Postsecondary Education Persistence of Adolescents With Specific Learning Disabilities or Emotional/Behavioral Disorders

Abstract

While experiences of students with disabilities transitioning from high school to college have been well documented, the influence exerted by selected factors on these experiences is less well understood. Using data from the Education Longitudinal Study of 2002, the influence of selected risk and resilience factors on the short-term postsecondary educational outcomes, that is, persistence, of adolescents with specific learning disabilities or emotional/behavioral disorders was examined. A logistic model revealed group differences between individuals with disabilities and peers without disabilities. All selected risk and resilience factors significantly predicted educational persistence. No significant differences were observed between adolescents with specific learning disabilities or emotional/behavioral disorders, but three factors—grade point average, socioeconomic status, and number of friends having plans to attend a 4-year college—were significant predictors of educational persistence for adolescents with disabilities. Implications of these findings are discussed.

Keywords

postsecondary educational outcomes risk and resilience framework high-incidence disabilities

An increasing number of adolescents with disabilities have pursued postsecondary education in recent years (Getzel, 2008; Newman, Wagner, Cameto, & Knokey, 2009). However, individuals with specific learning disabilities or emotional/behavioral disorders enroll in college and postsecondary training at rates far below those of the general population without disabilities (Wagner, Newman, Cameto, Garza, & Levine, 2005). Moreover, students with disabilities are less likely to maintain full-time status resulting in longer periods needed to complete a degree (Wessel, Jones, Markle, & Westfall, 2009), and more likely to drop out of postsecondary education (Murray, Goldstein, Nourse, & Edgar, 2000). Although a few comprehensive studies have been conducted on the transition of adolescents with disabilities from school to postsecondary education and work (e.g., Blackorby & Wagner, 1996; Newman et al., 2009; Wagner et al., 2005), relatively little is known about how risk and resilience factors contribute to the persistence of students with disabilities in postsecondary education.

Our study focused on adolescents with specific learning disabilities or emotional/behavioral disorders because of the many similarities these adolescents share, including overlap among the impact of disability on academic performance, work preparation, and career attainment (Dietz & Montague, 2006; Hallahan, Kauffman, & Pullen, 2012; Kaplan, Dewey, Crawford, & Wilson, 2001). Even so, Sabornie (Sabornie, Cullinan, Osborne, & Brock, 2005; Sabornie, Evans, & Cullinan, 2006) identified important differences in the cognitive and behavioral characteristics of these groups. For example, adolescents with specific learning disabilities exhibit higher cognitive abilities and higher academic achievement. Adolescents with emotional/behavioral disorders are more likely to face arrest, incarceration, or probation-parole violation (Newman et al., 2009).

The decision to examine adolescents with these disabilities in the same analysis was based on the similarity in characteristics and the common challenges faced as they prepare for and transition from school to postsecondary education and work (Gregg, 2009). Examining how adolescents with disabilities experience and cope with academic- and career-related issues can facilitate a better understanding of how career theories and interventions might be used to explain and promote career behavior, choice, and attainment for these individuals.

Persistence in Postsecondary Education

Postsecondary education plays a significant role for individuals with disabilities aspiring to career opportunities that cannot be attained with a high school degree (Lindstrom, Doren, & Miesch, 2011). Employment projections in the United States provide compelling evidence on the increasing importance of postsecondary education. Occupations requiring at least some type of postsecondary education for entry are expected to grow at quicker rates than other aspects of the workforce, while occupations requiring a high school degree or less will grow more slowly (Lockard & Wolf, 2012). While a majority of jobs held by individuals with disabilities are semiskilled and part-time (Barkley, 2006), individuals with disabilities who earn postsecondary degrees show comparable work outcomes (e.g., employment status, benefits, income) to individuals without disabilities (Madaus, 2006).

One positive indicator used to represent success in pursuing a postsecondary education degree is the concept of persistence. Berger and Lyon (2005) defined persistence as “the desire and action of a student to stay within the system of higher education” (p. 7). Students are more likely to enroll and persist in their degree program when engaged in the institution both academically and socially. Much of the work on persistence of students in postsecondary education has focused on individual and college factors (Tinto, 1987, 1992). Mamiseishvili and Koch (2011) found that women and African American college students were more likely than men and White students to persist. Students who held positive outcome expectations about attaining a higher degree were also more likely to persist.

Seo, Abbott, and Hawkins (2008) reported that adolescents with learning disabilities had significantly lower postsecondary school enrollments at age 21 than those without disabilities. Adolescents with disabilities from middle or high socioeconomic status (SES) families had higher postsecondary education enrollments than those with disabilities from low SES families (Wagner et al., 2005). Kortering, Braziel, and McClannon (2010) reported that students with learning disabilities were more likely to have lower educational and occupational aspirations than those without disabilities. Factors influencing these lowered rates of enrollment and persistence of students with disabilities in college are not clear.

Conceptual Considerations for Risk and Resilience Factors

Use of a risk-resilience framework offers researchers a balanced approach to understanding the outcomes of individuals with disabilities by stressing individual strengths and limitations, rather than an emphasis on limitations imposed by the presence of disability. Risk factors increase the likelihood that barriers or constraints exist that limit individual plans or activity, while resilience factors are those processes that reduce the risk or increase the probability of successful outcomes (Gregg, 2009; Murray, 2003). The presence of disability increases the possibility that adolescents will experience risk (Morrison & Cosden, 1997), while the nature of disability presents both common and unique career needs, experiences, and potential, as well as susceptibility to career-related risks. Career-related barriers associated with disability include social stigma, employment discrimination, inadequate vocational training and preparation, poor self-efficacy, and lower or restricted occupational aspirations (Fabian & Liesener, 2005).

Resilience factors can be divided into internal and external influences. Internal resilience factors include positive temperament, recognition of one’s disability, accommodation strategies, and knowledge of ways to be proactive and advocate for one’s self (Gregg, 2009; Murray, 2003). Disability identity may have a bearing on the career behavior of adolescents with disabilities by decreasing engagement with others (Mpofu & Harley, 2006). Other internal factors pertinent to educational persistence include an individual’s educational expectations and academic achievement (Murray, 2003). Family factors such as SES, family composition, parent−child discussions about work and education, and parents’ educational expectations may also influence educational persistence.

External resilience factors support individuals in their education- and work-related decision-making. The presence of a supportive adult appears to be one of the strongest resilience factors for individuals with learning disabilities (Goldberg, Higgins, Raskind, & Herman, 2003). Connections to supportive and helpful people (Reiff, Gerber, & Ginsberg, 1997), sustained emotional support from parents (Cosden, Brown, & Elliott, 2002), and positive parent−child interactions (Wong, 2003) also appear related to the potential of success for individuals with disabilities. It is critical that high school students with disabilities are knowledgeable of the Americans with Disabilities Act, realities of the workplace, and importance of self-determination (Madaus, Gerber, & Price, 2008). Outcomes for individuals with learning disabilities “reflect a combination of protective and risk factors, some of which are internal, whereas others are external to the individual” (Morrison & Cosden, 1997, p. 56).

Since risk and resilience factors may influence the career behavior and postsecondary educational outcomes of adolescents with specific learning disabilities or emotional/behavioral disorders, our understanding incorporated the work of both career theorists (e.g., Lent, Brown, & Hackett, 1994) and disability researchers (e.g., Murray, 2003; Wong, 2003). Another aspect to consider was the issue of outcomes. The unique interplay of risk and reliance factors will result in widely varied outcomes ranging from successful employment or unemployment, enrollment in a postsecondary academic or vocational programs, dropping out of school or successful completion of a postsecondary degree, job difficulties, job (dis)satisfaction, and life (dis)satisfaction (Barkley, 2006; Reiff et al., 1997). The question of how to incorporate risk-resilience factors to understand the persistence of young people with specific learning disabilities or emotional/behavioral disorders in postsecondary education deserves attention.

Purpose

Our analysis examined the influence of selected risk-resilience factors on college persistence for adolescents with (or without) specific learning disabilities or emotional/behavioral disorders. The outcome, persistence, was conceptualized as a dichotomous variable based on the pursuit or lack of pursuit of postsecondary education measured 2 years after high school completion. Educational persisters included those individuals who (a) had successfully completed or were enrolled in a 2-year postsecondary education institution (e.g., technical education, community college) or (b) were enrolled in a 4-year postsecondary education program at the time of data collection. Educational persisters were compared with a second group who (a) had never enrolled in postsecondary education or (b) had enrolled in a 2- or 4-year program but left before completion. Two research questions guided the analysis: (a) Do differences exist in short-term postsecondary educational outcomes between students with learning disabilities or behavior disorders, and individuals without disabilities? (b) What risk-resilience factors influenced short-term postsecondary educational outcomes for groups with or without disabilities?

Method

Educational Longitudinal Study of 2002 (ELS:2002)

The ELS:2002 database, administered by the National Center for Education Statistics, was selected because of its focus on the transition of adolescents from high school to postsecondary education and work. The ELS:2002 contains a base-year sample comprised of 15,362 tenth graders drawn from 752 public and private schools during the 2001–2002 school year. Subsequent administrations were conducted in 2004 and 2006. Our analysis included data from the first and second follow-up surveys as we focused on the transition period from 12th grade through 2 years after high school (Ingels et al., 2007). The initial sample was 10,760 and included 9,990 adolescents without disabilities (92.84%) and 770 adolescents with either specific learning disabilities or emotional/behavioral disorders (7.16%).

Identification of Students With Disabilities

We used two methods to identify individuals with specific learning disabilities or emotional/behavioral disorders. The first method examined students’ base year status (Grade 10) on two variables, provision of Special Education services through an Individualized Education Program (IEP), and indication of diagnosed disability using school transcript data. Drawing on previous studies (Hodapp & Krasner, 1994–1995; Reschly & Christenson, 2006), we also used a base-year question that asked parents about the presence and type of disability for their child. Transcript data identified students who received school credit in one or more of three types of special education-related coursework, including general special education (F1R54_C), special education-vocational (F1R55_C), and special education-resource curriculum (F1R56_C). We excluded students with other types of disabilities and those with multiple disabilities from our analysis. This approach was used in prior studies of National Education Longitudinal Study of 1988 (NELS:88) data (Hodapp & Krasner, 1994–1995; Ingels & Scott, 1993).

The final sample included all individuals without disabilities who participated in all three data collection points (N = 9,990) and individuals who met established disability criteria in the base year and participated in all three data collection points, including 670 (6.23%) participants who reported a learning disability and received one or more disability-related services, and 100 (0.93%) reporting an emotional/behavioral disorder and receiving at least one disability-related service. The non-disabled group did not include other types of students with disabilities. Our sample compared favorably with percentages of students with specific learning disabilities and emotional/behavioral disorder who received federally supported programs in 2002 and 2003 (5.9% and 1.0%, respectively; Snyder & Dillow, 2012). Over one-half of non-disabled adolescents were women (52.4%), while only 37.2% of the disability group was women (see Table 1).

Table 1.

Multiply Imputed Descriptive Data of Sample Used to Investigate Postsecondary Education Outcomes of Young Adults With High-Incidence Disabilities.

	Disability(n = 770)		No disability(n = 9,990)		All participants (n = 10,760)
Variable	Estimated proportion	SE	Estimated proportion	SE	Estimated proportion	SE
Individual factors
Disability type	0.875^a	0.018	—	—	0.067^b	0.003
Gender^c	0.372	0.021	0.524	0.006	0.514	0.006
Occupational aspirations
High	0.378	0.024	0.285	0.006	0.291	0.006
Medium	0.232	0.020	0.118	0.004	0.125	0.004
Low	0.200	0.020	0.176	0.005	0.178	0.005
Uncertain/don’t know	0.190	0.017	0.421	0.007	0.406	0.006
Educational expectations
Less than a 4-year college degree	0.489	0.022	0.235	0.006	0.252	0.006
More than a 4-year college graduate	0.350	0.021	0.691	0.007	0.668	0.007
Don’t know	0.161	0.017	0.074	0.004	0.079	0.004
GPA^d	2.167	0.032	2.703	0.016	2.667	0.016
Reading achievement^d	40.841	0.402	51.365	0.189	50.661	0.187
Math achievement^d	38.438	0.389	50.531	0.198	49.723	0.195
Family factors
SES^d	−0.205	0.034	0.002	0.015	−0.011	0.015
Family composition^e	0.500	0.023	0.585	0.007	0.580	0.007
Parent discussion: College
Never	0.155	0.018	0.048	0.003	0.055	0.003
Sometimes	0.362	0.022	0.264	0.006	0.271	0.006
Often	0.483	0.023	0.688	0.007	0.674	0.007
Parents’ educational expectations^f	0.646	0.022	0.892	0.004	0.876	0.005
School and peer factors
Friends dropped out
None	0.427	0.021	0.519	0.008	0.513	0.008
A few	0.400	0.021	0.363	0.007	0.366	0.007
Some	0.121	0.015	0.085	0.004	0.087	0.003
Most/All	0.052	0.010	0.032	0.002	0.034	0.002
Friends’ plan to go to 4-year college
None	0.199	0.020	0.059	0.003	0.069	0.003
A few	0.230	0.020	0.171	0.005	0.175	0.005
Some	0.283	0.021	0.250	0.006	0.252	0.006
Most/all	0.287	0.022	0.519	0.008	0.504	0.008
Community factors
Community service
None	0.705	0.020	0.587	0.007	0.595	0.006
Less than once a week	0.121	0.015	0.227	0.005	0.220	0.005
Once or twice a week	0.125	0.015	0.153	0.004	0.151	0.004
(Almost) Every day	0.049	0.009	0.032	0.002	0.033	0.002
Control variables
Native language^g	0.880	0.014	0.860	0.006	0.861	0.006
Race
Asian	0.017	0.004	0.045	0.003	0.044	0.003
Black	0.160	0.019	0.142	0.007	0.143	0.007
Hispanic, no race specified	0.048	0.009	0.075	0.006	0.073	0.006
Hispanic, race specified	0.094	0.013	0.085	0.006	0.086	0.006
More than one race	0.067	0.012	0.041	0.003	0.043	0.003
American Indian	0.014	0.005	0.009	0.002	0.010	0.002
White	0.600	0.023	0.602	0.011	0.602	0.011
Outcome variable
Persistence in college^h	0.324	0.021	0.630	0.008	0.610	0.008

Note. Hispanic, race specified means that specific ethnicity such as Mexican, Puerto Rican, Central American, South American, and so on was identified. GPA = grade point average; SES = socioeconomic status.

Emotional/behavioral disorders is reference. ^bNon-high-incidence disabilities is reference. ^cMale is reference. ^dIndicates mean scores. ^eOther is reference. ^fLess than college is reference. ^gOther than use of English is reference. ^hNon-persistence in college is reference.

Measures

Persistence in postsecondary education was constructed from second follow-up data using both postsecondary enrollment and completion status. Public high school graduates in 2004 who either enrolled in a postsecondary institution or completed a degree (or certificate) by 2006 were classified as persistent in postsecondary education, whereas those who had never enrolled or had enrolled and dropped out of postsecondary education were classified as not persistent.

Individual risk-resilience factors included gender, occupational aspirations in Grade 12, educational expectations in Grade 12, and academic achievement scores including high school grade point average (GPA; M = 2.667), reading achievement (M = 50.661), and math achievement (M = 49.723). Gender was coded “0” for adolescent men and “1” for adolescent women. Educational expectations were assigned the following values: less than college graduate = 0, more than college graduate = 1, uncertain/don’t know = 2. Occupational aspirations were grouped according to level of job status: high prestige = 0, medium prestige = 1, and low prestige = 2, and uncertain/don’t know = 3. High school GPA was used to reflect participants’ general academic achievement (Bozick et al., 2006).

Family risk-resilience factors were represented by SES, family composition (0 = others, 1 = two parents), parental communication about college attendance, and parents’ educational expectations. A standardized SES composite indicator (M = 0.00) was constructed by ELS:2002 researchers using five variables, including father’s and mother’s education, father’s and mother’s occupations, and family income (Bozick et al., 2006). Parental communication was measured with items asking how often respondents discussed going to college with their parents (never = 0, sometimes = 1, often = 2). Parental educational expectation for their children was conceptualized as a dichotomous variable, either less than a 4-year college degree or 4-year college degree or more.

School and peer risk-resilience factors were represented by the number of friends who had dropped out of high school and the number of friends planning to attend a 4-year college. Both variables consist of four options including none = 0, a few = 1, some = 2, and most/all = 3.

Community risk-resilience factor was measured using one item that asked respondents how often they performed community service. The variable consisted of four options: none = 0, less than once a week = 1, once or twice a week = 2, and (almost) every day = 3.

Control variables were used to describe the sample and included English as native language and race/ethnicity. English as a native language was coded 0 for other than use of English and 1 for use of English. Race/ethnicity was coded 0 for White; 1 for Asian; 2 for Black; 3 for Hispanic, no race specified; 4 for Hispanic, race specified; 5 for more than one race; and 6 for Native American. Additional information on variables is found in the appendix.

Data Analyses

To investigate college persistence among students with learning disabilities, emotional/behavioral disorders, and students without disabilities, we used a logistic regression model using a forced entry approach, which is generally used for modeling the probability of an occurrence of an event through the use of associated covariates. The model can be written as

logitPr (y_{i j} = 1 | X_{i j}) = β_{0} + X'_{i j} β, i = 1, \dots, C; j = 1, \dots, n_{i},

logit p = \log \frac{p}{1 - p}

where $y_{i j}$ denotes the outcome variable (1 = persistent, 0 = non-persistent), C is the number of clusters (schools), n_i is the number of individuals in cluster (school) i, $X_{i j}$ denotes the vector of covariates, and $β$ stands for the effects of covariates. The covariate vector $X_{i j}$ was composed of variables measured to represent risk-resilience factors and several control variables.

To adjust for school effects we incorporated the cluster (school) effect as a random effect in the logistic model, and a pseudo maximum likelihood estimation approach was adopted to estimate model parameters. We generated 20 multiply imputed data sets using a multiple imputation by the chained equations approach to handle missing values.

Results

Descriptive Results

Table 1 summarizes descriptive statistics from the multiply imputed data sets used for analysis. Only 6.7% of the sample was composed of individuals with disabilities (87.5% specific learning disabilities, 12.5% emotional/behavioral disorders). Approximately two thirds of adolescents with disabilities were not persistent in postsecondary education, almost twice the rate of the non-disabled group. There were no observable differences between adolescents in the learning disabilities or emotional/behavioral disorders groups.

The proportion of adolescents with disabilities expressing uncertain occupational aspirations was 19.0%, while the rate for adolescents without disabilities was 42.1%. In terms of educational expectations, the proportion of adolescents expecting less than a 4-year college degree was almost double for adolescents with disabilities (48.9%) compared with the non-disabled group (23.5%). Corresponding proportions of adolescents with educational expectations for more than a 4-year college degree were much lower among the group with disabilities (35.0%) than the group without disabilities (69.1%). The GPA of adolescents with disabilities (M = 2.167) was lower than adolescents without disabilities (M = 2.703). The reading achievement scores of adolescents with disabilities (M = 40.841) were lower than adolescents without disabilities (M = 50.661). Math achievement scores of adolescents with disabilities (M = 38.44) were also lower than those without disabilities (M = 49.723).

The SES of the non-disabled group was near zero (M = 0.002), reflecting the standardized mean. The group with disabilities showed a significantly negative SES (M = −0.205), indicating that adolescents with disabilities were from lower SES families. Adolescents with disabilities indicated that their friends were less likely to go to a 4-year college than students without disabilities. Although 51.9% of individuals without disabilities indicated that friends planned to go to college, only 28.7% of individuals with disabilities did so. A large difference existed in the outcome, persistence in college, between adolescents with disabilities (32.4%) and non-disabled peers (63.0%).

Comparison by Disability Type

The base model revealed that after holding native language and race/ethnicity constant, the persistence of adolescents without disabilities was significantly different from those with disabilities. The estimated odds ratio for individuals with emotional/behavioral disorders and college persistence was 0.305 (coefficient = −1.185, 95% confidence intervals [CI] for odds ratio [OR] = [0.156, 0.598]), whereas the odds ratio estimate for persons with learning disabilities and college persistence was 0.295 (coefficient = −1.221, 95% CI for OR = [0.239, 0.363]). Adolescents with emotional/behavioral disorders were approximately 70% [=(1 − 0.305) × 100] less likely to enroll or persist in college than adolescents without disabilities. Adolescents with learning disabilities were approximately 71% [=(1 − 0.295) × 100] less likely to enroll or persist in college than non-disabled peers. Design-adjusted Wald tests determined that disability status was a significant predictor of college persistence, F(2, 379) = 71.68, p < .001.

Risk-Resilience Factors for Persistence in Postsecondary Education

Risk-resilience factors were included in the base model to determine their influence on college persistence (see Table 2). The Hosmer−Lemeshow goodness-of-fit test suggested no evidence of lack of fit ( $\hat{C}$ = 10.87, df = 8, p = .21). Estimated coefficients of disability type revealed that while individuals with emotional/behavioral disorders were not statistically different from peers without disabilities in persistence after holding risks-resilience factors constant, individuals with learning disabilities experienced a significant deficiency in the odds of persisting when compared with persons without disabilities (coefficient = −0.300, OR = 0.741, 95% CI for OR = [0.574, 0.956]). The odds of not persisting were 25.9% higher for adolescents with learning disabilities than for peers without disabilities when controlling for other covariates. Educational expectations, high school GPA, and math achievement showed positive effects on persistence. Individuals who expected to attain more than a college degree had almost two times greater odds (coefficient = 0.708, OR = 2.029, 95% CI for OR = [1.735, 2.373]) of persisting in college than those expecting less than a college degree. The odds ratio for high school GPA was 2.346 (95% CI for OR = [2.107, 2.613]), meaning that adolescents with higher high school GPAs were more likely to persist in postsecondary education. Adolescents with higher math achievement scores were also more likely to persist in postsecondary education (coefficient = .024, OR = 1.024, 95% CI for OR = [1.014, 1.036]).

Table 2.

Logistic Regression Models for Risk-Resilience Factors Predicting Postsecondary Work Outcome.

	All participants (n = 10,760)			Disabilities (n = 770)
Variable	Coefficient	SE	Odds ratio	Coefficient	SE	Odds ratio
Individual factors
Disability type^a
Emotional/behavioral disorders	0.019	0.352	1.019	—	—
Specific learning disabilities	−0.300*	0.129	0.741	—	—
Gender^b	0.153*	0.069	1.165	0.411	0.234	1.508
Occupational aspirations^c
Medium	0.052	0.099	1.053	−0.095	0.303	0.909
Low	0.130	0.091	1.139	0.522	0.324	1.685
Don’t know	0.037	0.079	1.038	−0.099	0.330	0.906
Educational expectations^d
More than college graduate	0.708***	0.079	2.030	0.376	0.271	1.456
Don’t know	0.052	0.105	1.053	0.523	0.313	1.687
GPA	0.853***	0.055	2.347	0.572***	0.180	1.772
Reading achievement	−0.001	0.005	0.999	−0.006	0.017	0.994
Math achievement	0.024***	0.005	1.024	0.035	0.021	1.036
Family factors
SES	0.514***	0.050	1.672	0.902***	0.193	2.465
Family composition^e	0.154*	0.062	1.166	0.330	0.220	1.391
Parent discussion: College^f
Sometimes	0.330*	0.158	1.391	−0.153	0.389	0.858
Often	0.757***	0.148	2.132	0.276	0.361	1.318
Parents’ educational expectations^g	0.223*	0.093	1.250	−0.052	0.249	0.949
School and peer factors
Friends dropped out^h
A few	−0.125	0.065	0.882	0.113	0.233	1.120
Some	−0.410***	0.103	0.664	−0.333	0.372	0.717
Most/all	−0.316	0.176	0.729	−0.933	0.553	0.393
Friends’ plan to go to 4-year college^h
A few	0.125	0.136	1.133	0.837*	0.375	2.309
Some	0.168	0.134	1.183	0.467	0.361	1.595
Most/all	0.413**	0.137	1.511	0.549	0.370	1.732
Community factors
Community service^h
Less than once a week	0.292***	0.079	1.339	−0.143	0.366	0.867
Once or twice a week	0.133	0.095	1.142	−0.322	0.346	0.725
(Almost) Every day	0.268	0.170	1.307	0.563	0.491	1.756
Control variables
Native languageⁱ	−0.229*	0.103	0.795	−0.211	0.379	0.810
Race/ethnicity^j
Asian	0.350**	0.135	1.419	−0.208	0.765	0.812
Black	0.230*	0.086	1.259	0.247	0.329	1.280
Hispanic, no race	0.205	0.130	1.228	0.484	0.530	1.623
Hispanic, race	0.074	0.130	1.077	0.132	0.424	1.141
More than one race	−0.410**	0.154	0.664	−0.020	0.447	0.980
American Indian	−0.166	0.285	0.847	−0.173	1.072	0.841

Note. Adjusted Wald test for all parameters and McFadden’s R² in all participants model: F(30, 377.7) = 65.49, p < .001 and .269; Adjusted Wald test for all parameters and McFadden’s R² in Disabilities model: F(28, 255.6) = 3.70, p < .001 and .165. GPA = grade point average; SES = socioeconomic status.

The reference group is non-disabled. ^bThe reference group is male. ^cThe reference group is high. ^dThe reference group is less than college graduate. ^eThe reference group is other. ^fThe reference group is never. ^gThe reference group is less than college. ^hThe reference group is none. ⁱThe reference group is use of English. ^jThe reference group is White.

p < .05. **p < .01. ***p < .001.

Unlike individual factors, all family factors proved to be important for college persistence. SES was significantly favorable to persistence (coefficient = 0.514, OR = 1.672, 95% CI for OR = [1.515, 1.845]); individuals with higher family SES were more likely to persist in college than their peers with low SES (see “All Participants v Disabilities model” in Table 2). The group of adolescents indicating two-parent family composition had an odds ratio of 1.166 (coefficient = 0.154, 95% CI for OR = [1.033, 1.317]), indicating that adolescents who live with both parents were 17% more likely to persist in postsecondary education than their peers who lived with a single parent. Compared with those who had never discussed college with a parent, those who had discussed college with a parent were more likely to be involved and persist in college (sometimes: coefficient = 0.330, OR = 1.391, 95% CI = [1.018, 1.901]; often: coefficient = 0.757, odds ratio = 2.132, 95% CI = [1.594, 2.853]). In other words, the more often parents and children discussed college, the more likely children were to be enrolled and persistent in postsecondary education. Similarly, parents’ educational expectations were also important. Compared with the less-than-college group, the higher expectation group was 25% more likely to persist in postsecondary education (coefficient = 0.223, 95% CI for OR = [1.040, 1.503]).

School-peer and community factors had significant impact on college persistence, specifically pertaining to adolescents who had observed friends dropping out of school. Compared with those who did not have any friends dropping out of school, individuals with some friends dropping out of school (coefficient = −0.410, OR = 0.664, CI for OR = [0.542, 0.813]) were less likely to be persistent in postsecondary education. Although degraded persistence levels of the few and most/all friends had dropped out groups seem insignificant, the negative effects of experiencing friends leaving high school cannot be disregarded. More important, the design-adjusted Wald test demonstrated that the parameters associated with the student drop-out factor were significantly different from zero, suggesting that the peer factor was an important predictor of college persistence. Friends’ plans for attending a 4-year college also proved to be important. All estimates were positive at all levels with the none group as a reference group, which suggests that friends’ plans for college were likely to be corollary to persistence. In particular, the most/all friends going to college group was more likely to persist in postsecondary education than the none of friends going to college group (coefficient = 0.413,OR = 1.511, 95% CI for OR = [1.154, 1.980]). Moreover, significant results of the design-adjusted Wald test suggest that the peer factor was an important predictor. Students who participated in community service less than once a week during high school were more likely to persist in postsecondary education than peers who did not serve. The design-adjusted Wald test indicated that the parameters associated with community service were significantly different from zero.

Influential Risk-Resilience Factors for Persistence of Individuals With Disabilities

As no statistically significant differences in persistence existed between disability types, this factor was disregarded while fitting the logistic regression model. Table 2 (disabilities model) depicts the logistic regression model applied only to individuals with disabilities ( $\hat{C}$ = 8.92, df = 8, p = .34). High school GPA (OR = 1.772, 95% CI for OR = [1.241, 2.529]) and SES (OR = 2.465, 95% CI for OR = [1.685, 3.605]) were of particular importance for understanding the persistence dynamic. Adolescents with higher GPAs and higher SES families were more likely to persist in college than peers with lower GPAs and lower SES families.

Friends’ plans for college also favorably affected persistence (coefficient = 0.837, OR = 2.309, 95% CI = [1.104, 4.831]). The few friends going to college group was more likely to persist than the none of friends going to college group. However, the design-adjusted Wald test suggested that a friend’s plans for college might not be a significant predictor of college persistence when controlling for the associations between other variables with college persistence.

Discussion

This study investigated the usefulness of selected factors in explaining educational outcomes for young adults with and without disabilities. When the model containing all participants regardless of disability status was examined, all risk-resilience covariates, except for occupational aspirations and reading achievement, were significantly associated with the likelihood of persisting in postsecondary education. However, only a few variables—high school GPA, SES, and peers’ plans to attend a 4-year college—were significantly associated with the model of college persistence when only examining students with disabilities. This result indicated an important and noteworthy difference for adolescents with disabilities. It is possible that these three salient factors contribute more meaningfully to the college persistence of students with disabilities compared with students without disabilities. In this section, we discuss how these risk-resilience factors contribute to an understanding of postsecondary education persistence of adolescents with specific learning disabilities or emotional/behavioral disorders, and we provide limitations of the study and implications for practice and future research.

Impact of Risk-Resilience Factors on Educational Persistence

Influence of disability status

Results of our base model (control variables only) supported previous research findings on the association between disability status and college persistence (e.g., DaDeppo, 2009; Murray et al., 2000; Wagner et al., 2005). The adolescents with disabilities in our analysis persisted in postsecondary education at lower rates than students without disabilities. Disability status is an important, albeit indirect, risk factor in explaining college persistence. The presence of a disability may pose barriers because of self-perceptions. Students might experience failure in academics based on their disability status, perceived disability-related failure, or overly positive self-perception; the disability label itself, implying a difficulty in learning or actual difficulty with learning, contributes to a lowered chance of enrolling in or completing postsecondary programs or of not receiving the academic or emotional support necessary to be successful in a competitive academic environment (Cosden & McNamara, 1997; Heath, Roberts, & Toste, 2011; May & Stone, 2010).

Influence of high school GPA

Among individual risk-resilience factors, students with higher GPAs were more likely to persist in postsecondary education. Although the importance of academic achievement for success in postsecondary education for students with disabilities has been downplayed (e.g., DaDeppo, 2009), our results reflect its importance, and these outcomes coincide with studies that have demonstrated the consistent (and primary) role of academic achievement in postsecondary education (e.g., Bozick et al., 2006; Mamiseishvili & Koch, 2011). The importance of the high school GPA on college persistence can be further supported by the Social Cognitive Career Theory persistence model, highlighting a significant indirect effect of high school GPAs on college persistence via academic self-efficacy and goals (Brown et al., 2008).

Role of occupational aspirations

The uncertainty in occupational aspirations expressed by adolescents with disabilities was considerably less than the uncertainty of adolescents without disabilities. Despite this difference, occupational aspirations did not help explain college persistence, suggesting that persistence is more sensitive to shorter-term anticipation or experience than by perceived longer-term benefits or goals. In contrast, research on short-term work outcomes and work-bound adolescents with disabilities revealed that occupational aspirations significantly predicted the number of hours worked per week for work-bound adolescents with specific learning disabilities or emotional/behavioral disorders (Rojewski, Lee, & Gregg, 2013). It is likely that educational expectations and attainment may play significant roles as mediators of occupational aspirations (i.e., indirect influence of occupational aspirations on college persistence through educational expectations) in understanding our results because persistence and attainment in postsecondary education are considered to be more important short-term goals for college-bound adolescents with disabilities, compared with work-bound adolescents with disabilities.

Influence of SES

One of the most consistent findings on college persistence is the pervasive influence of SES. A recent study by Chen and St. John (2011) found that individuals from high SES families have 55% higher odds of persisting in college than adolescents from low SES families. Similar to past studies, in our analysis, SES played an important role in the persistence of students with disabilities. It is important to note that although all family risk-resilience factors were significantly associated with the college persistence of non-disabled adolescents, only SES was significantly associated with persistence for adolescents with disabilities. This finding suggests that SES-related differences in parenting or opportunities to access academic resources may contribute to college persistence.

Peer influence on college persistence

The literature provides limited information about the influence of high school friends or peers on postsecondary education outcomes. However, the importance of social networking and support influences social resources available to individuals in learning situations (Scott, 2000). In our study, only one social factor, friends’ plans to attend college, had a significant association with college persistence for adolescents with disabilities. Although the model containing all participants showed that students with most or all of their friends planning to attend a four-year college were more likely to persist in postsecondary education than were students with few friends planning to attend, results of the logistic model for disabilities showed that even a few friends planning to attend college contributed to a greater persistence for adolescents with disabilities. These findings suggest that students with specific learning disabilities or emotional/behavioral disorders are more likely to be influenced by a few close friends, who may provide them with the academic or emotional support necessary for college persistence, than those without disabilities. However, caution is warranted in interpreting this finding because of a statistically insignificant result of the design-adjusted Wald test.

Limitations of the Study

This study contains several limitations. Although the ELS:2002 database provided an excellent opportunity to examine our research questions, it is not specifically designed to examine disability issues. Fortunately, unlike previous national longitudinal databases, e.g., NELS:88, in which some students with disabilities were excluded from the base year sampling, this was not the case with the ELS:2002 sampling scheme (Ingels et al., 2007). Even so, reliance on a school-based diagnosis to identify students with disabilities likely resulted in our sample being less homogeneous than might have been expected. Therefore, we exercised caution in identifying individuals with disabilities, and relied on several different criteria depending on availability.

Our study was delimited by the time between high school completion and two years after high school graduation. As students with disabilities are both less likely to go on to postsecondary education and, when they do, are more likely to make a delayed entry into higher education, the 2-year window for studying persistence in postsecondary education provides a limited, albeit important, picture of persistence behaviors in postsecondary education. This delimitation can be addressed in the near future with the scheduled release of the ELS:2002 third follow-up data in 2014. These data will provide information on outcomes 8 years after high school. Although a full understanding of the transition process requires longer periods of assessment, it is important to acknowledge and understand this shorter period of time, especially as many adolescents, regardless of disability status, experience prolonged periods of floundering in the first few years after high school (Blackorby & Wagner, 1996). Because our results apply only to individuals with disabilities graduating on time with same-age peers, our results could be strengthened by future studies that analyze students who fall behind the modal sequence in high school.

Implications for Practice and Future Research

It is important for parents, school teachers, and counselors to continuously interact and engage with adolescents with specific learning disabilities or emotional/behavioral disorders by recognizing that these factors are important indicators of short-term postsecondary education outcomes. In particular, existing school programs may strengthen the relationship with parents to encourage their involvement at home in helping, motivating, and encouraging students to succeed academically (e.g., McDonnall, Cavenaugh, & Giesen, 2012) and to provide relevant resources and information for students, especially from low SES families, to moderate the influence of SES. Moreover, school teachers can develop peer mentoring programs to support peer interactions that may influence students’ study strategies or short-term postsecondary plans and outcomes. Future study is needed to examine how policy-related strategies or interventions (e.g., federal Pell Grants) may reduce the effect of SES on educational persistence for adolescents with disabilities and how school−parent partnerships and peer mentoring programs can increase students’ academic achievement and postsecondary outcomes.

Future research may take several directions. First, it may prove worthwhile to examine the combined influence of the 16 in-school predictors identified by Test et al. (2009), along with the risk-resilience factors we examined in this study on college persistence of other disability types using either the National Longitudinal Transition Study–2 (NLTS-2) or ELS:2002 database. A second avenue for research may be to incorporate aspects of Tinto’s (1987, 1992) theory of students’ departure/persistence, developed for students without disabilities, to students with specific learning disabilities or emotional/behavioral disorders. To date, a specific application of this theory to adolescents with disabilities is lacking. It is possible that all or part of Tinto’s work may transfer to adolescents with disabilities and help to explain why students with disabilities do or do not persist in postsecondary education environments. Third, the mediation effect of interactions between individuals’ attitudes and institutional experience, along with indirect influences of risk-resilience factors on college persistence via academic self-efficacy and goals for youths with disabilities, is worth pursuing.

Finally, we should not disregard the contribution of non-significant risk-resilience factors on college persistence. Indeed, risk-resilience variable coefficients in the disabled group model were higher than those for the non-disabled group model. We speculate that a relatively high number of variations might explain the lack of significance for most factors in the disabled group model. Future studies using relatively homogenous data such as NLTS-2 are necessary to determine whether other risk-resilience factors may contribute to college persistence.

Footnotes

Appendix

Definition and Measurement of Covariates and Outcomes From ELS:2002 Database

Variable	ELS:2002 variables	Variable labels
Outcome variable
Persistence in college	F2RTYPE; F2EDLEVL	F2 respondent type; Highest level of education attempted
Individual factors
Gender	F2SEX	Sex-composite;
Occupational aspirations	F1OCC30	F1 occupation at age 30-coded; the occupation the respondent expects/plans to have at age 30 was assigned to one of seventeen categories.
Educational expectations		F1 how far in school student thinks will get
GPA	F1RAGPH	GPA for all academic courses, honors weighted
Reading achievement	BYTXRIRR	Reading IRT estimated number right
Math achievement	F1TXM1IR	F1 math IRT estimated number right for F1 scores
Family factors
SES	F1SES1R	F1 socioeconomic status composite, v.1 (restricted)
Family composition	F1FCOMP	F1 Family composition
Parent discussion: college	F1S64H	How often discussed going to college with parents
Parents’ educational expectations	BYPARASP	How far in school parent wants 10th grader to go-composite
School and peer factors
Friends dropped out	F1S65A	How many friends dropped out of high school
Friends’ plan to go to 4-year college	F1S65D	How many friends plan to attend 4-year college/university
Community factors
Community service	F1S39C	How often performs community services
Control variables
Native language	F1STLANG	F1 whether English is student’s native language-composite
Race	F1RACE	F1 student’s race/ethnicity-composite

Note. F1 refers to data collected during the first follow-up. F2 refers to data collected during the second follow-up.

Acknowledgements

The authors thank anonymous reviewers for their constructive comments and suggestions on the manuscript.

Authors’ Note

The opinions expressed are those of the authors and do not represent views of the Institute or the U.S. Department of Education.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The research reported here was supported by the Institute of Education Sciences, U.S. Department of Education, through Grant R324A100232 to the University of Georgia.

References

Barkley

R. A.

(2006). Attention-deficit hyperactivity disorder: A handbook for diagnosis and treatment (3rd ed.). New York, NY: Guilford.

Berger

J. B.

Lyon

S. C.

(2005). Past to present: A historical look at retention. In Seidman

(Ed.), College student retention: Formula for stuent success (pp. 1-30). Westport, CT: Praeger.

Blackorby

Wagner

(1996). Longitudinal postschool outcomes of youth with disabilities: Findings from the National Longitudinal Transition Study. Exceptional Children, 62, 399–413.

Bozick

Lyttle

Siegel

P. H.

Ingels

S. J.

Rogers

J. E.

Lauff

Planty

(2006). Education Longitudinal Study of 2002: First follow-up transcript component data file documentation (NCES 2006–338). Washington, DC: U.S. Department of Education, National Center for Education Statistics.

Brown

S. D.

Tramayne

Hoxha

Telander

Fan

Lent

R. W.

(2008). Social cognitive predictors of college students’ academic performance and persistence: A meta-analytic path analysis. Journal of Vocational Behavior, 72, 298–308. doi:10.1016/j.jvb.2007.09.003

Chen

St. John

E. P.

(2011). State financial policies and college student persistence: A national study. The Journal of Higher Education, 82, 629–660. doi:10.1353/jhe.2011.0032

Cosden

M. A.

Brown

Elliott

(2002). Development of self-understanding and self-esteem in children and adults with learning disabilities. In Wong

B. Y. L.

Mavis

M. L.

(Eds.), The social dimensions of learning disabilities: Essays in honor of Tanis Bryan (pp. 33–51). Mahwah, NJ: Erlbaum.

Cosden

M. A.

McNamara

(1997). Self-concept and perceived social support among college students with and without learning disabilities. Learning Disability Quarterly, 20, 2–12. doi:10.2307/1511087

DaDeppo

L. M. W.

(2009). Integration factors related to the academic success and intent to persist of college students with learning disabilities. Learning Disabilities Research & Practice, 24, 122–131. doi:10.1111/j.1540-5826.2009.00286.x

10.

Dietz

Montague

(2006). Attention deficit hyperactivity disorder comorbid with emotional and behavioral disorders and learning disabilities in adolescents. Exceptionality, 14, 19–33. doi:10.1207/s15327035ex1401_3

11.

Fabian

E. S.

Liesener

J. J.

(2005). Promoting the career potential of youth with disabilities. In Brown

S. D.

Lent

R. W.

(Eds.), Career development and counseling: Putting theory and research to work (pp. 551–572). Hoboken, NJ: Wiley.

12.

Getzel

E. E.

(2008). Addressing the persistence and retention of students with disabilities in higher education: Incorporating key strategies and supports on campus. Exceptionality, 16, 207–219. doi:10.1080/0936283080241

13.

Goldberg

R. J.

Higgins

E. L.

Raskind

M. H.

Herman

K. L.

(2003). Predictors of success in individuals with learning disabilities: A qualitative analysis of a 20-year longitudinal study. Learning Disabilities Research & Practice, 18, 222–236. doi:10.1111/1540-5826.00077

14.

Gregg

(2009). Adolescents and adults with learning disabilities and ADHD: Assessment and accommodation. New York, NY: Guilford.

15.

Hallahan

D. P.

Kauffman

J. M.

Pullen

P. C.

(2012). Exceptional learners: Introduction to special education (12th ed.). Upper Saddle River, NJ: Merrill.

16.

Heath

Roberts

Toste

J. R.

(2011). Perceptions of academic performance: Positive illusions in adolescents with and without learning disabilities. Journal of Learning Disabilities, 46, 402–412. doi:10.1177/0022219411428807

17.

Hodapp

R. M.

Krasner

D. V.

(1994–1995). Reflections on “Using large, national data bases in special education research.” Exceptionality, 5, 103–108. doi:10.1207/s15327035ex0502_4

18.

Ingels

S. J.

Pratt

D. J.

Wilson

Burns

L. J.

Currivan

Rogers

J. E.

Hubbard-Bednasz

(2007). Education Longitudinal Study of 2002: Base-year to second follow-up data file documentation. (NCES 2008-347). Washington, DC: U.S. Department of Education, National Center for Education Statistics.

19.

Ingels

S. J.

Scott

L. A.

(1993). Exclusion of students with barriers to participation in NELS:88—Baseline excluded students two and four years later. Paper presented at the Annual Meeting of the American Educational Research Association, Atlanta, GA. Retrieved from ERIC database. (ED360371)

20.

Kaplan

B. J.

Dewey

D. M.

Crawford

S. G.

Wilson

B. N.

(2001). The term comorbidity is of questionable value in reference to developmental disorders: Data and theory. Journal of Learning Disabilities, 34, 555–565. doi:10.1177/002221940103400608

21.

Kortering

L. J.

Braziel

P. M.

McClannon

T. W.

(2010). Career ambitions: A comparison of youth with and without SLD. Remedial and Special Education, 31, 230–240. doi:10.1177/0741932508324404

22.

Lent

R. W.

Brown

S. D.

Hackett

(1994). Toward a unifying cognitive theory of career and academic interest, choice, and performance [Monograph]. Journal of Vocational Behavior, 45, 79–122. doi:10.1006/jvbe.1994.1027

23.

Lindstrom

Doren

Miesch

(2011). Waging a living: Career development and long-term employment outcomes for young adults with disabilities. Exceptional Children, 77, 423–434.

24.

Lockard

C. B.

Wolf

(2012). Occupational employment projections to 2020. Monthly Labor Review, 135(1), 84–108.

25.

Madaus

J. W.

(2006). Employment outcomes of university graduates with learning disabilities. Learning Disability Quarterly, 29, 19–31. doi:10.2307/30035529

26.

Madaus

J. W.

Gerber

P. J.

Price

L. A.

(2008). Adults with LD in the workforce: Lessons for secondary transition programs. Learning Disabilities Research & Practice, 23, 148–153.doi:10.1111/j.1540-5826.2008.00272

27.

Mamiseishvili

Koch

L. C.

(2011). First-to-second-year persistence of students with disabilities in postsecondary institutions in the United States. Rehabilitation Counseling Bulletin, 54, 93–105. doi:10.1177/0034355210382580

28.

May

A. L.

Stone

C. A.

(2010). Stereotypes of individuals with learning disabilities: Views of college students with and without learning disabilities. Journal of Learning Disabilities, 43, 483–499. doi:10.1177/0022219409355483

29.

McDonnall

M. C.

Cavenaugh

B. S.

Giesen

J. M.

(2012). The relationship between parental involvement and mathematics achievement for students with visual impairments. The Journal of Special Education, 45, 204–215. doi:10.1177/0022466910365169

30.

Morrison

G. M.

Cosden

M. A.

(1997). Risk, resilience, and adjustment of individuals with learning disabilities. Learning Disability Quarterly, 20, 43–60. doi:10.2307/1511092

31.

Mpofu

Harley

D. A.

(2006). Racial and disability identity: Implications for the career counseling of African Americans with disabilities. Rehabilitation Counseling Bulletin, 50, 14–23. doi:10.1177/00343552060500010301

32.

Murray

(2003). Risk factors, protective factors, vulnerability, and resilience: A framework for understanding and supporting the adult transitions of youth with high-incidence disabilities. Remedial and Special Education, 24, 16–26. doi:10.1177/074193250302400102

33.

Murray

Goldstein

D. E.

Nourse

Edgar

(2000). The postsecondary school attendance and completion rates of high school graduates with learning disabilities. Learning Disability Research & Practice, 15, 119–127. doi:10.1207/SLDRP1503_1

34.

Newman

Wagner

Cameto

Knokey

(2009). The post-high school outcomes of youth with disabilities up to 4 years after high school: A report from the National Longitudinal Transition Study–2 (NLTS2). Menlo Park, CA: SRI International.

35.

Reiff

H. B.

Gerber

P. J.

Ginsberg

(1997). Exceeding expectations: Successful adults with learning disabilities. Austin, TX: Pro-Ed.

36.

Reschly

A. L.

Christenson

S. L.

(2006). Prediction of dropout among students with mild disabilities. Remedial and Special Education, 27, 276–292. doi:10.1177/07419325060270050301

37.

Rojewski

J. W.

Lee

I. H.

Gregg

(2013). Intermediate work outcomes for adolescents with high-incidence disabilities. Career Development and Transition for Exceptional Individuals. Advance online publication. doi:10.1177/2165143412473352

38.

Sabornie

E. J.

Cullinan

Osborne

S. S.

Brock

L. B.

(2005). Intellectual, academic, and behavioral functioning of students with high-incidence disabilities: A cross-categorical meta-analysis. Exceptional Children, 72, 47–63.

39.

Sabornie

E. J.

Evans

Cullinan

(2006). Comparing characteristics of high-incidence disability groups. Remedial and Special Education, 27, 95–104. doi:10.1177/07419325060270020701

40.

Scott

J. G.

(2000). Social network analysis: A handbook (2nd ed.). Thousand Oaks, CA: SAGE.

41.

Seo

Abbott

R. D.

Hawkins

J. D.

(2008). Outcome status of students with learning disabilities at ages 21 and 24. Journal of Learning Disabilities, 41, 300–314. doi:10.1177/0022219407311308

42.

Snyder

T. D.

Dillow

S. A.

(2012). Digest of education statistics 2011 (NCES 2012-001). Washington, DC: U.S. Department of Education, National Center for Education Statistics.

43.

Test

D. W.

Mazzotti

V. L.

Mustian

A. L.

Fowler

C. H.

Kortering

Kohler

(2009). Evidence-based secondary transition predictors for improving postschool outcomes for students with disabilities. Career Development and Transition for Exceptional Individuals, 32, 160–181. doi:10.1177/0885728809346960

44.

Tinto

(1987). Leaving college: Rethinking the causes and cures of student attrition. Chicago, IL: University of Chicago Press.

45.

Tinto

(1992). Student attrition and retention. In Clark

B. R.

Neave

G. R.

(Eds.), The encyclopedia of higher education (pp. 1697–1709). Oxford, UK: Pergamon.

46.

Wagner

Newman

Cameto

Garza

Levine

(2005). After high school: A first look at the postschool experiences of youth with disabilities. A report from the National Longitudinal Transition Study–2. Menlo Park, CA: SRI International. Retrieved from ERIC database. (ED494935)

47.

Wessel

R. D.

Jones

J. A.

Markle

Westfall

(2009). Retention and graduation of students with disabilities: Facilitating student success. Journal of Postsecondary Education and Disability, 21, 116–125.

48.

Wong

B. Y. L.

(2003). General and specific issues for researchers’ consideration in applying the risk and resilience framework to the social domain of learning disabilities. Learning Disabilities Research & Practice, 18, 68–76. doi:10.2307/1511092