Delayed Enrollment and Student Involvement: Linkages to College Degree Attainment

ABSTRACT

Students who delay college enrollment after they graduate from high school have a lower chance of completing a college degree compared to students who enroll in college immediately after high school. This article explores delayers’ involvement in high-impact postsecondary campus activities to understand whether participation in high-impact activities is associated with bachelor’s degree attainment for students who delay enrollment. This study found that overall involvement in high-impact activities was associated with greater odds of bachelor’s degree attainment for all students, but students who delay entry into college do not benefit any differently than immediate-enrollment students from involvement in these activities. Participation in high-impact activities is only related to bachelor’s degree attainment in a minor way compared to other variables like students’ socioeconomic background and high school grade point average. This finding suggests that although high-impact practices may play a role in promoting student success in college, they are not as important as other social background and precollege student characteristics.

KEYWORDS:

• College completion
• delayed college enrollment
• high-impact practices

Attaining a bachelor’s degree has become increasingly important in the face of job polarization in the labor market (Wright & Dwyer, 2003), the declining labor market value of attending some college without earning a degree (McCall & Percheski, 2010), and the growth in the percentage of jobs requiring a bachelor’s degree (Barton, 2000). For aspiring Americans looking to become upwardly mobile, a college degree has become a necessary credential because of its growing labor market value relative to the high school diploma (McCall & Percheski, 2010) and its financial returns during the life course (Hout, 2012). Because of the benefit of attaining a bachelor’s degree, scholars and practitioners have grown increasingly interested in students’ transitions to higher education and their ability to navigate through higher education and attain a degree. Because degree attainment is connected to so many benefits later in the life course, inequality in the college-going and degree attainment process is connected to inequality in lifetime earnings and wealth accumulation.

Given this context, one topic of interest to scholars of students’ transitions to postsecondary education is delayed entry into higher education. Although a few studies have suggested that delaying entry into higher education can benefit students depending on the activities they participate in between high school and college (Martin, 2010; Martin, Wilson, Liem, & Ginns, 2013), the bulk of research on delayed entry into college paints a grim picture. Students from lower socioeconomic backgrounds are more likely to delay entry into higher education for a year or more (Bozick & DeLuca, 2005; Goldrick-Rab, 2006; Goldrick-Rab & Han, 2011; Roksa & Velez, 2012; Rowan-Kenyon, 2007; Wells & Lynch, 2012), and once enrolled, delayers are less likely to obtain a college degree compared to their immediate-enrollment peers (Bozick & DeLuca, 2005; Featherman & Carter, 1974; Horn, Cataldi, & Sikora, 2005; Jacobs & King, 2002; Kempner & Kinnick, 1990).

Two recent studies have suggested that delayed entry is negatively correlated with degree attainment because students who delay college enrollment transition into adult roles—such as parent, spouse, or employee—that may conflict with college responsibilities (Bozick & DeLuca, 2005; Roksa & Velez, 2012). Research in this area has not, however, investigated the college experiences of delayers to see if their experiences differ from their immediate-enrollment peers. One opportunity for advancement in this area of research is to look at differences in delayers’ levels of involvement in campus activities compared to nondelayers as a potential mechanism associated with students’ likelihood of attaining a degree.

Literature on college student involvement in higher education has suggested that the more college students are involved in campus activities, the more likely they will be to obtain a college degree (Astin, 1984; Kuh, 2009; Tinto, 1988). Additionally, scholars have paid particular attention to campus activities called high-impact practices that have been purported to provide a noteworthy benefit toward student engagement on campus (Brownell & Swaner, 2010; Kuh, 2008; Liberal Education and America’s Promise (LEAP) National Leadership Council, 2007). Research on high-impact practices has drawn significant attention from scholars and practitioners alike, largely because these activities appear to benefit students on multiple indicators of student success (e.g., engagement, persistence, deep learning), but more so because research has shown greater gains from participating in high-impact practices among underprivileged and underserved students (Kuh, 2008; Sweat, Jones, Han, & Wolfgram, 2013). According to Kuh (2008), high-impact practices include 1st-year seminars and experiences, common intellectual experiences, learning communities, writing-intensive courses, collaborative assignments and projects, undergraduate research, diversity/global learning, service learning or community-based learning, internships, and capstone courses and projects.

Does delaying entry into college affect student involvement in high-impact practices on campus? And how does participating in high-impact practices differ in its connection to bachelor’s degree attainment between delayers and nondelayers? Answering these questions could advance our understanding of why delayed enrollment in college is related to lower chances of obtaining a college degree. This study considered whether the level of involvement in high-impact practices differs between delayers and nondelayers and whether the relationship between involvement in high-impact practices and bachelor’s degree attainment differs between delayers and nondelayers.

With data from the National Center for Education Statistics Education Longitudinal Study of 2002 (ELS:2002), I investigated student involvement in what the ELS calls high-impact postsecondary activities. Because previous research has shown a positive connection between student involvement and student engagement in college leading to persistence to the 2nd year (Brownell & Swaner, 2009; Kuh, 2008; Kuh, Cruce, Shoup, Kinzie, & Gonyea, 2008), understanding the degree to which delayers are involved in college activities could provide useful information to inform educators and practitioners interested in helping delayers complete college. If student involvement in particular activities is especially conducive to staying in college and persisting to a degree, faculty and administrators might seek ways to ensure that students at risk for dropping out have access to such activities and participate in them. This information will be particularly useful for retention efforts, especially those targeting students from lower socioeconomic backgrounds.

Review of the literature

Delayed entry into college

Student pathways through postsecondary schooling are varied and changing (Goldrick-Rab, 2006). Increasingly, college students pursue varied enrollment patterns following high school graduation. Since 1990, college student enrollment in 2-year public and private, for-profit institutions has increased at a higher rate than student enrollment at 4-year public and private, not-for-profit institutions (Aud et al., 2010). These varied pathways are often characterized by interruptions, attendance at multiple institutions, and less than full-time status (Goldrick-Rab, 2006).

Variation in students’ college enrollment and attendance patterns begins immediately upon high school graduation. Thirty-seven percent of undergraduate students enrolled in 1999 to 2000 delayed their entry into college by at least 1 year after high school graduation (Horn et al., 2005).

Some research has suggested that delaying enrollment in college is beneficial for some college students (Martin, 2010; Martin et al., 2013). Studies have extolled the benefits of spending time outside the context of education while learning skills that apply to postsecondary experiences. Founded on Adelman’s (1999, 2006) academic momentum theory, Martin and collaborators (2010, 2013) suggested that students who accomplish something significant before college entry—particularly something related to their educational career—will have more academic momentum upon entry into college. These students then have a higher chance to attain a college degree (see also Attewell, Heil, & Reisel, 2012). Conversely, if students use their time between high school graduation and postsecondary enrollment in a noneducational way, they are less likely to attain a college degree (Martin et al., 2013). Most of the research supports this latter proposition.

Lower socioeconomic-status students are more likely to have varied pathways through higher education—including delayed entry—that diminish their chances of earning a degree (Bozick & DeLuca, 2005; Featherman & Carter, 1974; Goldrick-Rab, 2006; Horn et al., 2005; Roksa & Velez, 2012; Rowan-Kenyon, 2007; Wells & Lynch, 2012). Bozick and DeLuca (2005) found that students who delay entry into college by a year or more are 64% less likely to complete a bachelor’s degree compared with their immediate-enrollment counterparts, net of factors such as socioeconomic status, prior academic achievement, and other demographic characteristics.

What is the mechanism behind the relationship between delayed enrollment in college and the lower likelihood of college degree completion? Research dealing with this question is limited, and in some cases, it is contradictory. Using data from the National Education Longitudinal Study of 1988 (NELS:1988), Bozick and DeLuca (2005) found an effect of delayed enrollment on degree attainment while controlling for students’ socioeconomic status, academic achievement, the type of college they attended, and their responsibilities outside of school. Roksa and Velez (2012) explored the relationship between delayed college enrollment and degree attainment with data from the 1997 National Longitudinal Study of Youth (NLSY:1997) and maintained that the NLSY had better measures for understanding adulthood transitions for students who delayed enrollment in college. In contrast to Bozick and Deluca’s findings, Roksa and Velez found that adulthood roles, such as whether and how much a student works and whether they have family commitments like marriage or parenthood, negatively impact degree attainment. When Roksa and Velez considered student employment in addition to marital and parenthood status, the effect of delayed entry on degree attainment became statistically insignificant in their model. Their research corroborated previous research by Jacobs and King (2002) that suggested students who delay entry into college are less likely to obtain a degree because the competing demands in their life prevent them from dedicating adequate attention and effort to school.

College student involvement and high-impact practices

Researchers have yet to investigate a variety of plausible explanations to understand better the mechanism connecting delayed entry into college and degree attainment. While current research has documented delayers’ responsibilities outside college, researchers know very little about how delayers differ from students who enroll immediately after high school graduation regarding their involvement in extracurricular and cocurricular activities during college.

Research has shown that student involvement (Astin, 1984), integration (Tinto, 1988), and engagement (Kuh, 2009) in college activities—both in and out of the classroom—are positively related to degree attainment and other measures of success in college. Pascarella and Terenzini (2005) argued that students’ college-related responsibilities and level of involvement influence persistence choices and patterns and that disadvantaged students benefit more from engaging in social and academic activities on campus when compared with other students (see also Kuh, 2009). Research on high-impact practices has shown similar patterns: Participation in these activities is positively correlated with measures of student success such as deep learning, persistence to the 2nd year, and engagement in college (Kuh, 2008), and high-impact practices provide a compensatory effect toward these outcomes for students from disadvantaged backgrounds (Kuh, 2008; Sweat et al., 2013).

The current study

College student involvement in campus activities is a potentially important mechanism connecting delayed enrollment in college and degree attainment. Bozick and DeLuca (2005) and Roksa and Velez (2012) considered how delayers’ adult roles impact college completion, but there may be additional factors that matter for college completion. If delayers are less involved in campus activities, for whatever reason, they may be less engaged in activities that mean to help them feel integrated into the college experience, which may impact their persistence to completion. In both studies, researchers ignored these students’ involvement in on-campus activities as a factor that may influence the degree attainment process.

In his seminal article on involvement theory, Astin (1984) referred to involvement as the energy, both physical and psychosocial, that a student uses in their academic experience. I cannot utilize Astin’s concept of involvement or the similar concept of engagement (Trowler, 2010)¹ because the ELS data do not have measures of students’ qualitative experiences on campus. The ELS data, rather, have measures of student participation in activities. I focused on students’ participation in high-impact postsecondary activities, as defined by the ELS:2002 data set.

Scholars have suggested that participating in high-impact postsecondary activities—such as 1st-year seminars and experiences, common intellectual experiences (e.g., a core curriculum), learning communities, collaborative assignments and projects, and writing-intensive courses—are beneficial activities for increasing student engagement in the college experience and promoting higher grade point average (GPA) and likelihood to persist to the 2nd year (Brower & Inkelas, 2010; Kuh, 2008; LEAP National Leadership Council, 2007). ELS data include internships, co-ops, field experiences, student teaching, clinical assignments, study-abroad opportunities, community-based projects, mentoring, research projects with a faculty member outside of the course or program requirement, and culminating senior experiences within this high-impact postsecondary activities category.

I address the following research questions:

1. How do students who delay college enrollment differ in their involvement in high-impact postsecondary activities compared to students who enroll immediately after high school?

In response to this question, I hypothesized that students who delayed enrollment in college would be, on average, less involved in high-impact postsecondary activities than students who enroll in college immediately after high school because they may already feel disconnected from campus life after having a break between schooling from high school to college or perhaps have other responsibilities that conflict with their likelihood of participating in campus activities.

• (2) To what extent do students who are involved in high-impact postsecondary activities experience a benefit toward bachelor’s degree completion?

In response to this question, I hypothesized that students who were more involved in high-impact postsecondary activities would be more likely to obtain a bachelor’s degree than students who are less involved in high-impact postsecondary activities. This hypothesis pulled from the evidence in the literature showing that participating in high-impact practices increases college students’ level of engagement in college and extended it to apply to degree attainment. To be clear, this hypothesis has been widely assumed to be true in formal and informal conversations about high-impact practices, but up until this point it has not been empirically tested.

• (3) How does the relationship between participating in high-impact postsecondary activities and bachelor’s degree completion differ between delayers and nondelayers?

In response to this question, I hypothesized that delayers would experience a greater benefit toward bachelor’s degree completion by participating in high-impact practices compared to nondelayers because of previous work that has established a compensatory effect of participating in high-impact practices for disadvantaged groups on college campuses (Kuh, 2008; Sweat et al., 2013).

To test these predictions, I examined the overall effect of participation in high-impact activities on degree attainment but also considered individual activities to see which activities might provide students with the most benefit toward degree attainment.

Methods

Data

For this project, I utilized data from the ELS:2002. The ELS:2002 is a nationally representative study that originally sampled 10th graders in 2002. Data from these respondents were collected at three additional points: 2004, first follow-up; 2006, second follow-up; and 2012, third follow-up. The ELS:2002 data were collected with two main foci: (a) What are students’ trajectories from the beginning of high school into postsecondary education, the workforce, and beyond? And (b) What are the different patterns of college access and persistence that occur in the years following high school completion? (National Center for Education Statistics, 2014). These data are appropriate for this project because they are recent, they have information on delayed entry into college and participation in high-impact activities, and they have been recently used in research on this topic (Wells & Lynch, 2012).

From the original sample (n = 16,197), I selected 5,982 respondents who had information on the number of months between their high school graduation and college enrollment and their educational attainment as of the third follow-up in 2012. I restricted the sample to those respondents who graduated from high school in 2004 (n = 12,960), those who had ever attended a postsecondary institution (n = 10,015), and those who first entered a 4-year postsecondary institution by 2006 (n = 5,982) to provide adequate time for those who delayed to complete a bachelor’s degree. By restricting the sample in this fashion, I gave all respondents 6 years to complete a bachelor’s degree by the latest survey year of 2012.²

While these initial sample restrictions generated an analytical sample (n = 5,982) with a strong number of cases for statistical analysis, only 76.7% of the 5,982 respondents (n = 4,590) were available for the final analytical models after listwise deletion because of missingness on several of the control variables. Control variables ranged from having less than 1% missing values to having nearly 9% missing values on some of the high school and college cocurricular involvement measures. To deal with missing data on several variables, I multiply imputed the data for the statistical analyses to preserve as many cases for the statistical models as possible (see Manly & Wells, 2015; Rubin, 1987). This method of dealing with missing data assumes that missing values are missing at random and calculates pooled estimates of the missing values in each variable from several regression models using all the covariates included in the multiple imputation syntax. I conducted multiple imputation using Stata’s ice command, which produced five imputed data sets that included all variables in the final analytical models of this study. I conducted the statistical analyses using Stata’s mim command.

The sample was restricted to students who either did not complete a bachelor’s degree within the 6-year window or those who completed a bachelor’s degree in 6 years or less. The “no degree” category included students who had not completed a bachelor’s degree by 2012 and were still enrolled in college, as well as students who had not completed a bachelor’s degree by 2012 and were no longer enrolled in college. Furthermore, respondents who completed a bachelor’s degree in more than 6 years were included in the sample but were grouped in the “no degree” category.³ For example, if a student enrolled in college in 2005, that student had until 2011 to complete a bachelor’s degree and be counted as having attained a degree, while a student who enrolled in college in 2006 had until 2012 to complete a bachelor’s degree.

Of the 5,982 respondents, 345 respondents (5.77%) delayed entry into college for 6 months to two years. While I paid particular attention to the involvement patterns of these 345 delayers in my analyses, all 5,982 respondents were included in statistical models to compare delayers and nondelayers on important outcomes.

Variables

A central variable to this study was whether students delayed entry into postsecondary education by at least 6 months after their high school graduation following prior conceptualizations of this variable (Roksa & Velez, 2012). This variable was operationalized as a dichotomous variable in all statistical analyses and always served as an independent variable. That is, while interesting research exists that has investigated why students delay college entry (Wells & Lynch, 2012), this study only considered the outcomes of delayed college entry.

Student involvement in what the ELS:2002 calls high-impact postsecondary activities served as both a dependent variable and independent variable in the analyses. I focused on five dichotomous variables that indicated whether the respondents participated in a community-based project, an internship, mentoring, research with a faculty member, or a study-abroad program. To get at the difference between students’ level of involvement versus the type of involvement (e.g., in which activities students participated), I aggregated these five dichotomous variables into one variable that counted the total number of high-impact activities in which a student participated while in college. That is, I used different approaches to the high-impact activity variables of interest to investigate whether students participated in specific activities (e.g., internships or mentoring) and to what extent (how many activities) students were involved in these practices. The aggregate variable was a simple cumulative variable that added the individual high-impact practice variables together to get the number of activities in which a student participated.

To examine different relationships between participating in high-impact practices and bachelor’s degree attainment between delayers and nondelayers, I utilized interaction terms that multiplied each high-impact practice by a student’s delayer status. These variables allowed for the models to investigate whether delayers benefitted more from participating in high-impact practices than nondelayers regarding their likelihood of attaining a bachelor’s degree.

Bachelor’s degree attainment served as a dependent variable for analyses related to questions about the relationship between student participation in high-impact activities and degree outcomes. This variable was a dichotomous variable operationalized as “completed bachelor degree in 6 years” or “did not complete bachelor degree in 6 years.”

Following previous research, control variables included socioeconomic status, gender, race, employment status, marital status, whether or not the respondent had a child, high school cumulative GPA, high school standardized math and reading test scores, and high school cocurricular involvement. I operationalized socioeconomic status using the framework provided within the ELS data. All students were divided into four socioeconomic quartiles based on five components: father’s level of education, mother’s level of education, family income, father’s occupation, and mother’s occupation. These five indicators were equally weighted and standardized.

Gender, marital status, and parental status variables were all dichotomous variables. The gender variable indicated whether a respondent was female. The marital status variable considered whether respondents were single or married/previously married, and the parent variable considered whether respondents had biological children. Both of these variables documented the status of the respondent in 2006, 2 years after their high school graduation.

The race, employment, and high school GPA variables were categorical variables. Race was a nominal variable with five categories: White, Asian, Black, Hispanic, and Other. Employment status was an ordinal variable that documented the number of hours a respondent worked weekly during the 2005 to 2006 school year with three categories: did not work, worked 1 hour to 20 hours, and worked 21 or more hours. High school GPA was an ordinal variable with four categories: 0.00 to 2.00, 2.01 to 3.00, 3.01 to 3.50, and 3.51 to 4.00.

Finally, high school composite math and reading standardized test scores and number of high school cocurricular activities were continuous, numerical variables reflecting a student’s score and participation related to these metrics. The Appendix includes descriptive statistics for all variables.

Statistical analyses

To analyze how delayed entry into college impacts student involvement in high-impact postsecondary activities, I used student involvement as a dependent variable in several models. I used cross-tabulation and logistic regression to examine the extent to which delayers are involved in each specific activity compared to nondelayers. Understanding how delayers compare to nondelayers in their overall involvement in high-impact postsecondary activities provided insight into the differences in college experiences between these two groups. Examining each activity provided more nuanced information about which activities delayers are most and least involved in during college. I ran a series of logistic regression models to consider which variables were related to student participation in each of the five high-impact postsecondary activities, while controlling for pertinent variables.

For my second research question, “Do students who are involved in high-impact postsecondary activities experience a benefit toward bachelor’s degree completion?” I used student involvement in the five high-impact activities—both as a cumulative variable and as five independent dichotomous variables—as independent variables within logistic regression models to analyze how student involvement in high-impact postsecondary activities is related to bachelor’s degree attainment. I calculated odds ratios for attaining a bachelor’s degree compared to attaining no degree within 6 years of college matriculation. Because these analyses separated student participation into five different activities, they provided the added benefit of allowing for an investigation into the question of which activities provide a benefit toward bachelor’s degree completion.

Finally, to investigate whether the relationship between participating in high-impact postsecondary activities and bachelor’s degree completion differs between delayers and nondelayers, I calculated several logistic regression models that included interaction terms multiplying students’ participation in high-impact practices and whether they delayed entry into college. These interaction terms, if significant, would show that delayers have a different relationship between participating in these activities and their likelihood of attaining a bachelor’s degree compared to nondelayers.

Results

College student involvement in high-impact postsecondary activities

Figure 1 documents differences in student involvement based on the number of high-impact postsecondary activities in which respondents have participated by 2012. The graph compares students who delayed entry into college and immediate enrollees. Students who delayed entry into college were less involved in high-impact postsecondary activities than their immediate-enrollment peers: 58.4% of delayers had participated in no high-impact postsecondary activities compared to only 31.2% of nondelayers. For all remaining categories, the percentage of delayers involved was less than the percentage of nondelayers involved. More than 80% of delayers were included in the first two categories of participation in one activity or less compared with 61.1% of nondelayers.

Figure 1. Number of activities in which students were involved, comparison of delayers and nondelayers.

*Involvement differences are significant at the p < .001 level.

Figure 2 displays the specific activities in which delayers and nondelayers participated. Interestingly, more than half the students in the sample participated in the internship category. This category included activities such as an internship, a co-op, field experience, student teaching, or a clinical assignment.⁴ The high percentage of participation in the internship category may be due to the wide definition of activities included. Indeed, the percentage of participation for the overall sample never exceeded 23.4% in any other category. Similar to the differences in the number of activities in which delayers participated compared with the number in which immediate enrollees participated, fewer delayers than nondelayers were involved in each activity.

Figure 2. Level of student involvement by individual activities, comparison of delayers and nondelayers.

*Involvement differences in all activities are significant at the p < .001 level.

Table 1 presents six different regression models that aim to identify what covariates influence students’ participation in high-impact activities in college. Model 1 in Table 1 is an ordinary least-squares regression with the aggregate number of high-impact activities in which a student participates as the outcome variable. Models 2 through 6 are logistic regression models, each with a different high-impact activity as the outcome variable. The main focus of this table is to investigate whether delayers differ from nondelayers in their participation in these activities, while controlling for relevant covariates.

Table 1. Regression models testing whether covariates influence participation in high-impact practices (Model 1, ordinary least-squares regression; Models 2–6, logistic regression with odds ratios reported).

	(1)	(2)	(3)	(4)	(5)	(6)
VARIABLES	Number of HIPs	Community	Internship	Mentoring	Research	Study Abroad
Delayed College Entry by 6 Months or More	−0.25**	0.71	0.59**	0.72	0.65	0.42**
	(0.09)	(0.15)	(0.11)	(0.16)	(0.19)	(0.13)
Socioeconomic Status (Reference Category: Lowest Quartile)
Second Quartile	−0.04	0.87	0.89	0.85	1.25	1.13
	(0.07)	(0.14)	(0.12)	(0.14)	(0.25)	(0.29)
Third Quartile	0.06	0.92	1.22	1.08	1.14	1.18
	(0.06)	(0.15)	(0.15)	(0.18)	(0.22)	(0.28)
Highest Quartile	0.28***	1.08	1.45**	1.19	1.74**	2.28***
	(0.07)	(0.17)	(0.17)	(0.19)	(0.31)	(0.52)
Female	0.22***	1.49***	1.37***	1.26*	0.90	1.80***
	(0.04)	(0.13)	(0.10)	(0.11)	(0.09)	(0.20)
Race (Reference Category: White)
Asian	0.02	1.08	0.93	0.97	1.80***	0.59**
	(0.06)	(0.14)	(0.10)	(0.13)	(0.23)	(0.10)
Black	0.09	1.00	0.95	1.59**	1.65**	0.74
	(0.07)	(0.16)	(0.11)	(0.26)	(0.28)	(0.18)
Hispanic	0.10	0.82	1.00	1.38*	1.67*	1.13
	(0.07)	(0.14)	(0.13)	(0.22)	(0.32)	(0.22)
Other	−0.00	1.16	0.81	1.16	1.59*	0.58
	(0.09)	(0.23)	(0.14)	(0.23)	(0.35)	(0.17)
Married or Formerly Married (2006)	−0.47***	0.79	0.34**	0.61	0.20*	0.12*
	(0.10)	(0.27)	(0.11)	(0.23)	(0.15)	(0.12)
Whether Has Biological Children (2006)	−0.13	0.52	0.83	0.95	1.08	0.38
	(0.12)	(0.20)	(0.21)	(0.33)	(0.56)	(0.30)
Hours Worked Weekly 2005 to 2006 (Reference Category: 0 hrs)
1–20 hrs	0.21***	1.43***	1.36***	1.12	1.29*	1.21
	(0.05)	(0.13)	(0.11)	(0.11)	(0.13)	(0.13)
21+ hrs	−0.11*	1.01	0.91	0.89	0.86	0.47***
	(0.04)	(0.11)	(0.08)	(0.11)	(0.12)	(0.08)
High School Grade Point Average (Reference Category: 0.00–2.00)
2.01–3.00	0.05	1.59	1.35	0.95	1.09	0.91
	(0.10)	(0.50)	(0.36)	(0.27)	(0.40)	(0.44)
3.01–3.50	0.20	2.08*	1.86*	0.99	1.20	1.49
	(0.10)	(0.66)	(0.49)	(0.30)	(0.45)	(0.71)
3.51–4.00	0.38**	2.12*	2.22**	1.23	1.61	1.99
	(0.12)	(0.68)	(0.62)	(0.38)	(0.61)	(0.95)
High School Composite Math/Reading Score	0.00	0.99	1.00	0.99	1.02	1.04***
	(0.00)	(0.01)	(0.01)	(0.01)	(0.01)	(0.01)
Number of High School Cocurricular Activities	0.12***	1.14***	1.13***	1.18***	1.19***	1.11***
	(0.01)	(0.03)	(0.02)	(0.03)	(0.03)	(0.03)
Constant	.21	.11***	.29**	.14***	.02***	.00***
	(.19)	(.05)	(.12)	(.06)	(.01)	(.00)
Observations	5,982	5,982	5,982	5,982	5,982	5,982
R^2 (Ordinary Least Squares)/McFadden’s R^2 (Logistic)	.137	.038	.065	.035	.064	.135
Wald Chi-Square Value (18 df)		151.95	290.62	138.30	209.71	349.76

Note. Standard errors in parentheses. HIPs = High-Impact Practices.

*** p < .001. ** p < .01. * p < .05.

Model 1 in Table 1 shows that students who delayed college entry were overall less involved in high-impact activities than nondelayers. Furthermore, delayers were significantly less likely to participate in an internship and a study-abroad experience. Among the other regression models, however, the results showed no significant difference in participating in a community-based project, mentoring, or research with faculty between delayers and nondelayers. These findings partially support the first hypothesis of this study that delayers will be less involved in high-impact activities. The hypothesis was corroborated by delayers’ overall level of involvement but was not supported for some activities (e.g., community-based projects, mentoring, and research with faculty).

Although the main inquiry of this study focused on a student’s delayed-entry status, Table 1 shows several patterns that reflect differences in participation in these activities between demographic groups. Students from higher socioeconomic backgrounds were more likely to be involved in high-impact activities, as were women and students who participated in cocurricular activities in high school. Racial patterns of participation varied between activity type, and students who worked part-time were more involved than both students who did not work and students who worked 21 or more hours per week.

Delayed entry, student involvement, and bachelor’s degree attainment

To what extent do these activities benefit the students who participate in them? Table 2 presents odds ratios from two logistic regression models. Both models used bachelor’s degree attainment as a dependent variable, and the second model incorporated the aggregate high-impact activity variable to examine whether students who participated in more of these activities were more likely to attain a bachelor’s degree in 6 years.

Table 2. Logistic regression models for bachelor’s degree attainment (odds ratios reported).

	(1)	(2)
VARIABLES	Model 1	Model 2
Delayed College Entry by 6 Months or More	0.35***	0.38***
	(0.06)	(0.06)
Number of High-Impact Postsecondary Activities		1.42***
		(0.05)
Socioeconomic Status (Reference Category: Lowest Quartile)
Second Quartile	1.07	1.10
	(0.15)	(0.15)
Third Quartile	1.29*	1.28
	(0.17)	(0.16)
Highest Quartile	1.96***	1.81***
	(0.25)	(0.23)
Female	1.12	1.04
	(0.09)	(0.08)
Race (Reference Category: White)
Asian	0.93	0.93
	(0.11)	(0.11)
Black	0.92	0.89
	(0.12)	(0.11)
Hispanic	0.90	0.87
	(0.12)	(0.13)
Other	0.95	0.94
	(0.16)	(0.15)
Married or Formerly Married (2006)	0.27***	0.33**
	(0.09)	(0.11)
Whether Has Biological Children (2006)	0.40**	0.42**
	(0.13)	(0.13)
Hours Worked Weekly 2005 to 2006 (Reference Category: 0 hrs)
1–20 hrs	1.30**	1.23*
	(0.12)	(0.12)
21+ hrs	0.62***	0.63***
	(0.06)	(0.06)
High School Grade Point Average (Reference Category: 0.00–2.00)
2.01–3.00	2.51**	2.43**
	(0.76)	(0.74)
3.01–3.50	4.98***	4.65***
	(1.55)	(1.47)
3.51–4.00	10.35***	9.29***
	(3.27)	(2.99)
High School Composite Math/Reading Score	1.03***	1.03***
	(0.01)	(0.01)
Number of High School Cocurricular Activities	1.07**	1.03
	(0.03)	(0.02)
Constant	0.04***	0.04***
	(0.02)	(0.01)
Observations	5,982	5,982
McFadden’s R^2	.183	.203
Wald Chi-Square Value (M1 – 18 df | M2 – 19 df)	696.47	692.53

Note. Standard errors in parentheses.

*** p < .001. ** p < .01. * p < .05.

Model 1 is a skeleton model and provided base-level effects for comparison when considering the subsequent model in Table 2. Effectively, Model 1 provided us with an understanding of the relationship between delayed enrollment in college and bachelor’s degree attainment, while controlling for important variables of race, gender, socioeconomic status, employment status, marital status, whether or not the respondent had a child, high school GPA, high school composite math and reading standardized test scores, and number of high school cocurricular activities in which the respondent participated. Model 2 incorporated a variable that showed the number of high-impact activities in which students participated.

Model 1 showed that students who delayed college entry had odds for attaining a bachelor’s degree nearly a third the size of their odds for attaining no degree, while controlling for relevant variables. Model 2 built on Model 1 by adding a variable documenting the number of high-impact postsecondary activities in which a student participated. Model 2 corroborated the findings in Model 1 in that the odds ratio for attaining a bachelor’s degree among delayers changed only slightly. For every high-impact activity in which a student participated, their odds of attaining a bachelor’s degree multiplied by a factor of 1.42.

When the aggregate high-impact activity variable was added in Model 2 of Table 2, the coefficient for students from the highest socioeconomic quartile decreased and the statistically significant difference in bachelor’s degree attainment between students from the third socioeconomic quartile and those from the lowest quartile dropped out. These shifts suggest that at least part of the socioeconomic advantage toward bachelor’s degree attainment is related to the level at which students are involved in campus activities. That is, part of the reason students from higher socioeconomic backgrounds are more likely to attain a bachelor’s degree is that they are more involved in high-impact activities on campus.

Table 3 is an extension of the analyses in Table 2 but isolates specific high-impact activities in each model to identify which of these activities provide students with a benefit toward bachelor’s degree attainment. Each of the five models in Table 3 controlled for the amount students were involved in high-impact activities by including the aggregate high-impact activity variable. So, the interpretation of the individual high-impact activity variables in these models focused on whether they had a statistically significant relationship with a student’s likelihood of attaining a bachelor’s degree, while controlling for the extent to which a student was involved in high-impact activities overall.

Table 3. Logistic regression models for bachelor’s degree attainment, by activity (odds ratios reported).

	(1)	(2)	(3)	(4)	(5)
VARIABLES	Community	Internship	Mentoring	Research	Study Abroad
Delayed College Entry by 6 Months or More	0.38***	0.38***	0.38***	0.38***	0.38***
	(0.07)	(0.07)	(0.06)	(0.06)	(0.06)
Number of High-Impact Postsecondary Activities	1.55***	1.18**	1.53***	1.46***	1.42***
	(0.08)	(0.06)	(0.07)	(0.06)	(0.06)
Community-Based Project	0.68**
	(0.10)
Internship		1.85***
		(0.21)
Mentoring			0.72*
			(0.10)
Research With Faculty				0.84
				(0.12)
Study Abroad					1.00
					(0.15)
Socioeconomic Status (Reference Category: Lowest Quartile)
Second Quartile	1.09	1.11	1.09	1.10	1.10
	(0.15)	(0.16)	(0.15)	(0.16)	(0.15)
Third Quartile	1.26	1.26	1.28	1.28	1.28
	(0.16)	(0.16)	(0.16)	(0.17)	(0.16)
Highest Quartile	1.78***	1.81***	1.79***	1.81***	1.81***
	(0.23)	(0.23)	(0.23)	(0.23)	(0.23)
Female	1.04	1.03	1.04	1.03	1.04
	(0.08)	(0.08)	(0.08)	(0.08)	(0.08)
Race (Reference Category: White)
Asian	0.93	0.94	0.92	0.94	0.93
	(0.11)	(0.11)	(0.11)	(0.11)	(0.11)
Black	0.88	0.91	0.90	0.89	0.89
	(0.11)	(0.12)	(0.11)	(0.11)	(0.11)
Hispanic	0.85	0.88	0.87	0.87	0.87
	(0.12)	(0.13)	(0.13)	(0.13)	(0.13)
Other	0.95	0.98	0.94	0.95	0.94
	(0.16)	(0.16)	(0.16)	(0.16)	(0.16)
Married or Formerly Married (2006)	0.34**	0.34**	0.33**	0.33**	0.33**
	(0.11)	(0.11)	(0.11)	(0.11)	(0.11)
Whether Has Biological Children (2006)	0.41**	0.42**	0.43**	0.42**	0.42**
	(0.12)	(0.13)	(0.13)	(0.13)	(0.13)
Hours Worked Weekly 2005 to- 2006 (Reference Category: 0 hrs)
1–20 hrs	1.23*	1.22*	1.22*	1.23*	1.23*
	(0.12)	(0.12)	(0.12)	(0.12)	(0.12)
21+ hrs	0.64***	0.63***	0.64***	0.63***	0.63***
	(0.07)	(0.06)	(0.06)	(0.07)	(0.06)
High School GPA (Reference Category: 0.00–2.00)
2.01–3.00	2.46**	2.37**	2.41**	2.42**	2.43**
	(0.75)	(0.75)	(0.74)	(0.75)	(0.74)
3.01–3.50	4.73***	4.52***	4.57***	4.63***	4.65***
	(1.48)	(1.47)	(1.44)	(1.47)	(1.47)
3.51–4.00	9.36***	9.10***	9.14***	9.26***	9.29***
	(3.00)	(3.02)	(2.93)	(3.00)	(2.99)
High School Composite Math/Reading Score	1.03***	1.03***	1.03***	1.03***	1.03***
	(0.01)	(0.01)	(0.01)	(0.01)	(0.01)
Number of High School Cocurricular Activities	1.03	1.04	1.04	1.04	1.03
	(0.02)	(0.02)	(0.02)	(0.02)	(0.02)
Constant	.04***	.03***	.04***	.04***	.04***
	(.01)	(.01)	(.02)	(.01)	(.01)
Observations	5,982	5,982	5,982	5,982	5,982
McFadden’s R^2	.205	.210	.205	.204	.203
Wald Chi-Square Value (20 df)	693.71	716.28	687.10	692.63	698.02

Note. Standard errors in parentheses.

*** p < .001. ** p < .01. * p < .05.

Table 3 shows that when controlling for a student’s level of overall involvement in high-impact activities, participating in an internship was positively associated with bachelor’s degree attainment. Other models showed a different story, however, and revealed that participating in a community-based project and mentoring were negatively related to bachelor’s degree attainment, net of overall involvement. Students who participated in research with a faculty member or in study-abroad program were no more or less likely to attain a bachelor’s degree than were those who did not.

Tables 2 and 3 focus on the second research question of this study that investigated whether participating in high-impact activities was related to bachelor’s degree attainment. These results provided partial support for the hypothesis that participating in these activities is positively related to bachelor’s degree attainment. Students who participated in a higher number of high-impact activities were more likely to attain a bachelor’s degree in all models, but the relationship between individual activities and degree outcomes varied between each activity. In this way, these tables suggest that a higher level of overall involvement in these activities benefits students toward degree attainment, but not all of these activities individually help students toward degree attainment.

Testing the compensatory effect of participating in high-impact activities

The final research question of this study asked whether the relationship between participating in high-impact activities and bachelor’s degree completion varies between delayers and nondelayers. To test this research question, Table 4 presents six logistic regression models that each include a different interaction term multiplying a student’s delayed enrollment status by their participation in high-impact activities. If significant, these interactions would suggest that the relationship between participating in high-impact activities and bachelor’s degree attainment does, in fact, vary between these two groups.

Table 4. Logistic regression models for bachelor’s degree attainment, testing interaction terms (odds ratios reported).

	(1)	(2)	(3)	(4)	(5)	(6)
VARIABLES	Number of HIPs	Community	Internship	Mentoring	Research	Study Abroad
Delayed College Entry by 6 Months or More	0.32***	0.35***	0.37***	0.36***	0.36***	0.36***
	(0.07)	(0.07)	(0.09)	(0.07)	(0.07)	(0.07)
Number of High-Impact Postsecondary Activities	1.41***	1.54***	1.18**	1.53***	1.46***	1.42***
	(0.05)	(0.08)	(0.06)	(0.07)	(0.06)	(0.06)
Delay × Number of High-Impact Postsecondary Activities	1.21
	(0.18)
Community-Based Project		0.67**
		(0.09)
Delay × Community		1.77
		(0.73)
Internship			1.84***
			(0.21)
Delay × Internship			1.07
			(0.37)
Mentoring				0.71*
				(0.10)
Delay × Mentor				1.48
				(0.61)
Research With Faculty					0.83
					(0.12)
Delay × Research					1.63
					(0.83)
Study Abroad						0.98
						(0.15)
Delay × Study Abroad						2.47
						(2.25)
Socioeconomic Status (Reference Category: Lowest Quartile)
Second Quartile	1.10	1.09	1.11	1.09	1.10	1.10
	(0.15)	(0.15)	(0.16)	(0.15)	(0.16)	(0.15)
Third Quartile	1.28	1.27	1.26	1.28	1.28	1.28
	(0.17)	(0.16)	(0.16)	(0.16)	(0.17)	(0.16)
Highest Quartile	1.81***	1.78***	1.81***	1.79***	1.81***	1.81***
	(0.23)	(0.23)	(0.23)	(0.23)	(0.23)	(0.23)
Female	1.04	1.05	1.03	1.04	1.03	1.04
	(0.08)	(0.08)	(0.08)	(0.08)	(0.08)	(0.08)
Race (Reference Category: White)
Asian	0.92	0.92	0.94	0.92	0.94	0.93
	(0.11)	(0.11)	(0.11)	(0.11)	(0.11)	(0.11)
Black	0.89	0.88	0.91	0.90	0.90	0.89
	(0.11)	(0.11)	(0.12)	(0.11)	(0.12)	(0.11)
Hispanic	0.87	0.85	0.88	0.87	0.87	0.87
	(0.13)	(0.12)	(0.13)	(0.13)	(0.13)	(0.13)
Other	0.93	0.94	0.98	0.94	0.95	0.94
	(0.15)	(0.16)	(0.16)	(0.15)	(0.16)	(0.16)
Married or Formerly Married (2006)	0.33**	0.34**	0.34**	0.33**	0.33**	0.33**
	(0.11)	(0.11)	(0.11)	(0.11)	(0.11)	(0.11)
Whether Has Biological Children (2006)	0.42**	0.41**	0.42**	0.43**	0.42**	0.42**
	(0.13)	(0.12)	(0.13)	(0.13)	(0.13)	(0.13)
Hours Worked Weekly 2005 to 2006 (Reference Category: 0 hrs)
1–20 hrs	1.23*	1.23*	1.22*	1.22*	1.23*	1.23*
	(0.12)	(0.12)	(0.12)	(0.12)	(0.12)	(0.12)
21+ hrs	0.63***	0.64***	0.63***	0.63***	0.64***	0.64***
	(0.06)	(0.07)	(0.06)	(0.06)	(0.07)	(0.06)
High School Grade Point Average (Reference Category: 0.00–2.00)
2.01–3.00	2.44**	2.47**	2.37**	2.41**	2.43**	2.44**
	(0.75)	(0.76)	(0.75)	(0.74)	(0.75)	(0.75)
3.01–3.50	4.68***	4.77***	4.51***	4.58***	4.64***	4.68***
	(1.48)	(1.50)	(1.46)	(1.44)	(1.48)	(1.48)
3.51–4.00	9.35***	9.44***	9.10***	9.18***	9.28***	9.35***
	(3.01)	(3.03)	(3.02)	(2.94)	(3.02)	(3.01)
High School Composite Math/Reading Score	1.03***	1.03***	1.03***	1.03***	1.03***	1.03***
	(0.01)	(0.01)	(0.01)	(0.01)	(0.01)	(0.01)
Number of High School Cocurricular Activities	1.03	1.04	1.04	1.04	1.04	1.03
	(0.02)	(0.02)	(0.02)	(0.02)	(0.02)	(0.02)
Constant	.04***	.04***	.03***	.04***	.04***	.04***
	(.01)	(.02)	(.01)	(.02)	(.01)	(.01)
Observations	5,982	5,982	5,982	5,982	5,982	5,982
McFadden’s R^2	.203	.205	.210	.205	.204	.203
Wald Chi-Square Value (M1 – 20 df | M2–6 – 21 df)	690.30	692.21	717.62	686.26	691.73	698.82

Note. Standard errors in parentheses. HIPs = High-Impact Practices.

*** p < .001. ** p < .01. * p < .05.

Table 4 uses the same regression models presented in Table 3 but with an added interaction term in each model. The first model incorporated an interaction term that multiplied a student’s delayed enrollment status by the number of high-impact activities in which they participated in college. The following five models incorporated interaction terms relevant to the high-impact activity being tested in each model. That is, for Model 2 that tested the compensatory effect of participating in a community-based activity on bachelor’s degree attainment for delayers, Table 4 presents the interaction term Delay × Community. Table 4 follows this pattern for Models 2 through 6.

The story presented in Table 4 is less varied than the models presented in other tables for this study. None of the interaction terms in Table 4 are significant, suggesting that considering both a student’s overall level of involvement (e.g., number of activities in which a student participated) and a student’s participation in each independent high-impact activity, there is no compensatory effect toward bachelor’s degree completion for delayers who participate in these activities. This finding does not support the final hypothesis of this study that delayers would experience a compensatory effect toward bachelor’s degree attainment from participating in high-impact activities compared with their immediate-enrollment peers.

Discussion and conclusion

This study showed that students who delayed entry into college were involved in fewer high-impact postsecondary activities overall during their postsecondary experience compared with students who enrolled in college immediately after graduating from high school. Furthermore, students who participated in more high-impact activities in college were more likely to attain a bachelor’s degree than were students involved in fewer activities or not involved at all. Delayers, however, did not experience a compensatory benefit toward bachelor’s degree attainment by being highly involved in these activities compared with their immediate-enrollment peers.

Regarding which high-impact practices were related to degree outcomes—while controlling for the level to which a student was involved in these activities—only participating in an internship was positively associated with bachelor’s degree attainment. The other four high-impact activities showed varied relationships with degree outcomes, with some having a negative relationship (e.g., community-based projects, mentoring) and some showing no statistically significant relationship at all (e.g., research with faculty, study abroad). Delayers did not experience a compensatory benefit toward bachelor’s degree completion from participating in any of these high-impact activities after controlling for their overall level of involvement.

Overall, the findings in this study suggest that delayers’ lower level of involvement in high-impact practices while in college may explain a small part of their lower likelihood of attaining a bachelor’s degree in 6 years, but compared with the influence of other variables like students’ socioeconomic background and high school GPA, the effect of participating in these activities was very small indeed. Also, this study showed that a student’s overall involvement in high-impact activities was more important than participating in specific activities while in college. That is, students who participated in more activities were more likely to attain a bachelor’s degree in 6 years, but only one high-impact activity—internships—showed a positive relationship to bachelor’s degree attainment after controlling for students’ level of overall involvement. This particular finding—that internships have a uniquely positive benefit toward degree attainment—may hint at the varying qualitative experiences across high-impact activities. Semester-long or yearlong internships may require more time and energy from students, on average, than other high-impact activities. This article, however, can only hint at possible explanations considering that the data are only able to provide quantitative and not qualitative insights on college student participation in high-impact activities.

This study used nationally representative data to connect the impact of participating in high-impact activities to bachelor’s degree attainment while focusing on the specific role these activities play in the college experience of students who delay entry into college. Research in this area has not yet empirically tested the connection between high-impact practices and college degree completion. The widespread assumption in both research and practice is that high-impact practices promote a plethora of positive outcomes for students who participate in them because of the small evidence that these practices promote student persistence and engagement (Kuh, 2008) and are particularly beneficial for students from disadvantaged backgrounds (Sweat et al., 2013). Colloquially, higher education practitioners often lump higher chances of attaining a bachelor’s degree in with the positive effects of high-impact practices. This study showed that this assumption is empirically supported, at least when focusing on the number of high-impact activities in which students participate. At the same time, the analyses in this study showed that not all high-impact practices provide a benefit toward bachelor’s degree attainment for students who participate in them.

For delayers, participating in high-impact activities promoted bachelor’s degree attainment overall, but involvement in these activities did not benefit delayers more than nondelayers. This finding runs contrary to the suggestion in other research that disadvantaged students experience a compensatory benefit from these activities (Kuh, 2008; Sweat et al., 2013). In general, while this study revealed delayers’ on-campus participation in these activities may play a minor part in their likelihood of attaining a bachelor’s degree, their on-campus experiences played a much more insignificant role in this equation than other variables like their socioeconomic background and precollege characteristics. This finding supports the omission of college experience variables in previous research on students who delay entry into college (Bozick & DeLuca, 2005; Roksa & Velez, 2012).

Recent research has suggested that students from disparate backgrounds have different, often unequal experiences in college (Armstrong & Hamilton, 2015). This study provides additional evidence to this notion by demonstrating that disadvantaged groups are less likely to be involved in high-impact postsecondary activities in college. Although this study could not fully explore what prevents students who delay entry into college from participating in high-impact postsecondary activities at a rate similar to their immediate-enrollment counterparts, it provides a foundation for further research on how students from different backgrounds persist through college.

Furthermore, this study extends previous research on high-impact practices to show that while students who are more highly involved in these activities experience a benefit toward bachelor’s degree attainment, that benefit does not matter as much as outside-of-college variables. This finding has implications for scholars and practitioners in higher education. First, researchers and practitioners should consider a student’s experience in college as connected to a much broader picture of their social background and social forces that influence students’ trajectories into and through higher education. Without considering the broader context, scholars and practitioners cannot fully understand how to promote student success on campus.

Second, research on high-impact practices must increase its empirical validity by using nationally representative data instead of relying primarily on data from higher education institutions that self-select into the data set (e.g., National Survey of Student Engagement). This study suggests that the story around high-impact practices may be more nuanced than what is currently known in the research and scholars and practitioners should be more hesitant to implement sweeping changes in students’ experiences in higher education related to these practices before understanding more about them.

Finally, this article may raise more questions than it answers, and future research should investigate additional questions related to student involvement in high-impact activities and how these activities fit into the complicated equation of student success in college. The ELS data only provide a look at students’ participation in these activities. How do students’ experiences with these activities differ by institution or by activity? How might the quality of these activities influence degree attainment outcomes? Focusing on the experience of students who delay entry into college, why are delayers less involved in high-impact activities? What are some of the obstacles to participating in these activities for delayers? These questions are only a few of the many questions that connect to the findings in this article, questions that might help scholars and practitioners to understand more fully the experiences of delayers on college campuses and how high-impact activities play a role in student success-related outcomes among this group of students.

Notes

1. Trowler (2010) documented the varying definitions of student engagement in her review of the literature. Ultimately, despite numerous definitions of the word, student engagement typically refers to two things: (a) student involvement in campus activities and (b) students’ emotional connection to the university environment through these activities. With regard to the current study, ELS data have markers of the first characteristic of student engagement but not the second—hence the use of involvement instead of engagement.

2. While standardizing the window for degree attainment across the sample was a strength of this study in the sense that immediate enrollees did not have more time than delayers to complete their degrees, it also limited the number of students who qualified as delayers. This sample included students who delayed postsecondary education 6 months to 2 years after high school graduation, but many students delay longer than 2 years after high school graduation (Horn et al., 2005).

3. The sample was restricted to students who first enrolled in a 4-year institution for simplicity and clarity, but this restriction impacted how many students delayed entry into college. Delayed entry is much more common among students who first attend institutions that offer less than 4-year degrees.

4. Though varied, all these activities provided students with opportunities to work in professional settings within their respective fields of research. While co-ops, student teaching, and clinical assignments are often limited to certain disciplines, internships and field experiences extend to a broader population of students.

Appendix

Appendix: Descriptive statistics

Table

Variables	Mean	SD	Min	Max	N
Respondent delayed entry into college by 6 months or more	0.06	0.23	0	1	5,982
Number of high-impact educational activities in which respondent participated while in college	1.30	1.24	0	5	5,982
Respondent attained bachelor’s degree by 2012	0.61	0.49	0	1	5,982
Respondent participated in community-based project	0.23	0.42	0	1	5,982
Respondent participated in internship/co-op/field experience/student teaching/clinical assignment	0.54	0.50	0	1	5,982
Respondent participated in mentoring	0.20	0.40	0	1	5,982
Respondent participated in research project with faculty member outside course	0.17	0.38	0	1	5,982
Respondent participated in study-abroad program	0.15	0.36	0	1	5,982
Respondent’s socioeconomic status	—	—	0	3	5,982
First quartile	0.11	—	—	—	658
Second quartile	0.16	—	—	—	957
Third quartile	0.26	—	—	—	1,555
Fourth quartile	0.46	—	—	—	2,812
Respondent is female	0.55	0.50	0	1	5,982
Race	—	—	0	4	5,982
White	0.66	—	—	—	3,948
Black	0.11	—	—	—	658
Asian	0.10	—	—	—	598
Hispanic	0.08	—	—	—	479
Other	0.05	—	—	—	299
Respondent ever married in 2006	0.02	0.13	0	1	5,982
Respondent has biological child in 2006	0.02	0.14	0	1	5,982
Respondent’s work hours in 2005 to 2006	—	—	0	2	5,982
Not employed	0.38	—	—	—	2,273
1–20 hr	0.39	—	—	—	2,333
More than 20 hr	0.23	—	—	—	1,376
High school grade point average	—	—	0	3	5,982
0.00–2.00	0.03	—	—	—	179
2.01–3.00	0.29	—	—	—	1,735
3.01–3.50	0.33	—	—	—	1,974
3.51–4.00	0.35	—	—	—	2,094
Standardized test composite score: math and reading	56.53	8.16	25.35	81.04	5,982
Number of high school cocurricular activities in which respondent participated	2.84	1.83	0	11	5,982