U.S. Department of Education 

Institute of Education Sciences 

NCES 2006-328 

The Adult Lives of At-Risk 

Students 

The Roles of Attainment and 

Engagement in High School 

Statistical Analysis Report 

The Adult Lives of At-Risk 

Students 

The Roles of Attainment and 

Engagement in High School 

Statistical Analysis Report 

March 2006 

U.S. Department of Education 

Institute of Education Sciences 

NCES 2006-328 

Jeremy D. Finn 

State University of New York at Buffalo 

Jeffrey Owings 

Project Officer 

National Center for 

Education Statistics 
U.S. Department of Education 

Margaret Spellings 

Secretary 

Institute of Education Sciences 

Grover J. Whitehurst 

Director 

National Center for Education Statistics 

Mark Schneider 

Commissioner 

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Suggested Citation 

Finn, J.D. (2006). The Adult Lives of At-Risk Students: The Roles of Attainment and Engagement in High 

School. (NCES 2006-328). U.S. Department of Education, Washington, DC: National Center for 

Education Statistics. 

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Content Contact 

Michael Ross 

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michael.ross@ed.gov 
 

The Adult Lives of At-Risk Students 

Executive Summary 

This report examines heterogeneity in young adult outcomes among students at risk for 

school failure due to low socioeconomic status (SES). It addresses the question: “Among 

students at risk due to status characteristics, what are the relationships of high school 

engagement and attainments with post-high school outcomes?” Two sets of outcomes are 

considered: entry and persistence in postsecondary education, and employment and income as a 

young adult. 

The report distinguishes between “status risk factors” such as SES and race/ethnicity and 

two other sets of risk factors: behavioral risk factors and academic risk factors. Behavioral risk 

factors are behaviors and attitudes closely related to learning, for example, attendance, paying 

attention to the teacher, completing coursework, and developing a sense that schooling is 

important to future life successes. These behaviors and attitudes are referred to as school 

engagement. “Disengagement” (e.g., not attending class, not completing assignments) can create 

severe impediments to learning. 

Academic risk factors are less-than-successful outcomes at one point in a school career 

that can interfere with the chances of success at later stages. In this report, high school grades, 

test scores, and graduation are viewed as high school attainments which, if not adequate, become 

risk factors with respect to further schooling and employment. These outcomes were used to 

classify the sample into one of three high school attainment groups. Bivariate and multivariate 

analyses examined the relationships among behavioral risk (engagement/disengagement), 

academic risk (high school attainments), and post-high school outcomes in the sample of status 

risk students. 

The National Education Longitudinal Survey of 1988 (NELS:88) survey conducted by 

the National Center for Education Statistics (NCES) provided a rich dataset for addressing these 

issues. It followed a national sample of eighth grade students longitudinally until they were 8 

years beyond high school age; it collected extensive information on participants at three points 

during secondary school (when most students were in eighth, tenth, and twelfth grades); and it 

collected information about participants’ postsecondary activities including civic participation, 

further schooling, and employment, at two further time points. In the course of the five-wave 

survey, high school dropouts were also identified and followed through young adulthood. 

The Sample: Status Risk Students 

In all, 3,502 eighth-grade NELS:88 students were identified who were at risk for 

educational failure according to “status risk characteristics,” namely, the socioeconomic status of 

the students’ home1

 and of the school attended.2 Students in the sample were those who 

1

 NELS:88 composite variable BYSES, constructed from parents’ reports of their education, occupations, and 

household income. In some cases, the information was drawn from other sources. 

2

 Percentage of students eligible for free or reduced-price lunches. 

iii 
 

 

 

Executive Summary 

participated in all five waves of data collection and whose families and schools were both in the 

lower halves of the respective SES distributions. Approximately one-third (33 percent) of the 

five-wave cohort of 10,827 students was at risk by these criteria. Compared to students not at 

risk, status risk students were disproportionately minority (44 percent compared to 20 percent),3 

attended urban and rural public schools (71 percent compared to 49 percent), were from nonEnglish-speaking homes (20 percent compared to 9 percent), and not living with both biological 

parents (44 percent compared to 28 percent). 

Three Attainment Groups/Academic Risk 

Students were classified into one of three groups based on test scores, grades, and 

graduation status—successful completers, marginal completers, and noncompleters. Successful 

completers (21 percent) received acceptable grades in tenth grade,4

 attained reasonable scores on 

reading and mathematics standardized achievement tests in eighth and tenth grades,5

 and 

graduated on time from high school. Modest achievement criteria were used on the assumption 

that these represent noteworthy accomplishments for students with multiple risk factors. 

Nevertheless, approximately one in five students at risk due to a low-SES home and low-SES 

school passed their courses, received acceptable test scores, and graduated from high school on 

time. 

An additional 52 percent of status risk students were classified as marginal completers. 

Either their self-reported grade point average fell below the criterion of acceptability, their test 

scores fell below the cutoff for success used in the analysis, or both. All marginal completers 

graduated from high school,6

 despite receiving lower grades and/or test scores. 

The remaining 27 percent of status risk students left high school without graduating, and 

were referred to as noncompleters. They were viewed as being at high academic risk with 

respect to further endeavors. 

Attainment groups were compared to see if academic risk was related systematically to 

postsecondary education and employment. The specific outcomes were 

• entering a postsecondary program of study; 

• the number of credits accrued, for those who entered postsecondary education; 

• attaining a postsecondary certificate or degree, for those who entered; 

• being employed at the time of the fourth NELS:88 follow-up in year 2000;7 

• an index of consistent employment during 1997–1999; and 

• annual income in 1999, for those who were employed. 

3

 Asian/Pacific Islander students were the only minority group to constitute a greater percentage of non-risk students 

than of at-risk students. 

4

 Grade point average of “about half B’s and half C’s” or better, according to student report of grades. 5

 One-fourth of a standard deviation below the mean (X ̄ – 1/4 S) in reading/mathematics in eighth and tenth grades, 

or better; also used in Finn and Rock (1997).

6

 Graduated on time (90 percent), or else by the time of the third NELS:88 follow-up (1994). 

7

 Most participants were 26 years of age. 

iv 
 

 

 

 

 

The Adult Lives of At-Risk Students 

Behavioral Risk: School Engagement/Disengagement 

Engagement in school is “the attention, interest, investment, and effort students expend in 

the work of school” (Marks 2000, p. 155). Status risk students were scored on four measures of 

engagement, based on theory and prior research (e.g., Finn 1989, 1993; Rumberger and Larson 

1998; Wehlage et al. 1989; Voelkl 1997). They included three measures of behavioral 

engagement, constructed from the NELS:88 student and teacher questionnaires in tenth grade: a 

composite measure of absenteeism and tardiness, a composite measure of the student’s 

participation in learning activities, and a count of extracurricular activities in which the student 

participated. A fourth measure, indicative of affective engagement, was the student’s 

perceptions of the usefulness of school subjects in endeavors outside of school. This indicator 

was formed from questions about the utility of four school subjects, asked in the eighth-grade 

student questionnaire. 

The connections of school engagement/disengagement with postsecondary education and 

employment were examined to determine: (1) if behavioral risk is systematically related to 

postsecondary education and employment of young adults at risk due to status characteristics, 

and (2) if behavioral risk is related to postsecondary education and employment, above and 

beyond the effects of school attainments (academic risk).8 

The Analyses 

The relationships of attainment groups and engagement with postsecondary education 

and employment were first examined one variable at a time. Next, multivariate regression and 

logistic regression analyses were conducted that included other characteristics of the students and 

schools (i.e., student gender, race/ethnicity, school urbanicity). Each measure of postsecondary 

schooling (entering, accruing credits, completing a program of study) and each measure of 

employment (current employment, consistent employment, income) was considered in turn. In 

the analysis of postsecondary education, the type of program entered (less-than-2-year, 2-year, 4-

year) was also included in the analysis. In the analysis of employment outcomes, completing a 

postsecondary program of study was also included in the regressions. 

All statistical significance tests carry with them the possibility of error. Results found to 

be “statistically significant” may appear significant in the sample under study, but may not 

replicate in other samples drawn from the larger population. The usual practice is to limit the 

probability of this type of error (called a type I error) to a small value before interpreting the 

statistical outcomes. In this analysis, the probability of a type I error was set at .05.9 

Tests of significance reveal whether a relationship between variables is statistically 

reliable, but tell little about whether effects are weak or strong. Thus, strength-of-effect 

measures were obtained from the regressions for all statistically significant effects. Particular 

8

 The same six outcomes were examined as in the attainment group analysis (listed on the preceding page). 9

 Of the many statistical tests presented in the report, it can be expected that approximately 5 percent that appear 

significant do not represent real nonzero differences; unfortunately, it is not possible to identify which tests those 

may be. 

v 
 

Executive Summary 

strength-of-effect measures (odds ratios, effect sizes, or standardized regression weights) were 

used depending on the measurement scales involved. 

Academic Risk: High School Attainments and Young Adult Outcomes 

A number of differences were found among attainment groups. In all, 61 percent of 

participants attended some form of postsecondary schooling, but this included 83 percent of 

successful completers, 68 percent of marginal completers, and 29 percent of noncompleters (see 

table A). Almost half of successful completers entered a 4-year college or university (48 percent 

of all successful completers), while a 2-year school was the most common choice for marginal 

completers and noncompleters (43 percent and 22 percent of all marginal completers and 

noncompleters, respectively). 

Persistence in postsecondary schooling, in the form of accruing credits and completing a 

program of study, differed among the three attainment groups. High school noncompleters, with 

the highest level of academic risk, stood out in each case. Noncompleters earned the fewest 

credits of the three groups;10 the mean number of credits earned by noncompleters who entered a 

postsecondary program was 17.0, compared to 49.4 credits for marginal completers and 87.8 

credits for successful completers. Noncompleters also had the lowest completion rates. Of 

status-risk students who entered postsecondary schooling, 35 percent of noncompleters finished a 

program of study, compared to 44 percent of marginal completers and 58 percent of successful 

completers. 

Eighty-four percent of status risk students were employed for pay in 2000. The 

percentage was lower for high school noncompleters (77 percent) than for successful completers 

(88 percent) or marginal completers (86 percent). Likewise, noncompleters were less likely than 

successful completers or marginal completers to have consistent employment over a 3-year 

period; the mean consistent employment index for noncompleters was 2.2, compared to 2.5 for 

marginal completers and 2.6 for successful completers (on a scale from 0.0 to 3.0). 

As postsecondary education increased, the differences among the groups in consistent 

employment diminished. Specifically, among status risk students who did not enter 

postsecondary education, successful completers had more consistent employment than did 

marginal completers, and marginal completers had more consistent employment than did 

noncompleters; the mean consistent employment index for noncompleters with no postsecondary 

education was 2.2, compared to 2.5 for marginal completers and 2.7 for successful completers. 

Among students who completed postsecondary programs of study, there were no discernable 

differences among the attainment groups in consistent employment. 

The Regression Analyses. Multivariate regression and logistic regression analyses were 

conducted to examine the relationship of academic risk with post-high school outcomes in a 

fuller context. The regressions included student and school characteristics in the analysis 

together with attainment groups. Separate regressions were performed for each outcome variable 

(entering postsecondary education, accruing credits, and completing a program of study; and 

current employment, consistent employment, and salary). 

10 Type of institution entered was taken into account in this analysis. 

vi 
 

The Adult Lives of At-Risk Students 

High School Attainment and Postsecondary Education. In each analysis, successful 

completers showed clear advantages over marginal completers and marginal completers showed 

advantages over noncompleters. Marginal completers, who graduated from high school despite 

their modest academic performance, had over five times the odds of entering a postsecondary 

program. Of those who entered postsecondary programs, marginal completers earned more 

credits than did noncompleters by approximately one-half (0.55) standard deviation, and were 

about one and two-thirds times more likely to complete a program of study they entered.11 

Noncompleters, who were at the highest level of academic risk, had the poorest outcomes on all 

three postsecondary attainments. 

Table A. High school attainment groups of status risk students, by postsecondary

 education and employment/income outcomes: 2000 

High school attainment group 

Successful Marginal 

1 2 3 Outcome completers completers Noncompleters 

Percent of status risk students 21.3 51.6 27.1 

Percent who entered a postsecondary institution4 83.3 67.6 29.2 

 < 2-year school 1.7 ! 6.0 5.0 

 2-year school 33.3 42.6 21.6 

 4-year school 48.3 18.9 2.7 ! 

Mean credits earned in postsecondary institutions5,6,7 87.8 49.4 17.0 

Percent completed postsecondary education6 57.6 44.2 35.0 

Percent currently employed (2000) 88.3 86.1 76.7 

Consistent employment mean 1997–19998 2.62 2.51 2.24 

! Interpret data with caution. Unweighted n < 30. 

1

In school in twelfth grade in 1992; "reasonable" scores on reading and mathematics tests; passing grades; 

graduated from high school. 

2

Test scores not "reasonable" and/or grades not passing; high school graduate. 

3

Dropped out by 1992, or not in twelfth grade in 1992 and did not earn diploma by age 20. 

4

Classified by type of first institution attended. Percentages based on total number of successful completers, 

marginal completers, and noncompleters in status risk sample. 

5

Credits earned from all schools attended. 

6

Percentage based on total number of successful completers, marginal completers, and noncompleters who 

entered postsecondary education. 

7

Response rate on this variable less than 85 percent. 

8

0.0 to 3.0 indicator of part-time/full-time employment 1997–1999. 

NOTE: Standard errors are given in appendix B, table B-A. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, National Education 

Longitudinal Study of 1988 (NELS:88/2000). 

11 These regressions included the type of institution entered. 

vii 
 

 

 

Executive Summary 

High School Attainment and Adult Employment/Income. There were no discernable 

differences among attainment groups in current employment or income at age 26 when the 

analyses included student demographics and postsecondary schooling. A small difference was 

found in consistent employment between marginal completers and noncompleters.12 Not 

graduating from high school was accompanied by less consistent employment over a 3-year 

period. 

The analyses for employment were repeated excluding the postsecondary education 

variable. In these analyses, attainment groups differed with respect to current employment, 

consistent employment, and income. It appears that the relationship between high school 

attainment and employment and income is attributable in whole or in part to postsecondary 

schooling.13 Successful completers more than marginal completers, and marginal completers 

more than noncompleters, enter postsecondary schooling and subsequently are more likely to be 

employed and have higher incomes as young adults. 

Behavioral Risk: School Engagement/Disengagement and Young Adult Outcomes 

Behavioral risk was represented by four measures of school engagement/disengagement: 

attendance, classroom behavior, extracurricular participation, and students’ perceptions of the 

usefulness of school subjects. On three of the four measures (all except usefulness), successful 

completers were more engaged than were marginal completers and marginal completers were 

more engaged than were noncompleters. Noncompleters—students with the highest level of 

academic risk—were also the least engaged. Differences among the attainment groups in 

attendance and classroom behavior were moderate to large (effect sizes from 0.40 to 0.82 

standard deviations). 

Several engagement variables were significantly correlated with postsecondary 

schooling. Attendance and classroom behavior were related to entering a postsecondary 

program, earning postsecondary credits, and completing a program of study, with correlations 

ranging from 0.13 to 0.28. Extracurricular participation was related to all three postsecondary 

variables, but the correlations were small (from 0.08 to 0.18). The perceived usefulness of 

school subjects was related weakly to credits earned in postsecondary school (r = 0.09), but not 

to entering or completing a postsecondary program. 

Attendance was related to current employment, consistent employment, and income for 

the total sample, but the correlations were also small (0.08 to 0.13). Neither classroom behavior 

nor extracurricular participation was measurably related to any of the three key 

employment/income measures. The usefulness of school subjects was negatively related to 

consistent employment for some subgroups of students (correlations of -0.09 and -0.20). 

In general, behavioral risk, in the form of school engagement/disengagement, was related 

to measures of postsecondary education and related weakly, if at all, to employment and income. 

These relationships were examined in a fuller context in the regression analyses. 

12 Approximately one-fourth (0.27) of a standard deviation. 

13 The one outcome not attributable to postsecondary schooling is the relationship of high school noncompletion 

with consistent employment, which remained significant even with postsecondary education in the regressions. 

viii 
 

 

 

 

The Adult Lives of At-Risk Students 

The Regression Analyses. The relationship of behavioral risk with postsecondary 

education and employment was tested in regressions and logistic regressions with other 

characteristics of the students and schools also included in the analysis (i.e., student gender, 

race/ethnicity, school urbanicity, and, for employment outcomes, the amount of postsecondary 

education completed). Separate regressions were performed for each outcome variable (three 

indicators of postsecondary education and three indicators of employment/income). 

Engagement and Postsecondary Education. School engagement was related to all three 

postsecondary outcomes (entering a postsecondary program, accruing credits, and completing a 

program of study) when student and school background characteristics were included in the 

model. Attendance and classroom behavior had the most consistent relationships with 

postsecondary schooling. Both were associated with an increase in the likelihood of entering a 

postsecondary program,14 and in credits earned and the likelihood of completing a program 

among those who entered.15 Poor attendance and classroom behavior (disengagement) were 

associated with reduced postsecondary outcomes in each case. 

Participation in extracurricular activities was positively related to the likelihood of 

entering a postsecondary program.16 No relationship was detected between extracurricular 

activities and persistence in postsecondary schooling, however, as reflected in credits earned or 

program completion. The perceived usefulness of school subjects, rated in eighth grade, was not 

measurably related to any postsecondary outcome. 

Engagement and Employment/Income. As a set, the four engagement measures were related 

to current employment in 2000 and to consistent employment when other student and school 

characteristics were included in the analysis. Engagement was not discernably related to income. 

In terms of specific engagement variables, attendance was positively related to consistent 

employment,17 and classroom behavior was positively related to current employment.18 An 

analysis of interactions revealed that both relationships characterized some groups of status risk 

students more than others. 

The perceived usefulness of school subjects was negatively related to current 

employment in 2000 and to consistent employment. The usefulness measure was unique in other 

ways: it was assessed in eighth grade, and, although the internal consistency of the measure was 

adequate,19 the correlations between usefulness and the other engagement measures were low,20 

and it was not significantly related to postsecondary schooling. Follow-up regressions were 

14 Odds ratios = 1.23 for attendance and 1.54 for classroom behavior. For attendance, a 1 standard deviation 

improvement in attendance was associated with a 23 percent increase in the odds of entering a postsecondary school. 

This would be roughly the difference between two students, one of whom scores at about average on the 

absenteeism-tardiness index and one who scores at about the 25th percentile. 15 Standardized regression coefficients for credits earned were small (0.14 for attendance; 0.12 for classroom 

behavior). Odds ratios for program completion were 1.22 and 1.19, respectively. 

16 Odds ratio for the full sample = 1.39. This relationship was strongest for non-Hispanic White students. 17 The standardized regression coefficient was small (0.08). 18 Odds ratio = 1.31. 

19 Coefficient α = 0.71. 20 Correlations from 0.05 to 0.11. 

ix 
 

Executive Summary 

conducted omitting the usefulness scale. In these, there was no discernable relationship of the 

engagement measures with employment or income. 

In sum, behavioral risk, as reflected in a set of four engagement measures, is related to 

employment in the years following high school, but the relationship was not found for all 

engagement measures, for all employment measures, or for all groups of students. The 

relationship was explored further in analyses that included both attainment groups and high 

school engagement. 

Engagement Above and Beyond Attainment? A final set of analyses was conducted in which 

both high school attainment (academic risk) and engagement (behavioral risk) were included in 

the regressions. The purpose was to determine if the relationship of engagement and adult 

outcomes is attributable to the relationship of engagement with high school attainments, which in 

turn are related to later outcomes, or if engagement is related to post-high school 

accomplishments through other mechanisms not studied. Analyses were conducted with both 

postsecondary schooling and employment as outcomes. 

Engagement was related to all three postsecondary education variables—entering a 

postsecondary program, the number of credits earned, and completing a postsecondary 

program—above and beyond its relationship with high school attainment. The same engagement 

variables were related to postsecondary schooling as in the analysis without attainment, although 

several specific relationships were reduced to nonsignificance. In the full 

engagement/attainment analysis, attendance was positively related to credits earned, classroom 

behavior was positively related to entering postsecondary education and credits earned, and 

extracurricular participation was positively related to entering a postsecondary program. Again, 

in each instance, disengagement was associated with poorer postsecondary outcomes. 

Engagement was not discernably related to any of the employment/income measures at 

age 26 when attainment groups were included in the analysis. 

Conclusions 

This report supports three general conclusions: First, among students at risk due to status 

risk factors, there is diversity in high school accomplishments and in post-high school years in 

terms of further education and employment and income. Many status students at risk enter 

postsecondary programs (61%), and about one-half of those complete postsecondary studies 

(47%) including 2-year programs and 4-year programs leading to BA degrees; many are 

employed for pay at age 26 (84%) and/or maintain consistent employment as young adults. 

Second, academic risk factors, that is, less-than-successful outcomes in high school, are 

related to the likelihood of attaining further education and consistent employment as young 

adults. Among status risk students, attaining reasonable test scores, passing grades, and 

graduating from high school are associated with greater higher postsecondary attainments. High 

school noncompleters were least likely to enter or persist in postsecondary schooling, and were 

less likely than graduates to have sustained employment over a 3-year period. 

x 
 

The Adult Lives of At-Risk Students 

Third, behavioral risk factors, in the form of engagement/disengagement in high school, 

are related to postsecondary schooling among status risk students. Among status risk students, 

coming to class on time, attending classes regularly, working hard in class, and completing 

assignments were related to the likelihood of entering a postsecondary program and earning 

postsecondary credits. Extracurricular participation in high school was also related to entering a 

postsecondary program. Status risk students who were the least engaged in these ways 

(disengaged) were less likely to enter or persist in a postsecondary program of study. 

Limitations 

NELS:88 provided a particularly rich data set for this analysis. Nevertheless, certain 

design features limit the strength of the conclusions that can be drawn.21 First, a broad-based 

survey such as NELS:88 is designed to serve many purposes but may not address particular 

questions in depth. In this analysis, the measures of school engagement assessed key aspects of 

the construct discussed in previous research (Finn 1993; Marks 2000; Connell, Spencer, and 

Aber 1994). However, the affective component of engagement was represented by a single 

measure (usefulness of school subjects), assessed in eighth grade. The internal consistency of 

the measure was adequate, but a broader spectrum of affective measures, assessed later in the 

high school years, would have been desirable. Conclusions about school engagement are limited 

to the four aspects of engagement that could be assessed using NELS:88 data. 

Second, many of the NELS:88 measures are based on participants’ self-reports. The 

potential biases in self-report data are recognized widely (Tanur 1992; Paulhus 1991). In a 

survey similar to NELS:88 in many ways, researchers found that the correlations of self-reports 

with factual data (e.g., school grades) were highest, but tended to decrease with the passage of 

time (Fetter, Stowe, and Owings 1984). The correlations with less factual information (e.g., 

parents’ aspirations for students) were somewhat lower. In the analyses reported here, every 

attempt was made to use the most objective data available for each measure. 

In some instances, this principle resulted in reduced sample sizes for particular 

variables.22 Specifically, the number of credits earned in postsecondary education was taken 

from college transcripts, but was available only for 85 percent of those who entered 

postsecondary programs. Teachers’ ratings of student attendance and classroom behavior were 

only available for 87 percent of the eligible students.23 In all analyses, cases missing a value 

were omitted from the analysis of that particular variable. As a result, a degree of reporting bias 

may confound some of the statistical relationships given in this report. 

Third, NELS:88 did not involve any experimental intervention. Although it is desirable 

to know if high school attainments or engagement is causally connected to adult outcomes, 

survey methodology only provides a conclusion that events do or do not occur in the same 

individuals. 

21 More detailed technical issues are discussed in appendix A, for example, incomplete data on some variables. 22 See table A-1 in appendix A for response rates. 23 That is, those still in school at the time of the ratings. 

xi 
 

Executive Summary 

Further, in analyzing nonexperimental data, there is always the possibility of bias in the 

results due to “specification error,” that is, the omission of important variables from the 

regressions. For example, the analysis controlled home background to some extent by excluding 

all students but those from low-SES homes, and by including race/ethnicity and urbanicity in the 

regressions. Specific home processes that may vary within the group of low-SES families were 

not considered. Likewise, school policies and practices that may affect student engagement and 

attainment were not examined in these analyses. The results should be viewed within the 

constraints imposed by this limitation. 

xii 
The Adult Lives of At-Risk Students 

Acknowledgments 

The author would like to acknowledge the assistance of a number of people in preparing 

this report. Jeffrey Owings of the National Center for Education Statistics (NCES) and Leslie 

Scott of the Education Statistics Services Institute (ESSI) encouraged that the project be 

undertaken. Leslie provided answers to many questions about content and form and reactions to 

an early draft of the report. Her comments were especially helpful. 

Several people provided important technical support. Julia Wilkins of the University at 

Buffalo did an excellent job of finding and integrating background research for the introduction, 

as did Gina Pannozzo and Michelle McClellan. Cliff Adelman of the Institute for Education 

Sciences (IES), the U.S. Department of Education, provided well-informed feedback on issues 

related to NELS:88 and postsecondary schooling. Jon Cohen of American Institutes for 

Research was helpful in answering questions about data analysis with AM software, and even, on 

at least one occasion, augmented the software in response to our queries. Steven Ingels 

(Research Triangle Institute) provided insightful answers to questions that helped to define the 

foci of this analysis. 

A team of reviewers from NCES and ESSI made many valuable recommendations; these 

have resulted in a clearer report. The author is grateful to Lisa Hudson, Devon Carlson, Kerry 

Gruber, Geeta Kotak, Bruce Taylor, and Zeyu Xu for their thorough reading and commenting on 

a previous version. Bruce was especially helpful on issues of statistical reporting. Anne Ricciuti 

of IES and two anonymous reviewers provided feedback on conceptual issues that helped to 

produce the final version of the report. 

Finally, Reva M. Fish of the University at Buffalo (SUNY) deserves special recognition 

for the support she provided this project. Her contributions impacted every section of this report. 

She worked tirelessly from beginning to end to assist with data analysis, the preparation of 

tables, appendices, and references, and in locating technical and substantive information for the 

study. She did it all efficiently and well, always going one step beyond. 

xiii 

The Adult Lives of At-Risk Students 

Contents 

Executive Summary............................................................................... iii 

The Sample: Status Risk Students.................................................................................... iii 

The Analyses............................................................................................................... v 

Conclusions................................................................................................................. x 

Limitations................................................................................................................... xi 

Acknowledgments................................................................................. xiii 

Contents............................................................................................... xv 

Tables.................................................................................................. xvii 

1. Introduction....................................................................................... 1 

Status Risk Factors and School Outcomes......................................................................... 1 

Status Risk Factors and Post-High School Outcomes............................................................ 4 

Behavioral Risk Factors: Engagement/Disengagement in School............................................ 8 

Overview of This Report................................................................................................. 11 

2. Data Source and Methods of Analysis................................................... 15 

Participants in the Analysis............................................................................................. 16 

Variables..................................................................................................................... 17 

Data Analysis............................................................................................................... 22 

3. High School Attainments and Young Adult Outcomes............................ 27 

Status Risk and Non-Status Risk Students......................................................................... 27 

Status Risk Students...................................................................................................... 30 

4. Behavioral Risk: School Engagement/Disengagement and Young 

Adult Outcomes.................................................................................... 47 

Engagement/Disengagement and Attainment Groups........................................................... 47 

Engagement/Disengagement and Adult Outcomes............................................................... 49 

5. Summary of Results........................................................................... 67 

Academic Risk and Young Adult Outcomes........................................................................ 68 

Behavioral Risk and Young Adult Outcomes....................................................................... 69 

References........................................................................................... 71 

Appendixes

Appendix A.................................................................................................................. A-1 

Appendix B.................................................................................................................. B-1 

Appendix C.................................................................................................................. C-1 

Appendix D.................................................................................................................. D-1 

xv 
 

 

Tables 

Tables 

Table 

Executive Summary and Chapters 

Page 

A High school attainment groups of status risk students, by postsecondary 

education and employment/income outcomes: 2000..................................... vii 

1 Status risk sample, by demographic characteristics: 1988............................... 17 

2 Percentage distribution of status risk sample and non-status risk students, 

by selected demographic and school characteristics: 1988.............................. 28 

3 Status risk sample and non-status risk students, by selected home-related 

characteristics: 1988.......................................................................... 29 

4 Percentage distribution of status risk students in each attainment group, 

by gender and race/ethnicity: 1988......................................................... 31 

5 High school attainment groups of status risk students, by postsecondary 

education outcomes: 2000........................................................ ........... 32 

6 High school attainment groups of status risk students, by employment/ 

income outcomes: 2000...................................................................... 35 

7 Regression results for high school attainment groups and postsecondary 

education outcomes: 2000................................................................... 37 

8 Strength-of-effect measures for high school attainment groups and 

postsecondary education outcomes: 2000................................................. 39 

9 Regression results for high school attainment groups and employment/ 

income outcomes: 2000...................................................................... 42 

10 Strength-of-effect measures for high school attainment groups and 

employment/income outcomes: 2000....................................................... 44 

11 Mean engagement measures of high school attainment groups:1990.................. 48 

12 Correlations of engagement measures with postsecondary education 

and employment/income outcomes: 2000................................................. 50 

13 Regression results for engagement and postsecondary education 

outcomes: 2000............................................................................... 52 

14 Strength-of-effect measures for engagement and postsecondary 

education outcomes: 2000................................................................... 54 

15 Regression results for engagement and employment/income 

outcomes: 2000............................................................................... 57 

16 Strength-of-effect measures for engagement and employment/income 

outcomes: 2000............................................................................... 60 

17 Strength-of-effect measures for postsecondary education outcomes, by 

engagement factors and attainment groups: 2000........................................ 63 

18 Strength-of-effect measures for employment/income outcomes, by 

engagement factors and attainment groups: 2000........................................ 64 

19 Summary of regression analyses of school attainment and of school 

engagement on postsecondary education and employment/income: 2000............ 68 

xvi 
 

 

 

The Adult Lives of At-Risk Students 

Appendix A Table 

A-1 Response rates of variables used in descriptive or regression 

Analyses........................................................................................ A-7 

. 

Appendix B Tables 

B-A Standard errors for table A: High school attainment groups of 

status risk students, by postsecondary education and employment/ 

income outcomes:2000....................................................................... B-1 

B-1 Standard errors for table 1: Status risk sample, by demographic 

characteristics: 1988.......................................................................... B-2 

B-2 Standard errors for table 2: Percentage distribution of status risk and 

non-status risk students, by selected demographic and school 

characteristics: 1988.......................................................................... B-3 

B-3 Standard errors for table 3: Status risk sample and non-risk students, 

by selected home-related characteristics: 1988........................................... B-4 

B-4 Standard errors for table 4: Percentage distribution of status risk students 

in each attainment group, by gender and race/ethnicity: 1988.......................... B-5 

B-5 Standard errors for table 5: High school attainment groups of status risk 

students, by postsecondary education outcomes: 2000.................................. B-6 

B-6 Standard errors for table 6: High school attainment groups of status risk 

students, by employment/income outcomes: 2000....................................... B-7 

B-7 Standard errors for table 11: Mean engagement measures of high 

school attainment groups: 1990............................................................. B-8 

Appendix C Tables 

C-1 Unweighted sample sizes of attainment groups, by demographic 

characteristics: 1988.......................................................................... C-1 

C-2 Strength-of-effect measures for table 5: High school attainment 

groups, by postsecondary education outcomes: 2000.................................... C-2 

C-3 Strength-of-effect measures for table 6: High school attainment 

groups, by employment/income outcomes: 2000........................................ C-3 

C-4 Strength-of-effect measures for table 11: Mean engagement 

measures of high school attainment groups: 1990....................................... C-4 

C-5 Regression results for postsecondary education outcomes by engagement factors 

and attainment groups: 2000............................................................... C-5 

C-6 Regression results for employment/income outcomes, by engagement 

factors and attainment groups: 2000....................................................... C-6 

Appendix D Table 

D-1 Variables Used in This Report.............................................................. D-2 

xvii 

 

 

The Adult Lives of At-Risk Students 

1. Introduction 

The purpose of this report is to examine variation in outcomes among students at risk for 

school failure. The report addresses the question: “Among students at risk due to low 

socioeconomic status (SES), what are the relationships of high school engagement and 

attainments with post-high school outcomes?” Two sets of outcomes are considered: entry and 

persistence in postsecondary education, and employment and income as a young adult. 

Educational risk can be defined in terms of demographic or historical characteristics 

associated with an increased likelihood of adverse outcomes. These “status risk factors” include 

being a minority student, coming from a low-income home, or coming from a home in which 

English is not the primary language (Braswell et al. 2001; Grigg et al. 2003; Kaufman, Alt, and 

Chapman 2001; Persky, Daane, and Jin 2003). This report examines family SES (comprising 

parents’ education, parents’ occupations, and household income) and school SES (percent of 

students from low-income homes) as status characteristics associated with low grades, poor test 

scores, and leaving school without graduating. 

Risk has also been defined in terms of “behavioral risk factors” that create further 

impediments to learning.1

 These behaviors may include skipping classes or school, failing to pay 

attention to the teacher or to complete required coursework, and failing to develop a sense that 

schooling is important to future life success. In their positive forms (good attendance, 

completing coursework, positive attitudes, participation in the broader school environment), this 

cluster of behaviors has been referred to as engagement in school, and in their negative forms as 

disengagement (e.g., Finn and Rock 1997; Marks 2000; National Research Council and Institute 

of Medicine 2004; Rumberger and Larson 1998; Wehlage et al. 1989). This report examines the 

relationship of engagement/disengagement in high school to grades, test scores, and graduation, 

and to postsecondary education, employment, and income. 

A third set of risk factors is also examined in this report. “Academic risk factors”2 are 

less-than-successful outcomes at one point in a school career that can interfere with chances of 

success at later stages. The academic risk factors considered in this report are accruing a history 

of poor grades, low test scores, and leaving high school without graduating. These are examined 

as possible antecedents of school- and work-related difficulties in post-high school years. 

Status Risk Factors and School Outcomes 

Research shows that students’ economic status is associated with the likelihood of 

success in school. Whether measured directly by parents’ income or by characteristics such as 

parents’ occupations or the number of wage earners, students from low-income families are less 

1

 The distinction between status and behavioral risk factors is used in both educational and medical contexts (see 

Finn 1993). 

2

 A phrase first used by Croninger and Lee (2001). 

1 
 

2 

1. Introduction 

likely to obtain high grades and test scores, less likely to graduate with their entering class, and 

more likely to experience behavior problems than are students from higher-income families. 

For example, assessments of student performance in reading, writing, and mathematics 

conducted by the National Assessment of Educational Progress (NAEP) all show this pattern. 

Using free-lunch eligibility as an indicator of household income,3 40 percent of free-lunch 

eighth-grade students scored “at or above basic” in mathematics in 2000, compared to 76 percent 

of non-free-lunch students (Braswell et al. 2001). The percentages of students “at or above 

proficient” were 10 percent and 35 percent, respectively. The 2002 NAEP reading assessment 

reported that 60 percent of free-lunch eighth-grade students scored “at or above basic,” compared 

to 84 percent of non-free lunch students (Grigg et al. 2003). The percentages of students “at or 

above proficient” were 17 percent and 40 percent, respectively. And the 2002 NAEP writing 

assessment reported that 74 percent of free-lunch eighth-grade students scored “at or above 

basic,” compared to 91 percent of non-free lunch students (Persky, Daane, and Jin 2003). The 

percentages of students “at or above proficient” were 16 percent and 39 percent, respectively. 

A noteworthy feature of this difference is its pervasiveness. Similar achievement gaps 

are reported at every grade, at all performance levels, and for every subject in each NAEP 

assessment wave that collected free-lunch data. Although the relationship of income with 

academic achievement fluctuates slightly over time, the basic finding of poorer performance by 

low-income students remains (Braswell et al. 2001; Grigg et al. 2003; National Center for 

Education Statistics 2003; Persky, Daane, and Jin 2003). 

Family income is also related to high school completion. In 2000, young adults in 

families with incomes in the lowest 20 percent of all family incomes were six times more likely 

than those from families in the top 20 percent of income to have dropped out of high school 

(Kaufman, Alt, and Chapman 2001). Ten percent (10 percent) of students from families in the 

lowest one-fifth of the income distribution dropped out of high school compared with 5 percent 

in the middle of the income distribution and 1.6 percent of students from families in the top twofifths. These findings are corroborated by statistics from communities with dropout rates above 

the national average. For example, a study of school children in Baltimore (Alexander, Entwisle, 

and Kabbani 2001) found that 60 percent of school children from families in the lower half of the 

income distribution left high school without a diploma, compared to 15 percent of those in the 

top quarter of the distribution.4 In Chicago, where over 85 percent of students are from lowincome homes,5

 the 2002 dropout rate was over two and one-half times greater than the statewide 

rate (Illinois State Board of Education 2002). 

Economic disadvantage is more common among minority racial/ethnic groups, who are 

thus at increased risk for educational failure. The poverty rates for racial/ethnic groups in 2001 

were White: 8 percent; Black: 23 percent; Hispanic: 21 percent; Asian/Pacific Islanders: 10 

percent; and Native American/Alaska Natives: 25 percent (U.S. Department of Commerce 2002). 

3

 Eligibility for the U.S. Department of Agriculture’s National School Lunch Program is based on household income 

for a family, and depends on the number of members. 

4

 Based on the distribution in the Baltimore sample. 

5

 Low income was defined by a list of criteria including families receiving public aid, children otherwise supported 

by public funds, or children eligible to receive free or reduced-price school lunches. 

2 
The Adult Lives of At-Risk Students 

These overall differences also characterize school-age populations. For example, in 2002, 

approximately 58 percent of Black eighth-grade students, 58 percent of Hispanic eighth-grade 

students, and 55 percent of Native American/Alaska Native eighth-grade students were eligible 

for free/reduced-price school lunches, compared to 31 percent of eighth-grade Asian/Pacific 

Islander students and 19 percent of White students (Grigg et al. 2003). 

And, like income, race/ethnicity is related to achievement and school completion. In both 

grades 8 and 12, White students outperformed Black and Hispanic students on NAEP tests of 

reading, writing, and mathematics (Braswell et al. 2001; Grigg et al. 2003; Persky, Daane, and 

Jin 2003). The racial gaps persisted from 1990 through 2000 with few discernable changes in 

magnitude. 

As a group, Asian/Pacific Islander students had NAEP scores that were as high as or 

higher than those of White students in all three subjects (Braswell et al. 2001; Grigg et al. 2003; 

Persky, Daane, and Jin 2003). In contrast, Native American/Alaska Native students have 

performed consistently below White and Asian/Pacific Islander students; their average 

performance was close to that of Hispanic students (Braswell et al. 2001; Grigg et al. 2003; 

Persky, Daane, and Jin 2003). 

Dropout rates also differ by race/ethnicity. Over recent decades, the dropout rates for 

Black and Hispanic young adults have consistently been higher than those for Whites (Kaufman, 

Alt, and Chapman 2001). In 2000, 7 percent of Whites between ages 16 and 24 had not 

completed high school compared to 13 percent of Blacks and 28 percent of Hispanics. The high 

rate for Hispanics is attributable in part to the large number of Hispanic immigrants: over 44 

percent of Hispanic young adults born outside of the U.S. had not completed high school. The 

dropout rate for Asian/Pacific Islander students was the lowest of all groups at 4 percent 

(Kaufman, Alt, and Chapman 2001). 

School Characteristics and Academic Outcomes 

Schools with high percentages of low-income or minority students also tend to have poor 

academic performance and high dropout rates. Title 1 participation, based on measures of 

poverty of the student body, is an indicator of economic status. According to recent NAEP 

assessments, in the eighth grade, 28 percent of students in Title 1 schools performed at the lowest 

level—below basic—on the writing assessment, compared to 12 percent of students in non-Title 

1 schools (Persky, Daane, and Jin 2003). In reading, 45 percent of students in Title 1 schools 

performed below basic, compared to 20 percent of students in non-Title 1 schools (Grigg et al. 

2003). In terms of dropout rates, studies show repeatedly that rates are higher in schools serving 

high percentages of minority students or those from low-income homes (e.g., Bryk and Thum 

1989; Lippman, Burns, and McArthur 1996; McNeal 1997; Rumberger and Thomas 2000; 

Wiatrowski, Gottfredson, and Roberts 1983). 

Schools with the most low-income students are often concentrated in urban communities. 

In 1999, the percentage of school-aged children living in poverty in the central cities of large and 

mid-size metropolitan areas were 24 percent and 20 percent, well above the overall poverty rate 

3 
 

4 

1. Introduction 

of 16 percent6 (Wirt et al. 2003). Achievement and graduation rates are consistently lower in 

urban schools (Braswell et al. 2001; Finn, Gerber, and Boyd-Zaharias 2005; Grigg et al. 2003; 

Hauser, Simmons, and Pager 2004; Persky, Daane, and Jin 2003; Rumberger and Thomas 2000). 

One study used data from several national surveys to examine the associations of poverty and 

urbanicity with school outcomes (Lippman, Burns, and McArthur 1996). The analysis of 

academic achievement used reading-mathematics composite scores of tenth-graders who 

participated in the National Education Longitudinal Study of 1988 (NELS:88). Differences in 

achievement among urban, rural, and suburban schools were significant when school poverty 

concentration7

 was not controlled statistically; urban and rural schools had lower average 

achievement than did suburban schools. No significant differences were found among urban, 

rural, and suburban schools when poverty concentration was controlled statistically. An analysis 

of dropout rates used tenth-grade data from the High School and Beyond (HS&B) survey. 

Differences among urban, rural, and suburban schools remained significant when the 

concentration of disadvantaged students8 was controlled statistically; approximately 74 percent 

of urban students graduated on time, compared with 84 percent of suburban students. Together, 

the analyses suggest that urbanicity differences in achievement can be attributed to school 

poverty levels, but not differences in dropout rates. 

Many rural communities, but not all, have high poverty levels. In 1999, an estimated 18 

percent of school-age children in rural areas outside of metropolitan statistical areas were living 

in poverty (Wirt et al. 2003). According to the U.S. Department of Agriculture (2001), the 

poorest rural areas tend to cluster in particular geographic areas (e.g., Appalachia; the Ozarks; 

Native American reservations; the northern plains), where the populations are disproportionately 

poor and the local economies lag behind other rural areas. In general, NAEP assessments show 

that rural students in grades 8 and 12 score somewhat higher than students in central cities, but 

lower than students in urban fringe areas (see Loveless 2003). Among rural workers under the 

federal poverty level, an estimated 28 percent had not completed high school, compared with 11 

percent workers with higher incomes (Dagata 2000). 

Status Risk Factors and Post-High School Outcomes 

Characteristics of Academic Risk Factors 

Postsecondary Schooling. The effects of status factors such as coming from a lowincome home or attending a school with a high proportion of low-income students carry over to 

post-high school years. In terms of further schooling, the likelihood of completing a bachelor’s 

degree or higher increases with increased family income (Zucker and Dawson 2001). In an 

analysis of HS&B data, the researchers found that “a student with a family income of $40,000 

and above was more than three times as likely to have completed a bachelor’s degree and more 

than four times as likely to have completed a graduate degree than a student with a family 

income under $15,000”9

 (p. 9). At the same time, of those who enter 4-year colleges, the 

likelihood of remaining for more than 3 years is also lower for students from lower income 

6

 Based on the federal poverty threshold from 1998 U.S. Census information. 

7

 The percentage of students receiving free or reduced-price lunches. 

8

 The percentage identified as “disadvantaged” according to the school administrator. 9

 1980 dollars. 

4 
 

 

 

 

The Adult Lives of At-Risk Students 

families (Bradburn 2002); according to data from the Beginning Postsecondary Students 

Longitudinal Survey (BPS96/98), approximately one-fourth (24 percent to 27 percent)10 of 

students from the lowest income quartile depart from college in 3 years or less. The 

corresponding percentages were 17 percent to 19 percent in the middle income quartiles, and 11 

percent to 15 percent in the highest quartile. 

Minority racial/ethnic groups, with generally lower incomes than Whites, also attain less 

postsecondary schooling than do Whites. According to the U.S. Census Bureau (2003), 17 

percent of White adults 18 years and older have completed bachelors’ degrees, compared to 11 

percent of Black adults and 7 percent of Hispanic adults.11 There is a larger ratio of White-tominority degree holders at each successive stage in the educational ladder: 6 percent of White 

adults have completed masters’ degrees compared to 3 percent of Blacks and 2 percent of 

Hispanics; and 1 percent of White adults have completed Ph.D. degrees compared to 0.4 percent 

of Blacks and 0.3 percent of Hispanics (U.S. Census Bureau 2003). 

The results are somewhat different for other types of programs and schools. The 

likelihood of completing an associate’s degree or certificate was highest for HS&B students from 

the second lowest income category ($15,000–$25,000) (Zucker and Dawson 2001, table 1). 

Completion rates for 2-year public institutions were not discernably different among students in 

low, medium, and high family income quartiles (Bradburn 2002). Thus, while economic 

disadvantage is a risk factor with respect to higher education, some levels of postsecondary 

attainment are not hampered as seriously as others. 

Employment and Income. An analysis of long-term outcomes from the National 

Longitudinal Study of Youth (NLSY) examined the role of schools’ economic status (Lippman, 

Burns, and McArthur 1996). The authors compared the economic and educational activities of 

participants 7 to 15 years after high school. Those who had attended urban schools and schools 

with the highest proportions of disadvantaged students were less likely to be earning a steady 

income than were those who had attended other schools. In particular, 76 percent of participants 

from high-poverty schools were employed for pay compared to 81 to 88 percent from lowerpoverty schools. Although differences by urbanicity were statistically significant (urban and 

rural less than suburban), the differences “disappeared after taking into account differences in the 

concentration of disadvantaged students in their high schools”12 (p. 41). These findings 

emphasize that early economic disadvantage is related to later educational and work-related 

outcomes 

The findings apply particularly to minorities, who disproportionately come from lowincome homes and attend high-poverty schools (Grigg et al. 2003; Lippman, Burns, and 

McArthur 1996; U.S. Department of Commerce 2002). As adults, these groups trail Whites on 

virtually all indicators. The unemployment rates in October 2003 were 5 percent for Whites, 12 

percent for Blacks, and 7 percent for Hispanics (U.S. Department of Labor 2003). According to 

the U.S. Census Bureau (DeNavas-Walt, Cleveland, and Webster 2003), of all persons 15 years 

10 The range encompasses public and private 4-year institutions. 

11 Percentages computed from table 1 of U.S. Census Bureau (2003). 

12 Similar results were found for postsecondary degree attainment. 

5 
 

 

 

 

 

6 

1. Introduction 

and older, 27 percent of Whites earned less than $25,000 in 2002, compared to 44 percent of 

Blacks and 36 percent of Hispanics (of any race). 

The Relationship of Academic Risk Factors to Post-High School Endeavors 

Academic risk shapes the relationship of status risk factors with post-high school 

endeavors. Minority students and students from low-income homes have poorer academic 

achievement and lower graduation rates, on average, than do White students or those from 

higher-income homes.13 Poor grades and dropping out of school are also academic risk factors 

with respect to entry into postsecondary schooling, maintaining continuous employment, and 

earning high levels of income. 

Postsecondary Schooling. Data from longitudinal surveys illustrate the relationship 

between high school graduation/dropout and postsecondary schooling. According to NELS:88, 

87 percent of students who never dropped out of high school attended some form of 

postsecondary schooling within 8 years,14 compared to 43 percent of students who dropped out 

one or more times (NCES 2004). Further, the HS&B survey showed that, of students who 

graduated on time15 in 1982, 9 percent had earned an associate’s degree by 1992, and 28 percent 

had earned a bachelor’s degree or higher (Snyder, Tan, and Hoffman 2004).16 Of students who 

left high school for a period of time and then returned to graduate, 7 percent earned an 

associate’s degree and 4 percent earned a bachelor’s degree or higher. For students who did not 

complete high school, 2 percent earned an associate’s degree and 1 percent earned a bachelor’s 

degree or higher. 

Even among students who complete high school, grades are related to postsecondary 

schooling. Kane (1998) analyzed longitudinal data from the HS&B survey to study student 

characteristics related to college admission.17 A one-letter grade decrease in students’ grade 

averages (GPAs) was accompanied by a 7 percent decrease in the chances of being admitted to a 

4-year college, when race/ethnicity, high school activities, and college selectivity were controlled 

statistically (Kane 1998). The relationship of grades with college admission was stronger in 

more selective colleges18 (Kane 1998; see also Sax et al. 2001). 

Employment. Individuals from economically disadvantaged backgrounds spend less time 

employed for pay and have higher transition rates from work to unemployment (less “job 

constancy”) compared to individuals from more affluent backgrounds (Holzer and LaLonde 

2000; Royalty 1998). High school attainments also play a role in this connection. Using data 

from the National Longitudinal Survey of Youth (Center for Human Resource Research 1997), 

Holzer and LaLonde (2000) tabulated the percentage of time young adults (ages 23–27) spent in 

the labor force19 during a 5-year span from 1991 to 1995. With one exception, the percentages 

13 See “Status Risk Factors and School Outcomes,” pgs. 2-4 of this report. 

14 Within 8 years of the expected graduation date (1992). 15 That is, together with their entering freshman class. 

16 Calculated from table 309 of Snyder, Tan, and Hoffman (2004). 

17 Based on students’ reports of whether or not they had been accepted. 18 Selectivity based on the average scores of entering freshman on the Scholastic Aptitude Test (SAT). 19 Holzer and LaLonde (2000) analyzed data on individuals who held regular jobs for 30 or more hours per week. 

6 
 

 

The Adult Lives of At-Risk Students 

increased monotonically with educational attainment for both males and females.20 Male and 

female dropouts reported spending 76 percent and 37 percent of their time employed during the 

5-year period. In contrast, male and female high school graduates reported being employed 81 

percent and 65 percent of the time, respectively. Male and female college graduates reported 

that they were employed 88 percent and 86 percent of the time, respectively. 

Royalty (1998) calculated annual job-to-nonemployment turnover rates for 22- to 30-

year-olds. For males and females who had not continued past high school, the percentages were 

15 percent and 21 percent, respectively; for males and females with more than a high school 

education the rates were 13 percent and 14 percent, respectively. Less than a high school 

education increased job loss for both men and women. The high turnover rate for female 

dropouts was consistent with the low percentage of employed time reported by Holzer and 

LaLonde (2000). Further, the turnover rates were higher for non-Whites than for Whites in three 

of the four education/gender categories (all except females with greater than a high school 

education).21 

Farber (1998) used data from the Displaced Workers Survey (DWS), a supplement to the 

Current Population Survey (CPS), to examine the condition of the labor market over the period 

1979 through 1996. The analysis revealed that historically in weak job markets, job loss rates of 

less educated workers were higher than those of more educated workers; that is, recessions 

tended to have a bigger impact on less educated workers. This trend has reversed in recent years. 

The rise in jobless rates in 2001–2003 was higher for college graduates than for high school 

dropouts (Bernstein and Mishel 2003). The authors concluded that the unusual pattern occurred 

because of the specific sectors hit hardest by the recession—technology and financial services in 

particular. 

Income as Adults. That individuals from economically disadvantaged backgrounds have 

lower earnings as adults is attributable in part to school attainment. Data from the Census 

Bureau showed that the annual earnings of full-time year-round workers (ages 25–64 years) 

increased monotonically with educational attainment, from $23,400 for high school dropouts to 

$89,400 and above22 for persons with doctoral and professional degrees (Day and Newburger 

2002). Over a typical work life, today’s workers with a bachelor’s degree earn nearly twice as 

much as workers with a high school diploma. On the whole, individuals from low-income 

backgrounds acquire fewer years of schooling and thus may have fewer skills with which to 

obtain lucrative jobs. 

Large-scale studies have established a relationship between achievement test scores and 

wages earned as an adult. Using data from the National Longitudinal Study of the High School 

Class of 1972 (NLS72), Murnane, Willett, and Levy (1995) demonstrated that mathematics test 

scores in high school were significantly related to subsequent educational attainment and to 

wages at age 24. Neal and Johnson (1996) used NLSY data to ask whether years of schooling 

and scores on the Armed Forces Qualification Test (AFQT) explained wage differences between 

20 The exception was less time in the labor force for individuals still attending college after age 22 (see table 2 of 

Holzer and LaLonde 2000). 

21 The authors used the term “non-Whites” and did not distinguish among minority groups. 

22 1999 dollars. 

7 
 

8 

1. Introduction 

Black and White adults in their late 20s. The AFQT was used as a measure of cognitive skill 

attained through schooling. Scores on the AFQT explained nearly three-fourths of the BlackWhite wage gap for men and the entire Black-White gap for women. When years of schooling 

was used as a measure of skill in place of AFQT scores, it explained a significant portion of the 

wage gap but not as much as test scores did.23 The researchers concluded that the amount of 

human capital acquired by individuals by their late teens—in particular skills such as those 

represented by the AFQT—was a starting point that can limit future human capital and future 

wages. 

The relationship between high school achievement and income has been shown to get 

stronger as adults approach age 30. Data from the NLSY show that the income gap between 

those who scored higher and those who scored lower on tests of mathematics, science, and 

paragraph comprehension began to emerge at age 23 and increased up to age 31 (Decker, Rice, 

and Moore 1997; see also Murnane, Willett, and Levy 1995). 

Summary 

To be certain, these studies and other research on the relationships of status risk factors 

with adult outcomes do not permit causal conclusions; however, together they show that early 

economic disadvantage is associated with attenuated adult accomplishments. Further, degrees of 

success or non-success in high school (i.e., academic risk factors) are related to postsecondary 

education and employment. 

In this report, the hypothesis is tested that, despite the many impediments, some students 

at status risk attain more postsecondary education, maintain more consistent employment, and 

have higher incomes than do others. The antecedents considered are school related, that is, 

academic risk in the form of grades, test scores, and graduation from high school, and behavioral 

risk in the form of engagement/disengagement in school. 

Behavioral Risk Factors: Engagement/Disengagement in School 

Characteristics of Behavioral Risk Factors 

Engagement in school is “the attention, interest, investment, and effort students expend in 

the work of school” (Marks 2000, p. 155). Studies of engagement recognize two facets—a 

behavioral component, participation, and an affective component, sometimes called 

identification with school (see Finn 1989; NRC and IOM 2004). Students who participate 

actively in school and the classroom, and who identify with school, increase the likelihood of 

successful academic outcomes. Those who do not have been referred to as disengaged; this 

pattern is found disproportionately among minority students and those from low-income homes 

(Finn 1993; Rumberger and Larson 1998; Steele 1992; Wehlage et al. 1989). Failing to 

participate in classroom and school activities, exhibiting adverse social behavior, and feeling 

23 Years of schooling explained one-fifth of the gap for men and one-sixth of the gap for women (Neal and Johnson 

1996, p. 875). 

8 
 

 

The Adult Lives of At-Risk Students 

emotionally disconnected from school24 are risk behaviors that can compound the obstacles to 

success posed by status risk factors such as SES or race/ethnicity. 

Participation. Participation has been described as having three components (Finn 1989). 

Level 1 participation includes the student’s acquiescence to school and class rules, including the 

requirements to arrive at school and class on time, attend to the teacher, come prepared for class, 

and to respond to directions or questions initiated by the teacher. Noncompliant behavior (e.g., 

disruptive behavior or refusing to complete assigned work) represents a student’s failure to meet 

those basic requisites. Poor school attendance and failing to respond to the teacher are 

consistently found to be associated with low academic performance among high school students 

(e.g., deJung and Duckworth 1986; Finn and Rock 1997; Kerr et al., 1986; Laffey 1982; Lee and 

Smith 1995; Marks 2000; Rowe and Rowe 1992). 

Level 2 participation involves initiative-taking on the part of the student, for example, 

doing more work than required or seeking help from school staff when experiencing problems 

(see Nelson-LeGall and Jones 1991). Level 3 participation is involvement in the social, 

extracurricular, and athletic aspects of school life in addition to or in place of extensive 

participation in academic work. Data on the relationship of participation in the social or 

extracurricular aspects of school life suggest a positive relationship with academic achievement 

(e.g., Camp 1990; Cooper et al. 1999; Gerber 1996; Marsh 1992; Marsh and Kleitman 2002; 

Melnick, Sabo, and Vanfossen 1992). 

Identification With School. The affective component of engagement, identification with 

school, has been defined as an emotional bond, or attachment, that the youngster experiences 

with respect to the institution (Finn 1989; Voelkl 1997). Identification has two closely related 

components, students’ sense of school membership, sometimes referred to as school 

belongingness, school bonding, or school connectedness, and students’ valuing of school-related 

outcomes. For the most part, these components have weak or indirect relationships with 

academic achievement (e.g., Goodenow 1993; Voelkl 1997), although research has shown that 

high identification with school is associated with high motivation and effort (Goodenow 1993; 

Goodenow and Grady 1993; Osterman 2000; Pintrich and DeGroot 1990). 

Low identification (disidentification) is associated with a spectrum of adverse behaviors 

including academic cheating (Finn and Frone 2004), drug and alcohol use during the school day 

(Hawkins, Catalano, and Miller, 1992; Voelkl and Frone 2000), a range of health risk behaviors 

(Resnick et al. 1997), and dropping out of school (Jessor, Turbin, and Costa 1998; Newmann, 

Wehlage, and Lamborn 1992; Rumberger and Larson 1998). 

The valuing component of identification is the specific belief that school provides the 

individual with useful outcomes. This concept, also called utility (see Updegraff et al. 1996), is 

distinct from general valuing of education as expressed in statements such as “everyone should 

complete a high school education” or “a good education is important for all.” In an analysis of 

different meanings of valuing, Mickelson (1990; p.51) found that “concrete” school attitudes 

such as the belief that schooling pays off later with good jobs were associated with positive 

school outcomes for Black students, but more abstract attitudes were not (e.g., the belief that “If 

24 That is, “disidentifying” from school (Goodenow and Grady 1993; Voelkl 1997). 

9 
10 

1. Introduction 

everyone in American gets a good education, we can end poverty”). Because of its relationship 

to minority students’ beliefs about education, perceptions that school is providing information 

and skills that are useful to the individual may help sustain students’ participation in school. 

The Connection with Academic Risk Factors 

Risk factors are conditions or behaviors associated with a lower likelihood of positive 

outcomes. “Protective factors have the reverse effect; they enhance the likelihood of positive 

outcomes and lessen the likelihood of negative consequences from exposure to risk” (Jessor, 

Turbin, and Costa 1998, p. 195; see also Garmezy 1993; Rutter 1990; Werner 1995). Because of 

its direct relationship with positive outcomes and inverse relationship with negative outcomes, 

school engagement has been viewed as both: a behavioral risk factor when students are 

disengaged, and a protective factor when engagement is high (Finn and Rock 1997; Resnick et 

al. 1997; Steinberg and Avenevoli 1998). 

Two programs of research have examined the relationship of engagement with academic 

success among status risk students. One research team used path models with several data sets to 

study the antecedents of school performance in three samples of Black adolescents (Connell, 

Spencer, and Aber 1994; see also Connell 1990; Connell and Wellborn 1991). A measure of 

engagement was developed that included both behavioral and affective components. Significant 

positive correlations were obtained with an index of positive educational outcomes (e.g., good 

attendance; high test scores) and significant negative correlations with an index of negative 

outcomes (e.g., low achievement scores; in-grade retentions; suspensions). The results were 

consistent for all three samples, with correlations ranging as high as 0.51. 

Other research has been based on the “participation-identification model” (Finn 1989). 

The model explains school outcomes as resulting from a sequence of behaviors and attitudes that 

may begin in the early grades (Finn 1989). Research based on this model focuses on 

participation and identification as antecedents of academic achievement and graduating/dropping 

out. 

Two studies examined data from early waves of the NELS:88 survey (Finn 1993; Finn 

and Rock 1997). In the first (1993), 5,945 eighth-grade students at risk due to status 

characteristics were classified into one of three achievement groups based on standardized 

mathematics and reading tests. The groups were compared on measures of school engagement 

including indicators of attendance, classroom participation, disruptive behavior, extracurricular 

behavior, and identification with school. Differences were found among the achievement groups 

on all participation measures: students classified as academically unsuccessful scored lower than 

the other groups on teachers’ and students’ reports of attendance, academic preparation, and 

classroom participation. The three achievement groups were distinct on homework and 

extracurricular participation. And unsuccessful students rated school subjects as less useful to 

them than did the other two groups. The differences for other indicators of identification, 

including more abstract attitudes, were marginal or nonsignificant. 

10 
 

 

The Adult Lives of At-Risk Students 

In the second study (1997), 1,803 minority students from the lower half of the SES 

distribution25 were classified as resilient students, nonresilient completers, and dropouts, on the 

basis of grades, test scores, and high school graduation. Resilient students were those who 

obtained reasonable grades and test scores and graduated on time; nonresilient completers were 

students who persisted through high school graduation but with lower scores or grades; dropouts 

were those who left school without graduating. The groups were compared in terms of 

psychological characteristics (self-esteem and locus of control) and teacher and students’ reports 

of school and classroom participation. 

Significant differences were found on engagement measures even after background and 

psychological characteristics were statistically controlled. Resilient students scored higher than 

nonresilient completers on five out of six participation measures. Nonresilient completers, in 

turn, scored higher than noncompleters on the same measures. Effect sizes for both comparisons 

were moderate to large. 

The analyses reported here extend the study of school engagement/disengagement by 

examining its relationship with post-high school accomplishments. 

Overview of This Report 

This report presents the results of an analysis of the experiences of students at risk due to 

status characteristics (“status risk” students), and their educational and job-related attainments as 

young adults. The analysis focused on the question: Among students at risk due to status 

characteristics, what are the relationships of high school engagement (behavioral risk) and 

attainments (academic risk) with post-high school education and employment?” 

Data for the analysis were drawn from the fourth follow-up wave (2000) of the National 

Education Longitudinal Study of 1988 (NELS:88), in which students were followed from 1988, 

when they were in grade 8, until 2000, when most were 26 years old. A subsample of students 

was selected who met two criteria for being at status risk in grade 8: students were included if 

they came from low-SES homes26 and attended a school with a large proportion of low-income 

students. Despite that all students in the sample shared these characteristics, significant 

variability was found in short-term and long-term outcomes. 

The short-term outcomes examined were high school grades, test scores, and 

graduation/dropout. These were used to identify three high school attainment groups, referred to 

as successful completers, marginal completers, and noncompleters. Attainment group was 

considered to be an academic risk factor with respect to post-high school endeavors 

(postsecondary schooling and employment). The behavioral risk factors considered were 

indicators of students’ engagement/disengagement in school, constructed from student and 

teacher reports. 

25 Based on the NELS:88 SES composite of household income, parents’ educational attainments, and parents’ 

occupations.

26 Based on household income, parents’ educational attainments, and parents’ occupations. 

11 
 

 

12 

1. Introduction 

The specific questions for the analysis were: 

(1.1) Academic risk and postsecondary education: Are attainments in high school related 

to postsecondary education among status risk students? 

(1.2) Academic risk and employment: Are attainments in high school related to later 

employment and income among status risk students? 

(2.1) Behavioral risk and postsecondary education: Is engagement in high school related 

to postsecondary education among status risk students? 

(2.2) Behavioral risk and employment: Is engagement in high school related to later 

employment and income among status risk students? 

(2.3) Behavioral and academic risk: If engagement is related to young adult outcomes 

(education and employment), is the connection attributable to high school attainments? 

Bivariate and multivariate analyses were conducted. The bivariate analyses provided 

information about status-risk students and how they compare to their non-status-risk 

counterparts. Bivariate analyses were also used to examine relationships of high school 

attainment and engagement with postsecondary schooling and employment one variable at a 

time. Multivariate regression analyses provided information about the same relationships but 

considered the simultaneous effects of attainment or engagement with student background 

characteristics. Several regressions were conducted for each outcome measure. First, high 

school attainment groups were treated as the main independent variables to address questions 

(1.1) and (1.2). Second, measures of school engagement were treated as the main independent 

variables to address questions (2.1) and (2.2). A final analysis addressed question (2.3), with 

both sets of independent variables considered simultaneously. 

A type I error rate of .05 was used throughout. Of the many statistical tests presented in 

this report, it can be expected that approximately 5 percent that appear significant do not 

represent real nonzero differences; unfortunately, it is not possible to identify which tests those 

may be. All statistical findings were accompanied by corresponding strength-of-effect measures. 

Caveats 

NELS:88 provided a particularly rich data set for this analysis. A large, representative 

sample of students was followed for 13 years with age-appropriate measures collected at five 

time points. The measures spanned status characteristics (e.g., family income, race/ethnicity), 

psychological and behavioral processes (e.g., school attendance, classroom participation, 

attitudes toward school subjects), and measures of academic performance. Nevertheless, certain 

design features limit the strength of the conclusions that can be drawn.27 

27 More detailed technical issues are discussed in appendix A; for example, incomplete data on some variables. 

12 
 

 

The Adult Lives of At-Risk Students 

First, a broad-based survey such as NELS:88 is designed to serve many purposes but may 

not address particular questions in depth. To a certain extent, this is the case with this analysis. 

Several measures of school engagement were derived from student and teacher reports, but 

others may have been desirable as well. Although the questions asked about students’ learning 

and social behavior in the classroom were central to the construct “participation,” the number of 

questions was limited. And few questions tapped the construct “identification with school.” 

Conclusions about school engagement are limited to the four aspects of engagement that could 

be measured from available data.28 

Second, many of the NELS:88 measures are based on participants’ self-reports. The 

potential biases in self-report data are recognized widely, attributable to factors such as memory 

distortion (Tanur 1992) or the desire to make a good impression (Paulhus 1991). One study 

examined bias in HS&B responses (Fetter, Stowe, and Owings 1984), a survey similar to 

NELS:88 in many ways. The authors found that the correlations of self-reports with factual data 

(e.g., school grades) were highest, but tended to decrease with the passage of time. The 

correlations with less factual information (e.g., parents’ aspirations for students) were somewhat 

lower. 

In the analyses reported here, every attempt was made to use the most objective data 

available for each measure. Teacher ratings of absenteeism and classroom behavior were used 

instead of students’ self-reports; achievement tests constructed and scored by the NELS:88 

researchers were used as a component in classifying students into attainment groups; multiple 

indicators were used to determine whether or not a student graduated from high school; and 

transcripts from postsecondary schools were used to determine whether a student entered a 

program of study and the number of credits earned. 

In some instances, this resulted in reduced sample sizes for particular variables.29 

Specifically, the number of credits earned in postsecondary education, taken from transcripts, 

was available only for 85 percent of those who entered postsecondary programs. And teachers’ 

ratings of student attendance and classroom behavior were only available for 87 percent of the 

eligible students.30 In all instances, cases missing a value were omitted from analyses using that 

particular variable. As a result, there may be a degree of bias in some of the statistical 

relationships given in this report, especially for these three measures. 

Third, NELS:88 did not involve any experimental intervention. Although it may be 

desirable to know if high school attainment or engagement is causally connected to adult 

outcomes, survey methodology only provides a conclusion that events do or do not occur in the 

same individuals. 

Further, in nonexperimental research there is always the possibility of bias in the results 

due to “specification error,” that is, the omission of other variables from the regression analyses. 

For example, home background was controlled to some extent in this analysis by excluding all 

28 These four aspects of engagement have been used in these or similar forms in Finn (1993), Finn and Rock (1997), 

Gerber (1996), and Marsh and Kleitman (2002). 29 See table A-1 in appendix A for final response rates. 30 That is, 87 percent of those still in school at the time of the ratings. 

13 
14 

1. Introduction 

students but those from low-SES homes, and by including race/ethnicity and urbanicity in the 

regressions. Specific home processes that may vary within the group of low-SES families were 

not considered, however, nor were school policies and practices that may affect student 

engagement and attainment. The results should be viewed with these limitations in mind. 

Organization of the Report 

Chapter 1 of this report provides the background and rationale for the analysis, and 

introduces the concept of school engagement and its likely relationships with adult outcomes. 

Chapter 2 describes the NELS:88 survey in greater detail and the sample, measures, and methods 

used in this analysis. Chapter 3 presents the first set of statistical results. Status-risk students are 

compared to non-status-risk students, and three attainment groups are described. The questions 

about the relationship of academic risk factors with adult outcomes (questions 1.1 and 1.2) are 

addressed in this chapter. Chapter 4 presents statistical results about behavioral risk factors and 

addresses the questions about behavioral risk and adult outcomes (2.1, 2.2, and 2.3). Chapter 5 

summarizes the findings of the analysis. Appendixes A–D contain additional technical 

information about the analyses, including a full description of the measures used. 

14 
 

 

 

 

 

The Adult Lives of At-Risk Students 

2. Data Source and Methods of Analysis 

The analyses described in this report were based on data collected in the National 

Education Longitudinal Study of 1988 (NELS:88). NELS:88 was designed to follow a 

nationally representative sample of eighth-grade students until well beyond their high school 

years. The original eighth-grade base year sample was chosen through a two-stage stratified 

sampling design (see Spencer et al. 1990). At the first stage, 817 public and 240 private schools 

that enrolled eighth-grade students were selected.31 At the second stage, an average of 25 eighthgrade students were selected from each school, resulting in a total sample of over 26,000 

students, of whom 24,599 actually participated in the survey.32 

The survey followed the progress of each student during the remaining years of high 

school, with data collection points at the end of tenth grade (first follow-up, 1990) and twelfth 

grade (second follow-up, 1992).33 Students who left high school without graduating, as well as 

those who remained in school, were contacted and administered survey instruments. The tenthgrade and twelfth-grade samples were “freshened” with new students at the same grade levels so 

that the sample at each grade level was representative of the population of students in that year.34 

Further data collection points occurred in 1994, when most participants were 2 years past 

high school (third follow-up), and in 2000 when most participants were 8 years past high school 

and about 26 years old (fourth follow-up). To save on data collection costs, participants were 

subsampled in both post-high school waves of data collection. The final sample included 10,827 

respondents who participated in all five waves of data collection. Details of sampling and other 

technical issues are given in depth in Base-Year to Fourth Follow-up Data File User’s Manual 

(Curtin et al. 2002). 

During eighth grade and high school, the students completed extensive questionnaires 

and took achievement tests in reading, mathematics, science, and history. Additional 

information was collected from their parents, teachers, school principals, and high school 

transcripts. During the post-high school years, participants were interviewed through computerassisted telephone interviewing (CATI) and computer-assisted personal interviewing (CAPI) 

techniques. College transcripts were obtained for those who participated in formal 

postsecondary schooling, including 2- and 4-year college programs and certificate programs. 

31 Of the 1,057 schools, 1,052 provided student data. 

32 See appendix A for more information about the design and response rates for NELS:88. 

33 The follow-ups were conducted in 1990 and 1992. Not all students were in tenth or twelfth grade in those years. 

34 This permitted cross-cohort comparisons with other surveys of tenth-grade and twelfth-grade students, particularly 

the National Longitudinal Study of 1972 (NLS72) and the High School and Beyond Survey (HS&B), both 

conducted by the National Center for Education Statistics (NCES). 

15 
 

 

 

16 

2. Data Source and Methods of Analysis 

Participants in the Analysis 

This report is based on data for respondents who participated in all five waves of the 

NELS:88 survey and who were at risk for school failure in eighth grade (base year) due to status 

characteristics. Risk status was determined from the socioeconomic level of the student’s family 

and school in the base year of the survey. Students whose parents were in the lower half of the 

distribution of family socioeconomic status (SES), and whose school was in the upper half of the 

distribution in terms of the percentage of students receiving free lunches were classified as being 

at status risk. 

The family SES measure35 was a numerical composite of reports of the parents’ 

education and two measures directly related to the family’s economic status, namely, household 

income and parents’ occupations. The median of base year SES was computed for all students in 

the five-wave sample;36 students at or below the median were eligible to be included in the 

analysis. Second, the median percentage of students receiving free lunches in the base year was 

computed for schools;37 schools at or above this value were termed “low-SES schools.” 

Students attending a low-SES school, and who also met the family SES criterion, were included 

in the sample. 

Of 10,827 students who participated in all waves of data collection, 15 were missing 

school lunch information. The remaining 10,812 participants formed the basis for identifying 

status risk students. A total of 3,502 participants met the status risk criteria, or 32 percent of the 

full group (33 percent weighted38). 

The demographic characteristics of the sample are summarized in table 1.39 Roughly the 

same percentages of male and female eighth-grade students were identified as being at risk 

according to the two socioeconomic factors. Over half of eighth-grade Hispanic students (59 

percent), Black students (56 percent), and Native American/Alaska Native students (54 percent) 

were at status risk, and approximately one in four White students (26 percent) and one in five 

Asian/Pacific Islander students (21 percent) were identified as being at status risk. In terms of 

urbanicity, 38 percent of students attending urban schools, 46 percent of students attending rural 

schools, and 22 percent of students in suburban schools were at status risk according to the two 

socioeconomic factors. Thirty-seven percent of public-school students were identified as being 

at status risk, but only 9 percent of students attending Catholic schools and 1 percent of students 

attending other private schools.40 

35 NELS:88 variable BYSES. 

36 Weighted median, using the five-wave sampling weight F4PNLWT. 37 The reported percentage of students receiving free lunches was used, which is likely to be somewhat lower than 

the percentage who are eligible. The median was 16 percent of students receiving free lunches. For schools missing 

the free-lunch index in grade 8, the index was taken from tenth-grade school information (and compared to the 

tenth-grade). Sixty-eight of 3,502 status risk cases were identified in this way. 

38 The NELS:88 sampling weight F4PNLWT was computed so that the five-wave sample represented the population 

of 1988 eighth-grade students 12 years later, in 2000. 

39 Additional unweighted sample sizes are given in table C-1 (appendix C). 

40 “Other private schools” included religious schools other than Catholic and nonreligious private schools. Because 

of the small number of Catholic and other private school students in the sample, sometimes coupled with missing 

values on some variables, school types were not compared in any further analyses. 

16 
The Adult Lives of At-Risk Students 

Table 1. Status risk sample, by demographic characteristics: 1988 

Unweighted 

Characteristic sample size Weighted percentage 1 

 All 3,502 33.1 

Gender 

Male 1,570 31.9 

 Female 1,932 34.3 

Race/ethnicity 

 White, not Hispanic 1,976 25.6 

 Black, not Hispanic 526 56.1 

 Hispanic 759 58.7 

 Native American, Alaska Native 65 54.0 

 Asian, Pacific Islander 176 20.7 

School urbanicity - 8th grade

 Urban 887 37.8

 Suburban 1,040 21.7

 Rural 1,575 45.5 

School type - 8th grade

 Public 3,402 36.8

 Catholic 94 8.8

 Other private2 6 1.1 1

Weighted percentage of all members of the corresponding population or subpopulation (e.g., 31.9 percent 

of all eighth-grade males were at-risk due to status characteristics). Percentages weighted by F4PNLWT. 2

Includes 'private other religion' and 'private non-religious.' 

NOTE: Status risk classification based on eighth grade (base year) student and school socioeconomic 

status. Standard errors are given in appendix B, table B-1. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, National Education 

Longitudinal Study of 1988 (NELS:88/2000). 

Variables 

Four primary sets of variables were used for this report: 

• demographic characteristics (status risk variables); 

• engagement/disengagement measures (behavioral risk variables); 

• high school attainments (academic risk variables); 

• post-high school education and employment. 

17 
 

 

 

 

18 

2. Data Source and Methods of Analysis 

Details of the variables are given in appendix D, including those constructed from the NELS:88 

data specifically for this analysis. In some instances, when information was missing for a 

particular school year but was present for another year, data from the alternate year was used as 

long as the survey question was asked in the same way on both occasions and the information 

was likely to be relatively stable over time. The substitution rules are documented in appendix D. 

Status Risk: Demographic Characteristics 

In addition to the student’s family SES, school percent free lunch, and student and school 

demographics such as gender, race/ethnicity, urbanicity, and school type, a series of base year 

family characteristics was taken directly from the NELS:88 data files. The additional variables 

provided a more complete picture of the status risk sample as compared to non-status risk 

students. These included components of the SES composite (e.g., parents’ educational 

attainments, family income) and other factors closely related to SES (e.g., family composition,41 

the number of children in the family, whether a language other than English is typically spoken 

in the home, number of times the participant changed schools prior to eighth grade). 

Behavioral Risk: Engagement/Disengagement Measures 

Four composite measures of engagement in school were obtained. These spanned both 

the behavioral and affective components of engagement (Finn 1989; NRC and IOM 2004) and 

were similar to those used in earlier studies (Finn 1993; Finn and Rock 1997). Two of the 

measures were based on teachers’ ratings of students and two were based on students’ selfreports. Teacher ratings consisted of responses of two tenth-grade teachers to a set of classroom 

behaviors (either the mathematics teacher or the science teacher and either the English or history 

teacher). 

The behavioral indicators included an attendance composite (“ABSTARDY”) obtained 

from teachers’ responses to “How often is the student absent?” and “How often is the student 

tardy?” Response categories for each question ranged from “never” to “all the time.” Items 

were reverse-scored so that higher ratings meant better attendance. The two teachers’ responses 

to each item were averaged, and the two items were then averaged to obtain the final scale score. 

Scores on the composite ranged from 1 to 5 (mean = 3.9);42 the internal consistency of the scale 

(coefficient α) ranged between 0.59 and 0.65 for the status risk sample.43 

A second index (“CLASSBEH”) reflected the student’s behavior with respect to class 

work. The measure was obtained by combining teachers’ responses to the yes–no prompt 

“Student usually works hard,” and to three never-to-often questions: “How often does the student 

do homework?” “How often is the student attentive in class?” and “How often is the student 

disruptive in class?” Each item could contribute one point to the CLASSBEH score. “Work 

41 Family composition was used to determine if the student was living with both biological parents or not living with 

both biological parents. 

42 Mean of weighted responses using NELS:88 sampling weight F4PNLWT. Standard errors of engagement 

measures are given in appendix B, table B-7.

43 Four reliability analyses were performed, for students rated by their mathematics and English teachers, 

mathematics and history teachers, science and English teachers, and science and history teachers. 

18 
 

 

 

The Adult Lives of At-Risk Students 

hard” was scored 1 if both teachers answered “yes;” “Homework” and “Attentive” were each 

scored 1 if the average of the two teachers was in the “most” or “all” range; “Disruptive” was 

scored 1 if the average of the two teachers was in the “rarely” or “never” range. Scores on 

CLASSBEH ranged from 0 to 4 (mean = 2.3); the internal consistency of the scale (coefficient α) 

was 0.78. 

A third index reflected the student’s engagement in school-sponsored extracurricular 

activities in tenth grade. The student was presented with a list of 15 activities including both 

athletics and academically oriented programs.44 Variable “EXTRACUR” was the number of 

activities indicated by the student, up to a maximum of 6. Students indicating more than 6 

activities were assigned the value 6.45 For 102 students (3 percent) who did not answer the item 

in tenth grade, their twelfth-grade responses were used instead. Scores on EXTRACUR ranged 

from 0 to 6 (mean = 1.6); the internal consistency of the scale (coefficient α) was 0.56.46 

The affective measure of engagement was based on research emphasizing the importance 

of students’ perceptions that school provides information of value to the student (e.g., Eccles 

1983; Mickelson 1990; Updegraff et al. 1996). The variable “UTILITY” was obtained from 

students’ responses in eighth grade to a question asking how useful four school subjects 

(mathematics, English, science, social studies) will be in their future.47 Respondents were asked 

to choose “strongly agree,” “agree,” “disagree,” or “strongly disagree” that each subject “will be 

useful in my future.” The UTILITY score was the average of the four responses, recoded so 

higher scores indicated more positive values. Scores on UTILITY ranged from 1 to 4 (mean = 

3.0); the internal consistency of the scale (coefficient α) was 0.71. 

Students who dropped out of school before tenth grade were missing tenth-grade teacher 

ratings and extracurricular activities. Also some tenth-grade students were not rated by their 

teachers.48 To maximize the number of students with engagement data available, twelfth-grade 

ratings and extracurricular activities were substituted for missing tenth-grade ratings.49 The final 

sample sizes for engagement ranged from 2,991 cases for teacher ratings to 3,306 for student 

self-reports, out of 3,502 students in the full status risk sample. 

Correlations among the engagement measures for the status risk sample were all positive. 

The correlation between ABSTARDY and CLASSBEH was 0.49, between EXTRACUR and the 

two teacher ratings 0.12 and 0.11, and between UTILITY and the three other scales 0.05, 0.07, 

and 0.11, respectively.50 

44 This was a subset of 18 activities listed in the NELS:88 questionnaire. See appendix D for details. 

45 Higher values were obtained by 2 percent of the sample of tenth-grade students, including some who checked 

most or all of the 15 activities. 

46 This value, based on all 15 activities, reflects that correlations among the specific activities were low. The total 

score, constrained to a maximum value of 6, was likely to be more reliable than 0.56 indicates. 

47 This question was not posed in the tenth-grade or twelfth-grade NELS:88 questionnaire. 48 One or both teacher questionnaires missing for these students. 49 This was possible for 183 students on the ABSTARDY measure and 194 students on CLASSBEH. 

50 All significant at p < .05. 

19 
 

 

 

 

 

 

20 

2. Data Source and Methods of Analysis 

High School Attainments/Academic Risk 

The participants in the analysis were classified into one of three attainment groups based 

on their reading and mathematics test scores in eighth and tenth grades, self-reported grades in 

four school subjects in tenth grade, and whether or not they graduated from high school. The 

groups were referred to as “successful completers,” “marginal completers,” and 

“noncompleters,” based on the definitions used in Finn and Rock (1997). Attainment groups 

were viewed as outcomes of the high school experience and as an indicator of academic risk for 

postsecondary education and employment outcomes.51 

Successful completers (21 percent52) were students who met three conditions: they 

remained in school through twelfth grade and graduated on time,53 attained “reasonable” scores 

on the reading and mathematics tests in eighth and tenth grades, and reported receiving 

“acceptable” course grades in tenth grade. Conservative achievement criteria were used on the 

assumption that these represent noteworthy accomplishments for students with multiple risk 

factors. The reading/mathematics determination was based on an equally weighted composite of 

the two achievement tests, formed separately in each grade. Reasonable performance was 

defined as a score one-fourth of a standard deviation below the mean of the entire (status risk and 

nonrisk) population and higher;54 this was approximately the 45th percentile of performance in 

the eighth grade and the 42nd percentile in the tenth grade. A grade point average (GPA) was 

computed for students from their self-reported grades in mathematics, English, history, and 

science.55 Grades in each subject were recorded as “mostly A’s,” “about half A’s and half B’s,” 

“mostly B’s,” “about half B’s and half C’s,” “mostly C’s,” “about half C’s and half D’s,” mostly 

D’s,” and “mostly below D.” Acceptable grades were defined as a GPA of “about half B’s and 

half C’s” or better. 

Students who did not meet all three criteria were classified as marginal completers or 

noncompleters. Marginal completers (52 percent) were still in school in 1992, mostly (96 

percent) in twelfth grade. All marginal completers graduated from high school, either on time or 

else by the time of the third NELS:88 follow-up (1994).56 These students did not attain 

reasonable test scores and/or acceptable course grades, however. 

Noncompleters had not received a high school diploma by the time of the third NELS:88 

follow-up. This was confirmed through two NELS:88 variables: noncompleters (27 percent) 

were either classified as dropouts and had been asked to complete a dropout questionnaire in the 

51 Unweighted sample sizes for attainment groups, cross-classified by demographic characteristics, are given in table 

C-1 (appendix C). 

52 Weighted by NELS:88 sampling weight F4PNLWT. 

53 On-time graduation was determined by a combination of criteria. All successful students were in school in grade 

12 in the spring of 1992 (NELS:88 variable F4UNI2D) and none had dropped out of school (NELS:88 variable 

F2TRSTYP). Further validation was provided by F3DIPLOM, an indicator that students had received a high school 

diploma by the third follow-up (1994). 

54 That is, X ̄ – 1/4 S or better; also used in Finn and Rock (1997). 55 Self-reported grades tend to be somewhat inflated relative to actual grades received but the amount of bias is 

similar for sex, SES, and achievement groups (Fetters, Stowe, and Owings 1984). In this analysis, grades served as 

a supplement to scores on the externally constructed achievement tests. 

56 Ten percent of marginal completers graduated after the second follow-up. 

20 
 

 

 

 

 

The Adult Lives of At-Risk Students 

second follow-up, or were in school at the time of the second follow-up but not in twelfth grade 

and had not received a high school diploma by the third follow-up.57 

Postsecondary Education 

Three measures of postsecondary educational attainment were used in bivariate analyses. 

The first (“FRSTINST”) was the type of postsecondary institution, if any, the student entered. 

This information was taken from variables in the postsecondary transcript file;58 for students 

missing transcript information, it was taken from the fourth follow-up interview.59 The 

responses were combined into four categories: none, less-than-2-year school, 2-year school, and 

4-year school. 

For those who attended, two measures indicated the persistence in postsecondary 

schooling. One was the total number of credits earned in all postsecondary schooling 

(“TOTCRED”), taken directly from the NELS:88 postsecondary transcripts.60 The other 

(“HIGHDEGR”) indicated completion of a postsecondary program. Respondents reported the 

highest postsecondary degree they had attained by the time of the fourth follow-up (2000), if 

any. Because this measure was taken from self-reports instead of school transcripts, it indicated 

completion of any postsecondary program up to that time, even if the participant had attended 

several schools. Responses were classified as certificate/license, associate’s degree, or 

bachelor’s degree or higher. 

All three measures were also used in the regression analyses. The first was recoded to 

indicate whether or not the individual had entered any postsecondary institution. The second 

(number of credits) was used without recoding. The third was recoded to indicate whether or not 

an individual who began postsecondary schooling had completed a program of study. 

Employment and Income 

Five employment/income variables were used in the bivariate analyses, of which three 

were also used in the regressions. Two current-employment variables were whether or not the 

individual was employed for pay in 2000, at the time of the fourth follow-up (“EMPLOYED”) 

and, if the individual was employed, whether his/her current activity was a full-time job 

(“FULLTIME”). The third variable was the number of hours worked in a typical week during 

the previous year (1999), for respondents who were employed during that year (“HRSWEEK”).61 

Fourth, total annual income was recorded for participants who worked one or more hours in a 

typical week in 1999 (“INCOME99”). 

57 NELS:88 variables F4UNI2D and F3DIPLOM, respectively. 

58 Transcript variables REFITYPE and REFINST. 

59 For 231 out of 293 cases missing transcript information. No information was available for 62 cases; these were 

not included in analyses of postsecondary schooling. 60 NELS:88 variable TCREDN. The importance of this index of persistence is highlighted in the U.S. Department of 

Education’s publication Answers in the Tool Box (Adelman 1999). 61 The number of hours worked in 1999 was used instead of 2000 because annual income was also available for 

1999. 

21 
 

 

22 

2. Data Source and Methods of Analysis 

Fifth, a “consistent employment” index (“CNSTEMPL”) was formed from employment 

information from 1997, 1998, and 1999. The index combines information about the number of 

years worked and whether the work was full-time or part time. Respondents were first asked if 

they had worked for 6 months or more in 1997 and 1998. Those who responded “no” were given 

a value of 0.0 for that year. Those who answered “yes” were given a value of 1.0 for that year if 

they worked full-time and 0.77 if they worked part-time.62 The same values were assigned for 

1999 but the wording of the survey question was different. Rather than asking the number of 

months worked, the question requested the number of weeks worked, which was then split into 0 

to 25 or 26 to 52. And rather than asking full-time/part-time, the question requested the number 

of hours worked in a typical week, which was then split into 0 to 37 or more than 37. The values 

for 3 years were summed to obtain the consistent employment measure. 

Because the 1997 and 1998 survey items were worded in terms of 6-month periods of 

employment, CNSTEMPL reflects the number of consecutive years, out of 3, that the individual 

worked for at least half of the year. Seventy-four percent of the sample63 received values of 0, 1, 

2, or 3, indicating full-time employment for the respective number of half years. An additional 

16 percent received values of 0.77, 1.77, or 2.77, indicating that they worked full-time for 0, 1, 

or 2 half years, and part-time for an additional half year. The remaining 10 percent received 

other values, indicating multiple half years of part-time work. In 1999, the only year for which 

actual number of weeks was recorded, 85 percent of respondents who worked for 26 weeks or 

more actually worked 48–52 weeks; thus, the half year cutoff effectively identified respondents 

who worked for the full year. The range of CNSTEMPL was from 0.0 to 3.0 (mean = 2.46);64 

the internal consistency of the scale (coefficient α) was 0.77. 

Three employment/income variables were used in the regression analyses: whether or not 

the individual was employed for pay at the time of the fourth follow-up (“EMPLOYED”), annual 

income in 1999 (“INCOME99”), and consistent employment (“CNSTEMPL”). Because the 

distribution of income was skewed, it was transformed to log income in dollars for the analysis.65 

Data Analysis 

The data analysis was conducted in three phases. The first phase provided description of 

the sample of students at risk due to status characteristics, especially on SES-related variables. 

The second phase explored the relationships of attainment groups (academic risk) with 

adult outcomes. Variable-by-variable differences were examined among the three attainment 

groups on adult educational and employment/income outcome measures, without controlling for 

other factors. An overall test of all possible contrasts among the groups on each outcome 

measure was conducted first.66 When the overall test was significant, separate t-tests were used 

62 The value 0.77 is the proportion of a 37.5-hour work week worked by part-time employees in 1999, the only year 

in which hours worked per week was available. 

63 Unweighted percentage.

64 Standard error given in appendix B, table B-6. 

65 Medians are reported instead of means in the descriptive tables. 66 A Wald test was used (Wald 1943), which produces a chi-squared statistic. 

22 
The Adult Lives of At-Risk Students 

to compare successful completers with marginal completers, and marginal completers with 

noncompleters. Next, multivariate regression and logistic regression analyses were used to 

address questions 1.1 (Are attainments in high school related to postsecondary education?) and 

1.2 (Are attainments in high school related to later employment and income?), with other 

background variables included in the analysis. 

The regressions were performed for six selected outcome measures: entering a 

postsecondary institution; number of credits earned by those who entered a postsecondary 

program; completing a program of study for those who entered a postsecondary program; 

whether or not the individual was employed in 2000; consistent employment; and 1999 income 

for those who were employed in 1999. Binomial logistic regression was used for the categorical 

dependent variables (entering postsecondary education; completing a program of study; 

employed in 2000). 

The third phase of analysis examined the relationships of engagement/disengagement in 

high school (behavioral risk) with adult outcomes. Simple correlations between engagement and 

the six adult outcomes were examined first, without considering other variables. Next, 

regression and logistic regression analyses were used to address questions 2.1 (Is engagement in 

high school related to postsecondary education?) and 2.2 (Is engagement in high school related 

to later employment and incomes?). Demographic and background measures were also included 

in the regressions. 

To answer question 2.3 (If engagement is related to young adult outcomes, is the 

connection attributable to high school attainments?), regression analyses included attainment 

groups and the engagement measures simultaneously. 

The Regression Analyses 

The primary independent variables of the regressions were the three attainment groups 

(for questions 1.1, 1.2) and the four school engagement measures (for questions 2.1, 2.2, and 

2.3). In each analysis of attainment groups, two comparisons (“contrasts”) were made: 

successful completers were compared to marginal completers, and noncompleters were 

compared to marginal completers. In each analysis of engagement, the four engagement 

measures were entered in the regression simultaneously (as a “block”). 

Other independent variables were included in all the regressions: student gender, three 

race/ethnicity contrasts, and two school urbanicity contrasts. When the analysis involved 

educational outcomes (entering a postsecondary school; credits earned; completing a program of 

study), the type of postsecondary institution entered was also included in the regressions. When 

the analysis involved employment/income outcomes (current employment; consistent 

employment; income in 1999), the type of postsecondary education (PSE) completed was 

included in the analysis because both employment and income may be affected by attending 

postsecondary school. 

The analyses also included certain interactions. The interactions of gender and race with 

attainment groups, and gender and race with engagement, were also included in the analysis. 

23 
 

 

24 

2. Data Source and Methods of Analysis 

Significant interactions would indicate that the effects of academic risk or behavioral risk varied 

across the subgroups (i.e., males and females, or different racial/ethnic groups). The interactions 

of PSE with attainment groups and PSE with engagement were included in analyses of 

employment/income outcomes to see if academic risk or behavioral risk had differential 

relationships to employment depending on postsecondary school experiences. 

Each regression analysis was performed in two steps: First, school engagement and 

attainment groups were tested in a “main-effects-only” model. Interactions were tested in a 

second model that included main effects and interactions. Cases missing a value on one or more 

variables were excluded from all analyses involving the particular variable(s). 

A type I error rate of α = .05 was used throughout the analysis. In all tests of two or more 

effects (two or more contrasts, or two or more engagement measures), individual effects were 

examined only when the overall (“omnibus”) test was significant at the .05 level. A Wald test 

(Wald 1943) was used to decide overall significance. This 2-step procedure, referred to as 

“Fisher’s protected t-tests,” provides additional protection against type I errors when several 

statistical tests are performed (see Cohen 2001, chapter 13). 

Strength-of-Effect Measures 

Tests of significance reveal whether a relationship between variables is statistically 

reliable, but tell little about whether effects are weak or strong. Thus, strength-of-effect 

measures were obtained from the regressions for all statistically significant effects. Particular 

strength-of-effect measures were chosen depending on the measurement scales involved.67 

When the dependent variable was dichotomous (e.g., entering postsecondary education), 

odds ratios were computed. If the independent variable was also categorical (e.g., groups A and 

B), the ratio was the odds that a member of group A would enter postsecondary schooling 

divided by the odds that a member of group B would enter. For example, in comparing males to 

females, the odds ratio is the odds of males entering postsecondary education divided by the odds 

of females entering. Odds ratios much below 1.0 or much above 1.0 indicate strong effects; 1.0 

would be obtained if the odds for both groups were the same. When the independent variable 

was continuous (e.g., engagement variable x), the odds ratio was the change in odds associated 

with a one-standard deviation change in x. 

When the dependent variable was continuous (e.g., postsecondary credits earned) and the 

independent variable was categorical, effect sizes were computed. This is the estimated 

difference between the mean number of credits for population A (males) and mean number for 

population B (females), divided by the standard deviation of the dependent variable (credits).68 

The effect size is interpreted as the number of standard deviations separating the means of the 

two groups. Whether an effect size is “small,” “moderate,” or “large” depends on the variables 

being studied and the research context. However, general guidelines for interpreting effects have 

been given by Cohen (1988), who recommended that effect sizes of 0.2 or less should be 

67 More information about strength-of-effect measures in particular contexts is given in chapters 3 and 4. 

68 The standard deviation was estimated by the square root of the residual mean square from the regression analysis. 

24 
 

 

The Adult Lives of At-Risk Students 

considered small, those around 0.5 medium, and those of 0.8 or greater should be considered 

large. This report has adopted these conventions. 

When the dependent variable was continuous and the independent variable was also 

continuous, standardized regression coefficients were computed. This is the number of standard 

deviations change in the dependent variable associated with a one-standard-deviation increment 

in x. 

In all analyses, the strength-of-effect measures were computed from a “reduced” 

regression model that included only the main effects (that is, without interactions). This was 

done for consistency within and between analyses after it was found that most interactions were 

not statistically significant. Of those that were significant, some were based on small numbers of 

observations and some did not contradict the overall findings for the particular independent 

variables. When an interaction showed that a particular effect was significant for one group of 

participants (e.g., one racial/ethnic group) but not for others, this is discussed in the report. 

Special Considerations Required by the Data Set 

Two aspects of the NELS:88 data required special attention. First, sampling weights 

were used in the analysis so that the weighted sample was representative of the larger population 

from which participants were drawn; these took into account oversampling of some population 

subgroups (Hispanic students, Asian/Pacific Islanders, and students attending private schools), 

and student nonresponse.69 All results presented in this report, with the exception of actual 

sample sizes, are weighted. 

Second, the sampling design of NELS:88 involving sampling strata, schools within strata, 

and students within schools, complicates the problem of estimating full-population variances and 

standard errors. The variance of scores for the sample is biased downward because of 

“clustering,” that is, students within schools within strata are more homogeneous on any 

characteristic than would be a simple random sample of students from across the country. 

Several approaches to estimating population variances for complex surveys are available, among 

them the “Taylor series” approach (Binder 1983; Lee, Forthofer, and Lorimor 1989) incorporated 

in statistical packages such as SUDAAN (Research Triangle Institute 2001) and AM (Cohen et 

al. 2003). 

The AM program was used for all significance tests. It accepts sampling weights and 

performs multiple regression analysis and logistic regression, producing correct standard errors 

for all effects; AM accepts identifiers for the sampling stratum and the primary sampling unit 

(school) as input, in addition to the data. Omnibus tests of sets of predictor variables, for 

example, several contrasts among racial/ethnic groups, are provided in the form of Wald 

statistics (Wald 1943); these are compared to percentage points of chi-square distributions. 

Descriptive statistics including frequency distributions, plots of the data, and correlations 

were obtained with the SPSS program, also using the sampling weights. Statistical tests of 

69 NELS:88 fourth follow-up panel weight F4PNLWT was used which was created to correct for unequal sampling 

and nonresponse in the five-wave sample. 

25 
26 

2. Data Source and Methods of Analysis 

correlations were computed using the regression features of AM with one dependent variable and 

one predictor variable at a time. 

26 
 

 

The Adult Lives of At-Risk Students 

3. High School Attainments and Young Adult Outcomes 

Status Risk and Non-Status Risk Students 

How are students at risk due to status characteristics different from non-risk students? In 

tables 2 and 3, the full sample is classified by the two status risk criteria (home SES and school 

SES). Table 2 shows that 52 percent of status risk students were female, compared to 49 percent 

of non-risk students. Approximately two out of five status risk students were minorities (44 

percent minority; 56 percent White), compared to one in five non-status risk students (20 percent 

minority; 80 percent White). Asian/Pacific Islander students comprised a higher percentage of 

the non-status risk group than the status risk group (4 percent and 2 percent, respectively), the 

only minority population to have this pattern. Ninety-eight percent of status risk students 

attended public schools, whereas 83 percent of non-status risk students attended public schools. 

And 42 percent of status risk students attended rural schools, compared to 29 percent who 

attended urban schools and 29 percent who attended suburban schools. 

Table 3 summarizes home-related characteristics, including parents’ education and 

household income that are components of the base-year SES composite. Nineteen percent of 

parents of status risk students completed more than a high school education, lower than all three 

groups of non-status risk students including those from low-SES homes but high-SES schools 

(24 percent). Forty-six percent of the families of status risk students were earning more than 

$19,999 in 1988, again lower than all three groups of non-status risk students including those 

from low-SES homes but high-SES schools (63 percent). 

Status risk students were different from non-status risk students in other ways. A lower 

percentage of status risk students than students not at risk was living with both biological 

parents,70 and a higher percentage of status risk students came from a non-English-speaking 

home. Status risk students had more siblings than did non-status risk students, and had greater 

school mobility than did non-status risk students. 

On six of the seven indicators listed in table 3 (all except school changes), conditions 

were more adverse for status risk students than for any non-status risk group. In particular, status 

risk students had more adverse conditions than those from low-SES homes but who attended 

higher-SES schools. These findings are consistent with the research showing that attending a 

low-SES school is itself a status risk factor (e.g., Lee, Croninger, and Smith 1997; Lippman, 

Burns, and McArthur 1996; Rumberger and Thomas 2000). 

70 Approximately 21 percent of status risk students were living with a mother only, the most common alternative 

setting. An additional 13 percent were living with a mother and male guardian. 

27 
28 

3. High School Attainments and Young Adult Outcomes 

Table 2. Percentage distribution of status risk sample and non-status risk students, by selected demog

school characteristics: 1988 

Percentage of status risk sample Percentage of non-status risk students 

 Low-SES home Low-SES home High-SES home 

Group Low-SES school All High-SES school Low-SES school 

 Total 100.0 100.0 100.0 100.0 

Gender Male 47.9 50.6 48 50  Female 52.1 49.4 52 50 Race/ethnicity White, not Hispanic 55.9 80.3 76 74  Black, not Hispanic 20.9 8.1 10 13  Hispanic 18.8 6.5 11 7  Native American, Alaska Native 2.3 1.0 0.72 ! 2.2 ! 

 Asian, Pacific Islander 2.1 4.1 3 5 School urbanicity - 8th grade Urban 29.4 24.0 18 29  Suburban 28.6 51.1 54 31  Rural 42.0 24.9 28 40 School type - 8th grade Public 97.8 83.0 85.0 96.9 

 Catholic 2.0 10.4 12.3 2.3 

 Other private1 0.2 ! 6.6 2.8 0.8 ! 

! Interpret data with caution. Unweighted n < 30. 

1Includes 'private other religion' and 'private non-religious.' 

NOTE: Status risk classification based on eighth grade (base year) student and school socioeconomic status. Detail may not sum 

because of rounding. Standard errors are given in appendix B, table B-2. SOURCE: U.S. Department of Education, National Center for Education Statistics, National Education Longitudinal Study of 19
The A

Table 3. Status risk sample and non-status risk students, by selected home-related characteristics: 1988 

29 

Status risk sample Non-status risk students

 Low-SES home Low-SES home High-SES 

Characteristic Low-SES school All High-SES school Low-SES s

Percent 1987 annual household income over $19,9991 46.3 87.1 63.1 

Percent father's education beyond high school2,3 19.4 63.7 23.9 

Percent mother's education beyond high school2 19.2 59.2 24.3 Percent living with two biological parents2 56.1 71.8 67.5 Percent non-English speaking home4 19.5 8.5 13.4 

Mean number of siblings1 2.7 2.1 2.4 

Mean number of times changed schools1 1.3 1.1 1.1 

1Base year parent questionnaire. 

2Base year student questionnaire. 3Response rate on this variable less than 85 percent. 4Composite of base year student and teacher questionnaire. 

NOTE: Status risk classification based on eighth grade (base year) student and school socioeconomic status. Standard errors are given 

table B-3. SOURCE: U.S. Department of Education, National Center for Education Statistics, National Education Longitudinal Study of 1988 (N
 

 

 

 

 

3. High School Attainments and Young Adult Outcomes 

Status Risk Students 

The remainder of the analysis focused on just those students identified as at risk due to 

status characteristics. Diversity was apparent on many accomplishments by the time the 

participants were young adults (age 26). Forty-two percent reported that they were married as of 

the year 2000; 58 percent reported that they had one or more children; and 33 percent reported 

that they owned their own homes, while 19 percent were still living with their parents.71 

Three Attainment Groups/Academic Risk 

Status risk students were classified into one of three attainment groups based on high 

school outcomes: self-reported school grades, achievement test scores, and whether or not 

students graduated from high school.72 Successful completers graduated from high school on 

time, received acceptable grades, and attained reasonable scores on standardized reading and 

mathematics tests. Marginal completers graduated on time or by the NELS:88 third follow-up 

(1994), but had lower grades and/or test scores. Noncompleters left high school without 

graduating.73 The attainments were considered to be academic risk factors with respect to 

postsecondary education and employment. 

The gender and racial/ethnic composition of the attainment groups are shown in table 4.74 

Fifty-six percent of successful completers, 51 percent of marginal completers, and 51 percent of 

noncompleters were female. Seventy-five percent of successful completers were White, 

compared to 49 percent of marginal completers and 55 percent of noncompleters. Black and 

Hispanic students constituted 10 and 11 percent of successful completers, respectively. There 

was a greater percentage of Black and Hispanic students among marginal completers (26 and 20 

percent, respectively) and among noncompleters (19 and 22 percent) than among successful 

completers. Asian/Pacific Islander students constituted 3 percent and 2 percent of successful and 

marginal completers, respectively, and 1 percent of noncompleters. 

Postsecondary Education. Sixty-one percent of status risk students entered some form of 

postsecondary education (table 5). However, the attainment groups were distinct on all measures 

of postsecondary schooling—entering a postsecondary program, earning postsecondary credits, 

and completing a postsecondary program of study. The overall test of differences among the 

three attainment groups was statistically significant for every measure. Further, specific 

contrasts between successful completers and marginal completers, or between marginal 

completers and noncompleters, or both, were significant for every postsecondary education 

subgroup (table C-2, appendix C). 

Of successful completers, 83 percent entered some form of postsecondary institution, 

compared to 68 percent of marginal completers and 29 percent of noncompleters. Almost half of 

successful completers (48 percent) entered a 4-year college or university. Additionally, 

71 Values not tabled. 

72 See “High School Attainments/Academic Risk,” in chapter 2 for complete details. 

73 Some noncompleters were still in school at the second follow-up (1992) but not in twelfth grade, and had not 

graduated by the third follow-up in 1994. 

74 Unweighted sample sizes for demographic subgroups are given in table C-1, appendix C. 

30 
 

The Adult Lives of At-Risk Students 

33 percent of successful completers entered 2-year colleges (mostly community colleges). Two 

percent of successful completers entered less-than-2-year programs (e.g., specialized vocational 

programs in business, technology, or cosmetology). 

Among marginal completers and noncompleters, 2-year schools were the institutions of 

choice (43 percent and 22 percent respectively). Six percent of marginal completers and 5 

percent of noncompleters chose less-than-2-year programs that offered training in job-related 

skills. This was about 1 in 6 of noncompleters who entered postsecondary schooling (5 percent 

out of 29 percent). 

The number of credits earned in postsecondary schooling is one indicator of persistence. 

Although credits are best understood in relation to the type of institution (Adelman 1999), 

differences among the attainment groups were considerable regardless of the type of school 

entered.75 Among students who entered postsecondary programs, successful completers acquired 

more credits than did marginal completers in 2-year schools; the mean numbers of credits were 

68.2 and 40.0, respectively (table 5), or a ratio of 1.7-to-1.76 In 4-year colleges, the means were 

102.3 and 76.0, respectively, or a ratio of 1.3-to-1. 

Table 4. Percentage distribution of status risk students in each attainment group, by 

gender and race/ethnicity: 1988 

Percentage in each attainment group 

1 2 3 Successful completers Marginal completers Noncompleters 

Group (n = 874) (n = 1950) (n = 678) 

 Total 

Gender

 Male

 Female 

Race/ethnicity

 White, not Hispanic

 Black, not Hispanic

 Hispanic

 Native American, Alaska Native

 Asian, Pacific Islander 

100.0 100.0 100.0 

43.6 49.2 49.0 

56.4 50.8 51.0 

74.7 48.6 55.0 

10.2 26.2 19.0 

11.3 20.4 21.6

 1.1 ! 2.4 3.1 !

 2.7 2.4 1.3 ! 

! Interpret data with caution. Unweighted n < 30. 1

In school in twelfth grade in 1992; "reasonable" scores on reading and mathematics tests; passing grades; 

graduated from high school. 

2

Test scores not "reasonable" and/or grades not passing; high-school graduate. 

3

Dropped out by 1992, or not in twelfth grade in 1992 and did not earn diploma by age 20. 

NOTE: Detail may not sum to totals because of rounding. Standard errors are given in appendix B, table B-4. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, National Education 

Longitudinal Study of 1988 (NELS:88/2000). 

75 Overall differences were all statistically significant (table 5) and seven out of eight individual contrasts were also 

significant (table C-2). 

31 
 

3. High School Attainments and Young Adult Outcomes 

Table 5. High school attainment groups of status risk students, by postsecondary 

 education outcomes: 2000 

High school attainment group 

Successful Marginal 

Outcome All completers 1 2 completers Noncompleters 3 

Percent who entered a 

 postsecondary institution*4 60.8 83.3 67.6 29.2 

 < 2-year school* 4.8 1.7 ! 6.0 5.0 

 2-year school* 35.0 33.3 42.6 21.6 

 4-year school* 21.0 48.3 18.9 2.7 ! 

Mean credits earned in

 postsecondary institutions*5,6,7 58.8 87.8 49.4 17.0

 < 2-year school* 30.7 45.0 ! 33.4 20.7 

 2-year school* 43.2 68.2 40.0 15.1 

 4-year school* 89.1 102.3 76.0 31.2 ! 

Percent completed

 postsecondary education*6 47.0 57.6 44.2 35.0 

 Certificate/license* 16.5 7.3 18.6 29.0 

 Associate's degree* 11.8 12.0 13.1 4.9 !

 Bachelor's degree or higher* 18.8 38.3 12.4 1.1 ! 

! Interpret data with caution. Unweighted n < 30. 

*Overall test of differences among groups significant at p < .05; see table C-5 in appendix C for specific 

comparisons. 

1

Defined as: in school in twelfth grade in 1992; "reasonable" scores on reading and mathematics tests; 

passing grades; graduated from high school. 

2

Defined as: test scores not "reasonable" and/or grades not passing; high school graduate. 

3

Dropped out by 1992, or not in twelfth grade in 1992 and did not earn diploma by age 20. 

4

Classified by type of first institution attended. Percentages based on total number of successful 

completers, marginal completers, and noncompleters in status risk sample. 

5

Credits earned from all schools attended, classified by type of first institution attended. 

6

Percentage based on total number of successful completers, marginal completers, and noncompleters 

who entered postsecondary education. 

7

Response rate on this variable less than 85 percent. 

NOTE: Detail may not sum to totals because of rounding. Standard errors are given in appendix B, table B-5. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, National Education 

Longitudinal Study of 1988 (NELS:88/2000). 

76 These ratios are presented to illuminate the magnitude of differences between the means in table 5. 

32 
 

 

 

 

 

 

 

The Adult Lives of At-Risk Students 

 Marginal completers acquired more credits than did noncompleters in all three types of 

schools. In less-than-2-year schools, the mean number of credits completed by marginal 

completers and noncompleters were 33.4 and 20.7, respectively, or a ratio of 1.6-to-1. In 2-year 

schools the ratio was 2.6-to-1, and in 4-year schools the ratio was 2.4-to-1.77 Two-year schools 

were the most common alternative for marginal completers and noncompleters, but 

noncompleters earned just 38 percent of the number of credits earned by marginal completers.78 

Among students who entered postsecondary schooling, 58 percent of successful 

completers finished with a certificate or diploma, whereas 44 percent of marginal completers and 

35 percent of noncompleters finished with a certificate or diploma. The rates for marginal 

completers and noncompleters were both lower than that of successful completers.79 

Table 5 also indicates the type of certificate or diploma acquired. Of successful 

completers who finished postsecondary schooling, approximately two-thirds (66 percent) 

attained bachelor’s degrees. This represents 38 percent of all successful completers.80 Marginal 

completers who finished postsecondary programs were distributed across the three types of 

credentials (42 percent certificate, 30 percent associate’s degree, 28 percent bachelor’s degree or 

higher).81 Eighty-three percent of noncompleters who finished postsecondary schooling attained 

certificates or licenses in particular skill areas.82 

Academic risk, represented by the attainment group characteristics of high school grades, 

test scores, and graduation, is related to persistence (or nonpersistence) in postsecondary 

schooling in the form of earning credits and completing a program of study. 

Employment and Income. Employment outcomes for the three attainment groups are 

summarized in table 6; specific comparisons are given in table C-3 (appendix C). In terms of 

current employment, the difference between marginal completers and noncompleters was 

statistically significant; the odds ratio of 0.53 reflected the lower employment rate of 

noncompleters (table C-3). Successful completers, however, were not measurably different from 

marginal completers,. That is, employment 8 years after high school was related to high school 

graduation but not to grades or test scores (the characteristics that distinguish marginal 

completers from successful completers). 

The consistent employment index is a measure of the individual’s persistence in the work 

force and full-time/part-time status in 1997–1999. At the extremes, 0.0 indicates no employment 

during the 3-year period, and 3.0 indicates 3 years of full-time employment. Values between 

77 The sample size for noncompleters in 4-year schools is below 30; this ratio should be interpreted with caution. 78 That is, 1 ÷ 2.6. 

79 Completion rates were also computed for each type of school entered. Seventy-five percent of participants who 

entered short-term (less-than-2-year) programs completed their studies, compared to 39 percent of those who entered 

2-year programs, and 54 percent of those in 4-year schools. The lowest completion rate occurred in 2-year schools, 

which is the most common route taken by status risk students (35 percent of status risk students entered 2-year 

schools; table 5). 

80 Thirty-eight percent is approximately two-thirds of the 58 percent who finished. 81 That is, 19 percent, 13 percent, and 12 percent out of the 44 percent who finished. 

82 Twenty-nine percent out of the 35 percent who finished. 

33 
 

 

 

 

3. High School Attainments and Young Adult Outcomes 

these indicate fewer calendar years of full-time employment and/or one or more years of parttime work.83 

For all students combined, and for students with no postsecondary education, consistent 

employment was related to high school attainment (table 6). For these groups, the consistent 

employment index was greatest for successful completers, lower for marginal completers, and 

lowest for noncompleters. For the full sample, the difference between successful completers 

(mean index = 2.6) and marginal completers (mean index = 2.5) was small (effect size = 0.12σ; 

table C-3). The difference between marginal completers and noncompleters was about threetenths of a standard deviation (effect size = 0.31σ; table C-3). For students with no 

postsecondary education, the effect sizes were 0.26σ and 0.30σ, respectively. For students who 

completed a postsecondary program of any type, however, high school attainment was not 

discernably related to consistent employment.84,85 

In 1999, the median annual income of non-status risk NELS:88 participants was $25,000 

and the median income of status risk participants was $22,000 (table 6). However, several 

groups of status risk participants had incomes at least as high as those not at risk, namely, 

successful completers who acquired associate’s degrees or bachelor’s degrees, and marginal 

completers who acquired bachelor’s degrees. 

The incomes of attainment groups differed significantly in one way: for the entire status 

risk sample, successful completers, who attained high grades and test scores and graduated from 

high school, had higher incomes than other attainment groups at age 26 (effect size = 0.18σ; 

table C-3).86 When the sample was subclassified by postsecondary education outcomes, the 

incomes of the three attainment groups were not discernably different.87 

83 See “Employment and Income” in chapter 2 for further details. 84 These students also spent more years in school, leaving them less opportunity for long-term employment. 

85 As a follow-up analysis, the attainment groups were also compared for a combined group of all students who 

completed postsecondary programs (certificates and associate’s degrees and bachelor’s degrees); this test had greater 

power than tests on the individual groups. Both the overall difference and the individual contrasts remained 

nonsignificant.

86 The statistical test used mean log income in dollars. 

87 The larger sample size associated with “all participants” may have increased the likelihood of detecting a 

relatively small effect. 

34 
The Adult Lives of At-Risk Students 

Table 6. High school attainment groups of status risk students, by employment/income

 outcomes: 2000 

High school attainment group 

Outcome 

Percent currently employed (2000)* 

All 

84.0 

Successful 1 completers 

88.3 

Marginal 2 completers Non

86.1 

completers 

76.7 

3 

Percent employed full-time (2000)4 89.7 89.3 89.7 89.9 

Mean number of hours worked per week (1999)5 42.4 41.6 42.3 43.6 

Consistent employment mean 1997–1999 (all)*6 

 No postsecondary education* 

 Some postsecondary education, no degree 

 Certificate/license 

2.46

2.35

2.54

2.43

2.62 

2.71 

2.61 

2.63 

2.51 

2.46 

2.57 

2.39 

2.24 

2.17 

2.33 

2.46 

 Associate's degree 2.64 2.67 2.64 2.57 !

 Bachelor's degree or higher 2.53 2.55 2.51 2.55 ! 

Median 1999 annual income (all; in thousands)*5 

 No postsecondary education 

 Some postsecondary education, no degree 

 Certificate/license 

22.0

22.0

22.0

20.0

25.0 

26.0 

23.0 

24.0 

22.0 

22.0 

21.0 

20.0 

20.0 

22.0 

22.0 

19.0 

 Associate's degree 23.0 28.0 20.0 28.0 !

 Bachelor's degree or higher 

! Interpret data with caution. Unweighted n < 30. 

27.0 27.0 28.0 23.0 ! 

*Overall test of differences among groups significant at p < .05; see table C-6 in appendix C for specific 

comparisons. 

1

In school in twelfth grade in 1992; "reasonable" scores on reading and mathematics tests; passing grades; 

graduated from high school. 

2

Test scores not "reasonable" and/or grades not passing; high-school graduate. 

3

Dropped out by 1992, or not in twelfth grade in 1992 and did not earn diploma by age 20. 

4

Of those currently employed. 

5

Of those employed in 1999; annual income data were only available for 1999. 

6

0.0 to 3.0 indicator of part-time/full-time employment 1997–1999. Two decimal places shown for accuracy. 

NOTE: Standard errors are given in appendix B, table B-6. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, National Education 

Longitudinal Study of 1988 (NELS:88/2000). 

35 
 

3. High School Attainments and Young Adult Outcomes 

Question (1.1) Academic Risk and Postsecondary Education: Are Attainments in 

High School Related to Postsecondary Education Among Status Risk Students? 

Regression analyses were conducted to examine the relationship of high school 

attainments with postsecondary schooling in a more complete context—that is, in analyses that 

included students’ demographic characteristics and the type of postsecondary institution 

attended. The regression results are summarized in table 7; strength-of-effect measures are given 

in table 8. Each column of the tables gives results for one of three indicators of postsecondary 

schooling: entering a postsecondary institution and, for those who did, the number of credits 

earned and whether or not a degree or certificate was acquired. 

The rows of tables 7 and 8 indicate particular independent variables. For variables with 

more than two classifications (race; urbanicity; type of institution; attainment group), an overall 

test was conducted first to indicate whether there were any differences among the groups 

represented.88 If the overall test was significant, specific contrasts among the groups were then 

tested. 

Gender, race/ethnicity, urbanicity, and the type of program entered were primarily control 

variables; the type of institution entered was a control variable for outcomes connected to 

program duration (credits; completion). The interactions of attainment groups with gender and 

race were examined to see if the relationship of attainment groups with postsecondary schooling 

varied across gender or racial/ethnic groups. 

Strength-of-effect measures (table 8) are given in two forms, effect sizes and odds ratios. 

Effect sizes are given for the continuous dependent variable, number of credits earned. An effect 

size is the number of standard deviations between the means of the two groups being compared, 

for example, between male and female students. Effect sizes are positive if the mean of the first 

group (e.g., males) is higher and negative if the mean of the second group (females) is higher. 

Odds ratios are given for the yes/no dependent variables entering a postsecondary institution and 

completing a program of study. The odds of a “yes” for the first group89 (e.g., males) are divided 

by the odds for the second group (females). The odds ratio is less than 1.0 if the odds for the 

first group are less than the odds for the second, and greater than 1.0 if the odds for the first 

group are greater than the odds for the second. 

Background Variables. The results for gender, race/ethnicity, school urbanicity, and type 

of postsecondary institution, with high school attainment in the analysis, are given in tables 7 and 

8 for the full status risk sample. There were significant gender differences only for entering a 

postsecondary school (table 7). Males had about 0.66 of the odds that females had of entering a 

postsecondary school (table 8); stated another way, the odds of a female entering postsecondary 

schooling were about 1 1/2 times greater than the odds that a male would enter (1 ÷ 0.66). There 

were no discernable gender differences in persistence, measured either as credits earned or 

program completion. 

88 Using a Wald statistic; see “Data Analysis.” 

89 The “odds” is the ratio of the proportion of “yes’s” to the proportion of “no’s.” 

36 
The Adult Lives of At-Risk Students 

Table 7. Regression results for high school attainment groups and postsecondary

 education outcomes: 2000 

For those who entered 

postsecondary education 

 Entering a 

postsecondary Number of Completing a

Predictor variable institution 1 credits earned program of study

Gender (M - F) 

Race/ethnicity

 Black - White

 Hispanic - White

 Native American - White

 Asian - White 

School urbanicity - 8th grade

 Suburban - Urban

 Rural - Urban 

Type of postsecondary institution entered

 < 2-year - 2-year

 4-year - 2-year 

Attainment group

 (A1) Successful completers - Marginal completers

 (A2) Noncompleters - Marginal completers 

See notes at end of table. 

 -0.42 * -4.66 

(0.126) (2.650) 

0.31 -5.36

(0.214) ( ) (4.468) 

 0.57 * -5.18 

(0.287) (5.668) 

-0.49 -14.01 * 

(0.165) (3.342) 

 1.33 * 15.44 * 

(0.327) (6.131) 

-0.20 0.93

(0.182) (3.526) 

-0.29 7.56 * 

(0.184) (3.456) 

† -5.72 

(3.020) 

† 35.16 * 

(3.633) 

 0.98 * 24.25 * 

(0.238) (3.483) 

 -1.65 * -24.48 * 

(0.151) (2.487) 

-0.23 

(0.124)

 -0.42 

(0.201) 

-0.30 

(0.213) 

-0.47 

(0.162) 

-0.11 

(0.465) 

-0.05 

(0.189) 

0.20 

(0.171)

 1.70 * 

(0.222)

 0.40 * 

(0.136)

 0.39 * 

(0.131)

 -0.51 * 

(0.242) 

37 
3. High School Attainments and Young Adult Outcomes 

Table 7. Regression results for high school attainment groups and postsecondary

 education outcomes: 2000―Continued 

For those who entered 

Predictor variable 

 Entering a 

postsecondary

 institution 

postsecondary education 

 Number of Completing a

1 credits earned program of study

Gender x Attainment Group

 Gender x (A1)

 Gender x (A2) 

Race/ethnicity x Attainment Group

 Black x (A1)

 Black x (A2)

 Hispanic x (A1)

 Hispanic x (A2)

 Native American x (A1)

 Native American x (A2)

 Asian x (A1)

 Asian x (A2) 

-0.36 -11.22 -0.11 

(0.363) (5.566) (0.245)

0.36 -10.00 0.76

(0.312) (5.144) (0.472) 

-1.91 * 12.02 0.85 

(0.765) (12.589) (0.458)

-0.38 11.82 0.08

(0.431) (9.881) (0.658)

0.53 6.52 0.03 

(0.484) (7.991) (0.328)

-0.64 8.89 -0.71 

 (0.356) (5.145) (0.613)

-1.62 -11.47 -0.24

(0.940) (15.100) (0.707)

0.11 -10.68 -18.36 !* 

(0.710) (12.414) (1.049)

-0.08 0.19 0.38 

 (0.745) (10.747) (0.561)

 -1.48 * 8.50 -0.19 

(0.654) (21.692) (0.811) 

† Not applicable. 

! Interpret data with caution. Interaction based on small sample (five Native American/Alaska Native 

noncompleters of which none completed a postsecondary program). 

*p < .05 

1

Response rate on this variable less than 85 percent. 

NOTE: Table contains unstandardized regression coefficients. Standard errors in parentheses. Each 

coefficient is a contrast, group A-minus-group B (positive if group A has the higher value; negative if group 

B has the higher value). For effects with two or more contrasts, individual contrasts tested only when the 

overall test of differences among the groups was significant. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, National Education 

Longitudinal Study of 1988 (NELS:88/2000). 

38 
The Adult Lives of At-Risk Students 

Table 8. Strength-of-effect measures for high school attainment groups and 

 postsecondary education outcomes: 2000 

For those who entered

postsecondary education 

 Entering a

postsecondary Number of Completing a

Predictor variable 1 institution 2,3 1 credits earned program of study

Gender (M - F) 0.66 * -0.10σ 0.80 

Race/ethnicity

 Black - White ( ) 1.36 -0.12σ 0.66

 Hispanic - White 1.77 * -0.12σ 0.74

 Native American - White 0.61 -0.31σ * 0.62

 Asian - White 3.76 * 0.34σ * 0.90 

School urbanicity - 8th grade

 Suburban - Urban 0.82 0.02σ 0.95

 Rural - Urban 0.75 0.17σ ∗ 1.22 

Type of postsecondary institution entered

 < 2-year - 2-year † -0.13σ 5.46 *

 4-year - 2-year † 0.78σ * 1.50 * 

Attainment group

 (A1) Successful completers - Marginal completers 2.66 * 0.54σ * 1.47 *

 (A2) Noncompleters - Marginal completers 0.19 * -0.55σ * 0.60 * 

† Not applicable. 

*p < .05 

1

Odds ratio for specific predictor variable. 

2

Effect sizes; mean differences in standard deviation units. 

3

Response rate on this variable less than 85 percent. 

NOTE: Strength-of-effect measures estimated for main effects only. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, National Education 

Longitudinal Study of 1988 (NELS:88/2000). 

Racial/ethnic differences were found for entering postsecondary schooling and for 

number of credits earned. Among status risk students (low-SES homes, low-SES schools) 

Hispanic students were more likely to enter a postsecondary program than were non-Hispanic 

White students. Native American/Alaska Native students who entered postsecondary education 

earned fewer credits than did Whites. Asian/Pacific Islander students were more likely to enter a 

postsecondary program and earned more postsecondary credits compared to White students. 

The type of postsecondary institution was related to the number of credits earned and to 

the likelihood of students’ completing their programs of study. The odds of a student’s 

completing a less than 2-year program culminating in a job-related certificate or license were 

over five times greater than the odds of completing a 2-year degree (odds ratio = 5.46; table 8). 

The odds of students completing a 4-year program were one and one-half times greater than the 

39 
 

 

 

 

3. High School Attainments and Young Adult Outcomes 

odds of completing a 2-year program (odds ratio = 1.47). In general, students entering 2-year 

colleges were least likely to complete any program of study.90 

Academic Risk/High School Attainment Groups. Attainment groups differed on all 

indicators of postsecondary education (table 7). In terms of entering a postsecondary program, 

the odds of successful completers entering were over two and one-half times greater than the 

odds of marginal completers entering a program (odds ratio = 2.66; table 8). Marginal 

completers had about five times the odds of entering any kind of program compared to 

noncompleters (1 ÷ 0.19 = 5.26). 

In terms of persistence in a postsecondary program, the groups were ordered the same 

way (table 8). Successful completers earned more credits than did marginal completers and 

marginal completers earned more credits than did noncompleters. Both differences were 

approximately equal to one-half standard deviation (effect sizes = 0.54σ and 0.55σ, respectively). 

The odds of completing a program of study were almost one and one-half times greater for 

successful completers than for marginal completers (odds ratio = 1.47), and about one and twothirds times greater for marginal completers than for noncompleters (1 ÷ 0.60). 

Interactions. The interactions of gender and race with attainment groups were tested to 

determine if the differences in postsecondary education among attainment groups varied across 

gender or racial/ethnic groups. There were no significant interactions of gender with attainment 

groups on any postsecondary education measure studied. 

The interaction of race/ethnicity with attainment groups was significant for entering 

postsecondary education (among Black and Asian/Pacific Islander students) and for completing a 

program of study (among Native American/Alaska Native students). For entering postsecondary 

education, follow-up analyses91 indicated that there were differences between successful 

completers and marginal completers only among White and Hispanic students; no discernable 

differences were found between successful completers and marginal completers among Black, 

Asian/Pacific Islander, or Native American/Alaska Native students. The general finding that 

successful completers enter postsecondary education at a higher rate than do marginal completers 

does not apply to these racial/ethnic groups. Marginal completers of all five race/ethnicities had 

higher rates of entering postsecondary education than did noncompleters, however. 

For completing a program of study, the only significant interaction was for Native 

American/Alaska Native students, but the sample included just five Native American/Alaska 

Native noncompleters; none completed a postsecondary program. When the particular 

interaction was removed from the analysis, all tests of the interaction of race/ethnicity with 

attainment groups for completing a program of study were nonsignificant. 

Summary. In sum, in terms of entering postsecondary programs of study, successful 

completers had higher entrance rates than did marginal completers, but only among White and 

Hispanic status risk students. Successful completers had consistent advantages over marginal 

90 Information on program completion was taken from respondents’ self-reports at the time of the NELS:88 fourth 

follow-up (2000), and indicates the completion of any postsecondary program up to that time. 

91 Not shown in tables. 

40 
 

 

 

The Adult Lives of At-Risk Students 

completers in accruing postsecondary credits and completing a program of study. 

Noncompleters—students differentiated by their high academic risk—were the least likely of the 

three groups to enter or persist in postsecondary schooling. 

Question (1.2) Academic Risk and Employment: Are Attainments in High School 

Related to Later Employment and Income Among Status Risk Students? 

Analyses were conducted to examine the relationship of high school attainments with 

employment and income, in regressions that included students’ demographic characteristics and 

type of postsecondary education completed, if any. The regression results for employment and 

income are summarized in table 9; strength-of-effect measures are given in table 10. Each 

column of the tables gives results for one measure of employment/income: whether the 

respondent was employed for pay (full-time or part-time) in 2000, an index of consistent 

employment during 1997-1999, and, for those who were employed for pay in 1999,92 their 

annual income. The strength-of-effect measures in table 10 are odds ratios for the yes/no 

employed-in-2000 variable, and effect sizes (mean differences in standard deviation units) for 

consistent employment and 1999 annual income.93 

Background Variables. Gender differences favored males on all three 

employment/income variables (table 9). The odds of status risk males being employed in 2000 

were about 3 times greater than the odds of status risk females being employed (odds ratio = 

3.20; table 10). Further, the mean of the consistent employment index for males was 

approximately one-half standard deviation above that of females. And finally, in 1999, males 

were earning an income that was approximately one-half of a standard deviation greater than that 

of females (median incomes of $26,000 for males and $19,000 for females).94 

Background Variables. Gender differences favored males on all three 

employment/income variables (table 9). The odds of status risk males being employed in 2000 

were about 3 times greater than the odds of status risk females being employed (odds ratio = 

3.20; table 10). Further, the mean of the consistent employment index for males was 

approximately one-half standard deviation above that of females. And finally, in 1999, males 

were earning an income that was approximately one-half of a standard deviation greater than that 

of females (median incomes of $26,000 for males and $19,000 for females).95 

Differences were found in annual income by race/ethnicity, however. The incomes of 

Black and White young adults differed by about four-tenths of a standard deviation (effect size = 

0.44σ; table 10); the incomes of Native American and White young adults differed by about the 

same amount (effect size = 0.42σ). The median 1999 income of Whites was $24,000, compared 

to $19,000 for Blacks, $15,600 for Native Americans, and $22,000 for Hispanics. The 

disparities in income found among racial/ethnic groups on the whole (DeNavas-Walt et al. 2003) 

also characterized these low-SES respondents. The disparities were not explained by academic 

92 That is, for respondents who reported working one or more hours for pay in a typical week in 1999 (NELS:88 

variable F4BLHPW). 

93 See “Strength-of-Effect Measures” in chapter 2 for more discussion of effect sizes and odds ratios. 94 Median incomes not tabled. 

95 Median incomes not tabled. 

41 
 

3. High School Attainments and Young Adult Outcomes 

risk; they were statistically significant with educational attainment groups included in the 

regressions. 

Table 9. Regression results for high school attainment groups and employment/income

 outcomes: 2000 

Employed Consistent 1999 Annual 

Predictor variable in 2000 employment 1 income 2,3 

Gender (M - F) 1.16 * 0.42 * 0.41 * 

(0.180) (0.052) (0.060) 

Race/ethnicity 

 Black - White -0.12 -0.10 -0.33 * 

( ) (0.234) (0.071) (0.082) 

 Hispanic - White 0.07 -0.06 -0.01 

(0.175) (0.060) (0.053)

 Native American - White -0.56 -0.40 -0.32 * 

(0.280) (0.195) (0.116) 

 Asian - White 0.24 -0.13 -0.10 

(0.302) (0.104) (0.091) 

School urbanicity - 8th grade

 Suburban - Urban -0.01 0.07 0.02 

(0.197) (0.067) (0.065)

 Rural - Urban 0.09 0.08 -0.05

(0.197) (0.063) (0.060) 

Postsecondary education completed

 Some PSE, no degree - None 0.43 * 0.14 * 0.03 

(0.201) (0.060) (0.071)

 Certificate/License - None 0.77 * 0.12 -0.01

(0.257) (0.116) (0.111)

 Associate's degree - None 2.18 * 0.22 * 0.13

( ) (0.393) (0.064) (0.070) 

 Bachelor's degree or higher - None 1.02 * 0.10 0.32 * 

(0.219) (0.063) (0.067) 

Attainment group

 (A1) Successful completers - Marginal completers 0.07 0.08 0.05 

(0.177) (0.043) (0.045)

 (A2) Noncompleters - Marginal completers -0.35 -0.23 * -0.14 

(0.185) (0.065) (0.093) 

Gender x Attainment Group

 Gender x (A1) -0.20 -0.20 * -0.13 

(0.427) (0.091) (0.082)

 Gender x (A2) 0.63 0.32 * 0.07 

(0.368) (0.129) (0.160) 

See notes at end of table. 

42 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

The Adult Lives of At-Risk Students 

Table 9. Regression results for high school attainment groups and employment/income 

outcomes: 2000―Continued 

Employed Consistent 1999 Annual

Predictor variable in 2000 1 employment 2,3 income 

Race/ethnicity x Attainment Group 

Black x (A1) 1.63 * 0.05 0.22 

(0.715) (0.154) (0.150) 

Black x (A2) -0.08 -0.19 -0.67

(0.520) (0.203) (0.353)

 Hispanic x (A1) 0.85 0.15 0.05 

(0.431) (0.105) (0.100)

 Hispanic x (A2) 0.23 0.09 0.01 

(0.432) (0.138) (0.113)

 Native American x (A1) 0.17 0.30 -0.72 

(1.154) (0.282) (0.332)

 Native American x (A2) -0.38 -0.28 -0.26 

(0.693) (0.275) (0.296)

 Asian x (A1) -0.03 -0.16 -0.01 

(0.579) (0.177) (0.157)

 Asian x (A2) 16.08 * 0.08 0.21 

(0.532) (0.239) (0.197) 

Postsecondary education completed 

x Attainment group

 Some PSE, no degree x (A1) -1.06 -0.25 -0.11 

(0.550) (0.134) (0.109)

 Some PSE, no degree x (A2) 0.07 0.04 0.06 

(0.428) (0.150) (0.258)

 Certificate/license x (A1) -1.06 -0.07 0.14 

(0.669) (0.197) (0.193)

 Certificate/license x (A2) 0.32 0.34 0.43 

(0.570) (0.272) (0.247)

 Associate's degree x (A1) -0.97 -0.26 0.02 

(0.798) (0.143) (0.138)

 Associate's degree x (A2) -0.08 0.14 0.22 

(1.174) (0.212) (0.182)

 Bachelor's degree or higher x (A1) -1.33 -0.26 -0.17 

(0.567) (0.128) (0.116)

 Bachelor's degree or higher x (A2) -0.63 0.53 0.20 

(1.156) (0.339) (0.173) 

*p < .05 

1

0.0 to 3.0 indicator of part-time/full-time employment 1997–1999. 2

Of those employed in 1999; annual income data were only available for 1999. 

3

Log 1999 income in dollars. 

NOTE: Table contains unstandardized regression coefficients. Standard errors in parentheses. Each 

coefficient is a contrast, group A-minus-group B (positive if group A has the higher value; negative if group 

B has the higher value). For effects with two or more contrasts, individual contrasts tested only when the 

overall test of differences among the groups was significant. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, National Education 

Longitudinal Study of 1988 (NELS:88/2000). 

43 
3. High School Attainments and Young Adult Outcomes 

Table 10. Strength-of-effect measures for high school attainment groups and 

 employment/income outcomes: 2000 

Employed Consistent 1999 Annual 1 2, 3 4,5 Predictor variable in 2000 employment income 

Gender (M - F) 3.20 * 0.50σ * 0.54σ * 

Race/ethnicity

 Black - White ( ) 0.89 -0.11σ -0.44σ *

 Hispanic - White 1.08 -0.07σ -0.02σ

 Native American - White 0.57 -0.48σ -0.42σ *

 Asian - White 1.27 -0.15σ -0.13σ 

School urbanicity - 8th grade

 Suburban - Urban 0.99 -0.09σ 0.03σ

 Rural - Urban 1.09 -0.09σ -0.06σ 

Postsecondary education completed

 Some PSE, no degree - None 1.53 * 0.17σ * 0.04σ

 Certificate/license - None 2.15 * 0.14σ 0.00σ

 Associate's degree - None 8.86 * 0.26σ * 0.18σ

 Bachelor's degree or higher - None 2.78 * 0.12σ 0.42σ * 

Attainment group

 (A1) Successful completers - Marginal completers 1.07 0.10σ 0.06σ

 (A2) Noncompleters - Marginal completers 0.70 -0.27σ * -0.19σ 

*p < .05 

1

Odds ratio for specific predictor variable. 

2

0.0 to 3.0 indicator of part-time/full-time employment 1997–1999. 

3

Effect sizes; mean differences in standard deviation units. 

4

Of those employed in 1999; annual income data were only available for 1999. 5

Log 1999 income in dollars. 

NOTE: Strength-of-effect measures estimated for main effects only. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, National Education 

Longitudinal Study of 1988 (NELS:88/2000). 

44 
 

 

 

 

The Adult Lives of At-Risk Students 

Postsecondary Education and Employment/Income. Postsecondary education was 

included in this analysis because of its potential impact on employment: it can delay working for 

months or years but it can also help an individual obtain employment. Postsecondary education 

was significantly related to all three employment-related outcomes (see table 9). 

Current employment was associated with completion of a postsecondary program of 

study. Table 10 shows the strength of this relationship. A certificate or license and bachelor’s 

degree more than doubled the odds of being employed (odds ratios = 2.15 and 2.78, 

respectively). An associate’s degree, typically awarded by community colleges and other 2-year 

programs, increased the odds by a larger factor (odds ratio = 8.86).96 

Entering postsecondary education (even without completing the program) and obtaining 

an associate’s degree were associated with greater consistency of employment. A bachelor’s 

degree was not associated with greater consistency of employment. 

A degree from a 4-year college was associated with higher income levels among students 

who had been at risk due to status characteristics. The median income in 1999 for young adults 

with a bachelor’s degree was $27,000, whereas the median was $20,000 to $23,000 for other 

levels of postsecondary education (table 6). 

Academic Risk/High School Attainment Groups. No discernable differences were found 

among attainment groups on current employment or annual income (table 9), even though both 

were significant when tested in isolation in the bivariate analysis (table 6). In the bivariate 

analysis, the contrast between marginal completers and noncompleters was significant for current 

employment, and the contrast between successful completers and marginal completers was 

significant for income (table C-3). The bivariate analysis did not take into account gender, 

race/ethnicity, urbanicity, or postsecondary education. When these variables were added to the 

regression, the tests of attainment group differences were reduced to nonsignificance. 

Differences were found between marginal completers and noncompleters on consistent 

employment. The magnitude of the difference between these two groups was approximately 

one-quarter of a standard deviation (effect size = 0.27σ; table 10). The same contrast was 

significant in the bivariate analysis. Nongraduation from high school is accompanied by less 

persistence in employment—as is nongraduation and less persistence in postsecondary 

schooling.97 

Because postsecondary education may be a mediator of employment and income, followup regressions were conducted in which postsecondary education was omitted from the analysis. 

The analysis was rerun for all three employment/income measures, including all predictor 

variables except postsecondary education. In these analyses, five out of six comparisons of 

attainment group differences were statistically significant. Successful completers were more 

consistently employed and had higher incomes than did marginal completers. A higher 

percentage of marginal completers than noncompleters were employed in 2000, and marginal 

completers were more consistently employed and higher incomes than did noncompleters. 

96 Ninety-seven percent of respondents with associate’s degrees were employed at the time of the fourth follow-up. 

97 See “Background Variables” above for postsecondary education results. 

45 
 

 

3. High School Attainments and Young Adult Outcomes 

In sum, the relationships of high school attainments employment and income were 

attributable in large part to the role of postsecondary schooling. Successful completers more 

than marginal completers, and marginal completers more than noncompleters, acquire more 

postsecondary schooling (table 7) and subsequently experience greater employment outcomes as 

young adults. Only the difference between noncompleters and marginal completers in consistent 

employment was not explained in this way. Noncompleters—students with high status risk and 

high academic risk—were least likely to hold continuous employment, for reasons beyond the 

fact that they acquired less postsecondary education. 

Interactions. Several interactions were significant in the analysis of employment and 

income. For employment in 2000, two particular interaction effects of race/ethnicity with 

attainment groups were significant. For one, Black successful completers had a higher 

employment rate (96 percent) than Black marginal completers (84 percent);98 this contrasts with 

the overall finding of no significant association of employment with attainment groups. The 

second effect may be attributable to Asian/Pacific Islander noncompleters, of whom 100 percent 

were employed; however the group size was small (n = 19) and the particular result may not be 

reliable. 

The interaction of gender with attainment group was significant for consistent 

employment. Attainment group differences were smaller for males than for females in general,99 

but one group stood out in particular: female noncompleters had lower consistent employment 

scores than all other gender-attainment combinations. 

98 Not shown in table. 

99 Not shown in table. 

46 
 

 

 

 

The Adult Lives of At-Risk Students 

4. Behavioral Risk: School Engagement/Disengagement 

and Young Adult Outcomes 

This portion of the analysis addressed the question: “Is behavioral risk, in the form of 

disengagement from high school, related to postsecondary schooling and employment?” All 

respondents in the analysis were at risk due to status characteristics, that is, home and school 

socioeconomic status (SES). To assess behavioral risk, four indicators of school engagement 

were derived from student and teacher reports: attendance and timeliness, rated by classroom 

teachers; class participation and completing assigned work, rated by classroom teachers; 

participation in extracurricular activities, reported by the student; and students’ ratings of the 

usefulness of school subjects for post-high school endeavors. Except for extracurricular 

activities, the measures were not counts of activities, but composite scales on which higher 

scores represented better behavior (engagement), and lower scores represented degrees of 

disengagement. 

The relationships between engagement (behavioral risk) and high school attainments 

(academic risk) were examined by comparing the three attainment groups on average levels of 

engagement. Effect sizes revealed the magnitude of the differences between successful 

completers and marginal completers, and between marginal completers and noncompleters.100 

Two approaches were taken to studying the relationship of engagement/disengagement 

with adult outcomes. First, variable-by-variable relationships of engagement with adult 

outcomes were examined; correlation coefficients were used to show the strength of the 

relationships. Next, a set of multivariate regression analyses were performed to examine the 

relationship of engagement to adult outcomes in a more complete context. Strength-of-effect 

measures were obtained for each statistical relationship. 

Engagement/Disengagement and Attainment Groups 

The relationship between high school engagement and high school attainment was 

examined first, by comparing the three attainment groups on the means of the engagement 

variables. Table 11 gives the means, and table C-4 (appendix C) gives effect sizes and statistical 

significance for two specific contrasts—successful completers compared to marginal completers, 

and marginal completers compared to noncompleters.101 

Both contrasts were significant for attendance, classroom behavior, and extracurricular 

activities (table C-4). The effect sizes were small for extracurricular activities102 (0.24σ and 

100 See “Strength-of-Effect Measures” in chapter 2 for more information about effect sizes. 101 In the “protected test” approach, the contrasts are designated as statistically significant only if the “overall” test 

of all differences among the three groups is found to be significant first.

102 Using Cohen’s (1988) descriptors. 

47 
 

4. Behavioral Risk 

0.29σ), and moderate to large for attendance (0.40σ and 0.82σ) and classroom behavior (0.72σ

and 0.70σ). In all, the attainment groups were distinct on these three measures; status risk 

students with higher attainments in high school are more likely to have been engaged (and less 

disengaged) compared to those with lower grades, lower test scores, or who drop out. 

No appreciable differences among the groups were found on the usefulness measure 

(UTILITY), perhaps due to several factors.103 For one, the measure was gathered when students 

were in eighth grade, whereas the other measures were gathered in tenth and twelfth grade. 

Second, the measure reflects attitudes rather than actions. Despite theory that stresses the 

importance of these particular attitudes (Eccles 1983; Mickelson 1990; Updegraff et al. 1996), 

they may have little or no relationship with actual attainments several years later. 

Table 11. Mean engagement measures of high school attainment groups: 1990 

High school attainment group 

Successful Marginal 

1 2 3 Engagement measure All completers completers Noncompleters 

Attendance4

* 3.94 4.19 3.97 3.51 

Classroom behavior5

* 2.35 3.23 2.28 1.35 

Extracurricular participation6

* 1.63 2.01 1.63 1.16 

Usefulness of school subjects7 3.01 3.08 3.00 2.98 

*Overall test of differences among groups significant at p < .05; see table C-11 in appendix C for specific 

comparisons. 

1

In school in twelfth grade in 1992; "reasonable" scores on reading and mathematics tests; passing grades; 

graduated from high school. 

2

Test scores not "reasonable" and/or grades not passing; high-school graduate. 

3

Dropped out by 1992, or in school not in twelfth grade in 1992 and did not earn diploma by age 20. 

4

How often a student is absent or tardy, range: 1-5. 

5

Teacher rating of student's behavior, range: 1-4. 

6

Participation in extracurricular activities, range: 0-6. 

7

Student rating of the usefulness of math, English, social studies and science, range: 1-4. 

NOTE: Standard errors are given in appendix B, table B-11. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, National Education 

Longitudinal Study of 1988 (NELS:88/2000). 

103 UTILITY had low correlations with the other engagement measures (see “Variables” in chapter 2), but 

reasonably high internal consistency (α = 0.71; see p.19). 

48 
 

 

 

The Adult Lives of At-Risk Students 

Engagement/Disengagement and Adult Outcomes 

The correlations between the engagement variables and adult schooling and employment 

are given in table 12. Due to the large number of correlations in this table, some may be 

significant by chance factors alone.104 Nevertheless, attendance and classroom behavior had 

small but significant correlations with all three indicators of postsecondary schooling, that is, 

entering a postsecondary institution, credits earned in postsecondary institutions, and completing 

postsecondary education. Only the correlation of attendance with credits earned in less-than-2-

year programs was not statistically significant (r = 0.16). 

Extracurricular participation was related to all three postsecondary variables, although the 

correlations were small; the statistically significant correlations ranged from r = 0.08 to r = 0.18. 

The perceived usefulness of school subjects was related only to credits earned in postsecondary 

school, and only for the total sample. 

Attendance was related to four indicators of employment: employment in 2000, full-time 

employment, and consistent employment (for the total sample and for participants who did not 

enter or complete any postsecondary education), and income (for the full sample). The 

correlations were also small, however; the significant values ranged from r = 0.08 to r = 0.13. 

Classroom behavior was related negatively to hours worked (r = -0.10) and to the annual 

income of participants with no postsecondary education (r = -0.17). Extracurricular participation 

was not related significantly to any measure of employment or income. The perceived 

usefulness of school subjects was related negatively to consistent employment for the entire 

sample (r = -0.09) and for participants with no postsecondary education (r = -0.20). Of the 45 

correlations of classroom behavior, extracurricular participation, and usefulness of school 

subjects with adult employment and income, only five were statistically significant, and those 

were in a direction opposite to that suggested by prior research and theory.105 

In all, the behavioral risk level of engagement of status risk students, that is, 

engagement/disengagement, is related to entering and persisting in postsecondary education. It 

is related less, if at all, to employment and income at age 26. At the time of the NELS:88 fourth 

follow-up, only one indicator of engagement—regular, on-time attendance at school and in 

class—was significantly related to employment as a young adult. The regression analyses 

examined these relationships taking into account other characteristics of the status risk sample. 

104 The type I error rate of .05 suggests that, of the 84 correlations in the table, approximately 4 or 5 may appear 

significant but do not represent real nonzero relationships. 

105 See “Behavioral Risk Factors: Engagement and Disengagement in School” in the Introduction. 

49 
4. Behavioral Risk 

Table 12. Correlations of engagement measures with postsecondary education and

 employment/income outcomes: 2000 

Engagement measures 

 Classroom Extracurricular Usefulness of 

Postsecondary outcomes Attendance behavior participation school subjects

Student entered a postsecondary .167 * .256 * .154 * .038

 institution1,2 

Credits earned in postsecondary .282 * .274 * .175 * .090

 institutions3,4,5

 < 2-year school .158 .218 * .021 -.033 

 2-year school .224 * .221 * .090 * .034 

 4-year school .225 * .191 * .088 * .024

Student completed postsecondary .128 * .126 * .078 * .033

 education(all)2,4 

Currently employed (2000)2 .078 * .077 .000 -.065

Employed full-time (2000)2,6 .078 * .039 -.031 -.030 

Number of hours worked per week (1999)7 .030 -.097 * -.038 -.016 

Consistent employment 1997-19998 .129 * .047 -.012 -.090 

 No postsecondary education .104 * -.009 -.090 -.197

 Some postsecondary education, no degree .131 * .060 .019 .016 

 Certificate/license .176 -.025 .041 -.071 

 Associate's degree .175 .099 -.043 -.121 

 Bachelor's degree or higher -.003 .064 -.022 -.067 

1999 annual income7 .116 * .007 .025 .033 

 No postsecondary education .071 -.168 * -.050 -.025 

 Some postsecondary education, no degree .135 .087 .080 .104 

 Certificate/license .063 -.001 -.060 -.040 

 Associate's degree .206 .044 -.051 -.009 

 Bachelor's degree or higher -.040 -.059 .007 .026

*p < .05 1

Classified by type of first institution attended. 

2

Coded 0=No, 1=Yes. 

3

Credits earned from all schools attended, classified by type of first institution attended. 

4

Of those who attended postsecondary education. 

5

Response rate on this variable less than 85 percent. 

6

Of those currently employed. 

7

Of those employed in 1999; annual income data were only available for 1999. 

8

0.0 to 3.0 indicator of part-time/full-time employment 1997–1999. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, National Education 

Longitudinal Study of 1988 (NELS:88/2000). 

50 

* 

* 

* 

* 
 

 

The Adult Lives of At-Risk Students 

Question (2.1) Behavioral Risk and Postsecondary Education: Is Engagement in 

High School Related to Postsecondary Education Among Status Risk Students? 

Analyses were conducted to examine the relationship of engagement with postsecondary 

schooling in regressions that included students’ demographic characteristics and the type of 

postsecondary education attended. The results of the regressions are summarized in table 13; 

strength-of-effect measures are given in table 14. Each column of these tables corresponds to a 

particular outcome variable: entering a postsecondary school and, for those who did enter, the 

number of credits earned and whether a program of study was completed. The rows of tables 13 

and 14 correspond to particular independent variables. For variables with more than two 

classifications, and for the four engagement variables, an overall test was conducted first to 

indicate whether there was any relationship between the set of variables and the particular 

outcome measure.106 If the overall test was significant, specific contrasts or specific engagement 

variables were then tested. 

Strength-of-effect measures were obtained in several forms. Odds ratios were computed 

for the yes/no dependent variables entering a postsecondary institution and completing a program 

of study. For a categorical independent variable (e.g., gender), this was the ratio of the odds of a 

male belonging to a particular group (e.g., entering postsecondary schooling) to the odds of a 

female belonging to that group. The engagement variables were continuous. For these, the odds 

ratio is the change in odds associated with a one standard deviation increase in the engagement 

measure. For example, the odds ratio for the relationship of attendance with entering a 

postsecondary institution is the change in the odds of entering postsecondary schooling that 

accompanies a one standard deviation increase in attendance ratings. 

Effect sizes were computed for the relationship of the continuous dependent variable 

number of credits with categorical independent variables (e.g., gender). Standardized regression 

coefficients were computed for number of credits and the continuous engagement measures. A 

standardized regression coefficient indicates the number of standard deviations change in credits 

associated with a one-standard-deviation increase in the particular engagement measure. 

Background Variables.107 In this analysis, females at status risk were more likely to 

enter postsecondary schooling and to complete their postsecondary studies than were males, but 

there were no discernable gender differences in the number of credits earned (table 13). 

Racial/ethnic differences were found for entering postsecondary education and for the number of 

credits earned. Hispanic and Asian/Pacific Islander students at status risk were more likely than 

Whites to enter postsecondary education (odds ratios = 2.63 and 5.09, respectively; table 14). 

Native American/Alaska Native students were less likely than Whites to enter postsecondary 

schooling (odds ratio = 0.50), and when they did, they accrued fewer credits than did Whites. 

106 A Wald statistic; see “Special Considerations Required by the Data Set” in chapter 2. 107 The results for background variables (gender, race/ethnicity, school urbanicity, and type of postsecondary school) 

differ slightly from those given in the attainment-group analysis (tables 7 and 8). Each set of results depends to 

some extent on the other variables in the model—attainment group differences or engagement measures. 

51 
4. Behavioral Risk 

Table 13. Regression results for engagement and postsecondary education 

 outcomes: 2000 

For those who entered 

postsecondary education 

 Entering a 

postsecondary Number of Completing a

Predictor variable institution 1 credits earned program of study

Gender (M - F) 

Race/ethnicity

 Black - White

 Hispanic - White

 Native American - White

 Asian - White 

School urbanicity - 8th grade

 Suburban - Urban

 Rural - Urban 

Type of postsecondary institution

 < 2-year - 2-year

 4-year - 2-year 

Engagement

 Attendance

 Classroom behavior

 Extracurricular participation

 Usefulness of school subjects 

Gender x Engagement

 Gender x Attendance

 Gender x Classroom behavior

 Gender x Extracurricular Participation

 Gender x Usefulness of school subjects 

See notes at end of table. 

 -0.41 * -3.80 -0.35 * 

(0.141) (2.692) (0.129) 

0.35 -5.69 -0.29

( ) (0.204) (4.645) (0.202)

0.97 * -6.01 -0.32 

(0.198) (3.847) (0.170)

-0.69 * -26.57 * -0.82 

(0.348) (4.618) (0.432) 

1.63 * 7.8 -0.31 

(0.350) (5.936) (0.222) 

0.07 1.18 -0.004 

(0.207) (4.023) (0.192)

-0.18 5.56 0.19 

(0.201) (3.737) (0.175) 

† -7.97 * 1.63 * 

(3.787) (0.239)

† 40.66 * 0.54 * 

(3.023) (0.138) 

0.34 * 12.91 * 0.33 * 

(0.120) (2.782) (0.133)

 0.30 * 4.43 * 0.12 * 

(0.057) (1.084) (0.050)

 0.20 * 1.49 0.04 

(0.044) (0.836) (0.034)

-0.05 1.52 0.08 

(0.114) (2.130) (0.105) 

-0.26 2.99 0.17 

(0.250) (5.637) (0.299)

0.20 -1.72 -0.08 

(0.105) (2.079) (0.103)

-0.04 -0.12 -0.02 

(0.079) (1.646) (0.069)

0.35 5.08 0.06 

(0.203) (4.672) (0.223) 

52 
The Adult Lives of At-Risk Students 

Table 13. Regression results for engagement and postsecondary education outcomes:

 2000―Continued 

For those who entered 

postsecondary education

 Entering a 

postsecondary Number of Completing a 

Predictor variable institution credits earned 1 

program of study 

Race/ethnicity x Engagement

 Black x Attendance

 Black x Classroom behavior

 Black x Extracurricular participation

 Black x Usefulness of school subjects

 Hispanic x Attendance

 Hispanic x Classroom behavior

 Hispanic x Extracurricular participation

 Hispanic x Usefulness of school subjects

 Native American x Attendance

 Native American x Classroom behavior

 Native American x Extracurricular participation

 Native American x Usefulness of school subjects

 Asian x Attendance

 Asian x Classroom behavior

 Asian x Extracurricular participation

 Asian x Usefulness of school subjects 

0.08 

(0.349) 

0.11 

(0.176)

 -0.29 * 

(0.095) 

-0.19 

(0.270) 

0.03 

(0.297) 

0.18 

0.120

 -0.41 * 

(0.122) 

-0.02 

(0.291) 

-0.03 

(0.603)

 0.64 * 

(0.301) 

-0.14 

(0.207) 

-1.07 

(0.626) 

0.82 

(0.497) 

-0.20 

(0.250) 

-0.16 

(0.261)

 1.73 * 

(0.687) 

5.90 

(7.056) 

-0.58 

(3.075) 

-2.22 

(2.396) 

1.97 

(6.584) 

-4.00 

(6.765) 

-2.78 

(2.607) 

-2.45 

(2.635) 

3.90 

(5.968) 

-5.70 

(10.504) 

0.11 

(5.207) 

-2.40 

(6.084) 

-15.15 

(21.301) 

-11.20 

(9.209)

 6.83 

(4.059)

 -6.10 * 

(2.451) 

23.77 * 

(9.639) 

-0.08 

(0.362) 

0.07 

(0.142) 

-0.10 

(0.094) 

-0.32 

(0.273) 

-0.02 

(0.342) 

0.08 

(0.126) 

-0.13 

(0.104) 

0.16 

(0.367) 

2.05 

(1.341) 

0.59 

(0.489) 

-0.07 

(0.319) 

0.66 

(1.144) 

0.79 

(0.500) 

0.20 

(0.170) 

-0.15 

(0.134) 

0.34 

(0.465) 

† Not applicable. 

*p < .05 

1

Response rate on this variable less than 85 percent. 

NOTE: Table contains unstandardized regression coefficients. Standard errors in parentheses. Each 

coefficient is a contrast, group A-minus-group B (positive if group A has the higher value; negative if group 

B has the higher value). For effects with two or more contrasts, individual contrasts tested only when the 

overall test of differences among the groups was significant. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, National Education 

Longitudinal Study of 1988 (NELS:88/2000). 

53 
 

 

4. Behavioral Risk 

Table 14. Strength-of-effect measures for engagement and postsecondary

 education outcomes: 2000 

For those who entered 

postsecondary education

 Entering a 

postsecondary Number of Completing a 

1 2 1 Predictor variable institution credits earned program of study 

Gender (M - F) 0.67 * -0.08σ 0.71 * 

Race/ethnicity

 Black - White ( 1.42) -0.12σ 0.75

 Hispanic - White 2.63 * -0.13σ 0.73

 Native American - White 0.50 * -0.58σ * 0.44

 Asian - White 5.09 * 0.17σ 0.73 

School urbanicity - 8th grade

 Suburban - Urban 1.07 0.03σ 1.00

 Rural - Urban 0.83 0.12σ 1.21 

Type of postsecondary institution

 < 2-year - 2-year † -0.18σ * 5.12 *

 4-year - 2-year † 0.89σ * 1.72 * 

Engagement 

*

3 *4 *3 Attendance 1.23 0.14 1.22 

*

3 *4 *3 Classroom behavior 1.54 0.12 1.19

 Extracurricular participation 1.39 *

3 0.05 4 1.06 3 

3 4 3 Usefulness of school subjects 0.97 0.02 1.05 

† Not applicable. 

*p < .05 

1

Odds ratio for specific predictor variable. 

2

Response rate on this variable less than 85 percent. 

3

Odds ratios associated with a one standard deviation increase in the specific engagement measure. 

4

Standardized regression coefficient. 

NOTE: Strength-of-effect measures estimated for main effects only. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, National Education 

Longitudinal Study of 1988 (NELS:88/2000). 

Students who entered less-than-2-year programs were more likely to complete a program 

of study than were students who entered 2-year programs (odds ratio = 5.12; table 14). Students 

who entered 4-year programs were also more likely to complete their studies than were students 

in 2-year programs (odds ratio = 1.72). The noncompletion rate for 2-year colleges was 

especially high: 61 percent of status risk students who entered 2-year colleges had not completed 

any program of study by the time of the fourth follow-up wave of NELS:88.108 

Behavioral Risk: Engagement/Disengagement in High School. As a set, the four school 

engagement measures were related to all postsecondary outcomes. Individually, attendance and 

108 Including other programs to which they may have transferred (percentage not shown in tables). 

54 
 

 

The Adult Lives of At-Risk Students 

classroom behavior had positive relationships with postsecondary schooling. Both engagement 

measures were related to the odds of entering a postsecondary school (odds ratios = 1.23 and 

1.54, respectively; table 14), to credits earned (standardized coefficients = 0.14 and 0.12, 

respectively), and to the odds of completing a program of study (odds ratios = 1.22 and 1.19, 

respectively). Disengagement, in terms of less frequent attendance and poorer classroom 

behavior, was associated with reduced postsecondary outcomes. 

Extracurricular activities in high school were related to status risk students’ entering a 

postsecondary school, with less participation being associated with reduced chances of entering a 

postsecondary program. Extracurricular activities were not related to persistence in 

postsecondary school, however, either in the form of accruing credits or completing a program of 

study. 

In sum, among students at risk due to status characteristics, risk behaviors are related to 

postsecondary experiences, namely, being late or absent from classes, and nonparticipation in 

class work. The affective measure, the perceived utility of high school subjects, was not 

discernably related to any postsecondary education outcome. 

Interactions. Tests of interactions reveal whether the relationships between engagement 

and postsecondary education vary between gender groups or among racial/ethnic groups. The 

interaction of gender with engagement (Gender x Engagement in table 13) was nonsignificant; 

the effects of high school engagement/disengagement on later educational outcomes were not 

measurably different for males and females. 

The interaction of race/ethnicity by engagement was significant for entering a 

postsecondary institution and number of credits earned (table 13). For entering postsecondary 

education, two significant contrasts were found for participation in extracurricular activities. To 

explore further, the percentage entering postsecondary school was examined for each 

racial/ethnic group, with extracurricular activities classified into low (no activities), middle (1-2 

activities), and high (3 or more activities). The relationship between entering postsecondary 

education and extracurricular activities varied by racial/ethnic group. A relationship was found 

for White students, for whom the percentage entering postsecondary school increased from 47 

percent to 65 percent to 75 percent as the number of extracurricular activities increased.109 No 

discernable differences were found for Hispanic or Black students, however. Thus, the 

relationship between extracurricular activities and entering postsecondary education, found for 

the entire status risk sample, was not supported for Black or Hispanic students. 

A similar analysis was undertaken for the remaining four significant interactions of 

race/ethnicity with engagement (table 13). In each case, the engagement variable was classified 

into thirds and differences among the three groups were examined statistically. The only pattern 

to emerge was for White students: for Whites, one or both contrasts indicated a positive 

relationship between the usefulness of school subjects and entering postsecondary education, and 

between extracurricular participation and earning postsecondary credits. These were exceptions 

to the conclusion of no discernable relationships between these variables for the full sample. 

109 Values not tabled. 

55 
 

 

4. Behavioral Risk 

Question (2.2) Behavioral Risk and Employment: Is Engagement in High School 

Related to Later Employment and Income Among Status Risk Students? 

The relationship of engagement with adult employment and income was examined in 

analyses that included student demographic characteristics and postsecondary educational 

attainments (if any). In these analyses, the four indicators of school engagement were the main 

independent variables, and three indexes of employment/income were the outcome measures: 

whether the respondent was employed for pay (full-time or part-time) in 2000, an index of 

consistent employment during 1997-1999, and, for those who were employed in 1999, their 

annual income. 

The results are summarized in table 15; strength-of-effect measures are given in table 16. 

Each column of these tables gives results for one index of employment/income. The strength-ofeffect measures for the yes/no dependent variable employed in 2000 are odds ratios. For the 

continuous dependent variables (consistent employment and income), the strength-of-effect 

measures are effect sizes if the independent variable is categorical (gender, race/ethnicity, 

urbanicity, postsecondary education) and standardized regression coefficients if the independent 

variable is continuous (engagement variables). 

Background Variables.

110 Gender differences were found on all three outcomes. Males at 

status risk were more likely than females to be employed in 2000, were more consistently 

employed over the period 1997–1999, and earned higher incomes than did females. 

Racial/ethnic differences were found only for annual income; Black young adults earned less, on 

average, than did Whites. All urbanicity differences were nonsignificant. 

Postsecondary Education and Employment/Income. All three employment outcomes 

were related to postsecondary schooling. Status risk individuals who completed any 

postsecondary program (with a certificate or license, associate’s degree, or bachelor’s degree) 

had greater odds of being employed in 2000 than did those without postsecondary experience 

(odds ratios of 1.9, 7.5 and 2.2, respectively; table 16). In terms of consistent employment, 

associate’s degree holders and bachelor’s degree holders were more likely to be employed 

consistently over 3 years than those who had not completed any postsecondary program. In 

terms of income, individuals who had acquired an associate’s degree or bachelor’s degree111 had 

higher incomes in 1999 than those who had not completed a postsecondary program. The 

median incomes for young adults who had been at status risk as high school students were 

$22,000 with no postsecondary schooling, $23,000 with an associate’s degree, and $27,000 with 

a bachelor’s degree or higher (table 6). 

110 The results for background variables differ slightly from those found in the attainment-group analysis (tables 9 

and 10). Each set of results depends to some extent on the other variables in the model—attainment group 

differences or engagement measures. 

111 Or higher. 

56 
Table 15. 

The Adult Lives of At-Risk Students 

Table 15. Regression results for engagement and employment/income outcomes: 

1997-2000 

Employed Consistent 1999 Annual 

1 2,3 Predictor variable in 2000 employment income 

Gender (M - F) 

Race/ethnicity

 Black - White

 Hispanic - White

 Native American - White

 Asian - White 

School urbanicity - 8th grade

 Suburban - Urban

 Rural - Urban 

Postsecondary education completed

 Some PSE, no degree - None

 Certificate/license - None

 Associate's degree - None

 Bachelor's degree or higher - None 

Engagement

 Attendance

 Classroom behavior

 Extracurricular participation

 Usefulness of school subjects 

Gender x Engagement

 Gender x Attendance

 Gender x Classroom behavior

 Gender x Extracurricular Participation

 Gender x Usefulness of school subjects 

See notes at end of table. 

1.20 * 0.41 * 0.39 * 

(0.188) (0.049) (0.065) 

0.33 -0.08 -0.35 * 

(0.297) (0.089) (0.123) 

0.41 0.03 0.00 

(0.216) (0.068) (0.058)

-0.61 -0.46 -0.25 

(0.359) (0.199) (0.127)

0.30 -0.12 

( ) 

-0.14 

(0.325) (0.111) (0.099) 

0.26 0.15 0.11 

(0.239) (0.080) (0.091)

0.37 0.16 0.03

(0.230) (0.078) (0.083) 

0.24 0.12 -0.01

(0.206) (0.067) (0.086)

0.66 * 0.09 -0.01 

(0.310) (0.125) (0.138)

 2.02 * 0.25 * 0.16 * 

(0.432) (0.069) (0.075)

 0.79 * 0.15 * 0.34 * 

(0.231) (0.077) (0.083) 

0.05 0.11 * 0.10 

(0.167) (0.049) (0.068)

 0.18 * 0.02 -0.01 

(0.085) (0.021) (0.016)

-0.04 -0.02 0.00 

(0.045) (0.014) (0.014)

 -0.36 * -0.12 * 0.05

(0.138) (0.044) (0.055) 

-0.35 -0.09 -0.08 

(0.317) (0.088) (0.142)

0.17 -0.00 -0.01 

(0.132) (0.035) (0.036)

-0.02 -0.05 0.01

(0.093) (0.029) (0.028)

0.06 0.09 0.03 

(0.313) (0.087) (0.102) 

57 
 

4. Behavioral Risk 

Table 15. Regression results for engagement and employment/income outcomes: 

1997-2000―Continued 

Predictor variable 

Employed 

 in 2000 

 Consistent 1 employment

 1999 Annual 2,3 income

Race/ethnicity x Engagement

 Black x Attendance -0.80 * -0.05 0.37 

 Black x Classroom behavior 

(0.376) 

0.76 * 

(0.142) 

0.13 

(0.275)

0.00 

 Black x Extracurricular participation 

 Black x Usefulness of school subjects 

 Hispanic x Attendance 

 Hispanic x Classroom behavior 

 Hispanic x Extracurricular participation 

 Hispanic x Usefulness of school subjects 

 Native American x Attendance 

(0.199) 

0.03 

(0.133) 

-0.87 * 

(0.414) 

-0.27 

(0.381) 

0.26 

(0.139) 

-0.04 

(0.106) 

0.08 

(0.324) 

1.78 * 

(0.067) 

-0.07 

(0.036) 

0 

(0.124) 

-0.06 

(0.088) 

0.03 

(0.036) 

0.00 

(0.034) 

-0.09

(0.100) 

0.43 

(0.065)

0.02 

(0.050)

0.28 

(0.212)

-0.146

(0.092)

0.02

(0.032)

0.00 

(0.030)

0.03 

(0.077)

-0.33

 Native American x Classroom behavior 

(0.884) 

0.12

(0.499) 

0.03 

(0.169)

 0.04 

 Native American x Extracurricular participation 

 Native American x Usefulness of school subjects 

 Asian x Attendance 

(0.277) 

0.46 * 

(0.200) 

-1.22

(0.756) 

-0.40 

(0.117) 

0.04 

(0.077) 

-0.37 

(0.280) 

-0.07 

(0.101)

-0.15 

(0.126)

-0.00 

(0.208)

-0.12 

 Asian x Classroom behavior 

(0.484) 

-0.41 

(0.159) 

-0.01 

(0.185)

0.06 

 Asian x Extracurricular participation 

Asian x Usefulness of school subjects 

(0.432) 

0.04 

(0.165)

-0.65 

(0.830) 

(0.080) 

0.10 

(0.055)

0.15 

(0.157) 

(0.076)

0.01 

(0.045)

0.18 

(0.195) 

See notes at end of table. 

58 
Table 15. 

The Adult Lives of At-Risk Students 

Table 15. Regression results for engagement and employment/income outcomes: 

1997-2000―Continued 

Employed Consistent 1999 Annual 1 2,3 Predictor variable in 2000 employment income 

Postsecondary Education Completed x Engagement

 Some PSE, no degree x Attendance -0.29 -0.04 -0.11 

(0.340) (0.099) (0.195)

 Some PSE, no degree x Classroom behavior -0.10 0.05 0.12 

(0.131) (0.041) (0.044)

 Some PSE, no degree x Extracurricular participation 0.07 0.02 0.04 

(0.111) (0.040) (0.035)

 Some PSE, no degree x Usefulness of school subjects 0.78 0.26 * 0.11 

(0.361) (0.124) (0.161)

 Certificate/license x Attendance -0.57 0.15 -0.23 

(0.591) (0.190) (0.274)

 Certificate/license x Classroom behavior -0.14 -0.05 0.12

(0.193) (0.054) (0.058)

 Certificate/license x Extracurricular participation 0.22 0.03 -0.03 

(0.144) (0.055) (0.055)

 Certificate/license x Usefulness of school subjects 0.62 0.15 -0.15 

(0.381) (0.121) (0.112)

 Associate's degree x Attendance 0.32 -0.04 0.16 

(0.852) (0.140) (0.206)

 Associate's degree x Classroom behavior -0.12 0.07 0.08 

(0.303) (0.051) (0.051)

 Associate's degree x Extracurricular participation 0.45 -0.00 -0.01 

(0.282) (0.045) (0.041)

 Associate's degree x Usefulness of school subjects 1.89 0.14 0.02 

(0.777) (0.123) (0.123)

 Bachelor's degree or higher x Attendance -0.38 -0.29 * -0.26 

(0.489) (0.125) (0.170)

 Bachelor's degree or higher x Classroom behavior -0.01 0.13 * 0.10

(0.199) (0.056) (0.055)

 Bachelor's degree or higher x Extracurricular participation -0.05 -0.01 0.01 

(0.133) (0.042) (0.034)

 Bachelor's degree or higher x Usefulness of school subjects 0.36 0.12 0.04

(0.396) (0.116) (0.085) 

*p < .05 

1

0.0 to 3.0 indicator of part-time/full-time employment 1997–1999. 2

Of those employed in 1999; annual income data were only available for 1999. 

3

Log 1999 income in dollars. 

NOTE: Table contains unstandardized regression coefficients. Standard errors in parentheses. Each 

coefficient is a contrast, group A-minus-group B (positive if group A has the higher value; negative if group 

B has the higher value). For effects with two or more contrasts, individual contrasts tested only when the 

overall test of differences among the groups was significant. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, National Education 

Longitudinal Study of 1988 (NELS:88/2000). 

59 
4. Behavioral Risk 

Table 16. Strength-of-effect measures for engagement and employment/income 

 outcomes: 2000 

Employed Consistent 1999 Annual 

1 2 3,4 Predictor variable in 2000 employment income 

Gender (M - F) 3.32 * 0.51σ * 0.53σ * 

Race/ethnicity

 Black - White 1.38 -0.10σ -0.47σ *

 Hispanic - White 1.50 0.04σ 0.00σ

 Native American - White 0.55 -0.56σ ( -0.33σ)

 Asian - White 1.35 -0.15σ -0.19σ 

School urbanicity - 8th grade

 Suburban - Urban 1.29 0.19σ 0.15σ

 Rural - Urban 1.45 0.20σ 0.03σ 

Postsecondary education completed

 Some PSE, no degree - None 1.28 0.15σ -0.01σ

 Certificate/License - None 1.94 * 0.11σ -0.01σ

 Associate's degree - None 7.54 * 0.31σ * 0.21σ *

 Bachelor's degree or higher - None 2.21 * 0.19σ * 0.46σ * 

Engagement 

*

6 Attendance 1.03 5 0.08 0.07 6 

*

5 Classroom behavior 1.31 0.04 6 -0.02 6 

5 6 6 Extracurricular participation 0.94 -0.03 0.00

 Usefulness of school subjects 0.80 *

5 -0.08 *6 0.04 6 

*p < .05 

1

Odds ratio for specific predictor variable. 

2

0.0 to 3.0 indicator of part-time/full-time employment 1997–1999. 

3

Of those employed in 1999; annual income data were only available for 1999. 4

Log 1999 income in dollars. 

5

Odds ratios associated with a one standard deviation increase in the specific engagement measure. 

6

Standardized regression coefficient. 

NOTE: Strength-of-effect measures estimated for main effects only. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, National Education 

Longitudinal Study of 1988 (NELS:88/2000). 

60 
 

 

The Adult Lives of At-Risk Students 

Behavioral Risk: Engagement/Disengagement in High School. The links between school 

engagement/disengagement and employment and income are the most tentative of those 

examined in this analysis. The elapsed time between high school and employment 8 years later 

is substantial, and the theory connecting employment with high school engagement is more 

speculative than that connecting employment with school attainments. 

As a set, the four engagement measures were related to current employment in 2000 and 

to consistent employment. Classroom behavior was positively related to current employment 

(table 15). The attendance composite was positively but weakly related to consistent 

employment. Better attendance and coming to class on time were associated with more 

consistent employment as a young adult, and poorer attendance with less consistent employment, 

but the standardized regression coefficient was small (0.08; table 16). In the correlation analysis, 

which did not include student background variables, attendance was also weakly related to 

current employment (table 12).112 Extracurricular participation was not measurably related to 

any employment outcome. 

The perceived usefulness of school subjects (UTILITY) was related to current 

employment and consistent employment, but in the opposite direction than was hypothesized. 

Follow-up analyses were conducted omitting the UTILITY variable. These yielded 

nonsignificant overall tests of engagement with current employment and income. For consistent 

employment, no individual engagement measure was significant. With this reduced set of 

engagement measures, and with student demographics and postsecondary education included in 

the regressions, high school engagement was not discernably related to employment or income. 

In the analysis of high school attainments and employment/income (question 1.2), a set of 

follow-up analyses were run excluding postsecondary education.113 It was found that the 

relationship of attainment with employment was mediated by postsecondary schooling.114 

Likewise, follow-up analyses were conducted for question 2.2 to see if postsecondary education 

mediated the relationship between engagement and employment. If so, additional statistically 

significant relationships would emerge when postsecondary education was omitted from the 

analysis. In this follow-up analysis, engagement was still related to employment in 2000 and to 

consistent employment, and not related to income. In terms of specific engagement variables, 

attendance was still related to consistent employment, and classroom behavior was still related to 

current employment. UTILITY was inversely related to both outcomes. 

In sum, among student at risk due to status characteristics, specific risk behaviors are 

weakly related to employment in the years following high school. Poor classroom behavior is 

associated with a reduced likelihood of being employed, and poor attendance with less consistent 

employment over three years. When behavioral risk is measured by three of the four 

engagement variables, these relationships become more tentative still. 

112 In multiple regression, correlations among the independent variables (“collinearity”) can sometimes mask their 

association with the dependent variables. In these particular analyses, collinearity among the engagement measures 

may have masked some of the association with current employment or consistent employment. 

113 See “Question (1.2)” in chapter 3 of this report. 114 That is, the relationships of attainment and employment became nonsignificant when postsecondary schooling 

was included in the regressions. 

61 
 

 

 

 

 

 

4. Behavioral Risk 

Interactions. The interaction of gender with engagement was nonsignificant for every 

employment measure (table 15); that is, the relationships of engagement with employment did 

not vary discernably by gender. The overall test of the race/ethnicity-by-engagement interaction 

was significant for current employment. Specifically, the interactions of the Black–White 

difference with attendance, classroom behavior, and usefulness of school subjects were 

significant, as well as the Native American/Alaska Native–White difference with attendance and 

extracurricular participation. To investigate, respondents were classified into three equal-sized 

groups on each engagement measure and the percentage of employed respondents at each level 

was computed within racial/ethnic groups. 

Two patterns emerged. First, the percentages of White participants with low, middle, and 

high classroom behavior who were currently employed were 86 percent, 85 percent, and 85 

percent, respectively.115 Among Black participants, the percentages increased from 76 percent to 

90 percent to 94 percent employed as classroom behavior improved from low to high. Poor 

classroom behavior was related to lower employment rates among Black students but not among 

Whites. Second, the interactions for Native American/Alaska Native students were attributable 

to low employment rates for Native American/Alaska Natives who were low on attendance (45 

percent employed) and low on extracurricular participation (30 percent employed) compared to 

Native American/Alaska Native students who were mid-level on each measure (97 percent and 

87 percent, respectively).116 No such differences were found for White students. 

The overall test of the postsecondary education-by-engagement interaction was 

significant for consistent employment. The data were partitioned into low, middle, and high 

engagement groups. One result to emerge may account in part for the weak relationship between 

attendance and consistent employment (table 16). Among participants with a bachelor’s degree, 

no discernable differences in consistent employment were found among those who were low, 

middle, and high on the attendance composite. In the comparison group (those with no 

postsecondary education), as in the total sample,117 the consistent employment index was lower 

among low attendance students than among high attendance students. 

Question (2.3) Behavioral and Academic Risk: Is Engagement Related to Young 

Adult Outcomes, Above and Beyond High School Attainment? 

This portion of the analysis reexamined the relationship between behavioral risk 

(engagement/disengagement) and young adult outcomes with academic risk (attainment groups) 

also included in the regressions. Other portions of the analysis demonstrated a relationship 

between high school engagement and postsecondary education, and a weak relationship between 

high school engagement and later employment, without considering the effects of attainment.118 

This analysis asks if the relationship of engagement with adult outcomes is explained by the fact 

that engagement is related to attainment which, in turn, is related to postsecondary schooling and 

employment. The alternative is that engagement is related to adult outcomes or some other 

unspecified mechanism. 

115 Percentages not given in tables. 116 These percentages are based on sample sizes below 30 and may be unreliable. 117 That is, the test of attendance with consistent employment in the Engagement section of table 15. 118 Question (2.1) and question (2.2), respectively, in this chapter. 

62 
 

The Adult Lives of At-Risk Students 

The regressions for engagement (tables 13 and 15) were re-run with both attainment and 

engagement as predictor variables. All other predictor variables were also included, that is, 

gender, race/ethnicity, school urbanicity, the type of postsecondary institution attended (for 

credits earned and postsecondary program completion), and postsecondary education completion 

(for employment and income). Strength-of-effect measures for postsecondary education 

outcomes are given in table 17, and for employment and income are given in table 18. The 

complete regression results for all predictor variables are given in tables C-5 and C-6 in appendix 

C. 

Table 17. Strength-of-effect measures for postsecondary education outcomes, by 

 engagement factors and attainment groups: 2000 

For those who entered

postsecondary education 

 Entering a 

postsecondary Number of Completing a 

Predictor variable 1 institution 2 credits earned 1

program of study

Engagement

 Attendance 1.06 0.13 *

3 1.17 

 Classroom behavior 1.29 * 0.08 *

3 1.13 

 Extracurricular participation 1.26 * 0.03 3 1.05

 Usefulness of school subjects 0.97 0.01 3 1.05

Attainment group

 (A1) Successful completers - Marginal completers 2.70 * 0.40σ * 1.31 *

 (A2) Noncompleters - Marginal completers 0.30 * -0.34σ * 0.52 * 

*p < .05 

1

Odds ratio for specific predictor variable. 

2

Response rate on this variable less than 85 percent. 

3

Standardized regression coefficient. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, National Education 

Longitudinal Study of 1988 (NELS:88/2000). 

Engagement/Disengagement and Young Adult Outcomes. The main purpose of this 

analysis was to determine if the effects of engagement (behavioral risk) on postsecondary 

outcomes are significant independently of high school attainments (academic risk). The answer 

is “yes” with respect to postsecondary education. Engagement was related to entering a 

postsecondary program, number of credits earned, and completing a program of study,119 above 

and beyond the effects of high school attainment (table 17). In all, engagement is related to high 

school attainments—grades, test scores, and graduation; and engagement is related to long-term 

educational outcomes in ways not explained by high school accomplishments alone. 

119The overall test of four engagement variables was significant for completing a program of study, but no test of the 

individual variables was significant. 

63 
 

 

 

4. Behavioral Risk 

Table 18. Strength-of-effect measures for employment/income outcomes, by

 engagement factors and attainment groups: 2000 

Employed Consistent 1999 Annual 

1 2 3,4 Predictor variable in 2000 employment income 

Engagement

 Attendance 0.98 5 0.05 5 0.05 

 Classroom behavior 1.27 5 0.01 5 -0.04

 Extracurricular participation 0.93 5 -0.04 5 -0.01 

 Usefulness of school subjects 0.81 *

5 -0.08 5 0.04

Attainment group

 (A1) Successful completers - Marginal completers 1.05 *

6 0.14σ 6 0.07σ

 (A2) Noncompleters - Marginal completers 0.64 *

6 -0.30σ 6 -0.29σ

*p < .05 

1

Odds ratio for specific predictor variable. 

2

0.0 to 3.0 indicator of part-time/full-time employment 1997–1999. 

3

Of those employed in 1999; annual income data were only available for 1999. 

4

Log 1999 income in dollars. 

5

Standardized regression coefficient. 

6

Effect sizes; mean differences in standard deviation units. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, National Education 

Longitudinal Study of 1988 (NELS:88/2000). 

Several individual measures that were significant in the first analysis of engagement were 

reduced to nonsignificance when attainment groups were included in the regressions.120 Despite 

this, attendance and extracurricular participation remained positively related to the entering a 

postsecondary program, and attendance and classroom behavior remained related to the number 

of credits earned (table 17). Higher levels of engagement were associated with better 

postsecondary outcomes, and disengagement was associated with reduced outcome. 

In contrast, engagement was not discernably related to current employment or income at 

age 26 when high school attainment was included in the regression (table 18). Only the 

relationship of the perceived usefulness of school subjects with consistent employment was 

significant, but this was in the direction opposite of that hypothesized.121 

In sum, among student at risk due to status characteristics, behavioral risk 

(engagement/disengagement in high school) is related to high school attainments122 and to 

postsecondary schooling. Disengagement is associated with poorer outcomes in high school and 

120 The relationships of attendance with entering a postsecondary education and the relationship of attendance and 

classroom behavior with completing a program of study.

121 A follow-up analysis with the usefulness variable deleted produced a nonsignificant overall test of engagement 

with consistent employment. 

122 See “Engagement/Disengagement and Attainment Groups” in this chapter. 

64 
The Adult Lives of At-Risk Students 

with a reduced likelihood of entering or persevering in post-high school study. The relationship 

of behavioral risk with postsecondary education is not fully explained by academic risk factors 

(high school grades and graduation). 

65 

 

The Adult Lives of At-Risk Students 

5. Summary of Results 

The purpose of this analysis was to examine diversity in outcomes among students at risk 

for school failure because of traditional status characteristics. A sample of students was selected 

from the NELS:88 survey who were followed from eighth grade until they were 8 years beyond 

high school age. All members of the sample were at status risk because they came from a lowSES home (parents of limited educational attainments and/or limited incomes and/or jobs of 

limited status), and because they attended low-SES schools (schools serving high proportions of 

low-income students). 

The analyses focused on two additional sets of risk factors. Behavioral risk factors are 

behaviors which, in their positive forms (engagement), may be related to enhanced school 

performance even among students at status risk. Previous research suggests that engagement 

behaviors serve a “protective” function, to lessen the negative impact of status risk factors such 

as economic disadvantage. In their negative forms (disengagement) these behaviors can further 

impede learning. Engagement/disengagement behaviors examined in this analysis were patterns 

of attendance and timeliness, participation in learning activities in the classroom, participation in 

extracurricular activities, and attitudes about the utility of school subjects. 

Academic risk factors are less-than-successful outcomes at one point in a school career 

that can interfere with the chances of success at later stages. In this analysis, grades, test scores, 

and graduation status were viewed as high school outcomes that can be risk factors with respect 

to further schooling and employment. Based on these criteria, the sample was classified into one 

of three attainment groups: successful completers, marginal completers, or noncompleters. 

Modest definitions of success were used on the assumption that maintaining a reasonable grade 

point average and graduating from high school are noteworthy accomplishments for students 

who face the obstacles that accompany status risk. 

The analyses focused on the relationships among behavioral risk, academic risk, and 

postsecondary schooling and employment for the sample of status risk students. Five questions 

were addressed regarding risk and post-high school outcomes. 

• Question (1.1) Academic risk and postsecondary education: Are attainments in high 

school related to postsecondary education among status risk students? 

• Question (1.2) Academic risk and employment: Are attainments in high school related to 

later employment and income among status risk students? 

• Question (2.1) Behavioral risk and postsecondary education: Is engagement in high 

school related to postsecondary education among status risk students? 

• Question (2.2) Behavioral risk and employment: Is engagement in high school related to 

later employment and income among status risk students? 

67 
 

Table 19.

5. Summary of Results 

• Question (2.3) Behavioral and academic risk: If engagement is related to young adult 

outcomes (education and employment), is the connection attributable to high school 

attainments? 

The results of the multivariate regression analyses are summarized in Table 19, in which 

the asterisks (*) indicate statistically significant relationships between variables. 

Table 19. Summary of regression analyses of school attainment and of school

 engagement on postsecondary education and employment/income: 2000 

Postsecondary education Employment/income 

Entered Credits Completed Employed Consistent 1999 

Predictor variable institution earned 1 program in 2000 employment 2 income 3,4 

Attainment groups5 * * * *

 Successful - Marginal * * *

 Noncompleter - Marginal * * * * 

Engagement6 * * * * *

 Attendance * * * *

 Classroom behavior * * * *

 Extracurricular *

 Usefulness of subjects 

*p < .05 

1

Response rate on this variable less than 85 percent. 

2

0.0 to 3.0 indicator of part-time/full-time employment 1997–1999. 

3

Of those employed in 1999; annual income data were only available for 1999. 

4

Log 1999 income in dollars. 

5

Test of all contrasts among three groups (Wald test). 

6

Overall test of set of four engagement measures (Wald test). 

NOTE: Summary of separate analyses of attainment and engagement. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, National 

Education Longitudinal Study of 1988 (NELS:88/2000). 

Academic Risk and Young Adult Outcomes 

Even though all students in the analysis were at risk by virtue of poor socioeconomic 

conditions, there was considerable variability in high school and post-high school outcomes. 

Despite their risk status, a sizeable percentage of students in the sample maintained passing 

grades, graduated from high school,123 and went on to further accomplishments in school and the 

work place. 

123 Termed successful completers and marginal completers in this analysis. 

68 
 

The Adult Lives of At-Risk Students 

High school to post-high school connections (questions 1.1 and 1.2), were examined in a 

series of regression analyses that also included demographic characteristics; these are 

summarized in the top portion of table 19. 

The analyses found relationships between academic risk, in the form of high school 

attainments, and all aspects of postsecondary education: entering a postsecondary program, 

accruing postsecondary credits, and completing a program of study. High school achievement, 

even at modest levels, was associated with increased postsecondary schooling, and graduating 

from high school was associated with continued educational attainments of status risk students. 

Dropping out of high school was associated with reduced chances of entering or persisting in 

postsecondary education. 

The analyses also found an association between high school attainments and one measure 

of employment by age 26: consistent employment over a 3-year period. High school 

noncompleters had less consistent employment records than either of the other groups. Current 

employment and income were related to high school attainments but the relationships were 

explained by differences in postsecondary education. 

Behavioral Risk and Young Adult Outcomes 

Consistent with previous research,124 behavioral risk was related to high school 

attainments. All engagement measures except perceptions of utility discriminated among the 

attainment groups. Measures of attendance and classroom behavior had strong relationships with 

attainment, with high school noncompleters being the least engaged of the three groups (tables 

11 and C-4). 

The relationships of engagement/disengagement with young adult outcomes (questions 

2.1, 2.2, and 2.3) were examined in a series of regression analyses that also included student 

demographics and postsecondary education; these are summarized in the bottom portion of table 

19. 

Engagement was related to all postsecondary education outcomes: entering 

postsecondary schooling, accruing credits, and completing a program of study. In terms of 

specific measures, attendance and classroom behavior were positively related to all three 

outcomes, and extracurricular participation in high school was related to the likelihood of 

entering a postsecondary program. In each instance, disengagement was related to reduced 

postsecondary outcomes. The relationship of engagement with postsecondary education 

remained statistically significant when attainment groups were included in the regression 

analysis, although several relationships with specific variables became nonsignificant. In this 

follow-up analysis, attendance was still related to credits earned, classroom behavior was related 

to the likelihood of entering a postsecondary program and to credits earned, and extracurricular 

participation was related to the likelihood of entering a program. 

School engagement was related to two employment outcomes, current employment in 

2000 and consistent employment, but not to income. The relationships were weak and limited in 

124 See “The Connection with Academic Risk Factors” in chapter 1. 

69 
 

 

5. Summary of Results 

scope, however. Only one engagement measure was related to each employment measure: 

classroom behavior to current employment, and attendance to consistent employment. Further, 

these relationships described some groups of participants but not others.125 A third engagement 

measure (extracurricular activities) was not discernably related to employment, and a fourth 

measure (perceived usefulness of school subjects) was related inversely to employment. No 

discernable relationships between engagement and employment were found when high school 

attainments were also included in the regressions. 

In sum, behavioral risk in high school is related to postsecondary schooling among status 

risk students. Those who are more engaged in high school are more likely to undertake further 

education and to persist and complete a program of study. Those who are least engaged are least 

likely to enter postsecondary programs—even short-term career preparation programs or 2-year 

colleges. At the same time, there is little if any direct relationship between behavioral risk and 

employment or income by age 26 for individuals at risk due to status characteristics. 

125 The analysis of interactions showed that the behavior-to-current employment relationship did not hold for White 

students, and the attendance-to-consistent employment connections did not hold for participants with BA degrees. 

70 
The Adult Lives of At-Risk Students 

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78 
 

 

Appendixes 

Appendix A. Technical Notes 

Appendix B. Standard Errors Tables 

 Appendix C. Other Statistical Tables 

Appendix D. Variables Used in This Report 

The Adult Lives of At-Risk Students 

Appendix A. Technical Notes 

Overview of the NELS:88 Survey 

The NELS:88 survey was begun in 1988 by the National Center for Education Statistics 

(NCES) with a nationally representative sample of eighth-grade students. Participants were 

followed longitudinally through the year 2000, when most were 26 years of age. Data were 

collected on the students and their parents, teachers, and schools as they progressed through high 

school and beyond. 

NELS:88 focused on issues of education policy including the “identification of school 

attributes associated with achievement; the transition of different types of students from eighth 

grade to secondary school and to postsecondary institutions; the transition of secondary and 

postsecondary students to the workforce; the influence of ability grouping and program type on 

future educational experiences and achievements; ... and changes in educational practices over 

time” (Curtin et al. 2002, p. 10). 

To accomplish these purposes, data were collected in five waves: when students were in 

eighth grade, tenth grade, twelfth grade, 2 years beyond high school age, and 8 years beyond 

high school age. In eighth, tenth, and twelfth grades, students completed extensive 

questionnaires and tests of academic achievement. Further, teachers and school administrators 

completed questionnaires in each wave; teachers provided general information about themselves 

and their classes as well as ratings of each student in the survey. When the students were in 

eighth and twelfth grades, their parents also completed questionnaires providing information 

about themselves, their home setting, and their children. Post-high school information was 

collected through computer-assisted telephone interviews. 

Additionally, two rounds of transcript data were collected on the students. High school 

transcripts were collected for all participants in the school-age sample, including dropouts and 

early graduates. These made it possible to validate information in the questionnaires (e.g., 

grades, attendance) and to investigate other policy issues, for example, course taking patterns and 

their relationships to other attainments. Postsecondary transcripts were collected for students 

who reported attending a school beyond high school. The data included student information 

(e.g., courses taken; the number of credits earned; degree completion) and institutional 

characteristics (e.g., type of institution; location of the school). 

Students who dropped out of school after eighth grade continued to be followed at 

subsequent time points. A dropout questionnaire was administered in 1990 and 1992, and 

dropouts were contacted for further information through the telephone interviews in 1994 and 

2000. 

Detailed information about the data collected in NELS:88 is given in Base-Year to 

Fourth Follow-up Data File User’s Manual (Curtin et al. 2002). The data are available to the 

public for research purposes in public and restricted use versions. To increase their accessibility, 

A-1 
 

 

 

Appendix A. Technical Notes 

NCES has developed an “Electronic Codebook” for each data file that allows users to scan the 

data variable by variable, view the original questions that were posed in the questionnaires, 

obtain frequency distributions for responses to any question, and create a working file with 

specific variables in SPSS or SAS format. 

Sampling Design, Weighting, Standard Errors, 

and Statistical Procedures 

Sampling Design 

In the NELS:88 base year (1988), a two-stage stratified cluster sampling design was used 

to select a nationally representative sample of eighth grade schools and students (see Spencer et 

al. 1990). At the first stage, 1,655 public and private schools were contacted from a national 

pool of about 39,000 public and private schools that enrolled eighth grade students in the United 

States. The probability of selection was proportional to the number of eighth graders in the 

school. Private schools were oversampled so that the sample would include enough private 

schools for subset analyses. Of those selected, 1,057 (817 public and 240 private) schools agreed 

to participate, of which 1,052 (815 public and 237 private) schools ultimately provided eighthgrade student data.1

 At the second stage, an average of 25 eighth-grade students was selected 

from each school. Asian and Hispanic students were oversampled. In all, a national sample of 

approximately 26,000 students was identified of which 24,599 actually participated in the 

2 survey. 

The survey followed the progress of each student during the remaining years of high 

school, with data collection points at the end of grade 10 (first follow-up, 1990) and grade 12 

(second follow-up, 1992). Students who left high school without graduating were contacted and 

administered survey instruments, as well as those who remained in school. The tenth and twelfth 

grade samples were “freshened” with new students at the same grade levels so that the sample at 

each grade level was representative of the population of students in that year. In addition to the 

freshened students, students who were found to be ineligible during the base year (due to, for 

example, limited English proficiency or a disability that prevented them from completing 

assessments), but who were later determined to be eligible in tenth or twelfth grade, were added 

to the sample. 

Further data collection points occurred in 1994, when most participants were 2 years past high 

school (third follow-up), and in 2000 when most were 8 years past high school and about 26 

years old (fourth follow-up). For cost reasons, participants were subsampled in both post-high 

school waves of data collection. The final five-wave sample included 12,144 respondents. 

Details of sample design, including the sampling frame, size of clusters, use of stratification, and 

1 Table 2.8-2 of the Base Year Sample Design Report (Spencer et al. 1990) gives a 69.7 percent school participation 

rate based on a subset of the 1655 schools contacted. 

2

 The response rate for students was 93.1 percent unweighted, and 93.4 percent weighted (Spencer et al. 1990, table 

2.8-1). 

A-2 
 

The Adult Lives of At-Risk Students 

other technical issues are discussed in depth in Base-Year to Fourth Follow-up Data File User’s 

Manual (Curtin et al. 2002). 

Weighting 

The general purpose of weighting is to adjust for the probabilities of unequal selection of 

schools and students, and to compensate for the effects of non-response by schools and students 

selected to participate. 

The 24,599 eighth-grade participants in the base year represented the approximately 

3,000,000 students who attended eighth grade in the U.S. in 1988. Each student represented 

about 120 students (3,000,000/25,000 = 120). Oversampling of Asians, Hispanics, and private 

school students means that they were over-represented in the data file and required smaller 

weighting (i.e. they each represented fewer than 120 students.) 

To adjust for nonresponse, weights of nonrespondents were distributed among the 

respondents who had similar characteristics. The NELS:88 Base Year to Fourth Follow-up Data 

File User’s Manual (Curtin et al. 2002) provides response rates for each wave of the NELS:88 

data collection, both overall and by selected subgroups (e.g., gender, race/ethnicity, and high 

school enrollment). 

In data analysis, true population estimates can only be obtained by using the appropriate 

weights. The estimates in this report were produced using F4PNLWT, a panel weight that 

generalizes to the population of spring 1988 eighth graders, and applies to survey sample 

members who completed questionnaires in all five waves of NELS:88. 

Standard Errors 

The NELS:88 data collection involved stratification, the disproportionate sampling of 

certain strata, and clustered (i.e., multistage) probability sampling. This results in statistics that 

are more variable than they would have been if a simple random sample of the same size had 

been drawn. 

The calculation of standard errors must take these complexities into account. Standard 

errors obtained without these considerations will be artificially small3

 and will tend to exaggerate 

significance levels. Indeed, some popular statistical analysis packages calculate standard errors 

without accounting for the sampling complexities associated with surveys like NELS:88. This 

can result in statistical tests erroneously being reported as significant. 

The calculation of exact standard errors for survey estimates can be difficult. Several 

approaches to estimating population variances for complex surveys are available, among them 

the “Taylor series” approach (Binder 1983; Lee, Forthofer, and Lorimor 1989) incorporated in 

statistical packages such as SUDAAN (Research Triangle Institute 2001) and AM (Cohen et al. 

2003). 

3

 And confidence intervals will be artificially narrow. 

A-3 
 

 

 

Appendix A. Technical Notes 

The AM program was used for all tests of significance in this analysis. It accepts 

sampling weights and performs multiple regression analysis and logistic regression, producing 

correct standard errors for all effects; AM accepts identifiers for the sampling stratum and the 

primary sampling unit (school) as input, in addition to the data. 

Statistical Procedures 

This analysis used two types of statistical procedures, tests of differences between means 

or proportions, and tests of linear relationships between numerical variables. When two or more 

independent variables were involved in analysis, computations were performed by the AM 

software (Cohen et al 2003). Multiple regression analysis and logistic multiple regression 

analysis were used to determine the relationship between the independent variables (predictors) 

and the main outcomes of the analysis: postsecondary education and employment/income. 

Tests to confirm descriptive results, and follow-up tests (for example, simple main effect 

tests when interactions were significant in the regressions), used the t statistic: 

where E1 and E2 are the estimates to be compared (means or proportions), and se1 and se2 are the 

corresponding standard errors, obtained from AM. 

Throughout the report, a type-1 error rate of α = .05 was used. In addition, in all tests of 

significance of two or more contrasts (or two or more independent variables), specific effects 

were only examined when the overall or “omnibus” test was significant at the .05 level. 

Tests of significance reveal whether a relationship between variables is statistically 

reliable, but tell little about the strength of the relationship. Thus, strength-of-effect measures 

were obtained to accompany all statistical tests. 

Particular strength-of-effect measures were chosen depending on the nature of the 

measurement scales involved. When the dependent variable was dichotomous (e.g., entering 

postsecondary education), odds ratios were computed. If the independent variable was also 

categorical (e.g., groups A and B), the ratio was the odds that a member of group A would enter 

postsecondary schooling divided by the odds that a member of group B would enter. When the 

independent variable was numerical (e.g., engagement variable x), the ratio was the change in 

odds associated with a one-standard deviation change in x. 

When the dependent variable was numeric and the independent variable was categorical, 

effect sizes were computed. This is the estimated difference between the mean of population A 

and mean of population B divided by the standard deviation of the dependent variable.4 The 

effect size is interpreted as the number of standard deviations separating the means of the two 

groups. When the dependent variable was numeric and the independent variable was also 

4

 The standard deviation was estimated by the square root of the residual mean square from the regression analysis. 

A-4 
 

The Adult Lives of At-Risk Students 

numeric, standardized regression coefficients were computed, that is, the number of standard 

deviations change in the dependent variable associated with a one-standard-deviation increment 

in x. 

Response Rates for Variables Used in This Analysis 

Table A-1 reports the weighted response rates for variables used in this analysis, either in 

the descriptive portion or the regression analyses. The rates represent the final percentage of 

cases having the measure after all composite and derived variables were created. The effect of 

item nonresponse was reduced by deriving measures from multiple sources. When the same 

measure was available from multiple variables within a wave, all sources of information were 

used to reduce missing data. For example, to determine the type of postsecondary institution a 

student attended, two variables from the postsecondary education transcript data and one variable 

from the fourth follow-up student questionnaire were used. Similarly, when the same measure 

was available from several waves, and a student did not have the measure in the primary wave 

chosen, an alternate wave was used. For example, if measures used for the engagement variables 

ABSTARDY, CLASSBEH, and EXTRA were not available for a student in tenth grade, twelfthgrade responses were used if they were available. 

Appendix D gives detailed information about the variables used in this analysis, 

including constructed variables. 

Data Limitations 

The National Center for Education Statistics made every effort to collect complete 

responses, to clean the NELS:88 data, and to ensure consistency within and between responses. 

This work is described in detail in the NELS:88 Base Year to Fourth Follow-up Data File User’s 

Manual (Curtin et al. 2002). Nevertheless, some data limitations arise in any large survey, 

particularly one that draws data from many sources in several stages. Several of these may have 

affected the data used in this analysis. 

First, in the base year data collection, approximately 5 percent of the students were 

unable to participate in the survey due to limited English proficiency or to a physical or mental 

disability (Ingels 1996). Although some of these students were included in later waves of 

NELS:88, they were excluded from this analysis, which only included participants who 

participated in all five waves of data collection. 

Second, the collection of similar measures at different time points and from several 

sources resulted in occasional conflict among the values. Specifically, measures of students’ 

high school graduation status were collected in NELS:88 in the second, third, and fourth followups, and also from the high school transcripts. All school reports of students dropping out were 

confirmed by contacting the student directly, but the school and student report of dropping out 

were not in agreement in approximately five percent of the cases. The student report was used in 

NELS:88 to create the composite variable F4UNI2D, used in the present analysis. 

A-5 
 

 

 

 

Appendix A. Technical Notes 

Third, missing data due to questionnaire nonresponse may have affected the results. 

Father’s education was reported by 79 percent of eighth-grade students, and mother’s education 

was reported by 87 percent of students. These variables were used for descriptive purposes only 

(table 3). Three variables more central to the analyses had response rates close to 85 percent. 

Some students were not rated by their teachers either in grade 10 or in grade 12, and 

measures of classroom behavior could not be constructed; the response rates for variables 

ABSTARDY and CLASSBEH were 87 percent and 86 percent respectively. The number of 

credits earned in a postsecondary program of study was available for 85 percent of the 

participants who attended a postsecondary school; transcripts were unavailable for the remaining 

students. All other variables used in the analyses had response rates of 90 percent or above.5 

These response rates are comparable to those in other national surveys.6 Nevertheless, 

cases with missing values were excluded from all analyses involving the particular variable(s), 

and some of the relationships given in this report may contain some bias for that reason. 

5

 The response rate for the tenth-grade achievement composite, F12XCOMP, was 89.5 percent, but this variable was 

used together with others to decide students’ attainment classifications. It did not cause any cases to be eliminated. 6

 See Ingels et al. (2004) for nonresponse rates in the Education Longitudinal Study of 2002. 

A-6 
 

 

 

 

 

 

 

 

 

 

The Adult Lives of At-Risk Students 

Table A–1. Response rates of variables used in descriptive or regression analyses 

Source 

Variable variable Weighted 

name name Description response rate 

ABSTARDY Derived How often student is absent or tardy 86.7 

BY2XCOMP BY2XCOMP Standardized test composite - reading and math, grade 8 95.9 

BYSES BYSES Socioeconomic status 100.0 

CHILDREN F4GNCH Number of biological children 99.5 

CLASSBEH Derived Teacher rating of student's classroom behavior 86.5 

CNSTEMPL 

COMPPSE 

Derived 

Derived 

Constancy of employment across three years, 1997-1999 

Completed postsecondary education; recoded from HIGHDEGR 

93.1 

98.91 

EMPLOYED F4AEMPL Employed for pay at fourth follow-up 100.0 

ENTERPSE Derived Entered postsecondary education; recoded from FRSTINST 97.1 

EXTRACUR Derived Participation in extracurricular activities 98.7 

F12XCOMP 

FREELNCH 

F12XCOMP 

Derived2 

Standardized test composite - reading and math, grade10 

Percent of students in school who receive free lunch 

89.5 

100.0 

F4BWKSWK F4BWKSWK Number of weeks worked in 1999 95.3 

F4RACE F4RACE Race of respondent 100.0 

F4SEX F4SEX Gender of respondent 100.0 

F4UNI2D F4UNI2D Second follow-up status of sample member 100.0 

FAMCOMP BYFCOMP Family composition composite 98.6 

FAMILYINC BYFAMINC Base year parent report of total family income from all sources 90.7 

FATHERED BYS34A Student report of father's highest level of education attained 78.83 

FRSTINST Derived Type of first institution entered 97.1 

FULLTIME F4AACTF Fourth follow-up employment is full-time 100.0 

G10TYPE G10CTRL1 School classification reported by school, grade 10 97.4 

G8LUNCH G8LUNCH Percent of students in grade 8 school who receive free lunch 98.0 

G8TYPE G8CTRL School classification reported by school, grade 8 100.0 

G8URBAN G8URBAN Urbanicity of school, grade 8 100.0 

HIGHDEGR Derived Highest postsecondary degree attained; recoded F4HHDG 98.91 

HOMEOWNR F4HHOSE Participant's housing status at time of fourth follow-up 97.5 

HRSWEEK F4BLHPW Hours worked per typical week in 1999 93.54 

INCOME99 F4HI99 Income in 1999 92.0 

LANGMINR BYLM Language minority composite 99.9 

LIVEPARN F4GNGRD Parents/guardians in participant's household in fourth follow-up 97.6 

LOGINCME Derived Logarithm of 1999 income, respondents who worked for pay 92.0 

MARRIED F4GMRS Marital status in fourth follow-up 99.6 

MOBILITY BYP40 Number of times eighth grader has changed schools 91.5 

MOTHERED BYS34B Student report of mother's highest level of education attained 86.7 

SIBLINGS BYP3A Number of eighth grader's siblings 91.9 

TOTCRED TCREDN Total undergraduate credits earned 84.95 

UTILITY Derived Student rating of the usefulness of subjects 93.4 

VOLNTEER Derived Indicator of whether participant volunteered at fourth follow-up 97.6 

VOTED F4IVPRE Indicator of whether participant voted in 1996 election 96.6 1

Out of 2,264 cases who entered postsecondary education. 

2

Used G8LUNCH and F1C30A; grade 10 data used if grade 8 not available. 3

An additional 18.2 percent responded 'Don’t know;' total response rate was 97.0 percent. 4

This variable was also used for consistent employment calculation; 95.2 percent of cases provided 

usable information. 

5

Unweighted response rate was 88.2 percent. 

NOTE: See appendix D for details of measures used in this study. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, National Education 

Longitudinal Study of 1988 (NELS:88/2000). 

A-7 

The Adult Lives of At-Risk Students 

Appendix B. Standard Errors Tables 

Table B-A. Standard errors for table A: High school attainment groups of status risk

 students, by postsecondary education and employment/income

 outcomes: 2000 

High school attainment group 

Successful Marginal 

1 2 3 Outcome completers completers Noncompleters 

Percent of status risk students 1.14 1.53 1.55 

Percent who entered a postsecondary institution4 2.66 1.80 2.80 

 < 2-year school 0.51 ! 0.70 0.85 

 2-year school 2.32 1.95 2.62 

 4-year school 2.54 1.36 0.66 ! 

Mean credits earned in postsecondary institutions5,6,7 2.35 1.95 2.06 

Percent completed postsecondary education6 2.32 2.18 4.57 

Percent currently employed (2000) 1.38 1.29 2.31 

Consistent employment mean 1997–19998 0.031 0.038 0.059 

! Interpret data with caution. Unweighted n < 30. 

1

In school in twelfth grade in 1992; "reasonable" scores on reading and mathematics tests; passing grades; 

graduated from high school. 

2

Test scores not "reasonable" and/or grades not passing; high-school graduate. 

3

Dropped out by 1992, or not in twelfth grade in 1992 and did not earn diploma by age 20. 

4

Classified by type of first institution attended. Values based on total number of successful completers, 

marginal completers, and noncompleters in status risk sample. 

5

Credits earned from all schools attended. 

6

Values based on total number of successful completers, marginal completers, and noncompleters who 

entered postsecondary education. 

7

Response rate on this variable less than 85 percent. 

8

0.0 to 3.0 indicator of part-time/full-time employment 1997–1999. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, National Education 

Longitudinal Study of 1988 (NELS:88/2000). 

B-1 
Table B-1. 

Appendix B. Standard Errors Tables 

Table B-1. Standard errors for table 1: Status risk sample, by demographic 

characteristics: 1988 

Characteristic Weighted SE 1 

 All † 

Gender 

Male 1.60 

 Female 1.44 

Race 

 White, not Hispanic 1.35 

 Black, not Hispanic 3.41 

 Hispanic 3.52 

 Native American, Alaska Native 4.08 

 Asian, Pacific Islander 2.77 

School urbanicity - 8th grade

 Urban 2.60

 Suburban 1.77

 Rural 2.36 

School type - 8th grade

 Public 1.48

 Catholic 2.43

 Other2 0.67 

† Not applicable. 

1

Standard errors weighted by F4PNLWT. 

2

Includes "private other religion" and "private nonreligious." 

NOTE: Status risk classification based on eighth grade (base year) student and school socioeconomic 

status. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, National Education 

Longitudinal Study of 1988 (NELS:88/2000). 

B-2 
The A

Table B-2. Standard errors for table 2: Percentage distribution of status risk and non-status risk studen

 demographic and school characteristics: 1988 

B-3 

Group Total 

Percentage of status risk sample 

 Low-SES home

 Low-SES school

† 

All

† 

Percentage of non-status risk studen

 Low-SES home High-SES home 

 High-SES school Low-SES schoo

† † 

Gender Male 1.43 0.85 1.59 1.53 

 Female 1.43 0.85 1.59 1.53 

Race/ethnicity White, not Hispanic Black, not Hispanic  Hispanic  Native American, Alaska Native  Asian, Pacific Islander 

2.36 

1.89 

1.880.84 0.34 

1.12

0.88 

0.60 0.31 0.34 

2.36 1.87 1.66 0.18 0.73 

2.15 1.82 0.93 1.00 0.69 

School urbanicity - 8th grade Urban 2.57 1.61 2.35 2.83 

 Suburban 2.45 2.00 3.26 2.68 

 Rural 2.81 1.68 3.01 2.89 

School type - 8th grade Public 0.55 0.88 1.77 0.88 

 Catholic 0.54 0.79 1.67 0.65 

 Other1 0.09 0.46 0.62 0.61 

† Not applicable. 

1Includes 'private other religion' and 'private non-religious.' 

NOTE: Status risk classification based on eighth grade (base year) student and school socioeconomic status. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, National Education Longitudinal Study of 1988 (N
Appendix B. Standard Errors Tables 

Table B-3. Standard errors for table 3: Status risk sample and non-status risk students, by selected home

 characteristics: 1988 

B-4 

Status risk sample Non-status risk students

 Low-SES home Low-SES home High-SE

Characteristic Low-SES school All High-SES school Low-SEPercent 1987 annual household income over $19,9991 1.63 0.77 2.01 

Percent father's education beyond high school2 1.24 1.19 1.44 Percent mother's education beyond high school2 1.16 1.05 1.75 Percent living with two biological parents2 1.62 0.94 1.68 Percent non-English speaking home3 1.99 0.69 1.78 

Mean number of siblings1 0.05 0.03 0.05 Mean number of times changed schools1 0.06 0.03 0.05 

1Base year parent questionnaire 

2Base year student questionnaire 3Composite of base year student and teacher questionnaire 

NOTE: Status risk classification based on eighth grade (base year) student and school socioeconomic status. SOURCE: U.S. Department of Education, National Center for Education Statistics, National Education Longitudinal Study of 1988 (N
The Adult Lives of At-Risk Students 

Table B-4. Standard errors for table 4: Percentage distribution of status risk students

 in each attainment group, by gender and race/ethnicity: 1988 

Percentage in each attainment group 

Group Successful completers 1 Marginal completers 2 Noncompleters 3 

 Total 

Gender

 Male

 Female 

Race/ethnicity

 White, not Hispanic

 Black, not Hispanic

 Hispanic

 Native American, Alaska Native

 Asian, Pacific Islander 

† † 

2.51 1.87 

2.51 1.87 

3.02 2.67 

2.74 2.45

1.65 2.28 

0.43 ! 0.87

0.50 0.43 

† 

3.20 

3.20 

3.76 

2.79 

2.76 

1.32 ! 

0.36 ! 

† Not applicable. 

! Interpret data with caution. Unweighted n < 30. 

1

In school in twelfth grade in 1992; "reasonable" scores on reading and mathematics tests; passing grades; 

graduated from high school. 

2

Test scores not "reasonable" and/or grades not passing; high-school graduate. 

3

Dropped out by 1992, or not in twelfth grade in 1992 and did not earn diploma by age 20. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, National Education 

Longitudinal Study of 1988 (NELS:88/2000). 

B-5 
Table B-5.

Appendix B. Standard Errors Tables 

Table B-5. Standard errors for table 5: High school attainment groups of status 

risk students, by postsecondary education outcomes: 2000 

High school attainment group 

Successful Marginal 

Outcome All completers 1 completers 2 Noncompleters 3 

Percent who entered a 

 postsecondary institution4 1.56 2.66 1.80 2.80 

 < 2-year school 0.45 0.51 0.70 0.85 

 2-year school 1.53 2.32 1.95 2.62 

 4-year school 0.94 2.54 1.36 0.66 

Mean credits earned in

 postsecondary institutions5,6,7 1.75 2.35 1.95 2.06 

 < 2-year school 2.63 8.01 3.41 3.37 

 2-year school 2.03 4.26 2.08 2.24 

 4-year school 3.08 2.76 5.09 10.88 

Percent completed

 postsecondary education6 1.67 2.32 2.18 4.57 

 Certificate/license 1.16 1.28 1.47 4.66 

 Associate's degree 0.89 1.65 1.20 1.51 

 Bachelor's degree or higher 1.04 2.13 1.10 0.53 1

In school in twelfth grade in 1992; "reasonable" scores on reading and mathematics tests; passing 

grades; graduated from high school. 

2

Test scores not "reasonable" and/or grades not passing; high-school graduate. 

3

Dropped out by 1992, or not in twelfth grade in 1992 and did not earn diploma by age 20. 

4

Classified by type of first institution attended. Values based on total number of successful 

completers, marginal completers, and noncompleters in status risk sample. 

5

Credits earned from all schools attended, classified by type of first institution attended. 

6

Values based on total number of successful completers, marginal completers, and noncompleters 

who entered postsecondary education. 

7

Response rate on this variable less than 85 percent. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, National Education 

Longitudinal Study of 1988 (NELS:88/2000). 

B-6 
 

Table B-6. 

The Adult Lives of At-Risk Students 

Table B-6. Standard errors for table 6: High school attainment groups of status risk 

 students, by employment/income outcomes: 2000 

High school attainment group 

Successful Marginal 

1 2 3 Outcome All completers completers Noncompleters 

Percent currently employed (2000) 0.94 1.38 1.29 2.31 

Percent employed full-time (2000)4 0.88 1.31 1.34 1.77 

Mean number of hours worked per week (1999)5 0.38 0.51 0.45 0.93 

Consistent employment mean 1997-1999 (all)6 0.027 0.031 0.038 0.059

 No postsecondary education 0.047 0.067 0.065 0.081

 Some postsecondary education, no degree 0.035 0.059 0.048 0.106

 Certificate/license 0.119 0.103 0.172 0.174

 Associate's degree 0.044 0.089 0.055 0.211

 Bachelor's degree or higher 0.036 0.049 0.055 0.368 

1

In school in twelfth grade in 1992; "reasonable" scores on reading and mathematics tests; passing grades; 

graduated from high school. 

2

Test scores not "reasonable" and/or grades not passing; high-school graduate. 

3

Dropped out by 1992, or not in twelfth grade in 1992 and did not earn diploma by age 20. 

4

Of those currently employed. 

5

Of those employed in 1999; annual income data were only available for 1999. 

6

0.0 to 3.0 indicator of part-time/full-time employment 1997–1999. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, National Education 

Longitudinal Study of 1988 (NELS:88/2000). 

B-7 
Table B-7. 

Appendix B. Standard Errors Tables 

Table B-7. Standard errors for table 11: Mean engagement measures of high school 

 attainment groups: 1990 

High school attainment group 

Successful Marginal 

Engagement measure All completers 1 completers 2 Noncompleters 3 

Attendance4 0.019 0.025 0.023 0.052 

Classroom behavior5 0.044 0.048 0.056 0.078 

Extracurricular participation6 0.053 0.080 0.066 0.119 

Usefulness of school subjects7 0.018 0.033 0.025 0.036 

1

In school in twelfth grade in 1992; "reasonable" scores on reading and mathematics tests; passing grades; 

graduated from high school. 

2

Test scores not "reasonable" and/or grades not passing; high-school graduate. 

3

Dropped out by 1992, or not in twelfth grade in 1992 and did not earn diploma by age 20. 

4

How often a student is absent or tardy, range: 1-5. 

5

Teacher rating of student's behavior, range: 1-4. 

6

Participation in extracurricular activities, range: 0-6. 

7

Student rating of the usefulness of math, English, social studies and science, range: 1-4. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, National Education 

Longitudinal Study of 1988 (NELS:88/2000). 

B-8 
 

The Adult Lives of At-Risk Students 

Appendix C. Other Statistical Tables 

Table C-1. Unweighted sample sizes of attainment groups, by demographic

 characteristics: 1988 

Unweighted number in each attainment group 

Group Successful completers 1 Marginal completers 2 Noncompleters 3 

 Total 

Gender

 Male

 Female 

Race/ethnicity

 White, not Hispanic

 Black, not Hispanic

 Hispanic

 Native American, Alaska Native

 Asian, Pacific Islander 

School urbanicity - 8th grade

 Urban

 Suburban

 Rural 

School type - 8th grade

 Public

 Catholic

 Other private4 

870 1,950 680 

350 910 310 

530 1,040 370 

620 1,000 360 

50 370 110 

130 460 180 

10 40 20 

70 90 20 

170 520 200 

260 570 210 

440 860 270 

820 1,900 670 

40 40 10 

# # # 

# Rounds to zero. 

1

In school in twelfth grade in 1992; "reasonable" scores on reading and mathematics tests; passing grades; 

graduated from high school. 

2

Test scores not "reasonable" and/or grades not passing; high school graduate. 

3

Dropped out by 1992, or not in twelfth grade in 1992 and did not earn diploma by age 20. 

4

Includes "private other religion" and "private nonreligious." 

NOTE: Values rounded to protect data confidentiality. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, National Education 

Longitudinal Study of 1988 (NELS:88/2000). 

C-1 
 

 

 

 

 

 

Table C-2.

Appendix C. Other Statistical Tables 

Table C-2. Strength-of-effect measures for table 5: High school attainment groups, 

by postsecondary education outcomes: 2000 

High School Attainment Group

 Successful Completers - Noncompleters -

Outcome Marginal Completers Marginal Completers 

Percent who entered a postsecondary institution1 2.39 * 0.20 *

 < 2-year school 0.27 * 0.81

 2-year school 0.67 * 0.37 *

 4-year school 4.00 * 0.12 * 

Mean credits earned in postsecondary institutions2,3,4 0.80σ * -0.68σ *

 < 2-year school 0.52σ -0.56σ *

 2-year school 0.68σ * -0.60σ *

 4-year school 0.51σ * -0.86σ * 

Percent completed postsecondary education3 1.72 * 0.68 

 Certificate/license 0.34 * 1.79 *

 Associate's degree 0.90 0.34 *

 Bachelor's degree or higher 4.38 * 0.08 * 

*p < .05 1

Classified by type of first institution attended. Values based on total number of successful completers, 

marginal completers, and noncompleters in status risk sample. 

2

Credits earned from all schools attended, classified by type of first institution attended. 

3

Values based on total number of successful completers, marginal completers, and noncompleters 

who entered postsecondary education. 

4

Response rate on this variable less than 85 percent. 

NOTE: Strength-of-effect measures for percents are odds ratios. Strength-of-effect measures for means 

are mean differences in standard deviation units. Contrasts tested for significance only when the overall 

test of differences among the groups was significant. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, National Education 

Longitudinal Study of 1988 (NELS:88/2000). 

C-2 
Table C-3. 

The Adult Lives of At-Risk Students 

Table C-3. Strength-of-effect measures for table 6: High school attainment groups, by 

 employment/income outcomes: 2000 

High school attainment group

 Successful Completers - Noncompleters -

Outcome Marginal Completers Marginal Completers 

Percent currently employed (2000) 1.23 0.53 * 

Percent employed full-time (2000)1 0.96 1.02

Mean number of hours worked per week (1999)2 -0.07σ 0.12σ 

Consistent employment mean 1997-1999 (all)3 0.12σ * -0.31σ *

 No postsecondary education 0.26σ * -0.30σ *

 Some postsecondary education, no degree 0.06σ -0.30σ

 Certificate/license 0.26σ 0.08σ

 Associate's degree 0.05σ -0.11σ

 Bachelor's degree or higher 0.05σ 0.05σ

Median 1999 annual income (all)2 0.18σ * -0.18σ 

 No postsecondary education 0.12σ -0.19σ

 Some postsecondary education, no degree 0.07σ -0.25σ

 Certificate/license 0.36σ 0.19σ

 Associate's degree 0.19σ 0.16σ

 Bachelor's degree or higher -0.03σ -0.52σ

*p < .05 

1

Of those currently employed. 

2

Of those employed in 1999; annual income data were only available for 1999. 

3

0.0 to 3.0 indicator of part-time/full-time employment 1997–1999. 

NOTE: Strength-of-effect measures for percents are odds ratios. Strength-of-effect measures for means 

are mean differences in standard deviation units. Contrasts tested for significance only when the overall 

test of differences among the groups was significant. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, National Education 

Longitudinal Study of 1988 (NELS:88/2000). 

C-3 
Table C-4.

Appendix C. Other Statistical Tables 

Table C-4. Strength-of-effect measures for table 11: Mean engagement measures of

 high school attainment groups: 1990 

High school attainment group

 Successful completers - Noncompleters -

Outcome Marginal Completers 1 Marginal Completers 1 

Attendance2 0.40σ * 0.82σ * 

Classroom behavior3 0.72σ * 0.70σ * 

Extracurricular participation4 0.24σ * 0.29σ * 

Usefulness of school subjects5 0.12σ -0.04σ 

*p < .05 1

Effect sizes; mean differences in standard deviation units. 

2

How often a student is absent or tardy, range: 1-5. 

3

Teacher rating of student's behavior, range: 1-4. 

4

Participation in extracurricular activities, range: 0-6. 

5

Student rating of the usefulness of math, English, social studies and science, range: 1-4. 

NOTE: Strength-of-effect measures estimated for main effects only. Contrasts tested for significance only 

when the overall test of differences among the groups was significant. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, National Education 

Longitudinal Study of 1988 (NELS:88/2000). 

C-4 
Table C-5. 

The Adult Lives of At-Risk Students 

Table C-5. Regression results for postsecondary education outcomes, by engagement 

factors and attainment groups: 2000 

For those who entered 

postsecondary education

 Entering a 

postsecondary Number of Completing a 

Predictor variable institution credits earned 1 program of study 

Gender (M - F) -0.41 * -3.60 -0.35 * 

(0.141) (2.580) (0.129) 

Race/ethnicity

 Black - White 0.49 * -1.24 -0.22 

(0.230) (4.649) (0.205)

 Hispanic - White 1.08 * -2.61 -0.27 

(0.198) (3.661) (0.172)

 Native American - White -0.54 -20.45 * -0.68 

(0.353) (4.873) (0.427)

 Asian - White 1.17 * 10.23 -0.27 

(0.364) (5.723) (0.223) 

Urbanicity

 Suburban - Urban 0.11 0.39 -0.02 

(0.214) (3.729) (0.189)

 Rural - Urban -0.13 5.32 0.20 

(0.207) (3.497) (0.171) 

Type of postsecondary institution

 < 2-year - 2-year † -4.62 1.74 * 

(3.540) (0.243)

 4-year - 2-year † 35.49 * 0.45 * 

(3.149) (0.142) 

Engagement

 Attendance 0.10 11.34 * 0.26 

0.138 (2.817) (0.134)

 Classroom behavior 0.17 * 2.83 * 0.09 

(0.063) (1.089) (0.051)

 Extracurricular participation 0.14 * 1.11 0 

(0.038) (0.813) (0.034)

 Usefulness of school subjects -0.05 1.19 0.07 

(0.116) (2.083) (0.107) 

Attainment group

 (A1) Successful completers - Marginal completers 0.99 * 17.69 * 0.27 * 

(0.192) (3.400) (0.135)

 (A2) Noncompleters - Marginal completers -1.22 * -15.11 * -0.65 * 

(0.195) (3.370) (0.256) 

† Not applicable. 

*p < .05 1

Response rate on this variable less than 85 percent. 

NOTE: Table contains unstandardized regression coefficients. Standard errors in parentheses. Each 

coefficient is a contrast, group A minus group B (positive if group A has the higher value; negative if group 

B has the higher value). For effects with two or more contrasts, individual contrasts tested only when the 

overall test of differences among the groups was significant. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, National Education 

Longitudinal Study of 1988 (NELS:88/2000). 

C-5 
 

Table C-6.

Appendix C. Other Statistical Tables 

Table C-6. Regression results for employment/income outcomes, by engagement 

 factors and attainment groups: 2000 

Employed Consistent 1999 Annual 

1 2, 3 Predictor variable in 2000 employment income 

*p < .05 

1

0.0 to 3.0 indicator of part-time/full-time employment 1997–1999. 2

Of those employed in 1999; annual income data were only available for 1999. 

3

Log 1999 income in dollars. 

NOTE: Table contains unstandardized regression coefficients. Standard errors in parentheses. Each 

coefficient is a contrast, group A minus group B (positive if group A has the higher value; negative if 

group B has the higher value). For effects with two or more contrasts, individual contrasts tested only 

when the overall test of differences among the groups was significant. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, National Education 

Longitudinal Study of 1988 (NELS:88/2000). 

C-6 

* 

* 

* 

* 

Gender (M - F) 1.20 * 0.42 * 0.40 

(0.193) (0.049) (0.063) 

Race/ethnicity 

 Black - White 0.33 -0.06 -0.34 

(0.307) (0.091) (0.122) 

 Hispanic - White 0.40 0.05 0.01

(0.213) (0.067) (0.059)

 Native American - White -0.60 -0.44 -0.25 

(0.351) (0.189) (0.119) 

 Asian - White 0.29 -0.10 -0.13 

(0.324) (0.114) (0.102) 

Urbanicity

 Suburban - Urban 0.28 0.16 0.12 

(0.234) (0.080) (0.093)

 Rural - Urban 0.38 0.17 0.03 

(0.230) (0.079) (0.085 

Postsecondary education completed

 Some PSE, no degree - None 0.16 0.07 -0.04 

(0.224) (0.068) (0.091)

 Certificate/license - None 0.58 0.04 -0.04 

(0.325) (0.125) (0.133)

 Associate's degree - None 1.91 * 0.18 0.11 

(0.437) (0.067) (0.080)

 Bachelor's degree or higher - None 0.67 * 0.06 0.28 

(0.253) (0.073) (0.086) 

Engagement

 Attendance -0.03 0.08 0.07

(0.165) (0.049) (0.057)

 Classroom behavior 0.17 0.01 -0.02 

(0.092) (0.023) (0.017)

 Extracurricular participation -0.05 -0.02 -0.01

(0.043) (0.014) (0.014)

 Usefulness of school subjects -0.35 -0.12 * 0.05

(0.138) (0.044) (0.054) 

Attainment Group

 (A1) Successful completers - Marginal completers 0.05 0.11 * 0.05 

(0.175) (0.042) (0.050)

 (A2) Noncompleters - Marginal completers -0.45 -0.24 * -0.22

(0.248) (0.099) (0.133) 
The Adult Lives of At-Risk Students 

Appendix D. Variables Used in This Report 

This appendix provides details about how the measures used in this study were 

obtained from the NELS:88 data files. The data were gathered from three files: 

(1) NELS:88/2000 Public Use Data Files and Electronic Codebook: Base Year

 through Fourth Follow-Up 

(2) NELS:88/2000 Restricted Use Data Files and Electronic Codebook: Base

 Year through Fourth Follow-Up 

(3) NELS:88/2000 Restricted Use Postsecondary Education Transcript Study

 (PETS: 2000)Data Files and Electronic Codebook: Fourth Follow-Up 

Variables are from the NELS:88/2000 Public Use Data Files unless indicated 

otherwise. Ranges provided are for the status risk sample used in this study. 

Information about each of the missing values codes is available in the National 

Longitudinal Study: 1988-2000 Data Files and Electronic Codebook System 

(NCES 2002). 

D-1 
Appendix D Variables Used in This Report 

D-2 

VARIABLE 

NAME 

Variables Used in This Report 

DESCRIPTION 

SOURCE 

VARIABLE 

NAME 

F4PNLWT STRATUM PSU 

Weight and Design Variables 

Panel weight, BY, F1, F2, F3 and F4 Sampling stratum Primary sampling unit 

F4PNLWT 

STRATUM 

PSU 

R

R

R

BYSES G8LUNCH F1C30A 

Variables Used to Choose Status Risk Sample 

Socioeconomic status composite; constructed from parents' education, parents' occupations, and household income Percent of students in grade 8 school who receive free lunch Percent of students in grade 10 school who receive free lunch 

BYSES 

G8LUNCH1 

F1C30A1 

R

Variables Used to Assign Attainment Group 

BY2XCOMP Standardized achievement test composite, reading and math, grade 8 BY2XCOMP RF12XCOMP Standardized achievement test composite, reading and math, grade10 F12XCOMP R

F1S39A Respondent's self-reported math grades, grade 10 F1S39A 1 

F1S39B Respondent's self-reported English grades, grade 10 F1S39B 2 

F1S39C Respondent's self-reported history grades, grade 10 F1S39C 3 

F1S39D Respondent's self-reported science grades, grade 10 F1S39D 4 

5 6 7 8 9 9

F4UNI2D Second follow-up status of sample member F4UNI2D 1 

2 3 6 

F3DIPLOM Third follow-up high school diploma/GED status F3DIPLOM 1 

2 3 4 5 6 -

1Data source is the NELS:88 Restricted-Use Data File 
D-3 

The A

VARIABLE 

NAME 

DESCRIPTION 

SOURCE 

VARIABLE 

NAME 

Variables Used to Assign Attainment Group (continued) 

ATTNGRP Attainment group; determined by: Derived 1 

1)Math, English, history and science grades in grade 10 - F1S39A, F1S39B, F1S39C, F1S39D 2 2)Math and reading composite test scores in grades 8 and 10 - BY2XCOMP, F12XCOMP 3)Student status at second follow-up - F2UNI2D 4)Diploma status at third follow-up - F3DIPLOM Successful completer: Grade 10 average grades of B's and C's or better, and grade 8 and 10 composite scores ≥ 1/4 SD below the mean, and in school and in grade 12 at second follow-up 

Marginal completer: Grade 10 average grades, or grade 8 or 10 composite score, do not meet successful completer criteria; or in school but not in grade 12 at second follow-up, but did receive a high school diploma by third follow-up Noncompleter: Dropped out by second follow-up, or in school but not in grade 12 at second follow-up and did not receive a high school diploma by third follow-up 

3 

Demographic Variables 

F4SEX Gender of respondent F4SEX 1 

2 

F4RACE Race of respondent F4RACE 1 

2 3 4 5 -

G8URBAN Urbanicity of school, grade 8 G8URBAN 1 

2 3 9 

G8TYPE School classification reported by school, grade 8 G8CTRL 1 

2 3 4 9 

G10TYPE School classification reported by school, grade 10 G10CTRL1 1 

2 3 4 5 9
Appendix D Variables Used in This Report 

D-4 

VARIABLE 

NAME 

DESCRIPTION 

SOURCE 

VARIABLE 

NAME 

FAMLYINC FATHERED MOTHERED FAMCOMP LANGMINR SIBLINGS MOBILITY 

Base Year Family Characteristics 

Base year parent report of total family income from all sources 

Student report of father's highest level of education attained Student report of mother's highest level of education attained 

Family composition composite; used to determine percent living with two biological parents Language minority composite; used to determine percent from non-English speaking home Number of eighth-grader's siblings 

Number of times eighth grader has changed schools 

BYFAMINC 

BYS34A BYS34B 

BYFCOMP 

BYLM 

BYP3A 

BYP40 

1 = 

2 to tha98, 

1 = 2 = 

3 = 4 = 5 = 6 = 7 = 8, 9

1 = 

2 = 3 = 4 = 5 = 6 = 98, 

0 = 

1 = 8, 9 

0 to 

0 to 96, 

0 to 

0 to 98, 
D-5 

The A

VARIABLE 

NAME 

DESCRIPTION 

SOURCE 

VARIABLE 

NAME 

F4IYOUTH F4ICIVIC VOLNTEER VOTED HOMEOWNR 

LIVEPARN MARRIED CHILDREN 

Adult Lifestyle Characteristics 

Youth organization volunteer Civic/community volunteer 

Indicator of whether participant was doing volunteer work at time of fourth follow-up; 

composite of responses to F4IYOUTH and F4ICIVIC: if the value of either variable was 1, the value of VOLNTEER was set to 1 Indicator of whether participant voted in the 1996 presidential election Participant's housing status at time of fourth follow-up; used to determine percent who own home The number of parents/guardians in the participant's household in the fourth follow-up; used  to determine percent who live with parents Marital status in fourth follow-up; used to determine percent married Number of biological children; used to determine percent with one or more children 

F4IYOUTH 

F4ICIVIC 

Derived 

F4IVPRE 

F4HHOSE 

F4GNGRD 

F4GMRS 

F4GNCH 

0 = 1 = -1, 

0 = 

1 = -1, 

0 = 

1 = 

-9 = 

0 = 

1 = -1, 

1 = 

2 = 3 = 4 = -1, 

0 = 

1 = 

2 = -1, 

1 = 

2 = 3 = 4 = 5 = 6 = -1, 

0 to 

chi-1, 
Appendix D Variables Used in This Report 

VARIABLE 

NAME 

DESCRIPTION 

SOURCE 

VARIABLE 

NAME 

REFITYPE FRSTINST ENTERPSE TOTCRED F4HHDG HIGHDEGR COMPPSE 

Postsecondary Education Measures 

Carnegie type of first institution Type of first institution entered; derived using REFITYPE1 - values 1 to 4 were coded as 

4-year institutions, values 5 and 6 were coded as 2-year institutions, value 7 was coded as <2-year institution Alternative data: F4EFSECT - sector for first postsecondary institution entered REFINST1 - type of first institution; more comprehensive categories than REFITYPE1 

Entered postsecondary education; recoded from FRSTINST Total undergraduate credits earned 

Highest postsecondary education degree attained as of 2000 Highest postsecondary education degree attained; recoded F4HHDG - categories for 

Bachelor's degree, Master's degree and Ph.D./professional degree were collapsed into ' Bachelor's degree or higher' Completed postsecondary education; recoded from HIGHDEGR 

REFITYPE1 

Derived 

Derived 

TCREDN1 

F4HHDG Derived 

Derived 

1 = 2 = 3 = 4 = 5 = 6 = 7 = -1, 

0 = 

1 = 

2 = 

3 = 

-9 = 

0 = 

1 = -9 = -1, 

1 = 

2 = 3 = 4 = 5 = 6 = -3, 

1 = 2 = 

3 = 4 = -3, 

0 = 

1 = -9 = 

D-6 

1Data source is the NELS:88 Restricted-Use Transcript Study Data File 

Note: Variables with names in italics were outcome measures in regression analyses 
The A

D-7 

VARIABLE 

NAME 

DESCRIPTION 

SOURCE 

VARIABLE 

NAME 

EMPLOYED FULLTIME F4BWKSWK HRSWEEK INCOME99 LOGINCME F4BJ97A F4BJ97B F4BJ98A F4BJ98B CNSTEMPL 

Employment/Income Measures 

Employed for pay at fourth follow-up Fourth follow-up employment is full-time 

Weeks worked in 1999 

Hours worked per typical week in 1999 Income of respondent in 1999 Logarithm of 1999 income Employed for at least six months in 1997 

Held employment full time in 1997 

Employed for at least six months in 1998 

Held employment full time in 1998 

Constancy of employment across three years, 1997-1999; derived from six variables: F4BJ97A, F4BJ97B, F4BJ98A, F4BJ98B, F4BWKSWK, HRSWEEK Score: The sum of points based on amount of employment: 1 point for each year respondent worked full-time and 0.77 points for each year of part-time work. For 1999, respondents who worked at least 26 weeks were considered employed; those who typically worked 37 hours or more per week were considered full-time. Minimum data: If data were not available for any year, a consistent employment score was not computed. 

F4AEMPL 

F4AACTF 

F4BWKSWK 

F4BLHPW 

F4HI99 

Derived F4BJ97A 

F4BJ97B 

F4BJ98A 

F4BJ98B 

Derived 

0 = 

1 = -2 = 

0 = 

1 = -2 = Ra-1, 

Ran

-1, Ran-1, 

-9 = 

0 = 

1 = -1, 

1 = 

2 = -1, 

0 = 

1 = -1, 

1 = 

2 = -1, 

Ran

-9 = 

Note: Variables with names in italics were outcome measures in regression analyses 
Appendix D Variables Used in This Report 

D-8 

VARIABLE 

NAME 

DESCRIPTION 

SOURCE 

VARIABLE 

NAME 

F1T1_16 F1T5_16 F1T1_17 F1T5_17 ABSTARDY F1T1_2 F1T5_2 F1T1_15 F1T5_15 F1T1_18 F1T5_18 F1T1_20 F1T5_20 CLASSBEH 

Engagement Measures 

How often student is absent; responses from two grade 10 teachers 

How often student is tardy; responses from two grade 10 teachers 

How often student is absent or tardy; derived from four variables: F1T1_16, F1T5_16, 

F1T1_17, F1T5_17 Alternative data: When grade 10 data were missing, grade 12 measures were used. Minimum data: At least two teachers' responses from grade 10 or grade 12 were required. 

Score: ABSTARDY is the average of the available responses. The scale was reversed so higher scores meant better attendance. Student usually works hard; responses from two grade 10 teachers How often student does homework; responses from two grade 10 teachers 

How often student is attentive in class; responses from two grade 10 teachers 

How often student is disruptive in class; responses from two grade 10 teachers 

Teacher rating of student's classroom behavior; derived from eight variables: F1T1_2, F1T5_2, F1T1_15, F1T5_15, F1T1_18, F1T5 _18, F1T1_20, F1T5_20 Alternative data: When grade 10 data were missing, grade 12 measures were used. In grade 12, teachers were not asked if students work hard. To determine class behavior scores using grade 12 data, the mean of the grade 10 measure of working hard, for all cases with a class behavior score, was used, .54. Minimum data: At least one teacher's responses to all four questions in grade 10, or all three 

questions in grade 12, was required. Score: The CLASSBEH score was computed by summing points based on the average of two teachers' responses to each question: 1 point if 'works hard' = 1; 1 point if 'does homework' ≥ 4; 1 point if 'is attentive' ≥ 4; 1 point if ' is disruptive' ≤ 2 

F1T1_16 

F1T5_16 

F1T1_17 

F1T5_17 

Derived F1T1_2 

F1T5_2 

F1T1_15 

F1T5_15 

F1T1_18 

F1T5_18 

F1T1_20 

F1T5_20 

Derived 

1 

2 

3 

4 

5 6

R

-

1 

2 3

1 

2 

3 

4 

5 

6

R

-
The A

D-9 

VARIABLE 

NAME 

DESCRIPTION 

SOURCE 

VARIABLE 

NAME 

Engagement Measures (continued) 

F1S41AA Played baseball/softball at school F1S41AA 1 = F1S41AB Played basketball at school F1S41AB 2 = F1S41AC Played football at school F1S41AC 3 = F1S41AD Played soccer at school F1S41AD 4 = 

F1S41AE Participated on swim team at school F1S41AE 5 = F1S41AF Played other team sport F1S41AF 6 = 

F1S41AG F1S41AH F1S41AI 

Played an individual sport Participated in cheerleading Participated on pom-pom, drill team 

F1S41AG 

F1S41AH 

F1S41AI 

96, 

F1S41BA Participated in school band, orchestra F1S41BA 1 = F1S41BB Participated in school play, musical F1S41BB 2 = 

F1S41BC Participated in student government F1S41BC 3 = 

F1S41BD Paritcipated in academic honor society F1S41BD 4 = 

F1S41BE F1S41BG 

Paricipated in school yearbook, newspaper Participated in school academic clubs 

F1S41BE 

F1S41BG 

6, 8

EXTRACUR Participation in extracurricular activities; derived from student report of participation in 15 Derived 0 to 

extracurricular activities - F1S41AA-AI, F1S41BA-BE, F1S41BG Alternative data: When grade 10 information was not available, grade 12 data were used 

Minimum data: At least one valid response to a question about extracurricular participation 

was required for the student to receive an EXTRACUR score Score: EXTRACUR is the number of extracurricular activities a student reported participating in 

-9 = 

BYS69C Math will be useful in my future BYS69C 1 = 

BYS70C English will be useful in my future BYS70C 2 = BYS71C Social Studies will be useful in my future BYS71C 3 = 

BYS72C Science will be useful in my future BYS72C 4 = 

6, 8

UTILITY Utility score; student rating of the usefulness of math, English, social studies and science; Derived Ran

derived from base year student responses to BYS69C, BYS70C, BYS71C, BYS72C Minimum data: At least two responses were required for student to receive a UTILITY score Score: Average of available responses; reverse-scaled so higher value was more favorable 

-9 = 
 

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