Student Participation in Community Service Activity / Chapter 5
The Youth Civic Involvement (CI) component of the NHES:96, which is the primary basis of this report, employed a sample of students in grades 6 through 12. Up to three instruments were used to collect information on the school and family experiences of these students. A set of screening items (Screener), administered to a member of the household age 18 or older, was used to determine whether any children of the appropriate ages or grades lived in the household, to collect information on each household member, and to identify the appropriate parent/guardian respondent for the sampled child. For sampling purposes, children residing in the household were grouped into younger children (age 3 through grade 5) and older children (grades 6 through 12). If one child age 3 to 5th grade resided in the household, an interview was conducted about that child; if there were multiple children in this range, one child was sampled with equal probability. Similarly, if one child in 6th through 12th grade resided in the household, an interview was conducted about that child; if there were multiple children in this range, one child was sampled with equal probability. Up to two children could have been selected from a household, one in the lower age/grade range and one in the higher grade range.
For households with youth in 6th through 12th grade who were sampled for the survey, a Parent and Family Involvement in Education/Civic Involvement (PFI/CI) interview was conducted with the parent/guardian most knowledgeable about the care and education of the youth, usually the child's mother. Following completion of that interview and receipt of parental permission, a Youth CI interview was conducted with the student. This report was based on the responses of these students, and with few exceptions all variables used in the report are on the Youth CI public data file. The analysis includes one item taken from the Parent PFI/CI data file: the student's academic performance. Two variables were created from the 1990 Decennial Census (STF 3B data file) by matching the respondent's ZIP code to the census data. These items are the percentage of households below poverty and the percentage of households that are owner occupied. These variables are contained in the NHES:96 Household & Library restricted data file, which can be obtained from NCES under a special licensing agreement. More information about the adult, parent, and youth data can be found in the National Household Education Survey of 1996, Data File User's Manual, Volumes I-V (Collins et al. forthcoming).
For the NHES:96, item non response (the failure to complete some items in an otherwise completed interview) was very low. Most items in used in this analysis have response rates of 98 percent or more as shown in exhibit 1. Items in this report that had a response rate of less than 98 percent are number of weeks and number of hours per week the student performs community service, whether the school arranges and/or requires student community service, whether the student will participate in community service later this year or next year, whether the student participated in student government, and household income. Through a procedure known as "hot-deck" imputation (Kalton and Kasprzyk 1986), responses were imputed for missing values (i.e., "don't know" or "refused," for items not specifically designated to have those as legitimate response categories, or "not ascertained"). As a result, no missing values remain.
----------------------------------------------------------------------------- Item Number response Variable Label eligible rate ----------------------------------------------------------------------------- SEX S6-GENDER AT SCREENER 8,043 99.98% RACE SX21-RACE 8,043 99.52% HISPAN SX22-HISPANIC 8,043 99.45% HOWNHOME SX27-OWN, RENT HOME/OTHR ARRNGMNT 8,043 99.88% HINCMRNG SX33- TOTAL HH INCOME RANGE 8,043 94.89% HINCOME SX33- TOTAL HH INCOME RANGE 2 8,043 91.35% CSPEAK PA3-LANG CHLD SPEAKS MOST AT HOME 20,792 100.00% GRADE PB4-GRADE/YR CHLD IS ATTENDING 19,135 100.00% SPUBLIC PD1-CHLD ATTNDS PUBL/PRIV SCH 19,343 99.86% SCHOICE PD3-SCH ASSIGNED OR CHOSEN 7,130 99.99% SEGRADES PE3-CHLD'S GRADES ACROSS ALL SUBJECTS 16,151 99.00% MOMGRADE PL3-HIGHEST GRADE MOM COMPLETED 20,026 99.29% DADGRADE PM3-HIGHEST GRADE DAD COMPLETED 15,825 98.75% PRREPGOV YB2-SERVED/WORKED IN STUDENT GOVT 6,494 91.98% PRSCHACT YB3-PARTICIPATED IN SCH ACTIVITIES 7,940 99.87% PRGRPACT YB4-PARTICIPATED OUT-OF-SCH ACTIVITIES 8,043 99.93% PRWORK YB5-WORKS FOR PAY 8,043 99.91% SACTY YC1-DOES COMMTY SERVICE ACTY 8,043 99.69% SAREG1 YC4-SERVICE ACTIVITY #1 SCHEDULE 3,996 99.60% SAWKS1 YC5-FREQ OF SERVICE ACTIVITY #1 1,717 96.85% SAWKSNU1 YC5OV-NUM WKS FOR SERV ACTY #1 1,162 96.21% SAHRS1 YC6-HRS/WK FOR SERV ACTY #1 1,717 97.32% SAHRSNU1 YC6OV-NUM HRS/WK FOR SERV ACTY #1 1,691 96.69% SAREG2 YC4-SERVICE ACTIVITY #2 SCHEDULE 1,557 99.17% SAWKS2 YC5-FREQ OF SERVICE ACTIVITY #2 623 97.75% SAWKSNU2 YC5OV-NUM WKS FOR SERV ACTY #2 434 97.70% SAHRS2 YC6-HRS/WK FOR SERV ACTY #2 623 98.56% SAHRSNU2 YC6OV-NUM HRS/WK FOR SERV ACTY #2 606 98.02% SAREG3 YC4-SERVICE ACTIVITY #3 SCHEDULE 458 99.13% SAWKS3 YC5-FREQ OF SERVICE ACTIVITY #3 217 95.85% SAWKSNU3 YC5OV-NUM WKS FOR SERV ACTY #3 140 97.14% SAHRS3 YC6-HRS/WK FOR SERV ACTY #3 217 96.77% SAHRSNU3 YC6OV-NUM HRS/WK FOR SERV ACTY #3 211 95.73% SAARRSER YC8-SCH ARRANGES SERV ACTIVITIES 7,940 93.61% SAREQSER YC9-SCH REQUIRES SERV ACTY 7,940 92.49% SATALK YC11-TALK IN CLASS/GRP ABT SERV ACTY 3,956 99.32% SAJOURNAL YC12-REQUIRED TO WRITE ABT SERV ACTY 3,956 99.54% SAGRADE YC13-ACTIVITY FOR A GRADE IN CLASS 3,956 99.04% SASCHLYR YC14-WILL DO SERV ACTY LATER THIS SCH YR 4,047 85.37% SANEXTYR YC15-WILL DO SERV ACTY NEXT YR 8,043 87.87% SASERVC YC17-FAM PARTICIPATES COMMTY SERV 8,043 98.45% -----------------------------------------------------------------------------NOTE: The following variables were also used in the report analyses: CENREG, ZIP18POV, and ZIPOWNED. These were not questionnaire variables but rather variables derived from respondents' telephone area codes or ZIP codes.
The sample of telephone households selected for the NHES:96 is just one of many possible samples that could have been selected. Therefore, estimates produced from the NHES:96 sample may differ from estimates that would have been produced from other samples. This type of variability is called sampling error because it arises from using a sample of households with telephones, rather than all households with telephones.
The standard error is a measure of the variability due to sampling when estimating a statistic. Standard errors can be used as a measure of the precision expected from a particular sample. The probability that a complete census count would differ from the sample estimate by less than 1 standard error is about 68 percent. The chance that the difference would be less than 1.65 standard errors is about 90 percent, and that the difference would be less than 1.96 standard errors, about 95 percent.
In addition to properly weighting the responses, special procedures for estimating the statistical significance of the estimates were employed because the data were collected using a complex sample design. Complex sample designs, like that used in the NHES, result in data that violate some of the assumptions that are normally required to assess the statistical significance of the results. Frequently, the sampling errors of the estimates from the survey are larger than would be expected if the sample was a simple random sample and the observations were independent and identically distributed random variables.
Replication methods of variance estimation were used to reflect the actual sample design used in the NHES:96. A form of the "jackknife" replication method (Wolter 1985) was used to compute the standard errors for estimates presented in this report. The jackknife methods were used to estimate the precision of the estimates of the reported national totals, percentages, and regression parameters. The idea behind replication methods is to form subsamples, or replicates, and then calculate the estimate of interest from the full sample as well as each replicate. The variation among the replicate estimates is used to estimate the variance for the full sample. To form the replicates for the jackknife method used in this analysis, 80 subsets of telephone numbers were identified. These subsets were constructed to be approximately equal in size, with each subset resembling the full sample. Replicates were formed by deleting one subset at a time and adjusting the weights of units (households or persons) in the other subsets accordingly.
Standard errors for all of the estimates are presented. These standard errors can be used to produce confidence intervals. For example, an estimated 26 percent of students reported regular participation in a service activity. This figure has an estimated standard error of 0.6. Therefore, a 95 percent confidence interval for the percentage of students reporting regular participation in a service activity is approximately 25 to 27 percent.
To test the differences between two categories (e.g., 6th through 8th graders versus 9th and 10th graders), Student's t statistic was employed, using unbiased estimates of sampling errors derived by the replication methods mentioned above. As the number of comparisons at the same significance level increases, it becomes more likely that at least one of the estimated differences will be significant merely by chance, that is, it will be erroneously identified as different from zero. Even when there is no statistical difference between the means or percentages being compared, there is a 5 percent chance of getting a significant F or t value due to sampling error alone. As the number of comparisons increases, the chance of making this type of error also increases.
A Bonferroni adjustment was used to correct significance tests for multiple comparisons. This method adjusts the significance level for the total number of comparisons made with a particular classification variable. All the differences cited in this report are significant at the 0.05 level of significance after a Bonferroni adjustment.
In general, it is difficult to identify and estimate either the amount of non sampling error or the bias caused by this error. In the NHES:96, efforts were made to prevent such errors from occurring and to compensate for them where possible. For instance, during the survey design phase, focus groups and cognitive laboratory interviews were conducted for the purpose of assessing respondent knowledge of the topics, comprehension of questions and terms, and the sensitivity of items. The design phase also entailed CATI instrument testing and an extensive, multi-cycle field test in which about 3,200 Screeners, over 950 parent interviews, about 300 youth interviews, and about 40 adult interviews were conducted.
An important non sampling error for a telephone survey is the failure to include persons who do not live in households with telephones. About 93.3 percent of all students in grades 1 through 12 live in households with telephones. Estimation procedures were used to help reduce the bias in the estimates associated with youth who do not live in telephone households. Cross-classifications of race/ethnicity by household income, census region by urbanicity, and home tenure by child's grade were used for forming cells for raking. For more information about coverage issues and estimation procedures, see Brick and Burke (1992) and Montaquila and Brick (forthcoming).
Second, the number of hours for each service activity was calculated. If a specific number of hours had been reported, that number was used. For the few students who gave another response (e.g., "the hours change from week to week"), the modal value for number of hours for the appropriate service activity (first, second, or third) was assigned. Modal values were assigned to less than 3 percent of students in any given activity.
Third, the total number of hours of service in the current school year was calculated. The number of hours per week was multiplied by the number of weeks for each service activity. The total number of hours of community service for each student was calculated by summing the hours for each of the three possible service activities.
---------------------------------------------------------------------------------------------------------- Coefficient/1/ Standard error P-value Odds ratio/2/ ---------------------------------------------------------------------------------------------------------- Intercept -3.12** 0.180 0.000 0.04** Student characteristics Female 0.14* 0.052 0.010 1.15* English spoken most at home by student 0.39** 0.127 0.003 1.48** Number of types of student activities 0.60** 0.037 0.000 1.82** Student in grades 11 or 12 0.21** 0.066 0.002 1.23** Student received mostly As 0.50** 0.079 0.000 1.65** Student received mostly Bs 0.20* 0.079 0.013 1.22* White 0.08 0.082 0.356 1.08 Family characteristics Adult performed community service 0.32** 0.066 0.000 1.38** Parent/guardian had college degree or higher 0.25** 0.073 0.001 1.28** High-income household 0.01 0.075 0.882 1.01 Number of parents in household -0.02 0.068 0.730 0.98 School characteristics Church-related school 0.58** 0.136 0.000 1.79** Other private school 0.13 0.230 0.586 1.14 School policies School required service -0.39 0.302 0.206 0.68 School arranged service 0.95** 0.093 0.000 2.59** School both required and arranged service 0.55 0.318 0.090 1.73 Community characteristics Percent of households in ZIP code owner occupied 60 percent or less -0.04 0.076 0.615 0.96 61 to 70 percent 0.05 0.094 0.570 1.05 Percent of families below poverty in ZIP code Less than 5 percent -0.09 0.117 0.427 0.91 5 to 9 percent 0.03 0.128 0.798 1.03 10 to 19 percent 0.01 0.109 0.930 1.01 ----------------------------------------------------------------------------------------------------------*p<.05. **p<.01.
1\ These coefficients differ slightly from those in table 11 because a different model is being used. Each category is expressed in relation to an omitted category for the variable, controlling for all other variables in the model. The applicable coefficients can be summed to estimate the probability that students with certain characteristics will participate in community service. For example, for a male student who spoke English the most at home, who participated in four other types of activities, who received mostly Bs, who had an adult in the household who performed community service, who attended a church-related private school, and who fit none of the other criteria, the sum is -3.12 + .39 +4(.60) + .20 + .32 + .58 = .77. Based on these characteristics, the probability of participation is 1/(1 + e-.77) = .68, or 68 percent.
2\ The odds ratio can be used to estimate the change in probability of a student participating in community service. An odds ratio greater than one indicates that students in the indicated group were more likely to perform community service than the omitted group. More specifically, suppose a student would ordinarily have a probability of participating of 68 percent, but that student is in a school that arranges service. The student's original probability can be expressed as an odds [68/(100-68) = 68/32 = 2.125]. The odds ratio of 2.59 for schools that arrange service can be multiplied by the original odds (2.59 times 2.125 = 5.50) to estimate the revised odds that the student would participate, based on the school's policy. To express the revised odds as a probability, one can apply the formula: probability = (odds)/ (1 + odds) = 5.50/6.50 = .85. Thus, by being in a school that arranges services, the student's probability of participating would increase from 68 percent to 85 percent. The amount of the increase that is associated with the school policy varies from one student to another depending on the student's original probability of participating.
SOURCE: U.S. Department of Education, , National Household Education Survey, spring 1996, Youth Civic Involvement component.
[11] Students' expectations of serving later in the school year might have greater accuracy in at least one situation. The survey focuses on 6th through 12th graders, but in the case of those 12th graders who were required to participate in community service in order to graduate, the graduation requirements may help to ensure that the expectations were met (though the students could have satisfied the requirement in an earlier year). To examine this possibility, the responses of 12th graders were looked at separately. The data indicate that 12th grade students who attended schools in which students were required to participate in community service tended to have already participated in community service earlier in the school year; only a small group of all of the students who said they expected to participate later actually met the conditions of also being 12th graders who were required to participate. Further, the same pattern appeared for these students as for students overall, with students who responded later in the school year not showing increased rates of participation, but rather showing small (but not statistically significant) increases in the rate of non participation