Skip Navigation

Search Results: (1-15 of 23 records)

 Pub Number  Title  Date
NCES 2018020 U.S. TIMSS 2015 and TIMSS Advanced 1995 & 2015 Technical Report and User's Guide
The U.S. TIMSS 2015 and TIMSS Advanced 1995 & 2015 Technical Report and User's Guide provides an overview of the design and implementation in the United States of the Trends in International Mathematics and Science Study (TIMSS) 2015 and TIMSS Advanced 1995 & 2015, along with information designed to facilitate access to the U.S. TIMSS 2015 and TIMSS Advanced 1995 & 2015 data.
11/1/2018
NCES 2018121 Administering a Single-Phase, All-Adults Mail Survey: A Methodological Evaluation of the 2013 NATES Pilot Study
This report describes the methodological outcomes from an address-based sampling (ABS) mail survey, the 2013 pilot test of the National Adult Training and Education Survey. The study tested the feasibility of (1) using single-stage sampling, rather than two-stage sampling (with a screener to identify adults within households), and (2) mailing out three individual survey instruments per household versus a composite booklet with three combined instruments.
3/30/2018
NCES 2017095 Technical Report and User Guide for the 2015 Program for International Student Assessment (PISA)
This technical report and user guide is designed to provide researchers with an overview of the design and implementation of PISA 2015 in the United States, as well as information on how to access the PISA 2015 data. The report includes information about sampling requirements and sampling in the United States; participation rates at the school and student level; how schools and students were recruited; instrument development; field operations used for collecting data; detail concerning various aspects of data management, including data processing, scaling, and weighting. In addition, the report describes the data available from both international and U.S. sources, special issues in analyzing the PISA 2015 data, as well as a description of merging data files.
12/19/2017
NCEE 20184002 Asymdystopia: The threat of small biases in evaluations of education interventions that need to be powered to detect small impacts
Evaluators of education interventions are increasingly designing studies to detect impacts much smaller than the 0.20 standard deviations that Cohen (1988) characterized as "small." While the need to detect smaller impacts is based on compelling arguments that such impacts are substantively meaningful, the drive to detect smaller impacts may create a new challenge for researchers: the need to guard against smaller inaccuracies (or "biases"). The purpose of this report is twofold. First, the report examines the potential for small biases to increase the risk of making false inferences as studies are powered to detect smaller impacts, a phenomenon the report calls asymdystopia. The report examines this potential for both randomized controlled trials (RCTs) and studies using regression discontinuity designs (RDDs). Second, the report recommends strategies researchers can use to avoid or mitigate these biases. For RCTs, the report recommends that evaluators either substantially limit attrition rates or offer a strong justification for why attrition is unlikely to be related to study outcomes. For RDDs, new statistical methods can protect against bias from incorrect regression models, but these methods often require larger sample sizes in order to detect small effects.
10/3/2017
REL 2016119 Stated Briefly: How methodology decisions affect the variability of schools identified as beating the odds
This "Stated Briefly" report is a companion piece that summarizes the results of another report of the same name. Schools that show better academic performance than would be expected given characteristics of the school and student populations are often described as "beating the odds" (BTO). State and local education agencies often attempt to identify such schools as a means of identifying strategies or practices that might be contributing to the schools' relative success. Key decisions on how to identify BTO schools may affect whether schools make the BTO list and thereby the identification of practices used to beat the odds. The purpose of this study was to examine how a list of BTO schools might change depending on the methodological choices and selection of indicators used in the BTO identification process. This study considered whether choices of methodologies and type of indicators affect the schools that are identified as BTO. The three indicators were (1) type of performance measure used to compare schools, (2) the types of school characteristics used as controls in selecting BTO schools, and (3) the school sample configuration used to pool schools across grade levels. The study applied statistical models involving the different methodologies and indicators and documented how the lists schools identified as BTO changed based on the models. Public school and student data from one midwest state from 2007-08 through 2010-11 academic years were used to generate BTO school lists. By performing pairwise comparisons among BTO school lists and computing agreement rates among models, the project team was able to gauge the variation in BTO identification results. Results indicate that even when similar specifications were applied across statistical methods, different sets of BTO schools were identified. In addition, for each statistical method used, the lists of BTO schools identified varied with the choice of indicators. Fewer than half of the schools were identified as BTO in more than one year. The results demonstrate that different technical decisions can lead to different identification results.
4/6/2016
NCSER 2015002 The Role of Effect Size in Conducting, Interpreting, and Summarizing Single-Case Research
The field of education is increasingly committed to adopting evidence-based practices. Although randomized experimental designs provide strong evidence of the causal effects of interventions, they are not always feasible. For example, depending upon the research question, it may be difficult for researchers to find the number of children necessary for such research designs (e.g., to answer questions about impacts for children with low-incidence disabilities). A type of experimental design that is well suited for such low-incidence populations is the single-case design (SCD). These designs involve observations of a single case (e.g., a child or a classroom) over time in the absence and presence of an experimenter-controlled treatment manipulation to determine whether the outcome is systematically related to the treatment.

Research using SCD is often omitted from reviews of whether evidence-based practices work because there has not been a common metric to gauge effects as there is in group design research. To address this issue, the National Center for Education Research (NCER) and National Center for Special Education Research (NCSER) commissioned a paper by leading experts in methodology and SCD. Authors William Shadish, Larry Hedges, Robert Horner, and Samuel Odom contend that the best way to ensure that SCD research is accessible and informs policy decisions is to use good standardized effect size measures—indices that put results on a scale with the same meaning across studies—for statistical analyses. Included in this paper are the authors' recommendations for how SCD researchers can calculate and report on standardized between-case effect sizes, the way in these effect sizes can be used for various audiences (including policymakers) to interpret findings, and how they can be used across studies to summarize the evidence base for education practices.
1/7/2016
REL 2015077 Comparing Methodologies for Developing an Early Warning System: Classification and Regression Tree Model Versus Logistic Regression
The purpose of this report was to explicate the use of logistic regression and classification and regression tree (CART) analysis in the development of early warning systems. It was motivated by state education leaders' interest in maintaining high classification accuracy while simultaneously improving practitioner understanding of the rules by which students are identified as at-risk or not at-risk readers. Logistic regression and CART were compared using data on a sample of grades 1 and 2 Florida public school students who participated in both interim assessments and an end-of-the year summative assessment during the 2012/13 academic year. Grade-level analyses were conducted and comparisons between methods were based on traditional measures of diagnostic accuracy, including sensitivity (i.e., proportion of true positives), specificity (proportion of true negatives), positive and negative predictive power, and overall correct classification. Results indicate that CART is comparable to logistic regression, with the results of both methods yielding negative predictive power greater than the recommended standard of .90. Details of each method are provided to assist analysts interested in developing early warning systems using one of the methods.
2/25/2015
REL 2015071 How Methodology Decisions Affect the Variability of Schools Identified as Beating the Odds
Schools that show better academic performance than would be expected given characteristics of the school and student populations are often described as "beating the odds" (BTO). State and local education agencies often attempt to identify such schools as a means of identifying strategies or practices that might be contributing to the schools' relative success. Key decisions on how to identify BTO schools may affect whether schools make the BTO list and thereby the identification of practices used to beat the odds. The purpose of this study was to examine how a list of BTO schools might change depending on the methodological choices and selection of indicators used in the BTO identification process. This study considered whether choices of methodologies and type of indicators affect the schools that are identified as BTO. The three indicators were (1) type of performance measure used to compare schools, (2) the types of school characteristics used as controls in selecting BTO schools, and (3) the school sample configuration used to pool schools across grade levels. The study applied statistical models involving the different methodologies and indicators and documented how the lists schools identified as BTO changed based on the models. Public school and student data from one midwest state from 2007-08 through 2010-11 academic years were used to generate BTO school lists. By performing pairwise comparisons among BTO school lists and computing agreement rates among models, the project team was able to gauge the variation in BTO identification results. Results indicate that even when similar specifications were applied across statistical methods, different sets of BTO schools were identified. In addition, for each statistical method used, the lists of BTO schools identified varied with the choice of indicators. Fewer than half of the schools were identified as BTO in more than one year. The results demonstrate that different technical decisions can lead to different identification results.
2/24/2015
REL 2014064 Reporting What Readers Need to Know about Education Research Measures: A Guide
This brief provides five checklists to help researchers provide complete information describing (1) their study's measures; (2) data collection training and quality; (3) the study's reference population, study sample, and measurement timing; (4) evidence of the reliability and construct validity of the measures; and (5) missing data and descriptive statistics. The brief includes an example of parts of a report's methods and results section illustrating how the checklists can be used to check the completeness of reporting.
9/9/2014
REL 2014014 Developing a Coherent Research Agenda: Lessons from the REL Northeast & Islands Research Agenda Workshops
This report describes the approach that REL Northeast and Islands (REL-NEI) used to guide its eight research alliances toward collaboratively identifying a shared research agenda. A key feature of their approach was a two-workshop series, during which alliance members created a set of research questions on a shared topic of education policy and/or practice. This report explains how REL-NEI conceptualized and organized the workshops, planned the logistics, overcame geographic distance among alliance members, developed and used materials (including modifications for different audiences and for a virtual platform), and created a formal research agenda after the workshops. The report includes links to access the materials used for the workshops, including facilitator and participant guides and slide decks.
7/10/2014
REL 2014051 Going public: Writing About Research in Everyday Language
This brief describes approaches that writers can use to make impact research more accessible to policy audiences. It emphasizes three techniques: making concepts as simple as possible, focusing on what readers need to know, and reducing possible misinterpretations. A glossary of common concepts is included showing the approaches applied to a range of concepts common to impact research, such as ‘regression models’ and ‘effect sizes.’
6/24/2014
NCES 2013046 U.S. TIMSS and PIRLS 2011 Technical Report and User's Guide
The U.S. TIMSS and PIRLS 2011 Technical Report and User's Guide provides an overview of the design and implementation in the United States of the Trends in International Mathematics and Science Study (TIMSS) 2011 and the Progress in International Reading Literacy Study (PIRLS) 2011, along with information designed to facilitate access to the U.S. TIMSS and PIRLS 2011 data.
11/26/2013
NCES 2013190 The Adult Education Training and Education Survey (ATES) Pilot Study
This report describes the process and findings of a national pilot test of survey items that were developed to assess the prevalence and key characteristics of occupational certifications and licenses and subbaccalaureate educational certificates. The pilot test was conducted as a computer-assisted telephone interview (CATI) survey, administered from September 2010 to January 2011.
4/9/2013
NCES 2011463 The NAEP Primer
The purpose of the NAEP Primer is to guide educational researchers through the intricacies of the NAEP database and make its technologies more user-friendly. The NAEP Primer makes use of its publicly accessible NAEP mini-sample that is included on the CD. The mini-sample contains real data from the 2005 mathematics assessment that have been approved for public use. Only public schools are included in this subsample that contains selected variables for about 10 percent of the schools and students in this assessment. All students who participated in NAEP in the selected public schools are included. This subsample is not sufficient to make state comparisons. In addition, to ensure confidentiality, no state, school, or student identifiers are included.

The NAEP Primer document covers the following topics:
  • Introduction and Overview: includes a technical history of NAEP, an overview of the NAEP Primer mini-sample and its design and implications for analysis, and a listing of relevant resources for further information.
  • The NAEP Database describes the contents of the NAEP database, the NAEP Primer mini-sample and the types of variables it includes, the NAEP database products, an overview of the NAEP 2005 Mathematics, Reading, and Science Data Companion, and how to obtain a Restricted-Use Data License.
  • NAEP Data Tools: provides the user with the information on the resources available to prepare the data for analysis, and how to find and use the various NAEP data tools.
  • Analyzing NAEP Data: includes recommendations for running statistical analyses with SPSS, SAS, STATA, and WesVar, including addressing the effect of BIB spiraling, plausible values, jackknife, etc. Worked examples and simple analyses use the NAEP Primer mini-sample.
  • Marginal Estimation of Score Distributions: discusses the principles of marginal estimation as used in NAEP and the role of plausible values.
  • Direct Estimation Using AM Software: presents an approach to direct estimation using the AM software including examples of analyses.
  • Fitting of Hierarchical Linear Models: presents information and examples on the use of the HLM program to do hierarchical linear modeling with NAEP data.
  • An appendix includes excerpted sections from the 2005 Data Companion to give the reader additional insight on topics introduced in previous sections of the Primer.
Please note that national results computed from the NAEP Primer mini-sample will be close to—but not identical to—published results in NAEP reports. National estimates should not be made with these data, and these data cannot be published as official estimates of NAEP.

Also note that the NAEP Primer consists of two publications: NCES 2011463 and NCES 2011464
8/4/2011
NCES 2011464 NAEP Primer Mini-Sample
The purpose of the NAEP Primer is to guide educational researchers through the intricacies of the NAEP database and make its technologies more user-friendly. The NAEP Primer makes use of its publicly accessible NAEP mini-sample that is included on the CD. The mini-sample contains real data from the 2005 mathematics assessment that have been approved for public use. Only public schools are included in this subsample that contains selected variables for about 10 percent of the schools and students in this assessment. All students who participated in NAEP in the selected public schools are included. This subsample is not sufficient to make state comparisons. In addition, to ensure confidentiality, no state, school, or student identifiers are included.

The NAEP Primer document covers the following topics:
  • Introduction and Overview: includes a technical history of NAEP, an overview of the NAEP Primer mini-sample and its design and implications for analysis, and a listing of relevant resources for further information.
  • The NAEP Database describes the contents of the NAEP database, the NAEP Primer mini-sample and the types of variables it includes, the NAEP database products, an overview of the NAEP 2005 Mathematics, Reading, and Science Data Companion, and how to obtain a Restricted-Use Data License.
  • NAEP Data Tools: provides the user with the information on the resources available to prepare the data for analysis, and how to find and use the various NAEP data tools.
  • Analyzing NAEP Data: includes recommendations for running statistical analyses with SPSS, SAS, STATA, and WesVar, including addressing the effect of BIB spiraling, plausible values, jackknife, etc. Worked examples and simple analyses use the NAEP Primer mini-sample.
  • Marginal Estimation of Score Distributions: discusses the principles of marginal estimation as used in NAEP and the role of plausible values.
  • Direct Estimation Using AM Software: presents an approach to direct estimation using the AM software including examples of analyses.
  • Fitting of Hierarchical Linear Models: presents information and examples on the use of the HLM program to do hierarchical linear modeling with NAEP data.
  • An appendix includes excerpted sections from the 2005 Data Companion to give the reader additional insight on topics introduced in previous sections of the Primer.
Please note that national results computed from the NAEP Primer mini-sample will be close to—but not identical to—published results in NAEP reports. National estimates should not be made with these data, and these data cannot be published as official estimates of NAEP.

Also note that the NAEP Primer consists of two publications: NCES 2011463 and NCES 2011464
8/4/2011
   1 - 15     Next >>
Page 1  of  2