U.S. Department of Education

Institute of Education Sciences

NCES 2005–303

STATISTICS IN BRIEF September 2005

What Is the Status of High School Athletes 

8 Years After Their Senior Year?

Contact:

Jeffrey Owings

NCES

202-502-7423 

Authors:

Deven Carlson

Leslie Scott

Education Statistics 

Services Institute

Michael Planty

RTI

Jennifer Thompson

Branch Associates Inc.

This Statistics in Brief examines the status of high school athletes 8 years after 

their senior year in high school. Using a representative sample of 10th-graders 

in 1990, who were seniors in 1992, from the National Education Longitudinal 

Study of 1988 (NELS:88), this Brief provides information on the 1990–1992 high 

school athletes’ educational, labor market, and health status in the year 2000, 

eight years after scheduled high school graduation. Outcomes for persons who 

reported participation in high school athletics (persons who indicated involvement 

in any school sports, at any level, in 1990 or 1992) are compared to outcomes 

for persons who did not report any high school athletic participation. In addition, 

outcomes for different types of high school athletes—persons who participated as 

elite athletes (team captains or most valuable players [MVPs] in 1990 or 1992), 

varsity athletes, and junior varsity (JV)/intramural athletes1

—are compared.

Major fi ndings include the following:

Educational

High school athletes at the elite and varsity levels are more likely than nonathletes 

to have any postsecondary education by 2000 and to have earned a bachelor’s 

degree by 2000. No differences were detected for JV/intramural athletes.

Labor Market

Elite athletes are more likely than nonathletes to be employed and employed 

full time in 2000. Those who participated in high school athletics at the elite and 

varsity levels earned a higher income in 1999, when compared to those who did 

not participate in high school athletics.

Health

High school athletes at all three levels of participation—elite, varsity, and JV—are 

more likely than nonathletes to participate in physical fi tness activities and in 

group sports/recreation activities in 2000. Elite and varsity athletes were less 

likely to be daily smokers in 2000 than were nonathletes, but were more likely 

to binge drink in 2000 than their nonathletic counterparts.

1

The three categories of high school athletes discussed in this report—elite, varsity, and JV/intramural—are 

mutually exclusive.
2

Introduction

School-based organized sports play a large role on the 

campuses of America’s educational institutions. From elementary school gym classes to high school varsity teams 

to National Collegiate Athletic Association (NCAA) 

tournaments, participation in school-based team and 

individual sporting competitions is a prevalent activity. 

Virtually every state, district, and school in the United 

States requires physical education for its students (Pate 

et al. 1995). According to fi gures from state high school 

athletic/activity associations, 55.4 percent of high school 

students participated in athletics in 2002–03 (National 

Federation of State High School Associations 2003). 

Given the prevalence of school athletic programs and 

participation, it is important to understand and consider 

both the immediate and long-range impacts that such 

activities may have on the individual. Over the years, 

many studies have examined this impact and athletic 

participation has been shown to be associated with a 

number of different outcomes, ranging from educational 

aspirations and attainment to occupation and income to 

consumption of alcohol (Adelman 1990; Barber, Eccles, 

and Stone 2001; Long and Caudill 1991; Pascarella et 

al. 1999). Some of the studies have investigated athletic 

participation as one factor within the larger context of 

participation in extracurricular activities, while others 

have examined the infl uence of school athletic participation by itself (Adelman 1990; Marsh and Kleitman 2002). Regardless of the context in which school 

athletic participation is analyzed, the preponderance of 

the literature reaches similar conclusions. There is everincreasing evidence that school athletic participation 

is positively associated with many educational, labor 

market, and health outcomes. 

Barber, Eccles, and Stone (2001) found that school 

athletic participation was associated with academic 

achievement. Students who self-identifi ed themselves as 

“jocks” were found to have relatively higher grade-point 

averages in high school and to be relatively more likely 

to enroll in college full time than those who did not selfidentify themselves as “jocks.” In addition to educational 

outcomes, school athletic participation has also been 

shown to be positively associated with income for males 

later in life (Long and Caudill 1991). Pascarella et al.’s 

(1999) review of the literature noted Adelman’s (1990) 

fi nding that students who participated in college athletics 

have higher rates of employment than nonparticipants. In 

the same review, the authors also noted that Pascarella 

and Smart (1991) reported evidence linking athletic 

participation to persistence in college and completion of 

a bachelor’s degree. 

One exception to these positive patterns is alcohol consumption. A number of studies have found that students 

who participate in athletics have higher rates of drinking 

than those who do not participate in school athletics 

(Barber, Eccles, and Stone 2001; Eccles and Barber 1999; 

Long and Caudill 1991). In their research, Barber, Eccles, 

and Stone (2001) found that students who participated in 

high school athletics also had high rates of drinking relative to nonparticipants, both in high school and later in 

life. In a similar study, Eccles and Barber (1999) found 

that participation in team sports in high school was linked 

to higher rates of drinking alcohol, when compared to 

the general population. Along the same lines, Long and 

Caudill (1991) found that participants in college athletics 

had more alcohol- and drug-related problems than other 

students.

While some of the studies that examine the impact of 

athletic participation on a variety of outcomes do so using cross-sectional data, other studies, including this one, 

use longitudinal data in their analyses. Eccles and Barber 

(1999) and Barber, Eccles, and Stone (2001) used data 

from the Michigan Study of Adolescent Life Transitions, 

a longitudinal study that followed participants from their 

6th-grade year in 1983 until six years after their scheduled graduation. In his piece titled Light and Shadows on 

College Athletics: College Transcripts and Labor Market 

History, Adelman (1990) used data from the 1986 followup to the National Longitudinal Study of 1972 (NLS:72), 

one of the predecessors to the National Education Longitudinal Study of 1988 (NELS:88), and found positive 

labor market outcomes for college athletes. 

Marsh and Kleitman (2002) used NELS:88 data to examine the associations between participation in high 

school extracurricular activities, including athletics, and a 

number of grade 12 and postsecondary outcomes such as, 

school grades, coursework selection, homework, educational and occupational aspirations, self-esteem, substance 

abuse, number of university applications submitted, and 

college enrollment. Their conclusions supported many 

of the fi ndings of earlier studies. Participation in high 

school extracurricular activities, and especially athletics, was found to be associated with relatively higher 

rates of achieving a number of desirable outcomes than 

nonparticipation. Furthermore, these authors found that 

participation in high school athletics had a stronger positive impact on postsecondary outcomes than participation 

in any other type of extracurricular activity. 
3

As is evident from this review of the literature, a number 

of studies have examined the associations between school 

athletic participation and later-life outcomes. This study, 

however, goes one step farther and deconstructs athletic 

participation by intensity of participation. Following the 

methodology employed by Owings, Burton, and Daniel 

(1997), this report classifi es participants into one of three 

mutually exclusive categories of athletes—elite, varsity, 

or junior varsity/intramural (JV). These categories were 

created based upon self-reported student data. Outcomes 

are then examined by participation status and intensity of 

athletic participation. Other research has found that level 

of participation in extracurricular activities is often associated with outcomes (Broh 2002; Fejgin 1994; Marsh 1993; 

Melnick, Sabo, and Vanfossen 1992; Melnick et al. 2001; 

Sabo, Melnick, and Vanfossen 1993). 

The specifi c outcomes examined in this Brief are as follows:

Educational

• any postsecondary education after high school for 

academic credit (i.e., college, university, or vocational, technical, or trade school) by 2000 (8 years 

after scheduled high school graduation);

• attainment of a bachelor’s degree or higher by 

2000;

Labor Market

• employment in 2000;

• full-time employment in 2000;

• income in 1999;

Health

• cigarette use in 2000;

• alcohol use in 2000; 

• binge drinking in 2000;

• participation in physical fi tness activities in 2000; 

and 

• participation in group or team sports and recreation 

in 2000.

Like Marsh and Kleitman (2002), this report uses data 

from NELS:88 and examines many comparable outcomes. 

Despite this similarity, there are a number of unique aspects of this report, which expand upon and extend the 

work of Marsh and Kleitman. First, while both reports 

use NELS:88 data—Marsh and Kleitman looked at outcomes in 1992 and 1994, respondents’ scheduled senior 

year in high school and two years after their scheduled 

graduation from high school, respectively—this report 

examines outcomes in 2000—eight years after respondents’ scheduled graduation from high school. To this 

extent, this Brief investigates the possible long-term associations between high school athletic participation and 

later-life outcomes. Using data from the 2000 follow-up 

allows for postsecondary outcomes to be assessed more 

precisely and also allows for occupational outcomes, 

such as employment status and income, to be examined. 

In addition to using data from the year 2000, this report 

looks only at participation in school-based athletics, as 

opposed to examining athletic participation as one aspect 

of the larger realm of extracurricular activities, as Marsh 

and Kleitman have done. This study also extends previous 

work done on the topic by deconstructing high school 

athletic participation by level of intensity and examining 

the associations between selected outcomes and the three 

levels of high school athletic participation.

Research Hypotheses

There is no one specifi c theory that can be used to predict 

the associations between athletic participation and all 

outcomes studied in this Brief—education, labor market, 

and health. However, there are general tenets that can 

guide thinking on hypothesis formation. Long and Caudill 

(1991) hypothesized that participation in athletics enhances 

self-control, perseverance, and discipline, traits that lead to 

positive outcomes in education, society, and the labor market. In addition, athletes may develop better exercise and 

eating habits that lead to positive personal health outcomes. 

Given these theories and the fi ndings of previous research, 

it is hypothesized that, when compared to individuals who 

did not report participation in high school athletics, individuals who reported athletic participation will be more 

likely to experience positive outcomes for every outcome 

examined except two. The two exceptions are alcohol use 

and binge drinking. Given the number of studies that have 

found athletic participation associated with higher levels of 

alcohol consumption, it is hypothesized that similar results 

will be observed in this study.

It is also hypothesized that, among athletic participants, 

those who participate at the more intense levels will 

experience more positive outcomes than those who participate at less intense levels. That is, it is expected that 

more positive outcomes will be observed for elite athletes 

compared to varsity athletes and JV athletes. Similarly, it 

is hypothesized that varsity athletes will be observed to 

have more positive outcomes compared to JV athletes.
4

The National Education Longitudinal Study of 

1988 (NELS:88)

This Brief uses data from the National Education Longitudinal Study of 1988 (NELS:88). The NELS:88 is a 

nationally representative sample of eighth-graders who 

were fi rst surveyed in the spring of 1988. A sample of 

these respondents was then resurveyed through four follow-ups, conducted in 1990, 1992, 1994, and 2000. 

A sample of 9,840 NELS:88 students who were 10thgraders in 1990 and 12th-graders in 1992 was used to 

produce the estimates in this report. For each NELS:88 

follow-up from which this report draws data (1990, 1992, 

and 2000), persons were asked to complete a questionnaire. However, not all persons completed the questionnaire as requested. In 1990, the weighted completion 

rate of the student questionnaire was 91.1 percent. For 

the second follow-up, in 1992, the weighted completion 

rate of the student questionnaire was 91.0 percent. The 

weighted completion rate for the fourth follow-up, in 

2000, was 82.7 percent. Thus the unit nonresponse for 

the instruments used to produce the estimates in this Brief 

ranged from 82.7 percent to 91.1 percent. Weighted item 

response rates ranged from a low of 90.7 percent (for the 

variable measuring participation on a swim team during 

high school) to a high of 100 percent for a number of 

variables (including those measuring whether a person 

was named MVP of a team, gender, race, and attendance 

at a postsecondary institution).

Student-reported data from the fi rst and second followups, in 1990 and 1992 (sophomore and senior years 

in high school), were used to determine, among other 

variables, high school athletic participation status, level 

of athletic participation, participation in nonsport extracurricular activities, sex, race/ethnicity, and household 

socioeconomic status (SES), as well as 10th-grade test 

scores. On these questionnaires, students reported on a 

range of topics including school, work, and home experiences; educational and occupational aspirations; and 

other student perceptions. Additional topics included 

self-reports on smoking, alcohol and drug use, and extracurricular activities. 

NELS:88 surveyed students two additional times after 

1992, once in 1994 (two years after scheduled high school 

graduation) and once in 2000. In addition to drawing on 

data collected during the 1990 and 1992 follow-ups, this 

report utilizes data from the 2000 follow-up, 8 years after students’ expected graduation from high school. The 

comparisons drawn for educational, labor market, and 

health outcomes were based upon data from the 2000 

follow-up. 

Overview of Analyses

There are two main types of analyses used in this Brief. 

The fi rst type of analysis, bivariate analysis, is used to 

describe the characteristics of students that reported participating in high school athletics and those that did not 

participate in high school athletics. The purpose of this 

analysis is to provide a profi le of high school athletes. The 

specifi c characteristics described are as follows:

• sex; 

• race/ethnicity; 

• socioeconomic status; 

• school control;

• participation in nonsport extracurricular activities, 

• 10th-grade test scores; and 

• 10th-grade locus of control. 

Comparisons are then made contrasting the characteristics 

of students reporting high school athletic participation and 

those reporting no high school athletic participation. Similar comparisons are then made for each level of athletic 

participation intensity. That is, the characteristics of elite 

athletes are compared to the characteristics of varsity and 

JV athletes and the characteristics of varsity athletes are 

compared to the characteristics of JV athletes. Please refer 

to the Technical Notes for a complete description of the 

statistical techniques used to make these comparisons.

The second type of analysis employed in this report is 

multivariate regression analysis. Multivariate regression 

analysis is a technique that allows for the isolation of 

an association between an independent variable and a 

dependent variable (e.g., the association between athletic 

participation and earning a bachelor’s degree) while taking into account, or controlling for, other independent 

variables that may also be associated with the dependent 

variable (e.g., sex, academic achievement, socioeconomic 

status, or race/ethnicity).

The specifi c method of regression analysis employed in 

this report is called hierarchical regression. In hierarchical regression analyses, outcomes are fi rst modeled using 

only control variables. Then, additional independent variables are introduced into the model, and, based on entry 
5

of these new variables into the model, observations are 

made regarding any increases or decreases in the predictive power of the model. In addition, the relative sizes of 

coeffi cients and odds ratios can be compared.

The hierarchical regression analysis performed in this 

report is typical in nature. All outcomes selected for examination are fi rst modeled using fi ve selected control 

variables—sex, race/ethnicity, household socioeconomic 

status, 10th-grade test scores, and 10th-grade locus of 

control. These demographic and student achievement 

variables have been shown to be associated with future 

outcomes in previous research (Ingels et al. 2002), and 

also appear to be associated with athletic participation 

(see table 1). This is the control model. Three additional 

variables are then added to the model. These variables 

measure athletic participation at each level of intensity—elite, varsity, and JV. The model containing these 

additional variables is referred to as the full model. Each 

outcome is then modeled using the full model, and the 

results of the full model are compared to the results of 

the control model for each outcome. More specifi cally, 

likelihood ratio tests are used to determine if the addition 

of the three athletic participation variables helps predict 

differences among individuals for each outcome. 

It is important to note that these analyses do not offer a 

formal evaluation of the impact or effect of high school 

athletic participation on selected outcomes for young 

adults. Causal relationships are best determined with a 

design that randomly selects and assigns adolescents to 

the characteristics of athletic participation discussed in 

this Brief. Of course, this type of treatment is often diffi cult to perform. The strengths of this study, however, are 

the use of a nationally representative sample of students 

from both public and private schools, the incorporation of 

a time dimension to establish proper variable order, and 

a control for variables that may be related to high school 

athletic participation and the variety of outcomes in 2000 

(e.g., sex, race/ethnicity, household SES, 10th-grade test 

scores). It is possible that other important variables may 

be related to athletic participation and the outcomes for 

young adults but are not represented in the analyses, such 

as physical health and stature, two variables not available 

on the NELS:88 dataset. Also, participation in sporting 

activities outside of school was not specifi cally examined 

in this Brief. Given the nonexperimental, longitudinal 

design of the study, there is no way to determine if the 

samples of athletes and nonathletes were equivalent in all 

important ways in high school besides this athlete characteristic. This is a research design limitation that makes it 

impossible to draw causal conclusions from the data. All 

comparisons in the text are statistically signifi cant at the 

.05 level unless otherwise noted. The reader is referred 

to the Technical Notes for further details on the statistical 

methods and variables used in this report.

Findings

This Brief investigates the educational, labor market, and 

health status of high school athletes 8 years after their senior year in high school, beginning fi rst with a description 

of the characteristics of students who reported participating in high school athletics in 1990 or 1992. Following 

this description is a portrayal of students participating at 

the three mutually exclusive categories of high school 

athletes—elite, varsity, and JV. 

This section then turns to presenting and discussing results 

of the multivariate regression analyses. As stated earlier, 

the multivariate regression analyses were undertaken to 

evaluate the ability of high school athletic participation 

to predict a number of later-life outcomes after controlling for possible alternative predictors. Results from the 

control model are presented fi rst and are followed by 

results from the full model. 

Who Participated in High School Athletics: 

1990 and 1992

This section examines a number of demographic and 

school-related measures of high school athletes and 

nonathletes2 in 1990 and 1992. Athletes differed from 

nonathletes in many ways in the early 1990s (table 1). 

Following are highlighted fi ndings from table 1:

Sex

A greater percentage of males than females reported athletic participation (72 percent versus 49 percent). 

Race/ethnicity

No differences were detected in high school athletic participation rates between Whites and Blacks, and Whites 

and Hispanics.

2

Less than one percent of students in the sample were classifi ed as nonathletes 

because they attended schools that did not provide opportunities to participate 

in athletics.
6

Socioeconomic status

A greater percentage of students from high-socioeconomic 

status families3

 reported high school athletic participation 

than students from middle- or low- SES families (70 percent versus 60 and 48 percent, respectively). 

School control

A greater percentage of students in private high schools 

reported high school athletic participation than students 

in public schools (72 percent versus 60 percent). 

Extracurricular activity participation

A greater percentage of students who participated in 

other nonsport school extracurricular activities4

 reported 

athletic participation than students who did not participate 

in other activities (62 percent versus 57 percent). 

10th-grade test scores

A greater percentage of students with high test scores5

reported athletic participation than students with middle 

or low scores (66 percent versus 60 and 57 percent, respectively). 

Locus of control

A greater percentage of students with high or middle 

levels of locus of control than a low level participated in 

high school athletics6 (64 and 64 percent, respectively, 

versus 53 percent).

Who Participated at Different Levels of High 

School Athletic Intensity: 1990 and 1992

Of interest is not only the kind of student who participates 

in high school athletics, and the consequences of doing so, 

but also how participation in athletics at different levels 

of intensity— elite, varsity, JV/intramural—is associated 

with student characteristics and later outcomes. Following 

are highlighted fi ndings from table 1:

Sex

Of all high school athletes, a greater percentage of males 

than females participated at the elite level (42 percent 

versus 33 percent), while a greater percentage of females 

than males participated at the JV/intramural level (33 

percent versus 24 percent). 

Race/ethnicity

Of all high school athletes, a greater percentage of Blacks 

than Whites and Hispanics were elite athletes (51 percent 

versus 36 and 37 percent, respectively), while a greater 

percentage of Whites than Blacks were varsity athletes 

(36 percent versus 27 percent). 

Extracurricular activity participation 

Of all high school athletes, a greater percentage of students who participated in other extracurricular activities 

were elite athletes than those who did not participate 

in other extracurricular activities (40 percent versus 32 

percent). 

School control

Of all high school athletes, a greater percentage of students in public schools than students in private schools 

were varsity athletes (35 percent versus 29 percent). 

10th-grade test scores

Of all high school athletes, a greater percentage of athletes 

with low test scores than with high scores participated at 

the elite level (42 percent versus 34 percent). 

Of all high school athletes, a greater percentage of students with high scores than with middle scores were 

varsity athletes (39 percent versus 33 percent); and a 

greater percentage of students with middle scores than 

with low scores were JV/intramural athletes (30 percent 

versus 25 percent). 

3

Household socioeconomic status (SES) is divided into three categories—high, 

middle, and low. A greater percentage of students from high socioeconomic 

(SES) families reported high school athletic participation compared to students 

from middle- or low-SES families, and a greater percentage of students from 

middle-SES families reported participation than students from low-SES 

families. Defi nitions for socioeconomic status (SES) and other variables are 

included in the Technical Notes. 

4

Participation in extracurricular activities included participation in any of 

the following activities in either 1990 or 1992: cheerleading, pom-pom, drill 

team, school music group (band, orchestra), school play or musical, student 

government, academic honor society, school yearbook or newspaper, school 

service clubs, school academic clubs, school hobby clubs, school FTA, FHA, 

FFA.

5

Test scores are a composite score of reading and math tests taken by NELS 

10th-graders.

6

Locus of control measures individuals’ beliefs about whether personal 

outcomes are the result of personal actions (internal locus of control orientation) 

or events outside of personal control (external locus of control orientation). 

Locus of control is a composite of six questionnaire items from the fi rst followup survey. Locus of control was divided into three categories—high, middle, 

and low. Higher levels indicate a more internal locus of control; lower levels 

indicate a more external locus of control. Details on this and other composites 

are included in the Technical Notes.
7

Table 1. Percentage of student participation in high school athletics, by sex, race/ethnicity, socioeconomic status, 

school type, extracurricular activities, test scores, and self-concepts: 1990 and 1992

 Did not report Reported 

 participation in participation in 

 high school high school 

Characteristic Population total Total athletics2

 athletics Elite3 Varsity4 JV/intramural5

 Total 100 100 39.1 (0.97) 60.9 (0.97) 38.1 (1.14) 34.2 (1.00) 27.7 (1.04)

Sex

 Male 50.2 (0.91) 100 27.6 (1.16) 72.4 (1.16) 41.6 (1.54) 34.1 (1.49) 24.3 (1.40)

 Female 49.8 (0.91) 100 50.7 (1.28) 49.3 (1.28) 33.0 (1.88) 34.3 (1.59) 32.6 (1.57)

Race/Ethnicity6

 White 76.9 (1.40) 100 38.6 (1.00) 61.4 (1.00) 35.8 (1.08) 35.7 (1.07) 28.5 (1.13)

 Black 12.8 (1.09) 100 38.9 (3.44) 61.1 (3.44) 51.1 (5.40) 26.7 (4.08) 22.1 (3.68)

 Hispanic 10.3 (0.99) 100 45.9 (3.61) 54.1 (3.61) 36.8 (3.91) 37.4 (4.61) 25.8 (3.58)

Household socioeconomic status7

 Low 18.9 (0.89) 100 52.4 (2.23) 47.6 (2.23) 37.6 (3.09) 34.0 (2.81) 28.4 (2.58)

 Middle 51.5 (1.06) 100 39.7 (1.30) 60.3 (1.30) 38.5 (1.62) 33.7 (1.40) 27.8 (1.51)

 High 29.6 (1.13) 100 30.1 (1.55) 69.9 (1.55) 38.3 (1.74) 34.9 (1.72) 26.8 (1.63)

12th-grade school type

 Public 90.5 (0.65) 100 40.2 (1.02) 59.8 (1.02) 38.3 (1.25) 34.9 (1.09) 26.9 (1.11)

 Private 9.5 (0.65) 100 28.4 (3.04) 71.6 (3.04) 37.3 (2.95) 29.4 (2.31) 33.2 (3.18)

Extracurricular activities (nonsport)

 Yes 80.8 (0.79) 100 38.1 (1.02) 61.9 (1.02) 39.5 (1.21) 33.3 (1.01) 27.2 (1.10)

 No 19.2 (0.79) 100 43.4 (2.21) 56.6 (2.21) 31.8 (2.96) 38.5 (3.35) 29.7 (2.96)

10th-grade test scores8

 Low 17.9 (0.84) 100 43.0 (2.30) 57.0 (2.30) 42.3 (3.38) 33.2 (3.45) 24.5 (2.47)

 Middle 51.6 (0.89) 100 40.0 (1.33) 60.0 (1.33) 37.4 (1.55) 32.5 (1.28) 30.2 (1.50)

 High 30.6 (0.92) 100 34.2 (1.40) 65.8 (1.40) 33.7 (1.54) 39.4 (1.79) 27.0 (1.66)

10th-grade locus of control

 Low 22.4 (0.83) 100 46.9 (2.17) 53.1 (2.17) 35.1 (2.82) 33.9 (2.60) 31.0 (2.60)

 Middle 50.1 (0.91) 100 35.8 (1.12) 64.2 (1.12) 38.2 (1.78) 36.3 (1.60) 25.6 (1.39)

 High 27.5 (0.82) 100 36.1 (1.90) 63.9 (1.90) 39.8 (1.98) 30.2 (1.71) 30.0 (1.97)

1

Includes respondents who reported athletic participation as elite, varsity, or JV/intramural athletes. 

2

Includes respondents who reported not participating in high school athletics, or were in schools that did not have sports teams in 1990 and 1992.

3

Includes respondents who reported being a team captain or named a most valuable player (MVP) in 1990 or 1992.

4

Includes respondents who reported they were on a varsity sports team in 1990 or 1992, but were not considered “elite” athletes.

5

Includes respondents who reported they were on a junior varsity or intramural sports team in 1990 or 1992, but were not elite athletes or on varsity sports teams.

6

Asians/Pacifi c Islanders and American Indians/Alaska Natives were not reported due to small sample sizes.

7

Socioeconomic status (SES) was obtained from F2SES1Q, which is a quartile coding of the composite variable, F2SES1. F2SES1 provides an SES scale for each individual that is derived 

from the following parent-questionnaire data obtained in the base year (1988): father’s education level, mother’s education level, father’s occupation, mother’s occupation, and family income. 

Respondents were considered to be from a household of “high” SES if their households’ SES fell in the highest quartile of all respondents’ households. Households in the lowest quartile were 

considered “low” SES, and households in one of the two middle quartiles were considered “middle” SES. F2SES1Q was used in the regression analysis.

8

Test scores are a composite of reading and math scores in 10th grade.

NOTE: Detail may not sum to totals because of rounding. Standard errors appear in parentheses. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, National Education Longitudinal Study of 1988 (NELS:88), First Follow-Up Student Questionnaire (1990), and 

Second Follow-Up Questionnaire (1992).

High school athletic participation1

Percent of high school athletes in 

each level of participation

Locus of control

Of all high school athletes, a greater percentage of athletes with middle levels of locus of control than with high 

levels participated at the varsity level (36 percent versus 

30 percent).

The bivariate analyses demonstrate that signifi cant gender 

and other demographic differences exist between athletes 

and nonathletes. These differences suggest that comparisons need to control for these differences. The multivariate 

analysis provides these controls.

Multivariate Analysis

In an effort to determine whether participation in high 

school athletics predicts later-life outcomes independent 

of other factors—specifi cally sex, race/ethnicity, socioeconomic status, 10th-grade test scores, and students’ 

locus of control—multivariate logistic regression analyses were performed on each of the outcomes that were 

selected for examination in this Brief. 
8

The specifi c outcomes examined were

Educational

• any postsecondary education for academic credit 

(college, university, or vocational, technical, or trade 

school) by 2000 (8 years after scheduled high school 

graduation);

• attainment of a bachelor’s degree or higher by 

2000;

Labor Market

• employment in 2000;

• full-time employment in 2000;

• income in 1999;

Health

• cigarette use in 2000;

• alcohol use in 2000; 

• binge drinking in 2000;

• participation in physical fi tness activities in 2000; 

and 

• participation in group or team sports and recreation 

in 2000.

As described earlier, each of the outcomes above were 

regressed on two sets of independent variables—a control model and a full model. The control model contains 

fi ve student demographic and background characteristics—sex, race/ethnicity, household SES, 10th-grade test 

scores, and locus of control. Table 2 shows the results of 

the control model analyses. The full model contains the 

fi ve control variables plus the three athletic participation 

intensity variables—elite, varsity, and JV. Table 3 presents 

the results of the full model analyses. 

In both tables 2 and 3, logistic regression is used for all 

models, except when annual income is the dependent 

variable. Unstandardized OLS coeffi cients are presented 

for the annual income model (since it is a continuous outcome), and odds ratios are presented for the others (since 

they are binary outcomes). An odds ratio greater than one 

indicates a greater likelihood of achieving the outcome 

variable; a ratio equal to one indicates an equal likelihood 

of achieving the outcome; and a ratio of less than one indicates a lower likelihood of achieving the outcome. For 

example, in table 3, students who were elite athletes are 

1.49 times (about one and one-half times) more likely to 

be employed than non-athletes. Another way to state this 

is that elite athletes are 49 percent (computed: [odds ratio 

– 1]*100) more likely than nonathletes to be employed 

in 2000. See the Technical Notes section of this Brief for 

more information on the use of logistic regression and the 

interpretation of odds ratios and regression coeffi cients. 

Control Model Results

The results of the control model analyses show that sex is 

the most consistent predictor of all ten outcomes examined 

in this report, although in different ways depending on 

the outcome (table 2). 

There are two outcomes in the year 2000 that males are 

less likely to achieve than females. These are having any 

postsecondary education and earning a bachelor’s degree 

(odds ratios of 0.59 and 0.66, respectively). Specifi cally, 

males are 41 percent less likely to have any postsecondary education by 2000 and 34 percent less likely to have 

earned a bachelor’s degree by 2000 compared to females. 

The results also show that males are about two times 

more likely than females to be employed and to be employed full time in the year 2000. Males are more likely 

than females to smoke daily, use alcohol, binge drink, 

participate in physical fi tness activities, and participate 

in group sports/recreation activities (refer to table 2 for 

specifi c odds ratios and coeffi cients). 

The control models also show that race/ethnicity is a less 

consistent predictor of the variety of outcomes explored 

in this report than sex. Sex was a statistically signifi cant 

predictor of all ten outcomes that are examined in this 

report, whereas race/ethnicity was found to be associated with seven outcomes. Race/ethnicity was found to 

be associated with having any postsecondary education 

by 2000, but not with having earned at least a bachelor’s 

degree by 2000. Blacks were found to be almost two times 

more likely than Whites—the comparison group—to have 

any postsecondary education by 2000 (odds ratio of 1.97), 

while Hispanics and Other races/ethnicities were found to 

be 2.39 and 1.95 times more likely than Whites, respectively, to have any postsecondary education by 2000.7

Blacks, Hispanics, and Other races/ethnicities are also 

7

The fi nding that Blacks and Hispanics are more likely than Whites to have 

any postsecondary education may appear inconsistent with the literature. For 

example, using NELS:88 data, Ingels et al. (2002) found that 69.7 percent of 

Hispanics and 76.5 percent each of Blacks and Whites had any postsecondary 

experience. The seemingly inconsistent percentages reported in this Brief 

of Blacks and Hispanics having any postsecondary experience compared to 

Whites are attributable primarily to the fact that other factors, such as SES, 

10th-grade test scores, and locus of control, have been controlled in the analysis 

reported in this Brief.
9

shown to be less likely than Whites to smoke on a daily 

basis (odds ratios of 0.33, 0.39, and 0.50, respectively).

The other three control variables comprising the control 

model—SES, 10th-grade test scores, and locus of control—are shown to be statistically signifi cant predictors 

in various cases. Each of these variables was entered into 

the control model as “quarter” variables, for example 

measuring the top 25 percent, middle two 25 percents, and 

bottom 25 percent of students in terms of SES, achievement on the NELS 10th-grade test, and locus of control.8

None of these factors infl uence being employed in 2000 

or being employed full time in 2000; however, all three 

were positively associated with having any postsecondary education by the year 2000, having earned at least a 

bachelor’s degree by the year 2000, annual income in 1999, 

and participation in physical fi tness activities in 2000. 

That is, individuals from relatively higher SES families 

were more likely to have any postsecondary education, to 

have earned a bachelor’s degree by the year 2000, to have 

a higher annual income in 1999, and to have participated 

in physical fi tness activities in 2000 compared to students 

from relatively lower SES families. The same relationships—more likely to have any postsecondary education, 

to have earned a bachelor’s degree, to have a higher annual 

income in 1999, and to have participated in physical fi tness 

activities in 2000—hold for students who had relatively 

higher 10th-grade test scores and a relatively more internal 

control perspective compared to students who had relatively lower test scores and a relatively external control 

perspective. Table 2 presents coeffi cients and odds ratios 

for each variable in the control model.

Full Model Results

The full model results address the primary question of this 

Brief: Does participation in high school athletics predict 

later-life outcomes independent of other predictors, specifi cally sex, race/ethnicity, SES, and tested achievement? 

As shown in table 3, the answer is yes. After controlling 

for the possible infl uences of sex, race/ethnicity, SES, and 

tested achievement, participation in high school athletics—particularly at the highest level of intensity, elite 

participation—independently accounted for differences 

in later life outcomes. The secondary purpose of this 

Brief is to investigate the association between intensity of 

athletic participation and later life outcomes. The fi ndings 

show that participation in high school athletics at the elite 

level is associated with all the outcomes examined here, 

whereas participation at lower levels is associated with 

some, but not all, postsecondary outcomes.

Table 2. Coeffi cients, standard errors, and odds ratios from multivariate analysis of selected characteristics on 

post high school outcomes: 2000

 Bachelor’s degree Employed Annual income,

Characteristic Any PSE, 20001 or higher, 20002 Employed, 2000 full time, 2000 19993

Male -0.52* -0.42* 0.79* 0.73* 0.34*

 (0.12) (0.09) (0.11) (0.09) (0.03) 

 [0.59] [0.66] [2.20] [2.08}

Black 0.68* 0.13 0.17 -0.15 -0.06 

 (0.31) (0.17) (0.18) (0.19) (0.06) 

 [1.97] [1.14] [1.19] [0.86]

Hispanic 0.87* -0.14 -0.03 -0.29* -0.03 

 (0.28) (0.16) (0.18) (0.14) (0.04) 

 [2.39] [0.87] [0.97] [0.75] 

Other9 0.67* 0.31 -0.42* -0.43* -0.10 

 (0.25) (0.16) (0.17) (0.18) (0.07) 

 [1.95] [1.36] [0.66] [0.65]

SES 0.81* 0.71* 0.04 0.01 0.04*

 (0.07) (0.04) (0.04) (0.04) (0.01) 

 [2.25] [2.03] [1.04] [1.01] 

10th-grade tests10 0.51* 0.80* 0.03 -0.08 0.05*

 (0.07) (0.05) (0.05) (0.04) (0.01) 

 [1.67] [2.23] [1.03] [0.92]

Locus of control 0.25* 0.19* -0.02 -0.02 0.05*

 (0.06) (0.04) (0.05) (0.05) (0.01) 

 [1.28] [1.21] [0.98] [0.98]

Log likelihood or R2 11 -786,507 -1,143,190 -770,038 -1,159,390 0.068

See notes at end of table.

8

Please refer to the Technical Notes for the exact names of the variables used 

in the control model. 
10

Table 2. Coeffi cients, standard errors, and odds ratios from multivariate analysis of selected characteristics on 

post high school outcomes: 2000—Continued

 Group sports/

Characteristic Daily smoker, 20004 Alcohol use, 20005 Binge drinker, 20006 Physical fi tness, 20007 recreation, 20008

Male 0.21* 0.67* 1.23* 0.26* 0.92*

 (0.09) (0.09) (0.09) (0.10) (0.07) 

 [1.23] [1.95] [3.42] [1.30} [2.51] 

Black -1.10* -0.35* -0.71* -0.01 0.38*

 (0.22) (0.17) (0.23) (0.17) (0.15) 

 [0.33] [0.70] [0.49] [0.99] [1.46] 

Hispanic -0.95* -0.10 -0.10 -0.03 -0.13 

 (0.15) (0.15) (0.16) (0.17) (0.19) 

 [0.39] [0.90] [0.90] [0.97] [0.88] 

Other9

 -0.70* -0.22 -0.60* -0.29 # 

 (0.22) (0.15) (0.20) (0.25) (0.17) 

 [0.50] [0.80] [0.55] [0.75] [1.00]

SES -0.08 0.26* 0.13* 0.16* -0.01 

 (0.04) (0.04) (0.05) (0.05) (0.04) 

 [0.92] [1.14] [1.14] [1.17] [0.99] 

10th-grade tests10 -0.17* 0.21* -0.06 0.16* -0.09*

 (0.04) (0.04) (0.05) (0.05) (0.04) 

 [0.84] [1.17] [0.94] [1.17] [0.91]

Locus of control -0.20* -0.04 -0.07 0.22* 0.13*

 (0.04) (0.04) (0.04) (0.05) (0.04) 

 [0.82] [0.96] [0.93] [1.25] [1.14]

Log likelihood or R2 11 -1,067,020 -1,312,580 -1,203,530 -951,265 -1,472,940

*p<.05

# Rounds to zero.

1

Any postsecondary education by 2000.

2

Earned a bachelor’s degree or higher by 2000.

3

Includes those who worked full time or part time. Logged in OLS regression.

4

Reported smoking one or more cigarettes per day in 2000.

5

Reported drinking alcohol on one or more occasions in the 30 days before the survey in 2000.

6

Binge drinking is defi ned as fi ve or more drinks on a single occasion in the 2 weeks before the survey in 2000.

7

Reported participating in physical fi tness activities to get in or stay in shape at least once a week during the 12 months before the survey in 2000.

8

Reported participating in group or team sports and recreation at least once a month during the 12 months before the survey in 2000.

9

Includes Asian/Pacifi c Islanders and American Indian/Alaska Native.

10Test scores are a composite of reading and math scores in 10th grade.

11The R2

 statistic only applies to the outcome for annual income in 1999.

NOTE: Female is the omitted category for sex. White is the omitted category for race/ethnicity. SES, 10th-grade test scores, and locus of control are all quartile variables, and the coeffi cients 

and odds ratios are associated with moving up one quartile. All models are logistic regression, except where the dependent variable is annual income (1999), where OLS regression analysis is 

performed. Standard errors are in parentheses. Odds ratios are in brackets.

SOURCE: U.S. Department of Education, National Center for Education Statistics, National Education Longitudinal Study of 1988 (NELS:88), First Follow-Up Student Questionnaire (1990), 

Second Follow-Up Questionnaire (1992), and Fourth Follow-Up Questionnaire (2000).

Elite athletes were more than three times as likely as those 

who did not participate in high school athletics to participate in physical fi tness activities and in group sports/recreation activities in 2000 (odds ratios of 3.19 and 3.71, 

respectively) (table 3). Also, elite athletes were more than 

twice as likely as nonathletes to have any postsecondary 

education by 2000 and to have earned a bachelor’s degree 

by 2000. Furthermore, as was hypothesized, elite athletes 

were approximately one and a half times more likely than 

nonathletes to use alcohol and to binge drink in 2000. This 

contrasts with the positive infl uence of elite participation 

(as well as at the varsity level) on smoking behavior: elite 

athletes are about half as likely as nonathletes to smoke 

daily in 2000. Elite athletes were also shown to be more 

likely than nonathletes to have been employed in 2000, 

employed full time in 2000, and to have earned a higher 

income in 1999. 

Participation in high school athletics at the second highest 

level of intensity—varsity participation— also independently predicted many outcomes, though not as many as 

were predicted by participation at the elite level. In all, participation in high school athletics at the varsity level was 

associated with increased likelihood of the following: 
11

• any postsecondary education by 2000;

• earned a bachelor’s degree by 2000; 

• higher annual income in 1999;

• binge drinking in 2000;

• participating in physical fi tness activities in 2000; 

and

• participating in group sports/recreation activities in 

2000.

In addition, participation at the varsity level was associated 

with a decreased likelihood of daily smoking in 2000.

However, for each outcome, the odds ratios for participation in high school athletics at the varsity level are closer 

to 1 than the odds ratios for participation at the elite level. 

This indicates that, for each outcome, participation in 

high school athletics at the varsity level is not as strongly 

related as is participation at the elite level. For example, 

for the outcomes any postsecondary education by 2000 

and having earned a bachelor’s degree or higher by 2000, 

the odds ratios for participation at the varsity level are 

1.70 and 1.86, respectively. These are lower than the odds 

ratios associated with participation at the elite level, which 

are 2.05 and 2.27, respectively. To further illustrate, elite 

athletes are 2.05 times more likely than nonathletes to 

have any postsecondary education by 2000 while varsity 

athletes are 1.70 times more likely than nonathletes to 

achieve the same outcome. For all outcomes, the coeffi cients associated with participation at the varsity level 

can be found in table 3.

The full model results indicate a different story for participation at the JV/intramural level. Participation at this 

level was shown to be related, and positively, to only two 

outcomes, participation in physical fi tness activities and 

Table 3. Coeffi cients, standard errors, and odds ratios from multivariate analysis of high school athletic status and 

selected characteristics on post high school outcomes: 2000 

 Bachelor’s degree Employed Annual income,

Characteristic Any PSE, 20001 or higher, 20002 Employed, 2000 full time, 2000 19993

Male -0.67* -0.59* 0.73* 0.68* 0.32*

 (0.12) (0.09) (0.12) (0.09) (0.03)

 [0.51] [0.55] [2.08] [1.97}

Black 0.64* 0.09 0.15 -0.16 -0.06

 (0.30) (0.19) (0.18) (0.19) (0.06)

 [1.90] [1.09] [1.16] [0.85]

Hispanic 0.88* -0.13 -0.03 -0.29* -0.03

 (0.28) (0.16) (0.18) (0.14) (0.04)

 [2.41] [0.88] [0.97] [0.75] 

Other9 0.62* 0.29 -0.44* -0.44* -0.10

 (0.23) (0.16) (0.16) (0.18) (0.07)

 [1.86] [1.34] [0.64] [0.64]

SES 0.78* 0.68* 0.03 (#) 0.03*

 (0.07) (0.04) (0.04) (0.04) (0.01)

 [2.18] [1.97] [1.03] [1.00] 

10th-grade tests10 0.52* 0.82* 0.03 -0.08 0.05*

 (0.08) (0.05) (0.05) (0.05) (0.01)

 [2.18] [2.27] [1.03] [0.92]

Locus of control 0.24* 0.17* -0.03 -0.03 0.05*

 (0.06) (0.05) (0.05) (0.05) (0.01)

 [1.27] [1.19] [0.97] [0.97]

Participated at elite level11 0.72* 0.82* 0.40* 0.30* 0.13*

 (0.21) (0.11) (0.14) (0.13) (0.04)

 [2.05] [1.19] [1.49] [1.35] 

Participated at varsity level12 0.53* 0.62* 0.17 0.18 0.07*

 (0.15) (0.12) (0.14) (0.11) (0.04)

 [1.70] [1.86] [1.19] [1.20]

Participated at JV/

intramural level13 0.20 0.19 -0.17 -0.05 #

 (0.18) (0.12) (0.14) (0.11) (#)

 [1.22] [1.21] [0.84] [0.95]

Log likelihood or R2 14 -777,312* -1,122,710* -766,373* -1,156,400* 0.073*

See notes at end of table.
12

Table 3. Coeffi cients, standard errors, and odds ratios from multivariate analysis of high school athletic status and 

selected characteristics on post high school outcomes: 2000—Continued 

 Group sports/

Characteristic Daily smoker, 20004 Alcohol use, 20005 Binge drinker, 20006 Physical fi tness, 20007 recreation, 20008

Male 0.31* 0.59* 1.14* 0.05* 0.73*

 (0.09) (0.09) (0.09) (0.11) (0.08) 

 [1.36] [1.80] [3.13] [1.05} [2.08]

Black -1.05* -0.38* -0.77* -0.09 0.32*

 (0.22) (0.18) (0.23) (0.16) (0.14) 

 [0.35] [0.68] [0.46] [0.91] [1.38]

Hispanic -0.96* -0.09 -0.10 -0.02 -0.12 

 (0.15) (0.15) (0.16) (0.16) (0.19) 

 [0.38] [0.91] [0.90] [0.98] [0.89] 

Other9

 -0.67* -0.25 -0.63* -0.37 -0.08 

 (0.22) (0.15) (0.20) (0.25) (0.19) 

 [0.51] [0.78] [0.53] [0.69] [0.92]

SES -0.05 0.24* 0.10* 0.11* -0.05 

 (0.04) (0.04) (0.04) (0.05) (0.04) 

 [0.95] [1.27] [0.94] [1.12] [0.95] 

10th-grade tests10 -0.18* 0.21* -0.06 0.17* 0.09*

 (0.04) (0.04) (0.05) (0.05) (0.04) 

 [0.84] [1.23] [0.94] [1.19] [1.09]

Locus of control -0.19* -0.05 -0.09* 0.19* 0.10*

 (0.04) (0.04) (0.04) (0.05) (0.04) 

 [0.83] [0.95] [0.91] [1.21] [1.11]

Participated at elite level11 -0.58* 0.42* 0.52* 1.16* 1.31*

 (0.12) (0.12) (0.11) (0.16) (0.10) 

 [0.56] [1.52] [1.68] [3.19] [3.71] 

Participated at varsity level12 -0.30* 0.17 0.23* 0.63* 0.62*

 (0.13) (0.12) (0.12) (0.14) (0.11)

 [0.74] [1.19] [1.26] [1.88] [1.86]

Participated at JV/

intramural level13 -0.17 0.10 0.16 0.42 0.53*

 (0.12) (0.12) (0.12) (0.15) (0.10) 

 [0.84] [1.11] [1.17] [1.52] [1.70]

Log likelihood or R2 14 -1,059,760* -1,307,360* -1,195,900* -927,855* -1,414,660*

*p<.05

# Rounds to zero.

1

Any postsecondary education by 2000.

2

Earned a bachelor’s degree or higher by 2000.

3

Includes those who worked full time or part time. Logged in OLS regression.

4

Reported smoking one or more cigarettes per day in 2000.

5

Reported drinking alcohol on one or more occasions in the 30 days before the survey in 2000.

6

Binge drinking is defi ned as drinking fi ve or more drinks on a single occasion in the 2 weeks before the survey in 2000.

7

Reported participating in physical fi tness activities to get in or stay in shape at least once a week during the 12 months before the survey in 2000.

8

Reported participating in group or team sports and recreation at least once a month during the 12 months before the survey in 2000.

9

Includes Asian/Pacifi c Islanders and American Indian/Alaska Natives.

10Test scores are a composite of reading and math scores in 10th grade.

11Includes respondents who reported being a team captain or named a most valuable player (MVP) in 1990 or 1992.

12Includes respondents who reported they were on a varsity sports team in 1990 or 1992, but were not considered “elite” athletes.

13Includes respondents who reported they were on a junior varsity or intramural sports team in 1990 or 1992, but were not elite athletes or on varsity sports teams.

14The R2

 statistic only applies to the outcome for annual income in 1999.

NOTE: Female is the omitted category for sex. White is the omitted category for race/ethnicity. SES, 10th-grade test scores, and locus of control are all quartile variables, and the coeffi cients 

and odds ratios are associated with moving up one quartile. Nonathlete is the omitted category for high school athletic status. All models are logistic regression, except where the dependent 

variable is annual income (1999), where OLS regression analysis is performed. Standard errors are in parentheses. Odds ratios are in brackets.

SOURCE: U.S. Department of Education, National Center for Education Statistics, National Education Longitudinal Study of 1988 (NELS:88), First Follow-Up Student Questionnaire (1990), 

Second Follow-Up Questionnaire (1992), Third Follow-Up Student Questionnaire (1994) and Fourth Follow-Up Student Questionnaire (2000). 
13

participation in group sports/recreation activities in 2000. 

As was the case between participation at the elite level 

and the varsity level, participation at the JV level is not as 

powerful a predictor as is participation at the varsity level, 

evidenced by the fact that the odds ratios associated with 

participation at the JV level indicate smaller likelihoods 

than the odds ratios associated with participation at the 

varsity level. For example, for the outcome participation 

in physical fi tness activities in 2000, the odds ratio associated with participation at the JV level is 1.52, smaller 

than the 3.19 and 1.88 odds ratios that are associated with 

participation at the elite and varsity levels, respectively. 

Control model versus full model comparison

By performing a statistical test (called a likelihood ratio 

test) that compares the log likelihoods of the two models 

in this analysis (the control model and the full model), it 

can be determined whether athletic participation accounts 

for a statistically signifi cant additional amount of variation 

in each of the outcomes examined. 

The log likelihoods for each model are presented in the 

bottom row of tables 2 and 3. The results of the log likelihood tests indicate that the association between athletic 

participation and each outcome explains additional variance in that outcome. That is, for each outcome, a statistically signifi cant difference was found between the log 

likelihood of the control model and the log likelihood of 

the full model. In each case the log likelihood of the full 

model was larger than the log likelihood of the control 

model, meaning that the full model had more predictive 

power than the control model. For the annual income 

model, there was a statistically signifi cant increase in the 

R2

 statistic between the control model and the full model. 

Hence, the athletic participation variables are shown to 

be predictive of all outcomes examined in this Brief, 

independent of the control variables. More information 

can be found in the Technical Notes. 

Conclusion

This Brief examines the relationship between participation in high school athletics and later-life outcomes. 

Consistent with previous research, this report generally 

found positive relationships between high school athletic 

participation and educational, labor market, and health 

outcomes in 2000, eight years after scheduled high 

school graduation (Barron, Ewing, and Waddell 2000; 

Curtis, McTeer, and White 2003; Eide and Ronan 2001; 

Howell, Miracle, and Rees 1984; Marsh 1993; Marsh 

and Kleitman 2002; Picou, McCarter, and Howell 1985; 

Sabo, Melnick, and Vanfossen 1993; Spreitzer 1994). 

High school athletes at the elite and varsity levels of 

participation experienced greater educational and labor 

market success than nonathletes. The health outcomes 

for high school athletes included lesser rates of smoking 

at the elite and varsity levels of participation and greater 

rates of participation in fi tness activities and in group 

sports/recreation activities at all three levels of participation, compared to nonathletes. High school athletes at 

the elite and varsity level, however, were more likely to 

be binge drinkers. 

This Brief extended the work of previous researchers by 

examining the positive and negative associations of high 

school athletic participation by intensity of participation, 

as measured by participation at the elite, varsity, or JV 

level. It fi nds that the relationships described above are 

more apparent when comparing elite athletes—students 

who were team captains or MVPs in 1990 or 1992—with 

nonathletes than when comparing varsity or JV athletes 

with nonathletes. The high level of athletic involvement 

among elite athletes is, for the most part, associated with 

positive outcomes. Other researchers have also found that 

level of involvement matters (Broh 2002; Fejgin 1994; 

Marsh 1993; Melnick, Sabo and Vanfossen 1992; Melnick 

et al. 2001; Sabo, Melnick and Vanfossen 1993).

Future work in this area should include more extensive 

controls for possible selection effects of high school 

athletic participation. For example, certain students who 

are highly motivated may be more likely to participate in 

athletics compared to students who have low motivation. 

A highly motivated person is also more likely to succeed 

in the labor market (e.g., to get a job and to advance within 

the job market). Therefore, it is important to account for 

these underlying differences between athletes and nonathletes. For some research (i.e., Fejgin 1994), associations 

with athletic participation are diminished once selection 

controls are included, indicating that the associations may 

stem from underlying differences between athletes and 

nonathletes. In addition, as the fi ndings in this Brief on 

differences by sex, race/ethnicity, and SES demonstrate, 

the role of athletic participation may vary for different 

groups of individuals; thus the relationship between different variables should also be further investigated. While 

this report provides greater detail by examining different 

levels of athletes, another avenue of investigation would 

be to examine differences among sports and their relationships with later outcomes, as certain sports may teach or 

instill particular skills that are benefi cial to participants 

in postsecondary or professional environments.
14

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Chen, X. (2002). Coming of Age in the 1990s: 

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(NCES 2002–321). U.S. Department of Education. 

Washington, DC: National Center for Education 

Statistics.

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of Participation in Intercollegiate Athletics on 

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464–511.

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and Barnes, G.M. (2001). Tobacco Use Among 

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the 1997 Youth Risk behavior Survey. Adolescence 

36(144): 727–747.

Melnick, M.J., Sabo, D.F., and Vanfossen, B. (1992). 

Educational Effects of Interscholastic Athletic 

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15

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Technical Notes

High school athletes were divided into three mutually exclusive categories (elite, varsity, JV/intramural) based on 

responses to questions about athletic participation in 1990 

and 1992.9

 The 1990 student questionnaire items asked 

about participation in the following interscholastic and 

intramural sports: baseball/softball, basketball, football, 

soccer, swim team, other team sport (hockey, volleyball, 

etc.), and individual sport (cross-country, gymnastics, 

golf, tennis, track, wrestling, etc.). The questions also 

asked at what level the student participated (responses 

were: school does not have, did not participate, intramural, junior varsity, varsity, captain/co-captain). The 1992 

student questionnaire asked about interscholastic and 

intramural athletic participation (in separate questions) 

in team sports (baseball, basketball, football, soccer, 

hockey, etc.) and individual sports (cross-country, gymnastics, golf, tennis, track, wrestling, etc.), and at what 

level (responses for interscholastic sports were: school 

does not have, did not participate, junior varsity, varsity, 

captain/co-captain; responses for intramural sports were: 

school does not have, didn’t participate, participated, 

offi cer/leader10). Questions in 1990 and 1992 also asked 

if a student had been named an MVP of a sports team. 

Athlete categories were created based on responses to 

these variables in 1990 and 1992. Elite athletes were 

those students who were team captains in 1990 or 1992, 

or reported being named most valuable player (MVP) of 

their team in 1990 or 1992. Varsity athletes were students 

who were not elite athletes, but reported participating in a 

varsity sport in 1990 or 1992. Junior varsity or intramural 

participation included respondents who were not elite 

athletes or varsity athletes, but participated in a junior 

varsity or intramural sport in 1990 or 1992. These three 

categories together comprised students who reported 

participation in high school athletics. Nonathletes were 

students who did not participate in any of the above (team 

captains, most valuable players (MVPs), varsity, junior 

varsity, or intramural sports), including students whose 

schools did not have any sports teams. These categories 

were constructed with the following variables from the 

NELS:88/2000 public-use ECB/CD: F1S41AA-AG, 

F2S30AA-AB, F2S30BJ-BK, F1S8H, and F2S29G. 

Information on demographic characteristics was obtained 

from the following variables:

• Respondent’s sex was obtained from F4SEX.

• Respondent’s race/ethnicity was obtained from 

F4RACE, whose categories include: Asian/Pacifi c 

Islander; Hispanic; Black, not Hispanic; White, not 

Hispanic; and Native American/Alaska Native.

• Socioeconomic status (SES) was obtained from 

F2SES1Q, which is a quartile coding of the composite variable, F2SES1. F2SES1 provides an SES scale 

for each individual that is derived from the following 

parent-questionnaire data obtained in the base year 

(1988): father’s education level, mother’s education 

level, father’s occupation, mother’s occupation, and 

family income. Respondents were considered to be 

from a household of “high” SES if their households’ 

SES fell in the highest quarter of all respondents’ 

households. Households in the lowest quarter were 

considered “low” SES and households in one of the 

two middle quarters were considered “middle” SES. 

F2SES1Q was used in the regression analysis.

Other individual and school background information 

(table 1) were gathered from the following variables:

• Public versus private high school (G12CTRL1). 

Private schools included Catholic, private/other 

religion, private/non-religious, and private/not ascertained schools. 

• Extra-curricular activities (nonsport) (F1S41AA-AI, 

F1S41BA-BI, F2S30AC, F2S30BA-BK)—participation in any nonsport extracurricular activities in 

either 1990 or 1992. These questions asked about 

9

A similar categorization was employed by Owings, Burton, and Daniel 

(1997).

10Offi cer/leader was not considered the equivalent of captain/co-captain because 

it is for an intramural sport.
16

participation in the following activities in 1990 and 

1992: cheerleading, pom-pom, drill team, school 

music group (band, orchestra), school play or musical, school government, academic honor society, 

school yearbook, newspaper, school service clubs, 

school academic clubs, school hobby clubs, school 

FTA, FHA, and FFA.

• 10th-grade test scores (F12XQURT)—quartile coding of the composite of reading and math scores, 

F12XCOMP. Respondents had “high” test scores if 

their score fell in the highest quarter. Respondents in 

the lowest quarter had “low” scores, and respondents 

in one of the two middle quarters had “middle” scores. 

F12XQURT was used in the regression analysis.

• A locus of control orientation is a belief about 

whether personal outcomes are contingent on 

personal actions (internal locus of control) or 

on events outside of personal control (external 

control orientation). Tenth-grade locus of control 

(F1LOCU2Q)—quartile coding of composite locus 

of control variable, F1LOCUS2. Respondents had 

“high” locus of control (more internal orientation) if 

their score fell in the highest quarter. Respondents in 

the lowest quarter had “low” locus of control (more 

external orientation), and respondents in one of the 

two middle quarters had “middle” locus of control. 

The composite locus of control consisted of six 

variables (F1S62B,C,F,G,K,M). F1LOCU2Q was 

used in the regression analysis.

The outcome variables were all taken from the 4th follow-up in 2000, except for college athletic participation, 

which was only asked in the third follow-up (1994). The 

outcomes measures were obtained from the following 

variables:

Postsecondary Education

• Any postsecondary education after high school for 

academic credit (college, university, or vocational, 

technical, or trade school) by 2000 (F4ATTPSE).

• Bachelor’s degree or higher by 2000 (F4HHDG).

• Participation in college athletics by 1994 (VARATH, 

OTHERATH)—intercollegiate athletics at longest 

postsecondary institution, not including intramural 

sports.

Labor Market Status

• Employed and employed full time in 2000 

(F4AACTF, F4AACTP).

• 1999 Income (F4HI99) for full-time and part-time 

workers. This variable was logged in the regression 

analysis.

Health/Well-Being

• Cigarette use in 2000 (F4ISMOKE)—at least one 

cigarette per day (“daily smoker”).

• Alcohol use in 2000 (F4IDRINK, F4IBINGE)—alcoholic drink at least once in past 30 days; fi ve or 

more drinks on a single occasion in past 2 weeks.

• Physical fi tness in 2000 (F4IFITNS)—participated 

in physical fi tness activities to get in or stay in shape; 

at least once per week.

• Team sports/recreation in 2000 (F4ISPORT)—participated in group or team sports and recreation; at 

least once per month.

Survey Methodology

The National Center for Education Statistics (NCES) of the 

U.S. Department of Education has collected longitudinal 

data for almost 30 years. Starting in 1972 with the National 

Longitudinal Study of 1972 (NLS:72), and continuing 

to the most recent study, the Education Longitudinal 

Study of 2002 (ELS:2002), NCES provides longitudinal 

data to education policymakers and researchers that link 

secondary school educational experiences with important 

downstream outcomes like labor market experiences and 

postsecondary education enrollment and attainment.

Initiated in 1988 as the third in an NCES series of decadelength secondary school longitudinal studies, NELS:88 

was the most ambitious longitudinal study undertaken 

by NCES. It extended the age and grade span of earlier 

NCES longitudinal studies by initiating data collection 

with a middle school/junior high school cohort, the eighthgrade class of 1988. Along with student surveys, NELS:88 

included surveys of parents, teachers, and school administrators. The study also administered cognitive tests in 

reading, mathematics, science, and social studies to the 

sample members. High school transcripts were collected 

in 1992, and postsecondary education transcripts were 

collected in the fall of 2000. The sample was “freshened” 

at each of the fi rst two follow-up studies, generating a 

10th- and 12th-grade cohort in the fi rst follow-up survey 

(1990) and second follow-up (1992) survey, respectively. 

Thus, NELS:88 follows an 8th-, 10th- and 12th-grade 

cohort over time. The analysis population for this report 

was 10th-graders in 1990 who continued as high school 

seniors in 1992. 
17

The fourth follow-up (NELS:88/2000) took place in 2000, 

the year in which most sample members turned 26 years 

of age and typically were eight years removed from high 

school enrollment. This follow-up study focused on the 

educational, labor market, and social processes and transitions experienced by young adults. Interviewing began 

in January 2000. Interview topics included experiences 

with postsecondary education, labor market participation, job-related training, community integration, and 

marriage and family formation. The study also included 

student transcript data collection from the postsecondary 

institutions that NELS:88/2000 respondents reported attending after high school. Transcript data collection began 

in September 2000.

Sampling/Weighting

The NELS:88 base-year sample design began with a 

nationally representative, two-stage stratifi ed probability sample of 1,052 participating eighth-grade schools 

and 24,599 participating students (of 26,432 eighthgraders selected in those schools). Each subsequent 

round of NELS:88 has involved complex and diffi cult 

sampling and data collection decisions, culminating 

in the NELS:88/2000 sample, which was the result of 

two further subsampling activities. The general purpose 

of weighting survey data is to compensate for these 

unequal probabilities of selection and to adjust for the 

effects of nonresponse. Weights for the fourth follow-up 

study were developed in several steps. In the fi rst step, 

unadjusted weights were calculated as the inverse of the 

probabilities of selection, taking into account all stages 

of the sample selection process. In the second step, these 

initial weights were adjusted for nonresponse. In order 

to maintain consistency in weights across the multiple 

data collection waves of NELS:88 (i.e., to ensure that 

weighting totals refl ected the population totals of the 

original group of interest), multidimensional raking was 

also applied to these nonresponse adjusted weights. The 

raking was performed with respect to base-year through 

fourth-follow-up-study response status, dropout status, 

race/ethnicity, sex, and school status.

The estimates in this report were produced using 

F4F1PNWT, the panel weight for 10th-grade members 

of the NELS:88 sample who were res,pondents during 

the fi rst (1990), second (1992), third (1994), and fourth 

follow-ups (2000). In addition, students were selected 

who were part of the 12th-grade cohort in 1992. This 

unweighted sample size included 9,840 NELS:88/2000 

10th-grade students in 1990 who were seniors in 1992. 

Weighting procedures were then applied such that the 

9,840 respondents represent a population of 2,494,866 

individuals who were in 10th grade in 1988. This is somewhat smaller than the original weighted sample of 1990 

10th-graders who were seniors in 1992. This reduction in 

the target population between 1990 and 2000 is due to a 

variety of factors. Respondents who dropped out of high 

school between 1990 and 1992 were excluded from this 

report. Additional sample loss is a result of death, emigration out of the country, moving abroad, nonresponse, etc. 

In some cases the unweighted sample sizes are lower than 

9,840 because of item nonresponse.

Survey Standard Errors

Because the NELS:88 sample design involved stratifi cation, the disproportionate sampling of certain strata, and 

clustered (i.e., multistage) probability sampling, the resulting statistics are more variable than they would have 

been had they been based on data from a simple random 

sample of the same size. The NELS:88/2000 analyses 

included in this report used the Taylor Series procedure 

to calculate standard errors as generated by AM statistical software (http://am.air.org). This procedure is also 

found in advanced statistical programs like SUDAAN 

or STATA.

Statistical Tests

Differences Between Means

Comparisons that have been drawn in the text of this 

report have been tested for statistical signifi cance to ensure that the differences are larger than those that might 

be expected due to sampling variation. The statistical 

comparisons in this report were based on the t statistic. 

Whether the statistical test is considered signifi cant or not 

is determined by calculating a t value for the difference 

between a pair of means or proportions and comparing 

this value to published tables of values, called critical 

values (cv). The alpha level is an a priori statement of 

the probability that a difference exists in fact rather than 

by chance. All comparisons in the text are statistically 

signifi cant at the .05 level unless otherwise noted.

The t statistic between estimates from various subgroups 

presented in the tables can by computed by using the following formula:

,

where E1

 and E2 are the estimates to be compared (e.g., 
18

the means of sample members in two groups) and se1

 and 

se2 are their corresponding standard errors.

Given the large number of comparisons made in this 

report, it is likely that some relationships identifi ed as 

signifi cant may have occurred by chance. One solution 

is to employ a test for multiple comparisons, such as the 

Bonferroni adjustment. The Bonferroni adjustment is 

appropriate to test for statistical signifi cance when the 

analyses are mainly exploratory because it results in a 

more conservative critical value for judging statistical 

signifi cance. However, another solution—the one used 

in this report—is to fi rst describe the bivariate relationships and then test to see if these relationships persist in a 

multivariate analysis. The multivariate analyses employed 

here, and described below, examine bivariate relationships 

while accounting for other important variables. Presumably any marginal bivariate relationships would not persist 

once other factors are considered in concert.

Multivariate Analysis

To carry out the multivariate analysis presented in tables 

2 and 3, either a binary logistic regression or an ordinary 

least squares (OLS) regression was conducted based 

on the properties of the dependent variable. Where the 

dependent variable was continuous (annual income in 

1999), an OLS was run. In the case of income, past 

research supports taking the log of the actual reported 

income to better approximate the assumed distribution 

when using OLS. Where the dependent variable had only 

two meaningful categories, the assumptions related to 

OLS are violated. Therefore, a binary logistic regression 

is used instead. The logistic regression model addresses 

these violations by conducting a logit transformation of 

the dependent variable. All dependent variables except 

income in this report were binary and logistic regression 

was performed. 

As noted in the body of the report, the specifi c method of 

regression analysis employed in this report is known as 

hierarchical regression analysis. In hierarchical regression 

analyses, outcomes are fi rst modeled using only control 

variables. Then, additional independent variables are 

introduced into the model and observations are made 

regarding increases or decreases in the explanatory power 

of the model. The purpose of a hierarchical regression 

model is to determine whether the independent variables 

of interest (athletic participation in this report) add any 

predictive power. In OLS regressions, this can be determined by comparing R2

 statistics, but unlike an OLS 

regression, logistic regressions do not produce an R2

 statistic. Instead, logistic regressions produce what is known 

as a log likelihood. Like an R2 statistic, the log likelihood 

assesses the explanatory power of the regression model, 

but it cannot be interpreted in the same manner as an R2

statistic. However, the predictive powers of two logistic 

regression models can still be compared with a likelihood 

ratio test (LRT).

A LRT is calculated by subtracting the value of the log 

likelihood of the full model from the log likelihood of the 

control model. That number is then multiplied by two.

LRT = 2 * (LLc

 – LLf)

Since the LRT statistic approximately follows the Chisquare distribution, standard statistical tables can be 

used to determine the critical value that the LRT statistic 

must exceed to achieve statistical signifi cance. To do 

this the level of signifi cance used in the testing must be 

selected fi rst (.05 was the level used in this analysis). The 

number of degrees of freedom must then be considered. 

This number is determined by observing how many additional degrees of freedom are present in the full model, 

compared to the control model. In the case of this report, 

three additional degrees of freedom are present in the 

full model, compared to the control model. The critical 

value is then determined by consulting a standard Chisquare distribution table and fi nding the appropriate value, 

considering the level of signifi cance and the number of 

degrees of freedom.

In addition to understanding the relationship between 

the primary independent variable (e.g., high school 

athletic participation) and the dependent variables (e.g., 

education and labor market success in 2000), a number 

of control variables were examined. Because secondary analysis of survey data prevents the option of using 

random assignment for the characteristic of interest 

(e.g., athletic participation in high school), a number 

of variables are introduced into the regression model in 

an effort to control for a potentially spurious correlation between the independent and dependent variables 

(Allison 1999). For example, certain characteristics of 

the respondent might be related to their postsecondary 

educational attainment such as gender and the family’s 

socioeconomic status (SES). Without controlling for 

these other potentially confounding factors, we might 

fi nd a statistically signifi cant relationship between high 

school athletic participation and postsecondary educational attainment when one does not actually exist. 
19

In these analyses, we control for the respondent’s sex, 

race/ethnicity, the respondent’s family’s socioeconomic 

status, and 10th-grade test scores. 

The regression coeffi cients generated by the OLS procedure are interpreted as a slope. The b value, or slope, 

indicates how many units of change in the dependent 

variable occur for each unit change in the independent 

variable. For example, in this analysis in table 3, being 

an elite athlete, compared to a nonathlete, is associated 

with an increase of 0.14 in the respondent’s salary (natural 

log) in 1999. A signifi cant negative coeffi cient means that 

for every unit change in the independent variable there 

is a b units decrease in the dependent variable. Since it is 

diffi cult to interpret effects on logged earnings, the coeffi cients can also be interpreted as, for example, being an 

elite athlete versus a nonathlete is associated with a 15 

percent (100*[e0.14 – 1]) increase in the logged dependent 

variable (income). 

In the binary logistic regression models, the independent 

variables of interest (high school athletic participation) are 

categorical and the coeffi cient is expressed in relation to 

an omitted value of those variables (nonathlete), controlling for all other variables in the model. For example, in 

this analysis in table 3, being an elite athlete, compared to 

a nonathlete, is associated with an increase of 0.41 in the 

probability that the respondent is employed in 2000. 

Often the odds ratio (computed e ) can be used to estimate 

the change in probability of a particular variable in the 

logistic regression model making the coeffi cients easier 

to interpret. An odds ratio greater than one indicates a 

greater likelihood of having the characteristic than the 

omitted group, a ratio equal to one indicates no greater or 

lesser likelihood of having the characteristic, and a ratio 

less than one indicates a lower likelihood of having the 

characteristic compared to the omitted value. Most statistical packages will generate both the coeffi cients and the 

odds ratios. For example, in table 3, students who were 

elite athletes are 1.49 times (about one and one-half times) 

more likely to be employed than nonathletes. Another way 

to state this is that elite athletes are 9 percent (computed: 

[odds ratio – 1]*100) more likely than non-athletes to be 

employed in 2000.

Some NELS:88 variables are analyzed in such a way that 

makes it impossible to have an omitted group. The variables in this analysis for which there is no omitted group 

are the variables measuring household socioeconomic 

status, 10th-grade test scores, and locus of control. All of 

these are quartile variables, and the odds ratio presented 

is the increase (or decrease) in likelihood in achieving an 

outcome that a person would experience by moving up one 

quartile. For example, the independent variable household 

socioeconomic status has an odds ratio of 2.18 for the outcome any postsecondary education by 2000. This means 

that a person in the second quarter of the scale is 2.18 times 

more likely than a person in the lowest quarter of the scale 

to have any postsecondary education by 2000. Similarly, a 

person in the third quarter of the scale is 2.18 times more 

likely than a person in the second quarter of the scale to 

have any postsecondary education by 2000.

Acknowledgments

The authors would like to thank the following individuals 

for their thoughtful reviews and suggestions that greatly 

improved the quality of this report: Ben Dalton and Robert 

Stillwell of the Education Statistics Services Institute/

American Institutes for Research (ESSI/AIR); Marilyn 

McMillen Seastrom, Jeffrey Owings, Patrick Rooney, 

and Tom Snyder from the National Center for Education 

Statistics (NCES), and Lisa Bridges from the Institute of 

Education Sciences (IES), U.S. Department of Education; 

and two anonymous reviewers.

For More Information

For more information about this Statistics in Brief or 

the data set, contact Jeffrey Owings, Project Offi cer, 

National Center for Education Statistics, 1990 K Street 

NW, Washington, DC 20006-5651; or call 202-502-7423; 

or e-mail jeffrey.owings@ed.gov. To download fi les and 

documentation, visit the NELS website (http://nces.

ed.gov/surveys/nels88/).