postsecondary qsOffpsOntsOff
Baccalaureate and Beyond: 2000/2001 2000/2001 Adult Education qsOffpsOntsOff
Baccalaureate and Beyond: 1993/2003 Graduate students 1993/2003 Postsecondary qsOffpsOntsOff
Baccalaureate and Beyond: 1993/2003 Graduate students 1993/2003 Adult Education qsOffpsOntsOff
Baccalaureate and Beyond: 1993/2003 1993/2003 Postsecondary qsOnpsOntsOff
Baccalaureate and Beyond: 1993/2003 1993/2003 Adult Education qsOnpsOntsOff
Adult Training and Education Survey: 2016 2016 Adult Education qsOnpsOntsOff
Adult Education Survey: 2005 2005 Adult Education qsOffpsOntsOff
High School and Beyond 1980 Postsecondary qsOnpsOntsOff
High School and Beyond 1980 P-12 qsOnpsOntsOff
National Postsecondary Student Aid Study: 1987 Undergraduates 1987 Postsecondary qsOffpsOntsOff
National Postsecondary Student Aid Study: 1987 Graduate Students 1987 Postsecondary qsOffpsOntsOff
National Study of Postsecondary Faculty: 1988 Faculty 1988 Postsecondary qsOffpsOntsOff
National Education Longitudinal Study of 1988 1988 Postsecondary qsOnpsOntsOff
National Education Longitudinal Study of 1988 1988 P-12 qsOnpsOntsOff
National Postsecondary Student Aid Study: 1990 Undergraduates 1990 Postsecondary qsOffpsOntsOff
National Postsecondary Student Aid Study: 1990 Graduate Students 1990 Postsecondary qsOffpsOntsOff
Beginning Postsecondary Students: 1990/1994 1990/1994 Postsecondary qsOnpsOntsOff
National Study of Postsecondary Faculty: 1993 Faculty 1993 Postsecondary qsOffpsOntsOff
National Postsecondary Student Aid Study: 1993 Undergraduates 1993 Postsecondary qsOffpsOntsOff
National Postsecondary Student Aid Study: 1993 Graduate Students 1993 Postsecondary qsOffpsOntsOff
Baccalaureate and Beyond: 1993/2003 Graduate students 1993/2003 Postsecondary qsOffpsOntsOff
Baccalaureate and Beyond: 1993/2003 Graduate students 1993/2003 Adult Education qsOffpsOntsOff
Baccalaureate and Beyond: 1993/2003 1993/2003 Postsecondary qsOnpsOntsOff
Baccalaureate and Beyond: 1993/2003 1993/2003 Adult Education qsOnpsOntsOff
National Postsecondary Student Aid Study: 1996 Undergraduates 1996 Postsecondary qsOffpsOntsOn1
National Postsecondary Student Aid Study: 1996 Graduate Students 1996 Postsecondary qsOffpsOntsOn2
Beginning Postsecondary Students: 1996/2001 1996/2001 Postsecondary qsOnpsOntsOff
National Study of Postsecondary Faculty: 1999 Faculty 1999 Postsecondary qsOffpsOntsOff
Schools and Staffing Survey, Public and Private Teachers: 1999-00 1999-2000 P-12 qsOffpsOntsOff
Schools and Staffing Survey, Public and Private Schools: 1999-00 1999-2000 P-12 qsOffpsOntsOff
Schools and Staffing Survey, Public and Private School Principals: 1999-00 1999-2000 P-12 qsOffpsOntsOff
Schools and Staffing Survey, Library Media Centers: 1999-00 1999-2000 P-12 qsOffpsOntsOff
Schools and Staffing Survey, Districts: 1999-00 1999-2000 P-12 qsOffpsOntsOff
School Survey on Crime and Safety: 1999-2000 1999-2000 P-12 qsOnpsOntsOff
National Postsecondary Student Aid Study: 2000 Undergraduates 2000 Postsecondary qsOffpsOntsOn1
National Postsecondary Student Aid Study: 2000 Graduate Students 2000 Postsecondary qsOffpsOntsOn2
Baccalaureate and Beyond: 2000/2001 2000/2001 Postsecondary qsOffpsOntsOff
Baccalaureate and Beyond: 2000/2001 2000/2001 Adult Education qsOffpsOntsOff
Early Childhood Longitudinal Study: Birth Cohort 2001-02 P-12 qsOffpsOntsOff
Education Longitudinal Study of 2002 2002 Postsecondary qsOnpsOntsOff
Education Longitudinal Study of 2002 2002 P-12 qsOnpsOntsOff
Pre-Elementary Education Longitudinal Study, Waves 1-5 2003/2008 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Public and Private Teachers: 2003-04 2003-2004 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Public and Private Schools: 2003-04 2003-2004 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Public and Private School Principals: 2003-04 2003-2004 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Library Media Centers: 2003-04 2003-2004 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Districts: 2003-04 2003-2004 P-12 qsOnpsOntsOff
School Survey on Crime and Safety: 2003-04 2003-2004 P-12 qsOnpsOntsOff
National Study of Postsecondary Faculty: 2004 Institution 2004 Postsecondary qsOnpsOntsOff
National Study of Postsecondary Faculty: 2004 Faculty 2004 Postsecondary qsOnpsOntsOff
National Postsecondary Student Aid Study: 2004 Undergraduates 2004 Postsecondary qsOnpsOntsOn1
National Postsecondary Student Aid Study: 2004 Graduate Students 2004 Postsecondary qsOnpsOntsOn2
Beginning Postsecondary Students: 2004/2009 2004/2009 Postsecondary qsOnpsOntsOff
Adult Education Survey: 2005 2005 Adult Education qsOffpsOntsOff
School Survey on Crime and Safety: 2005-06 2005-2006 P-12 qsOnpsOntsOn3
Schools and Staffing Survey, Public and Private Teachers: 2007-08 2007-2008 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Public and Private Schools: 2007-08 2007-2008 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Public and Private School Principals: 2007-08 2007-2008 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Library Media Centers: 2007-08 2007-2008 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Districts: 2007-08 2007-2008 P-12 qsOnpsOntsOff
School Survey on Crime and Safety: 2007-08 2007-2008 P-12 qsOnpsOntsOn3
National Postsecondary Student Aid Study: 2008 Undergraduates 2008 Postsecondary qsOnpsOntsOn1
National Postsecondary Student Aid Study: 2008 Graduate Students 2008 Postsecondary qsOnpsOntsOn2
Baccalaureate and Beyond: 2008/2012 2008/2012 Postsecondary qsOnpsOntsOff
Baccalaureate and Beyond: 2008/2012 2008/2012 Adult Education qsOnpsOntsOff
High School Longitudinal Study of 2009 2009 Postsecondary qsOnpsOntsOff
High School Longitudinal Study of 2009 2009 P-12 qsOnpsOntsOff
School Survey on Crime and Safety: 2009-10 2009-2010 P-12 qsOnpsOntsOn3
Schools and Staffing Survey, Public and Private Teachers: 2011-12 2011-2012 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Public and Private Schools: 2011-12 2011-2012 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Public and Private School Principals: 2011-12 2011-2012 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Library Media Centers: 2011-12 2011-2012 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Districts: 2011-12 2011-2012 P-12 qsOnpsOntsOff
Private School Universe Survey: 2011-12 2011-2012 P-12 qsOnpsOntsOff
Parent and Family Involvement in Education: 2012 2012 P-12 qsOnpsOntsOn7
National Postsecondary Student Aid Study: 2012 Undergraduates 2012 Postsecondary qsOnpsOntsOn1
National Postsecondary Student Aid Study: 2012 Graduate Students 2012 Postsecondary qsOnpsOntsOn2
Early Childhood Program Participation: 2012 2012 P-12 qsOnpsOntsOn6
Beginning Postsecondary Students: 2012/2017 2012/2017 Postsecondary qsOnpsOntsOff
Private School Universe Survey: 2013-14 2013-2014 P-12 qsOnpsOntsOff
School Survey on Crime and Safety: 2015-16 2015-2016 P-12 qsOnpsOntsOn3
Private School Universe Survey: 2015-16 2015-2016 P-12 qsOnpsOntsOff
National Teacher and Principal Survey, 2015-16 Public Schools 2015-2016 P-12 qsOnpsOntsOff
National Teacher and Principal Survey, 2015-16 Public School Principals 2015-2016 P-12 qsOnpsOntsOff
Parent and Family Involvement in Education: 2016 2016 P-12 qsOnpsOntsOn7
National Postsecondary Student Aid Study: 2016 Undergraduates 2016 Postsecondary qsOnpsOntsOn1
National Postsecondary Student Aid Study: 2016 Graduate Students 2016 Postsecondary qsOnpsOntsOn2
Early Childhood Program Participation: 2016 2016 P-12 qsOnpsOntsOn6
Adult Training and Education Survey: 2016 2016 Adult Education qsOnpsOntsOff
Baccalaureate and Beyond: 2016/2017 2016/2017 Postsecondary qsOnpsOntsOff
Baccalaureate and Beyond: 2016/2017 2016/2017 Adult Education qsOnpsOntsOff
Parent and Family Involvement in Education: 2019 2019 P-12 qsOffpsOntsOn7
Early Childhood Program Participation: 2019 2019 P-12 qsOffpsOntsOn6
Parent and Family Involvement in Education: 2019 2019 P-12 qsOffpsOntsOn7
Early Childhood Program Participation: 2019 2019 P-12 qsOffpsOntsOn6
Baccalaureate and Beyond: 2016/2017 2016/2017 Postsecondary qsOnpsOntsOff
Baccalaureate and Beyond: 2016/2017 2016/2017 Adult Education qsOnpsOntsOff
Parent and Family Involvement in Education: 2016 2016 P-12 qsOnpsOntsOn7
National Postsecondary Student Aid Study: 2016 Undergraduates 2016 Postsecondary qsOnpsOntsOn1
National Postsecondary Student Aid Study: 2016 Graduate Students 2016 Postsecondary qsOnpsOntsOn2
Early Childhood Program Participation: 2016 2016 P-12 qsOnpsOntsOn6
Adult Training and Education Survey: 2016 2016 Adult Education qsOnpsOntsOff
School Survey on Crime and Safety: 2015-16 2015-2016 P-12 qsOnpsOntsOn3
Private School Universe Survey: 2015-16 2015-2016 P-12 qsOnpsOntsOff
National Teacher and Principal Survey, 2015-16 Public Schools 2015-2016 P-12 qsOnpsOntsOff
National Teacher and Principal Survey, 2015-16 Public School Principals 2015-2016 P-12 qsOnpsOntsOff
Private School Universe Survey: 2013-14 2013-2014 P-12 qsOnpsOntsOff
Beginning Postsecondary Students: 2012/2017 2012/2017 Postsecondary qsOnpsOntsOff
Parent and Family Involvement in Education: 2012 2012 P-12 qsOnpsOntsOn7
National Postsecondary Student Aid Study: 2012 Undergraduates 2012 Postsecondary qsOnpsOntsOn1
National Postsecondary Student Aid Study: 2012 Graduate Students 2012 Postsecondary qsOnpsOntsOn2
Early Childhood Program Participation: 2012 2012 P-12 qsOnpsOntsOn6
Schools and Staffing Survey, Public and Private Teachers: 2011-12 2011-2012 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Public and Private Schools: 2011-12 2011-2012 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Public and Private School Principals: 2011-12 2011-2012 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Library Media Centers: 2011-12 2011-2012 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Districts: 2011-12 2011-2012 P-12 qsOnpsOntsOff
Private School Universe Survey: 2011-12 2011-2012 P-12 qsOnpsOntsOff
School Survey on Crime and Safety: 2009-10 2009-2010 P-12 qsOnpsOntsOn3
High School Longitudinal Study of 2009 2009 Postsecondary qsOnpsOntsOff
High School Longitudinal Study of 2009 2009 P-12 qsOnpsOntsOff
Baccalaureate and Beyond: 2008/2012 2008/2012 Postsecondary qsOnpsOntsOff
Baccalaureate and Beyond: 2008/2012 2008/2012 Adult Education qsOnpsOntsOff
National Postsecondary Student Aid Study: 2008 Undergraduates 2008 Postsecondary qsOnpsOntsOn1
National Postsecondary Student Aid Study: 2008 Graduate Students 2008 Postsecondary qsOnpsOntsOn2
Schools and Staffing Survey, Public and Private Teachers: 2007-08 2007-2008 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Public and Private Schools: 2007-08 2007-2008 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Public and Private School Principals: 2007-08 2007-2008 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Library Media Centers: 2007-08 2007-2008 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Districts: 2007-08 2007-2008 P-12 qsOnpsOntsOff
School Survey on Crime and Safety: 2007-08 2007-2008 P-12 qsOnpsOntsOn3
School Survey on Crime and Safety: 2005-06 2005-2006 P-12 qsOnpsOntsOn3
Adult Education Survey: 2005 2005 Adult Education qsOffpsOntsOff
Beginning Postsecondary Students: 2004/2009 2004/2009 Postsecondary qsOnpsOntsOff
National Study of Postsecondary Faculty: 2004 Institution 2004 Postsecondary qsOnpsOntsOff
National Study of Postsecondary Faculty: 2004 Faculty 2004 Postsecondary qsOnpsOntsOff
National Postsecondary Student Aid Study: 2004 Undergraduates 2004 Postsecondary qsOnpsOntsOn1
National Postsecondary Student Aid Study: 2004 Graduate Students 2004 Postsecondary qsOnpsOntsOn2
Schools and Staffing Survey, Public and Private Teachers: 2003-04 2003-2004 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Public and Private Schools: 2003-04 2003-2004 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Public and Private School Principals: 2003-04 2003-2004 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Library Media Centers: 2003-04 2003-2004 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Districts: 2003-04 2003-2004 P-12 qsOnpsOntsOff
School Survey on Crime and Safety: 2003-04 2003-2004 P-12 qsOnpsOntsOff
Pre-Elementary Education Longitudinal Study, Waves 1-5 2003/2008 P-12 qsOnpsOntsOff
Education Longitudinal Study of 2002 2002 Postsecondary qsOnpsOntsOff
Education Longitudinal Study of 2002 2002 P-12 qsOnpsOntsOff
Early Childhood Longitudinal Study: Birth Cohort 2001-02 P-12 qsOffpsOntsOff
Baccalaureate and Beyond: 2000/2001 2000/2001 Postsecondary qsOffpsOntsOff
Baccalaureate and Beyond: 2000/2001 2000/2001 Adult Education qsOffpsOntsOff
National Postsecondary Student Aid Study: 2000 Undergraduates 2000 Postsecondary qsOffpsOntsOn1
National Postsecondary Student Aid Study: 2000 Graduate Students 2000 Postsecondary qsOffpsOntsOn2
Schools and Staffing Survey, Public and Private Teachers: 1999-00 1999-2000 P-12 qsOffpsOntsOff
Schools and Staffing Survey, Public and Private Schools: 1999-00 1999-2000 P-12 qsOffpsOntsOff
Schools and Staffing Survey, Public and Private School Principals: 1999-00 1999-2000 P-12 qsOffpsOntsOff
Schools and Staffing Survey, Library Media Centers: 1999-00 1999-2000 P-12 qsOffpsOntsOff
Schools and Staffing Survey, Districts: 1999-00 1999-2000 P-12 qsOffpsOntsOff
School Survey on Crime and Safety: 1999-2000 1999-2000 P-12 qsOnpsOntsOff
National Study of Postsecondary Faculty: 1999 Faculty 1999 Postsecondary qsOffpsOntsOff
Beginning Postsecondary Students: 1996/2001 1996/2001 Postsecondary qsOnpsOntsOff
National Postsecondary Student Aid Study: 1996 Undergraduates 1996 Postsecondary qsOffpsOntsOn1
National Postsecondary Student Aid Study: 1996 Graduate Students 1996 Postsecondary qsOffpsOntsOn2
Baccalaureate and Beyond: 1993/2003 Graduate students 1993/2003 Postsecondary qsOffpsOntsOff
Baccalaureate and Beyond: 1993/2003 Graduate students 1993/2003 Adult Education qsOffpsOntsOff
Baccalaureate and Beyond: 1993/2003 1993/2003 Postsecondary qsOnpsOntsOff
Baccalaureate and Beyond: 1993/2003 1993/2003 Adult Education qsOnpsOntsOff
National Study of Postsecondary Faculty: 1993 Faculty 1993 Postsecondary qsOffpsOntsOff
National Postsecondary Student Aid Study: 1993 Undergraduates 1993 Postsecondary qsOffpsOntsOff
National Postsecondary Student Aid Study: 1993 Graduate Students 1993 Postsecondary qsOffpsOntsOff
Beginning Postsecondary Students: 1990/1994 1990/1994 Postsecondary qsOnpsOntsOff
National Postsecondary Student Aid Study: 1990 Undergraduates 1990 Postsecondary qsOffpsOntsOff
National Postsecondary Student Aid Study: 1990 Graduate Students 1990 Postsecondary qsOffpsOntsOff
National Study of Postsecondary Faculty: 1988 Faculty 1988 Postsecondary qsOffpsOntsOff
National Education Longitudinal Study of 1988 1988 Postsecondary qsOnpsOntsOff
National Education Longitudinal Study of 1988 1988 P-12 qsOnpsOntsOff
National Postsecondary Student Aid Study: 1987 Undergraduates 1987 Postsecondary qsOffpsOntsOff
National Postsecondary Student Aid Study: 1987 Graduate Students 1987 Postsecondary qsOffpsOntsOff
High School and Beyond 1980 Postsecondary qsOnpsOntsOff
High School and Beyond 1980 P-12 qsOnpsOntsOff
Baccalaureate and Beyond: 2016/2017 2016/2017 Adult Education qsOnpsOntsOff
Baccalaureate and Beyond: 2008/2012 2008/2012 Adult Education qsOnpsOntsOff
Baccalaureate and Beyond: 2000/2001 2000/2001 Adult Education qsOffpsOntsOff
Baccalaureate and Beyond: 1993/2003 Graduate students 1993/2003 Adult Education qsOffpsOntsOff
Baccalaureate and Beyond: 1993/2003 1993/2003 Adult Education qsOnpsOntsOff
Adult Training and Education Survey: 2016 2016 Adult Education qsOnpsOntsOff
Adult Education Survey: 2005 2005 Adult Education qsOffpsOntsOff
Schools and Staffing Survey, Public and Private Teachers: 2011-12 2011-2012 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Public and Private Teachers: 2007-08 2007-2008 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Public and Private Teachers: 2003-04 2003-2004 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Public and Private Teachers: 1999-00 1999-2000 P-12 qsOffpsOntsOff
Schools and Staffing Survey, Public and Private Schools: 2011-12 2011-2012 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Public and Private Schools: 2007-08 2007-2008 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Public and Private Schools: 2003-04 2003-2004 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Public and Private Schools: 1999-00 1999-2000 P-12 qsOffpsOntsOff
Schools and Staffing Survey, Public and Private School Principals: 2011-12 2011-2012 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Public and Private School Principals: 2007-08 2007-2008 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Public and Private School Principals: 2003-04 2003-2004 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Public and Private School Principals: 1999-00 1999-2000 P-12 qsOffpsOntsOff
Schools and Staffing Survey, Library Media Centers: 2011-12 2011-2012 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Library Media Centers: 2007-08 2007-2008 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Library Media Centers: 2003-04 2003-2004 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Library Media Centers: 1999-00 1999-2000 P-12 qsOffpsOntsOff
Schools and Staffing Survey, Districts: 2011-12 2011-2012 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Districts: 2007-08 2007-2008 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Districts: 2003-04 2003-2004 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Districts: 1999-00 1999-2000 P-12 qsOffpsOntsOff
School Survey on Crime and Safety: 2015-16 2015-2016 P-12 qsOnpsOntsOn3
School Survey on Crime and Safety: 2009-10 2009-2010 P-12 qsOnpsOntsOn3
School Survey on Crime and Safety: 2007-08 2007-2008 P-12 qsOnpsOntsOn3
School Survey on Crime and Safety: 2005-06 2005-2006 P-12 qsOnpsOntsOn3
School Survey on Crime and Safety: 2003-04 2003-2004 P-12 qsOnpsOntsOff
School Survey on Crime and Safety: 1999-2000 1999-2000 P-12 qsOnpsOntsOff
Private School Universe Survey: 2015-16 2015-2016 P-12 qsOnpsOntsOff
Private School Universe Survey: 2013-14 2013-2014 P-12 qsOnpsOntsOff
Private School Universe Survey: 2011-12 2011-2012 P-12 qsOnpsOntsOff
Pre-Elementary Education Longitudinal Study, Waves 1-5 2003/2008 P-12 qsOnpsOntsOff
Parent and Family Involvement in Education: 2019 2019 P-12 qsOffpsOntsOn7
Parent and Family Involvement in Education: 2016 2016 P-12 qsOnpsOntsOn7
Parent and Family Involvement in Education: 2012 2012 P-12 qsOnpsOntsOn7
National Teacher and Principal Survey, 2015-16 Public Schools 2015-2016 P-12 qsOnpsOntsOff
National Teacher and Principal Survey, 2015-16 Public School Principals 2015-2016 P-12 qsOnpsOntsOff
National Education Longitudinal Study of 1988 1988 P-12 qsOnpsOntsOff
High School Longitudinal Study of 2009 2009 P-12 qsOnpsOntsOff
High School and Beyond 1980 P-12 qsOnpsOntsOff
Education Longitudinal Study of 2002 2002 P-12 qsOnpsOntsOff
Early Childhood Program Participation: 2019 2019 P-12 qsOffpsOntsOn6
Early Childhood Program Participation: 2016 2016 P-12 qsOnpsOntsOn6
Early Childhood Program Participation: 2012 2012 P-12 qsOnpsOntsOn6
Early Childhood Longitudinal Study: Birth Cohort 2001-02 P-12 qsOffpsOntsOff
National Study of Postsecondary Faculty: 2004 Institution 2004 Postsecondary qsOnpsOntsOff
National Study of Postsecondary Faculty: 2004 Faculty 2004 Postsecondary qsOnpsOntsOff
National Study of Postsecondary Faculty: 1999 Faculty 1999 Postsecondary qsOffpsOntsOff
National Study of Postsecondary Faculty: 1993 Faculty 1993 Postsecondary qsOffpsOntsOff
National Study of Postsecondary Faculty: 1988 Faculty 1988 Postsecondary qsOffpsOntsOff
National Postsecondary Student Aid Study: 2016 Undergraduates 2016 Postsecondary qsOnpsOntsOn1
National Postsecondary Student Aid Study: 2016 Graduate Students 2016 Postsecondary qsOnpsOntsOn2
National Postsecondary Student Aid Study: 2012 Undergraduates 2012 Postsecondary qsOnpsOntsOn1
National Postsecondary Student Aid Study: 2012 Graduate Students 2012 Postsecondary qsOnpsOntsOn2
National Postsecondary Student Aid Study: 2008 Undergraduates 2008 Postsecondary qsOnpsOntsOn1
National Postsecondary Student Aid Study: 2008 Graduate Students 2008 Postsecondary qsOnpsOntsOn2
National Postsecondary Student Aid Study: 2004 Undergraduates 2004 Postsecondary qsOnpsOntsOn1
National Postsecondary Student Aid Study: 2004 Graduate Students 2004 Postsecondary qsOnpsOntsOn2
National Postsecondary Student Aid Study: 2000 Undergraduates 2000 Postsecondary qsOffpsOntsOn1
National Postsecondary Student Aid Study: 2000 Graduate Students 2000 Postsecondary qsOffpsOntsOn2
National Postsecondary Student Aid Study: 1996 Undergraduates 1996 Postsecondary qsOffpsOntsOn1
National Postsecondary Student Aid Study: 1996 Graduate Students 1996 Postsecondary qsOffpsOntsOn2
National Postsecondary Student Aid Study: 1993 Undergraduates 1993 Postsecondary qsOffpsOntsOff
National Postsecondary Student Aid Study: 1993 Graduate Students 1993 Postsecondary qsOffpsOntsOff
National Postsecondary Student Aid Study: 1990 Undergraduates 1990 Postsecondary qsOffpsOntsOff
National Postsecondary Student Aid Study: 1990 Graduate Students 1990 Postsecondary qsOffpsOntsOff
National Postsecondary Student Aid Study: 1987 Undergraduates 1987 Postsecondary qsOffpsOntsOff
National Postsecondary Student Aid Study: 1987 Graduate Students 1987 Postsecondary qsOffpsOntsOff
National Education Longitudinal Study of 1988 1988 Postsecondary qsOnpsOntsOff
High School Longitudinal Study of 2009 2009 Postsecondary qsOnpsOntsOff
High School and Beyond 1980 Postsecondary qsOnpsOntsOff
Education Longitudinal Study of 2002 2002 Postsecondary qsOnpsOntsOff
Beginning Postsecondary Students: 2012/2017 2012/2017 Postsecondary qsOnpsOntsOff
Beginning Postsecondary Students: 2004/2009 2004/2009 Postsecondary qsOnpsOntsOff
Beginning Postsecondary Students: 1996/2001 1996/2001 Postsecondary qsOnpsOntsOff
Beginning Postsecondary Students: 1990/1994 1990/1994 Postsecondary qsOnpsOntsOff
Baccalaureate and Beyond: 2016/2017 2016/2017 Postsecondary qsOnpsOntsOff
Baccalaureate and Beyond: 2008/2012 2008/2012 Postsecondary qsOnpsOntsOff
Baccalaureate and Beyond: 2000/2001 2000/2001 Postsecondary qsOffpsOntsOff
Baccalaureate and Beyond: 1993/2003 Graduate students 1993/2003 Postsecondary qsOffpsOntsOff
Baccalaureate and Beyond: 1993/2003 1993/2003 Postsecondary qsOnpsOntsOff
National Study of Postsecondary Faculty: 2004 Institution 2004 Postsecondary qsOnpsOntsOff
National Study of Postsecondary Faculty: 2004 Faculty 2004 Postsecondary qsOnpsOntsOff
National Study of Postsecondary Faculty: 1999 Faculty 1999 Postsecondary qsOffpsOntsOff
National Study of Postsecondary Faculty: 1993 Faculty 1993 Postsecondary qsOffpsOntsOff
National Study of Postsecondary Faculty: 1988 Faculty 1988 Postsecondary qsOffpsOntsOff
National Postsecondary Student Aid Study: 2016 Undergraduates 2016 Postsecondary qsOnpsOntsOn1
National Postsecondary Student Aid Study: 2016 Graduate Students 2016 Postsecondary qsOnpsOntsOn2
National Postsecondary Student Aid Study: 2012 Undergraduates 2012 Postsecondary qsOnpsOntsOn1
National Postsecondary Student Aid Study: 2012 Graduate Students 2012 Postsecondary qsOnpsOntsOn2
National Postsecondary Student Aid Study: 2008 Undergraduates 2008 Postsecondary qsOnpsOntsOn1
National Postsecondary Student Aid Study: 2008 Graduate Students 2008 Postsecondary qsOnpsOntsOn2
National Postsecondary Student Aid Study: 2004 Undergraduates 2004 Postsecondary qsOnpsOntsOn1
National Postsecondary Student Aid Study: 2004 Graduate Students 2004 Postsecondary qsOnpsOntsOn2
National Postsecondary Student Aid Study: 2000 Undergraduates 2000 Postsecondary qsOffpsOntsOn1
National Postsecondary Student Aid Study: 2000 Graduate Students 2000 Postsecondary qsOffpsOntsOn2
National Postsecondary Student Aid Study: 1996 Undergraduates 1996 Postsecondary qsOffpsOntsOn1
National Postsecondary Student Aid Study: 1996 Graduate Students 1996 Postsecondary qsOffpsOntsOn2
National Postsecondary Student Aid Study: 1993 Undergraduates 1993 Postsecondary qsOffpsOntsOff
National Postsecondary Student Aid Study: 1993 Graduate Students 1993 Postsecondary qsOffpsOntsOff
National Postsecondary Student Aid Study: 1990 Undergraduates 1990 Postsecondary qsOffpsOntsOff
National Postsecondary Student Aid Study: 1990 Graduate Students 1990 Postsecondary qsOffpsOntsOff
National Postsecondary Student Aid Study: 1987 Undergraduates 1987 Postsecondary qsOffpsOntsOff
National Postsecondary Student Aid Study: 1987 Graduate Students 1987 Postsecondary qsOffpsOntsOff
National Education Longitudinal Study of 1988 1988 Postsecondary qsOnpsOntsOff
High School Longitudinal Study of 2009 2009 Postsecondary qsOnpsOntsOff
High School and Beyond 1980 Postsecondary qsOnpsOntsOff
Education Longitudinal Study of 2002 2002 Postsecondary qsOnpsOntsOff
Beginning Postsecondary Students: 2012/2017 2012/2017 Postsecondary qsOnpsOntsOff
Beginning Postsecondary Students: 2004/2009 2004/2009 Postsecondary qsOnpsOntsOff
Beginning Postsecondary Students: 1996/2001 1996/2001 Postsecondary qsOnpsOntsOff
Beginning Postsecondary Students: 1990/1994 1990/1994 Postsecondary qsOnpsOntsOff
Baccalaureate and Beyond: 2016/2017 2016/2017 Postsecondary qsOnpsOntsOff
Baccalaureate and Beyond: 2008/2012 2008/2012 Postsecondary qsOnpsOntsOff
Baccalaureate and Beyond: 2000/2001 2000/2001 Postsecondary qsOffpsOntsOff
Baccalaureate and Beyond: 1993/2003 Graduate students 1993/2003 Postsecondary qsOffpsOntsOff
Baccalaureate and Beyond: 1993/2003 1993/2003 Postsecondary qsOnpsOntsOff
Schools and Staffing Survey, Public and Private Teachers: 2011-12 2011-2012 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Public and Private Teachers: 2007-08 2007-2008 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Public and Private Teachers: 2003-04 2003-2004 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Public and Private Teachers: 1999-00 1999-2000 P-12 qsOffpsOntsOff
Schools and Staffing Survey, Public and Private Schools: 2011-12 2011-2012 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Public and Private Schools: 2007-08 2007-2008 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Public and Private Schools: 2003-04 2003-2004 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Public and Private Schools: 1999-00 1999-2000 P-12 qsOffpsOntsOff
Schools and Staffing Survey, Public and Private School Principals: 2011-12 2011-2012 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Public and Private School Principals: 2007-08 2007-2008 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Public and Private School Principals: 2003-04 2003-2004 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Public and Private School Principals: 1999-00 1999-2000 P-12 qsOffpsOntsOff
Schools and Staffing Survey, Library Media Centers: 2011-12 2011-2012 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Library Media Centers: 2007-08 2007-2008 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Library Media Centers: 2003-04 2003-2004 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Library Media Centers: 1999-00 1999-2000 P-12 qsOffpsOntsOff
Schools and Staffing Survey, Districts: 2011-12 2011-2012 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Districts: 2007-08 2007-2008 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Districts: 2003-04 2003-2004 P-12 qsOnpsOntsOff
Schools and Staffing Survey, Districts: 1999-00 1999-2000 P-12 qsOffpsOntsOff
School Survey on Crime and Safety: 2015-16 2015-2016 P-12 qsOnpsOntsOn3
School Survey on Crime and Safety: 2009-10 2009-2010 P-12 qsOnpsOntsOn3
School Survey on Crime and Safety: 2007-08 2007-2008 P-12 qsOnpsOntsOn3
School Survey on Crime and Safety: 2005-06 2005-2006 P-12 qsOnpsOntsOn3
School Survey on Crime and Safety: 2003-04 2003-2004 P-12 qsOnpsOntsOff
School Survey on Crime and Safety: 1999-2000 1999-2000 P-12 qsOnpsOntsOff
Private School Universe Survey: 2015-16 2015-2016 P-12 qsOnpsOntsOff
Private School Universe Survey: 2013-14 2013-2014 P-12 qsOnpsOntsOff
Private School Universe Survey: 2011-12 2011-2012 P-12 qsOnpsOntsOff
Pre-Elementary Education Longitudinal Study, Waves 1-5 2003/2008 P-12 qsOnpsOntsOff
Parent and Family Involvement in Education: 2019 2019 P-12 qsOffpsOntsOn7
Parent and Family Involvement in Education: 2016 2016 P-12 qsOnpsOntsOn7
Parent and Family Involvement in Education: 2012 2012 P-12 qsOnpsOntsOn7
National Teacher and Principal Survey, 2015-16 Public Schools 2015-2016 P-12 qsOnpsOntsOff
National Teacher and Principal Survey, 2015-16 Public School Principals 2015-2016 P-12 qsOnpsOntsOff
National Education Longitudinal Study of 1988 1988 P-12 qsOnpsOntsOff
High School Longitudinal Study of 2009 2009 P-12 qsOnpsOntsOff
High School and Beyond 1980 P-12 qsOnpsOntsOff
Education Longitudinal Study of 2002 2002 P-12 qsOnpsOntsOff
Early Childhood Program Participation: 2019 2019 P-12 qsOffpsOntsOn6
Early Childhood Program Participation: 2016 2016 P-12 qsOnpsOntsOn6
Early Childhood Program Participation: 2012 2012 P-12 qsOnpsOntsOn6
Early Childhood Longitudinal Study: Birth Cohort 2001-02 P-12 qsOffpsOntsOff
Baccalaureate and Beyond: 2016/2017 2016/2017 Adult Education qsOnpsOntsOff
Baccalaureate and Beyond: 2008/2012 2008/2012 Adult Education qsOnpsOntsOff
Baccalaureate and Beyond: 2000/2001 2000/2001 Adult Education qsOffpsOntsOff
Baccalaureate and Beyond: 1993/2003 Graduate students 1993/2003 Adult Education qsOffpsOntsOff
Baccalaureate and Beyond: 1993/2003 1993/2003 Adult Education qsOnpsOntsOff
Adult Training and Education Survey: 2016 2016 Adult Education qsOnpsOntsOff
Adult Education Survey: 2005 2005 Adult Education qsOffpsOntsOff
1                 	                     	Percentage distribution of 1995–96 beginning postsecondary students' highest degree attained by 2001, by work status                      
                                              Highest degree completed as of June 2001                        	Certificate
(%)                          Associate
(%)                          Bachelor
(%)                          Never attained
(%)                          Total                      
                                              Estimates                      
                                              Total                          11.7                          9.8                          29.8                          48.6                          100%                      
                                              Job 1995–96: hours worked per week while enrolled                      
                                              Did not work while enrolled                          14.0                          9.8                          38.5                          37.8                        	100%                    	
                                           	Worked part time                          8.9                          11.6                          35.5                          44.0                        	100%                    	
                                              Worked full time                          14.5                          7.2                          8.3                          69.9                        	100%                    	
                                          	NOTE: Rows may not add up to 100% due to rounding.

SOURCE: U.S. Department of Education, National Center for Education Statistics, 1995–96 Beginning Postsecondary Students Longitudinal Study, Second Follow-up (BPS:96/01).

                         Computation by NCES QuickStats on 6/22/2009                      
                 
 
ckeakb7
2                 	                     	Percentage distribution of 1995–96 beginning postsecondary students' highest degree attained by 2001, by number of advanced placement tests taken                      
                                              Persistence and completion at any institution as of 2000-01                          Never attained
(%)                          Certificate
(%)                          Associate
(%)                          Bachelor
(%)                          Total                      
                                              Estimates                      
                                              Total                          48.6                          11.7                          9.9                          29.8                          100%                      
                                              Number of Advanced Placement tests taken                      
                                              0                          51.1                          7.7                          12.1                          29.1                        	100%                    	
                                           	1                          38.1                          2.6                          6.0                          53.4                        	100%                    	
                                              2                          33.6                          0.4                          3.4                          62.6                        	100%                    	
                                           	Three or more                          13.8                          0.1                          1.4                          84.8                        	100%                    	
                                          	NOTE: Rows may not add up to 100% due to rounding.

SOURCE: U.S. Department of Education, National Center for Education Statistics, 1995–96 Beginning Postsecondary Students Longitudinal Study, Second Follow-up (BPS:96/01).

                         Computation by NCES QuickStats on 6/22/2009                      
                 
  
ckeak19
3                 	                     	Percentage of beginning postsecondary students who received Pell grants, by race/ethnicity: 1995–96                      
                                                                         Pell Grant amount 1995-96
(%>0)                      
                                              Estimates                      
                                              Total                          26.4                      
                                              Race/ethnicity                      
                                              White, non-Hispanic                        	19.0                    	
                                           	Black, non-Hispanic                        	49.3                    	
                                              Hispanic                        	42.4                    	
                                           	Asian/Pacific Islander                        	35.5                    	
                                              American Indian/Alaska Native                        	33.2                    	
                                           	Other                          ‡                      
                                          	‡ Reporting standards not met. 

Source: U.S. Department of Education, National Center for Education Statistics, 1995–96 Beginning Postsecondary Students Longitudinal Study, Second Follow-up (BPS:96/01).

                         Computation by NCES QuickStats on 3/10/2009                      
                 
 
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4                 	                     	Percentage distribution of 1995–96 beginning postsecondary students' grade point average (GPA) through 2001, by income percentile rank                      
                                              Cumulative Grade Point Average (GPA) as of 2001                          Mostly A’s 
(%)                          A’s and B’s 
(%)                          Mostly B’s 
(%)                          B’s and C’s 
(%)                          Mostly C’s 
(%)                          C’s and D’s 
(%)                          Mostly D’s or below 
(%)                          Total                      
                                              Estimates                      
                                              Total                          13.3                          31.8                          35.3                          14.4                          4.4                          0.7                          0.1                          100%                      
                                              Income percentile rank 1994                      
                                              1-25                          13.1                          28.2                          37.8                          14.7                          4.7                          1.4                          0.2                        	100%                    	
                                           	26-50                          13.5                          30.2                          37.3                          12.8                          5.8                          0.3                          0.2                        	100%                    	
                                              51-75                          12.9                          36.1                          33.1                          14.0                          3.4                          0.4                          0..                        	100%                    	
                                           	More than 75                          13.7                          32.7                          33.0                          16.3                          3.7                          0.7                          0.0                        	100%                    	
                                          	NOTE: Rows may not add up to 100% due to rounding.

SOURCE: U.S. Department of Education, National Center for Education Statistics, 1995–96 Beginning Postsecondary Students Longitudinal Study, Second Follow-up (BPS:96/01).

                         Computation by NCES QuickStats on 6/22/2009                      
                 
ckeak03 
5                 	                     	Percentage distribution of 1995–96 beginning postsecondary students' persistence at any institution through 2001, by gender                      
                                              Persistence at any institution through 2001                          Attained, still enrolled
(%)                          Attained, not enrolled
(%)                          Never attained, still enrolled
(%)                          Never attained, not enrolled
(%)                          Total                      
                                              Estimates                      
                                              Total                          5.9   						45.5                          14.9                          33.7                          100%                      
                                              Gender                      
                                              Male                          5.9                          41.8                          15.8                        	36.5                          100%                    	
                                           	Female                          5.8                          48.5                          14.2                        	31.5                          100%                    	
                                          	NOTE: Rows may not add up to 100% due to rounding.

SOURCE: U.S. Department of Education, National Center for Education Statistics, 1995–96 Beginning Postsecondary Students Longitudinal Study, Second Follow-up (BPS:96/01).

                         Computation by NCES QuickStats on 6/22/2009                      
                 
cgeakd4
1                 	                     	Percent of graduate students who borrowed, by type of graduate program: 2003–04                      
                                                                         Loans: total student loans all sources
(%>0)                      
                                              Estimates                      
                                              Total                          40.0                      
                                              Graduate study: program                      
                                          	Business administration (MBA)                          39.1                      
                                              Education (any master's)                        	34.8                    	
                                           	Other master of arts (MA)                        	41.3                    	
                                              Other master of science (MS)                          31.8                      
                                          	Other master's degree                          49.3                      
                                           	PhD except in education                          19.9                      
                                              Education (any doctorate)                        	27.1                    	
                                           	Other doctoral degree                        	49.5                    	
                                              Medicine (MD)                          77.3                      
                                          	Other health science degree                          81.7                      
                                                                   Law (LLB or JD)                        	81.0                    	
                                           	Theology (MDiv, MHL, BD)                        	30.0                    	
                                              Post-baccalaureate certificate                          30.1                      
                                          	Not in a degree program                          28.0                      
                                          	SOURCE: U.S. Department of Education, National Center for Education Statistics, 2003–04 National Postsecondary Student Aid Study (NPSAS:04).

                         NOTICE OF REVISIONS: The NPSAS:04 weights were revised in June 2009. The revised weights will produce 2003-04 estimates that differ somewhat from those in any tables and publications produced before June 2009. See the description for the total Stafford loan variable (STAFFAMT) for details.

 Computation by NCES QuickStats on 8/25/2009                      
                 
bbfak2a
2                 	                     	Percentage of graduate students with assistantships, by graduate field of study: 2003–04                      
                                                                         Assistantships
(%>0)                      
                                              Estimates                      
                                              Total                          15.3                      
                                              Graduate study: major field                      
                                          	Humanities                          20.8                      
                                              Social/behavioral sciences                        	31.7                    	
                                           	Life sciences                        	47.4                    	
                                                               	Math/Engineering/Computer science                          37.9                      
                                              Education                        	7.6                    	
                                           	Business/management                        	7.9                    	
                                           	Health                        	10.3                    	
                                                                	Law                        	5.8                    	
                                           	Others                        	23.8                    	
                                                                	Undeclared or not in a degree program                        	5.4                    	
                                          	SOURCE: U.S. Department of Education, National Center for Education Statistics, 2003–04 National Postsecondary Student Aid Study (NPSAS:04).

                         NOTICE OF REVISIONS: The NPSAS:04 weights were revised in June 2009. The revised weights will produce 2003-04 estimates that differ somewhat from those in any tables and publications produced before June 2009. See the description for the total Stafford loan variable (STAFFAMT) for details.

 Computation by NCES QuickStats on 8/25/2009                      
                 
 
ckeak39
3                 	                     	Percentage distribution of graduates students' student/employee role, by graduate field of study: 2003–04                      
                                              Work: primarily student or employee                          A student working to meet expenses
(%)                          An employee enrolled in school
(%)                          No job
(%)                          Total                      
                                              Estimates                      
                                              Total                          35.8                         45.1                         19.1                         100%                      
                                              Graduate study: major field                      
                                              Humanities                          44.9                         35.9                         19.2                       	 100%                    	
                                           	Social/behavioral sciences                          58.9                         24.6                         16.5                       	 100%                    	
                                              Life sciences                          61.0                         20.7                         18.3                       	 100%                    	
                                           	Math/Engineering/Computer science                          47.4                         38.3                         14.3                       	 100%                    	
                                              Education                          26.3                         63.3                         10.4                       	 100%                    	
                                              Business/management                          24.8                         61.8                         13.3                       	 100%                    	
                                           	Health                          39.4                         19.0                         41.6                       	 100%                    	
                                              Law                          39.6                         11.6                         48.8                       	 100%                    	
                                           	Others                          47.0                         38.5                         14.5                       	 100%                    	
                                              Undeclared or not in a degree program                          20.5                         67.3                         12.2                       	 100%                    	
                                          	NOTE: Rows may not add up to 100% due to rounding.

                         SOURCE: U.S. Department of Education, National Center for Education Statistics, 2003–04 National Postsecondary Student Aid Study (NPSAS:04).

                         NOTICE OF REVISIONS: The NPSAS:04 weights were revised in June 2009. The revised weights will produce 2003-04 estimates that differ somewhat from those in any tables and publications produced before June 2009. See the description for the total Stafford loan variable (STAFFAMT) for details.

 Computation by NCES QuickStats on 8/25/2009                      
                 
 
ckeakce
4                 	                     	Percentage of graduate students who have ever borrowed loans, by institution type: 2003–04                      
                                                                         Total loan debt (cumulative)
(%>0)                      
                                              Estimates                      
                                              Total                          65.2                      
                                              Type of 4-year institution                      
                                          	Public 4-year nondoctorate                          61.4                      
                                              Public 4-year doctorate                        	60.6                    	
                                           	Private not-for-profit 4-yr nondoctorate                        	61.6                    	
                                              Private not-for-profit 4-year doctorate                          71.3                      
                                              Private for-profit 4-year                          85.9                      
                                              Attended more than one institution                          68.9                      
                                          	SOURCE: U.S. Department of Education, National Center for Education Statistics, 2003–04 National Postsecondary Student Aid Study (NPSAS:04).

                         NOTICE OF REVISIONS: The NPSAS:04 weights were revised in June 2009. The revised weights will produce 2003-04 estimates that differ somewhat from those in any tables and publications produced before June 2009. See the description for the total Stafford loan variable (STAFFAMT) for details.

 Computation by NCES QuickStats on 8/25/2009                      
                 
 
ckeak5f
5                 	                     	Average loan amount for graduate students, by parents' education, 2003–04                      
                                                                         Loans: total student loans all sources
(Mean[0])                      
                                              Estimates                      
                                              Total                          6,302.0                      
                                              Parent's highest education                      
                                          	Do not know parent's education level                          7,677.5                      
                                              High school diploma or less                        	5,878.7                    	
                                           	Some college                        	6,016.3                    	
                                              Bachelor's degree                          5,794.3                      
                                          	Master's degree or higher                          7,185.9                      
                                          	SOURCE: U.S. Department of Education, National Center for Education Statistics, 2003–04 National Postsecondary Student Aid Study (NPSAS:04).

                         NOTICE OF REVISIONS: The NPSAS:04 weights were revised in June 2009. The revised weights will produce 2003-04 estimates that differ somewhat from those in any tables and publications produced before June 2009. See the description for the total Stafford loan variable (STAFFAMT) for details.

 Computation by NCES QuickStats on 8/25/2009                      
                 
 
ckeakef
1                 	                     	Percentage of undergraduate students who applied for aid, by parents' income: 2003–04                      
                                              Aid: applied for federal aid                          Yes
(%)                          No
(%)                          Total                      
                                              Estimates                      
                                              Total                          58.3                            41.7                          100%                      
                                              Income: dependent student household income                      
                                              Less than $32,000                          78.7                          	21.3                          100%                    	
                                           	$32,000-59,999                          66.6                          	33.4                            100%                    	
                                              $60,000-91,999                          56.9                          	43.1                          100%                    	
                                           	$92,000 or more                          47.1                          	52.9                           100%                     	
                                          	NOTE: Rows may not add up to 100% due to rounding.

 SOURCE: U.S. Department of Education, National Center for Education Statistics, 2003–04 National Postsecondary Student Aid Study (NPSAS:04).

 NOTICE OF REVISIONS: The NPSAS:04 weights were revised in June 2009. The revised weights will produce 2003-04 estimates that differ somewhat from those in any tables and publications produced before June 2009. See the description for the total Stafford loan variable (STAFFAMT) for details.

 Computation by NCES QuickStats on 8/25/2009                       
                 
 
cgeak8c
2                 	                     	Percentage distribution of undergraduates' cumulative grade point average (GPA) categories, by major field of study: 2003–04                    	
                                              Cumulative Grade Point Average (GPA) as of 2003-04                        	Less than 2.75
(%)                        	2.75 - 3.74
(%)                        	3.75 or higher
(%)                          Total                      
                                              Estimates                      
                                              Total                          34.4                            49.0                        16.7                        100%                    	
                                              College study: major                    	
                                              Humanities                        	35.9                           50.4                      	  13.6                         100%                    	
                                           	Social/behavioral sciences                        	35.0                           52.1                      	  	12.8                           100%                     	
                                              Life sciences                        	34.9                          52.7                       	 	12.4                          100%                   	 
                                           	Physical sciences                        	31.5                            54.3                     	  	14.2                            100%                    	
                   	                         Math                     	  	29.1                           55.3                      	  	15.6                          100%                   	
                   	                      	Computer/information science                        	34.0                           48.1                         	17.9                           100%                      	
                                              Engineering                        	37.4                           48.1                      	  14.5                         100%                    	
                                           	Education                        	31.9                           52.6                      	  	15.5                            100%                    	
                                              Business/management                        	35.6                          49.3                       	 	15.1                          100%                   	 
                                           	Health                        	32.2                            50.7                     	  	17.0                          100%                      	
                   	                         Vocational/technical                     	  	33.3                           47.1                      	  	19.6                          100%                   	
                   	                      	Other technical/professional                        	36.7                           49.9                         	13.4                            100%                     	
                                              Undeclared or not in a degree program                     	  	33.2                           44.1                      	  	22.8                          100%                   	
                                          	NOTE: Rows may not add up to 100% due to rounding.

 SOURCE: U.S. Department of Education, National Center for Education Statistics, 2003–04 National Postsecondary Student Aid Study (NPSAS:04).

 NOTICE OF REVISIONS: The NPSAS:04 weights were revised in June 2009. The revised weights will produce 2003-04 estimates that differ somewhat from those in any tables and publications produced before June 2009. See the description for the total Stafford loan variable (STAFFAMT) for details.

 Computation by NCES QuickStats on 8/25/2009                        
                 
 
cgeake9
3                 	                     	Mean net price of attendance for undergraduate students, by type of institution: 2003–04                      
                                                                         Net price after all aid
(Mean[0])                      
                                              Estimates                      
                                              Total                          6,656.0                      
                                              Institution: type                      
                                              Public less-than-2-year                         	5,616.5                     	
                                           	Public 2-year                        	4,716.3                      	
                                              Public 4-year nondoctorate                          	6,253.5                    	
                                           	Public 4-year doctorate                        	7,564.1                      	
                                              Private not-for-profit less-than-4-year                        	7,382.3                    	
                                              Private not-for-profit 4-yr nondoctorate                        	9,208.7                    	
                                           	Private not-for-profit 4-year doctorate                           14,812.2                        
                                              Private for-profit less-than-2-year                         	7,842.9                     	
                                           	Private for-profit 2 years or more                        	6,737.6                      	
                                              Attended more than one institution                          	‡                    	
                                          	‡ Reporting standards not met. 

                         SOURCE: U.S. Department of Education, National Center for Education Statistics, 2003–04 National Postsecondary Student Aid Study (NPSAS:04).

                         NOTICE OF REVISIONS: The NPSAS:04 weights were revised in June 2009. The revised weights will produce 2003-04 estimates that differ somewhat from those in any tables and publications produced before June 2009. See the description for the total Stafford loan variable (STAFFAMT) for details.

 Computation by NCES QuickStats on 8/25/2009                      
                 
 
bcfak0c
4                 	                     	Percentage distribution of undergraduates' parents' highest level of education, by type of institution: 2003–04                      
                                              Parent's highest education                          High school or less
(%)                          Some college
(%)                          Bachelor's degree or higher
(%)                          Total                      
                                              Estimates                      
                                              Total                          37.1                          21.6                            41.3                          100%                      
                                              Institution: type                      
                                              Public less-than-2-year                          54.2                          17.4                          	28.4                           100%                    	
                                           	Public 2-year                          43.3                           23.9                         	32.7                            100%                    	
                                              Public 4-year nondoctorate                          28.7                          20.5                          	50.8                          100%                    	
                                           	Public 4-year doctorate                          46.9                           18.8                         	34.2                            100%                    	
                                              Private not-for-profit less than 4-year                        	29.6                           18.1                         	52.3                          100%                    	
                                           	Private not-for-profit 4-year nondoctorate                          55.6                          17.4                           27.0                           100%                       
                                               Private not-for-profit 4-year doctorate                        	53.8                           20.2                         	25.9                          100%                    	
                     	NOTE: Rows may not add up to 100% due to rounding.

SOURCE: U.S. Department of Education, National Center for Education Statistics, 2003–04 National Postsecondary Student Aid Study (NPSAS:04).

                         NOTICE OF REVISIONS: The NPSAS:04 weights were revised in June 2009. The revised weights will produce 2003-04 estimates that differ somewhat from those in any tables and publications produced before June 2009. See the description for the total Stafford loan variable (STAFFAMT) for details.

                         Computation by NCES QuickStats on 10/14/2009                      
                 
 
cgeakd5
5                 	                     	Average amount of Pell grants received by undergraduates, by income and dependency status: 2003–04                      
                                                                         Grants: Pell Grants
(Avg>0)                    
                                            Estimates                      
                                              Total                          2,449.7                      
                                              Income: categories by dependency status                      
                                              Dependent: Less than $10,000                         	3,242.2                     	
                                           	Dependent: $10,000-$19,999                        	3,176.1

Missing data code	Description
-1	Don't know
-4	Item not administered: abbreviated interview
-5	Suppressed
-6	Component not applicable
-7	Item legitimate skip/NA
-8	Unit nonresponse
-9	Missing

Missing data code	Description
-1	Missing
-2	Not applicable
-3	Skipped
-4	B&B:97 nonrespondent not sampled
-6	Uncodeable, out of range
-7	Not reached
-8	Item was not reached due to an error
-9	Missing, blank

Missing data code	Description
-1	Don’t know (CATI variables), Data not available (CADE variables)
-2	Refused (CATI variables only)
-3	Not applicable (CADE and CATI variables only)
-4	B&B:97 nonrespondent not sampled
-6	Bad data, out of range
-7	Item was not reached (abbreviated and partial CATI interviews)
-8	Item was not reached due to a CATI error
-9	Data missing, reason unknown (CATI variables)

Missing data code	Description
-1	Not classified
-2	Not applicable
-3	Skipped
-8	Double non-respondent
-9	Data missing

Missing data code	Description
-1	Don’t know
-2	Refused
-3	Legitimate skip (item was intentionally not collected because variable was not applicable to this student)
-6	Bad data, out of range, uncodeable userexit string
-7	Not reached
-8	Missing, CATI error
-9	Missing

Missing data code	Description
-1	Not classified
-2	Not applicable
-6	Out of range
-8	Item was not reached due to an error
-9	Missing

Missing data code	Description
-1	Legitimate skip
-7	Missing, refused
-8	Missing, don't know
-9	Missing, blank

Missing data code	Description
-1	Don''t know
-2	Refused
-3	Skipped
-8	Data source not available
-9	Data missing

Adult Education Survey: 2005	2005	Adult Education	qsOffpsOntsOff
Adult Training and Education Survey: 2016	2016	Adult Education	qsOnpsOntsOff
Baccalaureate and Beyond: 1993/2003	1993/2003	Postsecondary	qsOnpsOntsOff
Baccalaureate and Beyond: 1993/2003	1993/2003	Adult Education	qsOnpsOntsOff
Baccalaureate and Beyond: 1993/2003 Graduate students	1993/2003	Postsecondary	qsOffpsOntsOff
Baccalaureate and Beyond: 1993/2003 Graduate students	1993/2003	Adult Education	qsOffpsOntsOff
Baccalaureate and Beyond: 2000/2001	2000/2001	Postsecondary	qsOffpsOntsOff
Baccalaureate and Beyond: 2000/2001	2000/2001	Adult Education	qsOffpsOntsOff
Baccalaureate and Beyond: 2008/2012	2008/2012	Postsecondary	qsOnpsOntsOff
Baccalaureate and Beyond: 2008/2012	2008/2012	Adult Education	qsOnpsOntsOff
Baccalaureate and Beyond: 2016/2017	2016/2017	Postsecondary	qsOnpsOntsOff
Baccalaureate and Beyond: 2016/2017	2016/2017	Adult Education	qsOnpsOntsOff
Beginning Postsecondary Students: 1990/1994	1990/1994	Postsecondary	qsOnpsOntsOff
Beginning Postsecondary Students: 1996/2001	1996/2001	Postsecondary	qsOnpsOntsOff
Beginning Postsecondary Students: 2004/2009	2004/2009	Postsecondary	qsOnpsOntsOff
Beginning Postsecondary Students: 2012/2017	2012/2017	Postsecondary	qsOnpsOntsOff
Early Childhood Longitudinal Study: Birth Cohort	2001-02	P-12	qsOffpsOntsOff
Early Childhood Program Participation: 2012	2012	P-12	qsOnpsOntsOn6
Early Childhood Program Participation: 2016	2016	P-12	qsOnpsOntsOn6
Early Childhood Program Participation: 2019	2019	P-12	qsOffpsOntsOn6
Education Longitudinal Study of 2002	2002	Postsecondary	qsOnpsOntsOff
Education Longitudinal Study of 2002	2002	P-12	qsOnpsOntsOff
High School and Beyond	1980	Postsecondary	qsOnpsOntsOff
High School and Beyond	1980	P-12	qsOnpsOntsOff
High School Longitudinal Study of 2009	2009	Postsecondary	qsOnpsOntsOff
High School Longitudinal Study of 2009	2009	P-12	qsOnpsOntsOff
National Education Longitudinal Study of 1988	1988	Postsecondary	qsOnpsOntsOff
National Education Longitudinal Study of 1988	1988	P-12	qsOnpsOntsOff
National Postsecondary Student Aid Study: 1987 Graduate Students	1987	Postsecondary	qsOffpsOntsOff
National Postsecondary Student Aid Study: 1987 Undergraduates	1987	Postsecondary	qsOffpsOntsOff
National Postsecondary Student Aid Study: 1990 Graduate Students	1990	Postsecondary	qsOffpsOntsOff
National Postsecondary Student Aid Study: 1990 Undergraduates	1990	Postsecondary	qsOffpsOntsOff
National Postsecondary Student Aid Study: 1993 Graduate Students	1993	Postsecondary	qsOffpsOntsOff
National Postsecondary Student Aid Study: 1993 Undergraduates	1993	Postsecondary	qsOffpsOntsOff
National Postsecondary Student Aid Study: 1996 Graduate Students	1996	Postsecondary	qsOffpsOntsOn2
National Postsecondary Student Aid Study: 1996 Undergraduates	1996	Postsecondary	qsOffpsOntsOn1
National Postsecondary Student Aid Study: 2000 Graduate Students	2000	Postsecondary	qsOffpsOntsOn2
National Postsecondary Student Aid Study: 2000 Undergraduates	2000	Postsecondary	qsOffpsOntsOn1
National Postsecondary Student Aid Study: 2004 Graduate Students	2004	Postsecondary	qsOnpsOntsOn2
National Postsecondary Student Aid Study: 2004 Undergraduates	2004	Postsecondary	qsOnpsOntsOn1
National Postsecondary Student Aid Study: 2008 Graduate Students	2008	Postsecondary	qsOnpsOntsOn2
National Postsecondary Student Aid Study: 2008 Undergraduates	2008	Postsecondary	qsOnpsOntsOn1
National Postsecondary Student Aid Study: 2012 Graduate Students	2012	Postsecondary	qsOnpsOntsOn2
National Postsecondary Student Aid Study: 2012 Undergraduates	2012	Postsecondary	qsOnpsOntsOn1
National Postsecondary Student Aid Study: 2016 Graduate Students	2016	Postsecondary	qsOnpsOntsOn2
National Postsecondary Student Aid Study: 2016 Undergraduates	2016	Postsecondary	qsOnpsOntsOn1
National Study of Postsecondary Faculty: 1988 Faculty	1988	Postsecondary	qsOffpsOntsOff
National Study of Postsecondary Faculty: 1993 Faculty	1993	Postsecondary	qsOffpsOntsOff
National Study of Postsecondary Faculty: 1999 Faculty	1999	Postsecondary	qsOffpsOntsOff
National Study of Postsecondary Faculty: 2004 Faculty	2004	Postsecondary	qsOnpsOntsOff
National Study of Postsecondary Faculty: 2004 Institution	2004	Postsecondary	qsOnpsOntsOff
National Teacher and Principal Survey, 2015-16 Public School Principals	2015-2016	P-12	qsOnpsOntsOff
National Teacher and Principal Survey, 2015-16 Public Schools	2015-2016	P-12	qsOnpsOntsOff
Parent and Family Involvement in Education: 2012	2012	P-12	qsOnpsOntsOn7
Parent and Family Involvement in Education: 2016	2016	P-12	qsOnpsOntsOn7
Parent and Family Involvement in Education: 2019	2019	P-12	qsOffpsOntsOn7
Pre-Elementary Education Longitudinal Study, Waves 1-5	2003/2008	P-12	qsOnpsOntsOff
Private School Universe Survey: 2011-12	2011-2012	P-12	qsOnpsOntsOff
Private School Universe Survey: 2013-14	2013-2014	P-12	qsOnpsOntsOff
Private School Universe Survey: 2015-16	2015-2016	P-12	qsOnpsOntsOff
School Survey on Crime and Safety: 1999-2000	1999-2000	P-12	qsOnpsOntsOff
School Survey on Crime and Safety: 2003-04	2003-2004	P-12	qsOnpsOntsOff
School Survey on Crime and Safety: 2005-06	2005-2006	P-12	qsOnpsOntsOn3
School Survey on Crime and Safety: 2007-08	2007-2008	P-12	qsOnpsOntsOn3
School Survey on Crime and Safety: 2009-10	2009-2010	P-12	qsOnpsOntsOn3
School Survey on Crime and Safety: 2015-16	2015-2016	P-12	qsOnpsOntsOn3
Schools and Staffing Survey, Districts: 1999-00	1999-2000	P-12	qsOffpsOntsOff
Schools and Staffing Survey, Districts: 2003-04	2003-2004	P-12	qsOnpsOntsOff
Schools and Staffing Survey, Districts: 2007-08	2007-2008	P-12	qsOnpsOntsOff
Schools and Staffing Survey, Districts: 2011-12	2011-2012	P-12	qsOnpsOntsOff
Schools and Staffing Survey, Library Media Centers: 1999-00	1999-2000	P-12	qsOffpsOntsOff
Schools and Staffing Survey, Library Media Centers: 2003-04	2003-2004	P-12	qsOnpsOntsOff
Schools and Staffing Survey, Library Media Centers: 2007-08	2007-2008	P-12	qsOnpsOntsOff
Schools and Staffing Survey, Library Media Centers: 2011-12	2011-2012	P-12	qsOnpsOntsOff
Schools and Staffing Survey, Public and Private School Principals: 1999-00	1999-2000	P-12	qsOffpsOntsOff
Schools and Staffing Survey, Public and Private School Principals: 2003-04	2003-2004	P-12	qsOnpsOntsOff
Schools and Staffing Survey, Public and Private School Principals: 2007-08	2007-2008	P-12	qsOnpsOntsOff
Schools and Staffing Survey, Public and Private School Principals: 2011-12	2011-2012	P-12	qsOnpsOntsOff
Schools and Staffing Survey, Public and Private Schools: 1999-00	1999-2000	P-12	qsOffpsOntsOff
Schools and Staffing Survey, Public and Private Schools: 2003-04	2003-2004	P-12	qsOnpsOntsOff
Schools and Staffing Survey, Public and Private Schools: 2007-08	2007-2008	P-12	qsOnpsOntsOff
Schools and Staffing Survey, Public and Private Schools: 2011-12	2011-2012	P-12	qsOnpsOntsOff
Schools and Staffing Survey, Public and Private Teachers: 1999-00	1999-2000	P-12	qsOffpsOntsOff
Schools and Staffing Survey, Public and Private Teachers: 2003-04	2003-2004	P-12	qsOnpsOntsOff
Schools and Staffing Survey, Public and Private Teachers: 2007-08	2007-2008	P-12	qsOnpsOntsOff
Schools and Staffing Survey, Public and Private Teachers: 2011-12	2011-2012	P-12	qsOnpsOntsOff

Highest degree completed as of June 2001	Certificate (%)	Associate (%)	Bachelor (%)	Never attained (%)	Total
Percentage distribution of 1995–96 beginning postsecondary students' highest degree attained by 2001, by work status
Estimates
Total	11.7	9.8	29.8	48.6	100%
Job 1995–96: hours worked per week while enrolled
Did not work while enrolled	14.0	9.8	38.5	37.8	100%
Worked part time	8.9	11.6	35.5	44.0	100%
Worked full time	14.5	7.2	8.3	69.9	100%
NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, 1995–96 Beginning Postsecondary Students Longitudinal Study, Second Follow-up (BPS:96/01). Computation by NCES QuickStats on 6/22/2009

	Pell Grant amount 1995-96 (%>0)
Percentage of beginning postsecondary students who received Pell grants, by race/ethnicity: 1995–96
Estimates
Total	26.4
Race/ethnicity
White, non-Hispanic	19.0
Black, non-Hispanic	49.3
Hispanic	42.4
Asian/Pacific Islander	35.5
American Indian/Alaska Native	33.2
Other	‡
‡ Reporting standards not met. Source: U.S. Department of Education, National Center for Education Statistics, 1995–96 Beginning Postsecondary Students Longitudinal Study, Second Follow-up (BPS:96/01). Computation by NCES QuickStats on 3/10/2009

	Loans: total student loans all sources (%>0)
Percent of graduate students who borrowed, by type of graduate program: 2003–04
Estimates
Total	40.0
Graduate study: program
Business administration (MBA)	39.1
Education (any master's)	34.8
Other master of arts (MA)	41.3
Other master of science (MS)	31.8
Other master's degree	49.3
PhD except in education	19.9
Education (any doctorate)	27.1
Other doctoral degree	49.5
Medicine (MD)	77.3
Other health science degree	81.7
Law (LLB or JD)	81.0
Theology (MDiv, MHL, BD)	30.0
Post-baccalaureate certificate	30.1
Not in a degree program	28.0
SOURCE: U.S. Department of Education, National Center for Education Statistics, 2003–04 National Postsecondary Student Aid Study (NPSAS:04). NOTICE OF REVISIONS: The NPSAS:04 weights were revised in June 2009. The revised weights will produce 2003-04 estimates that differ somewhat from those in any tables and publications produced before June 2009. See the description for the total Stafford loan variable (STAFFAMT) for details. Computation by NCES QuickStats on 8/25/2009

	Assistantships (%>0)
Percentage of graduate students with assistantships, by graduate field of study: 2003–04
Estimates
Total	15.3
Graduate study: major field
Humanities	20.8
Social/behavioral sciences	31.7
Life sciences	47.4
Math/Engineering/Computer science	37.9
Education	7.6
Business/management	7.9
Health	10.3
Law	5.8
Others	23.8
Undeclared or not in a degree program	5.4
SOURCE: U.S. Department of Education, National Center for Education Statistics, 2003–04 National Postsecondary Student Aid Study (NPSAS:04). NOTICE OF REVISIONS: The NPSAS:04 weights were revised in June 2009. The revised weights will produce 2003-04 estimates that differ somewhat from those in any tables and publications produced before June 2009. See the description for the total Stafford loan variable (STAFFAMT) for details. Computation by NCES QuickStats on 8/25/2009

Work: primarily student or employee	A student working to meet expenses (%)	An employee enrolled in school (%)	No job (%)	Total
Percentage distribution of graduates students' student/employee role, by graduate field of study: 2003–04
Estimates
Total	35.8	45.1	19.1	100%
Graduate study: major field
Humanities	44.9	35.9	19.2	100%
Social/behavioral sciences	58.9	24.6	16.5	100%
Life sciences	61.0	20.7	18.3	100%
Math/Engineering/Computer science	47.4	38.3	14.3	100%
Education	26.3	63.3	10.4	100%
Business/management	24.8	61.8	13.3	100%
Health	39.4	19.0	41.6	100%
Law	39.6	11.6	48.8	100%
Others	47.0	38.5	14.5	100%
Undeclared or not in a degree program	20.5	67.3	12.2	100%
NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, 2003–04 National Postsecondary Student Aid Study (NPSAS:04). NOTICE OF REVISIONS: The NPSAS:04 weights were revised in June 2009. The revised weights will produce 2003-04 estimates that differ somewhat from those in any tables and publications produced before June 2009. See the description for the total Stafford loan variable (STAFFAMT) for details. Computation by NCES QuickStats on 8/25/2009

	Total loan debt (cumulative) (%>0)
Percentage of graduate students who have ever borrowed loans, by institution type: 2003–04
Estimates
Total	65.2
Type of 4-year institution
Public 4-year nondoctorate	61.4
Public 4-year doctorate	60.6
Private not-for-profit 4-yr nondoctorate	61.6
Private not-for-profit 4-year doctorate	71.3
Private for-profit 4-year	85.9
Attended more than one institution	68.9
SOURCE: U.S. Department of Education, National Center for Education Statistics, 2003–04 National Postsecondary Student Aid Study (NPSAS:04). NOTICE OF REVISIONS: The NPSAS:04 weights were revised in June 2009. The revised weights will produce 2003-04 estimates that differ somewhat from those in any tables and publications produced before June 2009. See the description for the total Stafford loan variable (STAFFAMT) for details. Computation by NCES QuickStats on 8/25/2009

	Loans: total student loans all sources (Mean[0])
Average loan amount for graduate students, by parents' education, 2003–04
Estimates
Total	6,302.0
Parent's highest education
Do not know parent's education level	7,677.5
High school diploma or less	5,878.7
Some college	6,016.3
Bachelor's degree	5,794.3
Master's degree or higher	7,185.9
SOURCE: U.S. Department of Education, National Center for Education Statistics, 2003–04 National Postsecondary Student Aid Study (NPSAS:04). NOTICE OF REVISIONS: The NPSAS:04 weights were revised in June 2009. The revised weights will produce 2003-04 estimates that differ somewhat from those in any tables and publications produced before June 2009. See the description for the total Stafford loan variable (STAFFAMT) for details. Computation by NCES QuickStats on 8/25/2009

Aid: applied for federal aid	Yes (%)	No (%)	Total
Percentage of undergraduate students who applied for aid, by parents' income: 2003–04
Estimates
Total	58.3	41.7	100%
Income: dependent student household income
Less than $32,000	78.7	21.3	100%
$32,000-59,999	66.6	33.4	100%
$60,000-91,999	56.9	43.1	100%
$92,000 or more	47.1	52.9	100%
NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, 2003–04 National Postsecondary Student Aid Study (NPSAS:04). NOTICE OF REVISIONS: The NPSAS:04 weights were revised in June 2009. The revised weights will produce 2003-04 estimates that differ somewhat from those in any tables and publications produced before June 2009. See the description for the total Stafford loan variable (STAFFAMT) for details. Computation by NCES QuickStats on 8/25/2009

Cumulative Grade Point Average (GPA) as of 2003-04	Less than 2.75 (%)	2.75 - 3.74 (%)	3.75 or higher (%)	Total
Percentage distribution of undergraduates' cumulative grade point average (GPA) categories, by major field of study: 2003–04
Estimates
Total	34.4	49.0	16.7	100%
College study: major
Humanities	35.9	50.4	13.6	100%
Social/behavioral sciences	35.0	52.1	12.8	100%
Life sciences	34.9	52.7	12.4	100%
Physical sciences	31.5	54.3	14.2	100%
Math	29.1	55.3	15.6	100%
Computer/information science	34.0	48.1	17.9	100%
Engineering	37.4	48.1	14.5	100%
Education	31.9	52.6	15.5	100%
Business/management	35.6	49.3	15.1	100%
Health	32.2	50.7	17.0	100%
Vocational/technical	33.3	47.1	19.6	100%
Other technical/professional	36.7	49.9	13.4	100%
Undeclared or not in a degree program	33.2	44.1	22.8	100%
NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, 2003–04 National Postsecondary Student Aid Study (NPSAS:04). NOTICE OF REVISIONS: The NPSAS:04 weights were revised in June 2009. The revised weights will produce 2003-04 estimates that differ somewhat from those in any tables and publications produced before June 2009. See the description for the total Stafford loan variable (STAFFAMT) for details. Computation by NCES QuickStats on 8/25/2009

	Net price after all aid (Mean[0])
Mean net price of attendance for undergraduate students, by type of institution: 2003–04
Estimates
Total	6,656.0
Institution: type
Public less-than-2-year	5,616.5
Public 2-year	4,716.3
Public 4-year nondoctorate	6,253.5
Public 4-year doctorate	7,564.1
Private not-for-profit less-than-4-year	7,382.3
Private not-for-profit 4-yr nondoctorate	9,208.7
Private not-for-profit 4-year doctorate	14,812.2
Private for-profit less-than-2-year	7,842.9
Private for-profit 2 years or more	6,737.6
Attended more than one institution	‡
‡ Reporting standards not met. SOURCE: U.S. Department of Education, National Center for Education Statistics, 2003–04 National Postsecondary Student Aid Study (NPSAS:04). NOTICE OF REVISIONS: The NPSAS:04 weights were revised in June 2009. The revised weights will produce 2003-04 estimates that differ somewhat from those in any tables and publications produced before June 2009. See the description for the total Stafford loan variable (STAFFAMT) for details. Computation by NCES QuickStats on 8/25/2009

	Grants: Pell Grants (Avg>0)
Average amount of Pell grants received by undergraduates, by income and dependency status: 2003–04
Estimates
Total	2,449.7
Income: categories by dependency status
Dependent: Less than $10,000	3,242.2
Dependent: $10,000-$19,999	3,176.1

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