The effect of learning disabilities,attention deficit hyperactivity disorder,and psychiatric disabilities on three-year persistence outcomes at four-year higher education institutions

Abstract

BACKGROUND:

Increasing numbers of children and youths are being diagnosed with learning disabilities, attention deficit hyperactivity disorder, and psychiatric disabilities. These youths are also enrolling in U.S. colleges and universities at increasing rates. In fact, in combination, they represent the largest group of students with disabilities in higher education.

OBJECTIVE:

The purpose of this investigation was to (a) summarize the demographic characteristics, in-college experiences, and persistence outcomes of students with and without learning disabilities, attention deficit hyperactivity disorder, and psychiatric disabilities and (b) examine the effect of having one of these disabilities on students’ three-year persistence outcomes at four-year higher education institutions.

METHODS:

Secondary data analyses were conducted for a sample of 7,750 students who enrolled at four-year institutions utilizing data from the Beginning Postsecondary Students Longitudinal Study (BPS:04/06). Descriptive analyses were used to summarize demographic characteristics, in-college experiences, and persistence outcomes of students with and without learning disabilities, attention deficit hyper activity disorder, and psychiatric disabilities. Univariate and multinomial logistic regression analyses were used to examine the effect of having one of these disabilities on students’ three-year persistence outcomes at four-year higher education institutions.

RESULTS:

We discovered that students with these disabilities in our sample (a) did not have the background characteristics typically associated with non-persistence and (b) reported being more academically and socially integrated into their institutions than participants without disabilities. However, they still withdrew from college in higher rates both by the end of the first and second years of college. The results from the multinomial logistic regression analysis revealed that when holding students’ demographic and in-college predictors constant, having one of these disabilities still increased the odds of non-persistence. Hierarchical multinomial logistic regression analysis further confirmed that the disability status significantly contributed to the likelihood of non-persistence over and above the combination of both the background characteristics and the in-college experiences factors.

CONCLUSION:

These findings have several important vocational rehabilitation practice and research implications for better facilitating the academic persistence of these students.

Keywords

Persistence postsecondary ADHD LD psychiatric disabilities academic integration social integration

1 Introduction

The purpose of this article is to present the methods, results, and implications of an investigation completed by the authors that (a) summarized the demographic characteristics, in-college experiences, and persistence outcomes of students with and without learning disabilities, attention deficit hyperactivity disorder, and psychiatric disabilities and (b) examined the effect of having one of these disabilities on students’ three-year persistence outcomes at four-year higher education institutions. This group of three disabilities combined represents the largest and fastest growing number of disabilities found in postsecondary college students (Koch, Mamiseishvili, & Wilkins, 2016).

In 2013, the United States Department of Education stated that 64% of students with disabilities enrolled in two year and four year postsecondary institutions had hidden disabilities such as LD (31% ), ADD/ADHD (18% ), and/or a psychiatric disability (15% ; as cited in Koch, Mamiseishvili, & Wilkins, 2016). A report released by the National Center for Learning Disabilities in 2014 indicated that 67% of young adults with LD reported being enrolled in some type of postsecondary education within eight years of leaving high school, which is the same rate as the general population. According to the same report, young adults with LD attended two-year or community colleges at more than double the rate of the general population, and students with LD attended four year institutions at half the rate of the general population. The National Alliance on Mental Illness (NAMI) reported in 2016 that one in five students enrolled in United States colleges and universities has a mental health condition (as cited in Koch, Mamiseishvili, & Wilkins, 2016). Between 2000 and 2010, the proportion of undergraduate students with LD or ADD/ADHD nearly doubled, and represented more than two percent of the total undergraduate population in the United States (Vickers, 2010).

Students with disabilities that are hidden, such as those described above, face certain challenges in college or university settings such as being unaware of available resources, fear of being stigmatized by faculty and peers, and lack of awareness of their conditions by faculty and staff. Other challenges include disability symptoms interfering with functioning, disruptive medication side effects, failure to use academic accommodations, and inadequate support from disability service offices (Koch et al., 2016). It is important for all students, but especially students with LD, ADHD, and psychiatric disabilities to experience both academic and social integration in order to persist to graduation (Koch, Mamiseishvili, & Higgins, 2014; Koch et al., 2016). Academic integration can be achieved through meetings with an academic advisor and utilizing services available through the college or university disability services office. Social integration can be achieved through involvement in clubs, sporting events, and fine arts activities. However, these students have been found to be less socially and academically integrated and less likely to persist to degree completion in comparison to their peers without disabilities.

Because LD, ADHD, and psychiatric disabilities have emerged as the most common disabilities in American college students, it is critical to examine and compare students with these disabilities to students without disabilities in terms of their persistence rates, background characteristics, and social and academic integration. Doing so will increase our understanding of areas in which assistance for these students is needed to facilitate persistence especially past (a) the first year of college during which students are most likely to drop out, (b) the critical second year when students typically declare their majors and apply to degree programs within their majors or alternative programs if not accepted into their first program of choice, and (c) the third year when they begin their studies in their major.

2 Purpose and research questions

The study examined the effect of having a learning disability, ADD (note: the data base we used was developed before the DSM V was published and replaced the term ADD with ADHD), or a psychiatric disability on students’ three-year persistence outcomes at four-year higher education institutions. Specifically, the following questions guided the investigation:

What differences exist in the background characteristics, in-college experiences, and persistence outcomes among students with LD, ADD, or a psychiatric disability and students without a disability at four-year institutions?

Does having LD, ADD, or a psychiatric disability affect students’ three-year persistence outcomes at four-year institutions when controlling for their background characteristics and in-college experiences?

Does having LD, ADD, or a psychiatric disability affect students’ three-year persistence outcomes at four-year institutions over and above their background characteristics and in-college experiences?

3 Method

3.1 Data source and sample

This study used the Beginning Postsecondary Students Longitudinal Study (BPS:04/06) data set that provided a nationally representative sample of students, including transfers, persisters, stopouts/dropouts, and vocational completers (Cominole et al., 2007). From this dataset, we selected 7,750 students who first enrolled at four-year institutions in fall 2003. Of this sample, 7,420 reported no disability and 330 students had ADD, LD, or a psychiatric disability. Specifically, students with disabilities in the sample included individuals who reported having (1) a specific learning disability (14.4% ), (2) attention deficit disorder (42.8% ), (3) an emotional or psychiatric condition (14.2% ), or (4) depression (28.6% ). Students who reported any other disability were excluded from this study to allow comparison amnong students without a disability and students with ADD, LD, and psychiatric disabilities.

3.2 Measures

Available longitudinal data from 2003 to 2006 were examined to identify appropriate indicators of students’ background characteristics, in-college experiences, disability status, and persistence outcomes.

3.2.1 Persistence outcomes

The persistence variable indicated students’ three-year persistence outcome at four-year institutions. Specifically, students who left by the end of their first year (2003–2004 academic year) were classified as first-year non-persisters and coded as 1. Students who left by the end of their second year (2004–2005 academic year) were classified as second-year non-persisters and coded as 2. Students who transferred to a 2-year institution were also classified as non-persisters for the purposes of this study. Finally, students who remained continuously enrolled at a four -year institution and persisted through the third-year of college (2005–2006 academic year) were classified as persisters and coded as 0 (reference group).

3.2.2 Disability status

Students reported having either a learning disability/ADD or a psychiatric disability were coded as 1. In contrast, students without a disability were coded as 0 and treated as the reference group.

3.2.3 Background characteristics

Several relevant demographic characteristics were included in the study: gender, race/ethnicity, family income, and first-generation college student status. The variable of gender was coded as a dichotomous variable (1 = male, 0 = female). Race/ethnicity was also coded as a dichotomous variable with two categories: 0 = white and 1 = students of color. The family income was divided into two categories: 1 = low-income family and 0 = not low-income family. Low-income family was defined as individuals who came from families with an income of $25,000 or lower in 2003. Finally, individuals without a parent with a bachelor’s degree were classified as first-generation college students. The first-generation student status variable was coded as 1 = first-generation college student and 0 = not first-generation college student. The definitions of low-income and first generation in the BPS data set were based on the eligibility criteria from the federal TRIO programs in 2003–2004.

3.2.4 In-college experiences

The study included four variables that represented participants’ in-college characteristics including living environment, attendance intensity, and social and academic integration. Living environment was classified as off campus (coded 2), off campus but living with parents (coded 1), or on campus (coded 0). The variable of attendance intensity represented students’ enrollment status during the 2003–2004 academic year: 1 = part-time/mixed and 0 = full-time.

The composite variable of academic integration indicated how often students (1) participated in study groups, (2) had social contact with faculty, (3) met with an academic advisor, or (4) talked with faculty about academic matters outside of class in their first year of college. Each of these four academic integration variables asked the respondents to report the frequency of their participation on a 3-point scale: 0 = never, 1 = sometimes, 2 = often. These responses were aggregated in a final composite academic integration score ranging from 0 to 8. Similarly, the social integration variable indicated how often students (1) participated in intramural or varsity sports, (2) attended fine arts activities, or participated in school clubs in their first-year in college. Each of these three variables were measured on a 3-point scale: 0 = never, 1 = sometimes, 2 = often. The final aggregate social integration score resulted in a composite variable ranging from 0 to 6.

3.3 Data analysis

Given that the student persistence outcome variable consisted of more than two categories (i.e., persisted through the third year, withdrew by the end the first year, and withdrew by the end the second year), we conducted a multinomial logistic regression analysis consisting of two steps (e.g., Menard, 2002). First, we conducted univariate multinomial logistic regression analyses to investigate whether or not each predictor was associated with persistence. Odds ratios were computed to aid in interpreting the relationship between each predictor and persistence. In order to select the potential indicators in the multivariate analysis, we adjusted the alpha level to 0.25 because the traditional alpha level (i.e., α= 0.05) is too conservative and often fails to identify variables known to be important at this stage (Bendel & Afifi, 1977; Mickey & Greenland, 1989). Second, we conducted the multivariate logistic regression analysis (i.e., including two or more predictors simultaneously in a single model) to examine the effects of each predictor while controlling for other variables in the model. Finally, we conducted a hierarchical multinomial logistic regression analysis to investigate whether disability status contributed to the prediction of non-persistence over and above background characteristics and in-college experiences. The SPSS 23.0 and SAS 9.4 were used to perform the analyses.

4 Results

4.1 Descriptive review of the data

Tables 1 and 2 summarize frequencies and percentages (i.e., for categorical variables) and means and standard deviations (i.e., for continuous variables) separately for students with and without a disability. Descriptive analysis of the data showed that 83.1% of students with a disability were white compared to 70% of whites in the students without a disability sample. Additionally, there were fewer first-generation students among the students with disabilities and less who also came from low-income backgrounds compared to the students without a disability (32.2% and 13.9% versus 42.7% and 16.9%, respectively). Students with a disability in this study were also more integrated both academically (M = 3.77) and socially (M = 2.07) compared to their peers without a disability (M = 3.57 and M = 1.96, respectively). However, despite the fact that students with a disability were more likely to be white, not first-generation, not low-income, and more socially and academically integrated, they still withdrew in higher numbers compared to students without a disability, especially by the end of their first year. For example, 17.2% of students with a disability left by the end of the first year compared to only 11.8% of students without a disability.

Table 1
Descriptive statistics for the categorical variables

Variables Students with Disability (% ) (n = 330) Students without Disability (% ) (n = 7,420)

Persistence

Persisted 71.4 79.0

Withdrew within first year 17.2 11.8

Withdrew within second year 11.5 9.2

Background Characteristics

Gender

Male 41.0 43.6

Female 59.0 56.4

Race/Ethnicity

Students of color 16.9 30

White 83.1 70

Family income

Low-income 13.9 16.9

Not low-income 86.1 83.1

First-generation

First-generation 32.2 42.7

Not first-generation 67.8 57.3

In-College Experiences

Attendance intensity

Full-time 87.7 89.2

Part-time/Mixed 12.3 10.8

Living environment

On campus 69.6 68.4

Off campus 13.3 13.5

Living with parents 17.2 18.1

Variables	Students with Disability (% ) (n = 330)	Students without Disability (% ) (n = 7,420)
Persistence
Persisted	71.4	79.0
Withdrew within first year	17.2	11.8
Withdrew within second year	11.5	9.2
Background Characteristics
Gender
Male	41.0	43.6
Female	59.0	56.4
Race/Ethnicity
Students of color	16.9	30
White	83.1	70
Family income
Low-income	13.9	16.9
Not low-income	86.1	83.1
First-generation
First-generation	32.2	42.7
Not first-generation	67.8	57.3
In-College Experiences
Attendance intensity
Full-time	87.7	89.2
Part-time/Mixed	12.3	10.8
Living environment
On campus	69.6	68.4
Off campus	13.3	13.5
Living with parents	17.2	18.1

Table 2

Descriptive statistics for the continues variables

Variables	Students with Disability (n = 330) M (SD)	Students without Disability (n = 7,420) M (SD)
Academic integration	3.77 (1.67)	3.57 (1.67)
Social integration	2.07 (1.64)	1.96 (1.60)

4.2 Univariate associations with persistence

As noted earlier, we first conducted the univariate logistic regression analyses to estimate the effect of each individual predictor on the likelihood of persistence. As illustrated in Table 3, univariate results indicated that when using persisters as the reference group, being male, low-income, first-generation, a student of color, living with parents or off campus, and enrolling part-time or mixed increased the likelihood of non-persistence both in the first and second years. On the other hand, academic and social integration decreased the likelihood of non-persistence. Odds ratios reported in Table 3 aid in interpreting the relationships between each predictor and persistence. An odds ratio (e^b) can be described as the probability of not persisting in either the first year or second year divided by the probability of persisting, conditioned on a one-unit increase in the predictor. An odds ratio of 1 indicates that a one-unit increase in the predictor does not yield any change in the likelihood of persisting. Odds ratios above 1 indicate a positive relationship between the predictor and non-persistence, while odds ratios below 1 indicate a negative relationship. For example, the odds ratio for the disability status variable indicates that students with ADD, LD, or psychiatric disabilities are 1.6 times more likely to withdraw within the first year of college at a four-year institution than students without a disability. We caution that the odds ratios must not be compared quantitatively across predictors because the scales of the predictors vary. Additionally, it is important to note that the odds ratios presented on the left side of Table 3 are univariate effects and do not control for other predictors in the model.

Table 3
Summary of multinomial logistic regression results for both univariate and multivariate models

Predictors Univariate models Multivariate model

Non-persisters in 1st year vs. Persisters Non-persisters in 2nd year vs. Persisters Non-persisters in 1st year vs. Persisters Non-persisters in 2nd year vs. Persisters

OR 95% CI OR 95% CI OR 95% CI OR 95% CI

Disability (ref: without a disability) 1.60^** [1.19, 2.16] 1.37^* [0.97, 1.95] 1.85^* [1.35, 2.51] 1.56^ [1.09, 2.23]

Background characteristics

Gender (ref: Female) 1.20^** [1.04, 1.37] 1.16^* [1.00, 1.36] 1.26^ [1.10, 1.46] 1.21^ [1.03, 1.42]

Race (ref: white) 1.32^* [1.14, 1.53] 1.61^* [1.37, 1.89] 1.03 [0.88, 1.21] 1.31^* [1.10, 1.56]

Family Income (ref: Not low-Income) 1.85^* [1.57, 2.19] 1.88^* [1.56, 2.26] 1.29^ [1.08, 1.56] 1.35^ [1.11, 1.66]

First-Generation (ref: No) 2.12^* [1.85, 2.44] 1.77^* [1.51, 2.07] 1.63^* [1.40, 1.90] 1.41^*** [1.20, 1.67]

In-College Experiences

Living environment (ref: On campus)

Living with parents 2.33^* [1.96, 2.76] 1.93^* [1.60, 2.34] 1.45^* [1.20, 1.75] 1.37^ [1.11, 1.69]

Off campus 3.09^* [2.58, 3.70] 2.35^* [1.92, 2.89] 1.74^* [1.42, 2.13] 1.57^* [1.25, 1.98]

Attendance intensity (ref: Full time) 2.72^* [2.27, 3.26] 2.14^* [1.73, 2.65] 1.88^* [1.55, 2.29] 1.68^* [1.34, 2.11]

Academic Integration 0.79^* [0.75, 0.82] 0.89^* [0.85, 0.93] 0.89^* [0.85, 0.93] 0.96 [0.91, 1.01]

Social Integration 0.74^* [0.71, 0.78] 0.84^* [0.80, 0.88] 0.88^* [0.83,0.93] 0.94 [0.89, 1.00]

Predictors	Univariate models	Multivariate model
Disability (ref: without a disability)	1.60^**	[1.19, 2.16]	1.37^*	[0.97, 1.95]	1.85^***	[1.35, 2.51]	1.56^**	[1.09, 2.23]
Background characteristics
Gender (ref: Female)	1.20^**	[1.04, 1.37]	1.16^*	[1.00, 1.36]	1.26^**	[1.10, 1.46]	1.21^**	[1.03, 1.42]
Race (ref: white)	1.32^***	[1.14, 1.53]	1.61^***	[1.37, 1.89]	1.03	[0.88, 1.21]	1.31^***	[1.10, 1.56]
Family Income (ref: Not low-Income)	1.85^***	[1.57, 2.19]	1.88^***	[1.56, 2.26]	1.29^**	[1.08, 1.56]	1.35^**	[1.11, 1.66]
First-Generation (ref: No)	2.12^***	[1.85, 2.44]	1.77^***	[1.51, 2.07]	1.63^***	[1.40, 1.90]	1.41^***	[1.20, 1.67]
In-College Experiences
Living environment (ref: On campus)
Living with parents	2.33^***	[1.96, 2.76]	1.93^***	[1.60, 2.34]	1.45^***	[1.20, 1.75]	1.37^**	[1.11, 1.69]
Off campus	3.09^***	[2.58, 3.70]	2.35^***	[1.92, 2.89]	1.74^***	[1.42, 2.13]	1.57^***	[1.25, 1.98]
Attendance intensity (ref: Full time)	2.72^***	[2.27, 3.26]	2.14^***	[1.73, 2.65]	1.88^***	[1.55, 2.29]	1.68^***	[1.34, 2.11]
Academic Integration	0.79^***	[0.75, 0.82]	0.89^***	[0.85, 0.93]	0.89^***	[0.85, 0.93]	0.96	[0.91, 1.01]
Social Integration	0.74^***	[0.71, 0.78]	0.84^***	[0.80, 0.88]	0.88^***	[0.83,0.93]	0.94	[0.89, 1.00]

Note. N = 7,750. OR = odds ratio; 95% CI = 95% confidence intervals of OR. ^*p < 0.25. ^**p < 0.05. ^***p < 0.001.

4.3 Multivariate associations with persistence

After the univariate logistic regression analyses, we proceeded with the multinomial logistic regression model that included all of the potential predictors of persistence. Parameter estimates, standard errors, significance test results, and odds ratios for the multivariate model are displayed on the right side of Table 3. Holding all other predictors constant, students’ disability status remained a significant predictor. Specifically, the odds of withdrawing within the first year were 1.85 times greater for students with LD, ADD, or a psychiatric disability than their peers without a disability. Similarly, when controlling for all other variables in the model, the odds of withdrawing within second year were 1.56 times greater for students with these disabilities compared to students without a reported disability. One other interesting finding that emerged in the multivariate model was that the low academic and social integration variables increased the likelihood of non-persistence throughout the first year of college but did not have a significant effect on whether the students would persist to the third-year or withdraw by the end of the second-year.

4.4 Hierarchical multinomial logistic regression

To examine whether disability status contributed to persistence beyond both the background characteristics and the in-college experiences, we conducted a hierarchical multinomial logistic regression analysis and evaluated the progressive contributions of these three sets (i.e., background characteristics, in-college experiences, and disability status) to the prediction of persistence. The background characteristics included gender, race, family income, and the first-generation student status. The second set consisted of four indicators related to in-college experiences: housing environment, attendance intensity, and social and academic integration. Because the primary purpose of this study was to examine the impact of disability status on persistence, the final variable in the model was whether or not the student had one of the three disabilities we examined. Table 4 reports the results of the hierarchical multiple logistic regression analysis. The likelihood ratio χ² tests indicated a significant change for each block, implying that disability status did significantly contribute to the likelihood of persistence over and above the combination of both the background characteristics and the in-college experiences factors.

Table 4
Summary of hierarchical multinomial logistic regression results

Block Model total Likelihood ratio χ² test

– 2LL df Δ – 2LL Δ df

0. Intercept 10308.230 0

1. Background characteristics^a 10094.570 8 213.663^ 8

2. In-college experiences^b 9813.642 18 280.925^ 10

3. Disability status^c 9796.817 20 16.825^** 2

Block	Model total	Likelihood ratio χ² test
0. Intercept	10308.230	0
1. Background characteristics^a	10094.570	8	213.663^**	8
2. In-college experiences^b	9813.642	18	280.925^**	10
3. Disability status^c	9796.817	20	16.825^**	2

Note: – 2LL = – 2 Log Likelihood. ^aGender, race, family income, and first-generation student. ^bLiving environment, attendance intensity, social and academic integration. ^cWith or without learning or psychiatric disability. ^**p < 0.01.

5 Discussion

5.1 Limitations

The results of this investigation highlight research findings that have important implications to be taken into consideration by VR professionals in supporting students with LD, ADHD, and psychiatric disabilities to achieve their postsecondary goals. The results also highlight future research directions for better understanding the demographic characteristics, in-college experiences, and persistence rates of these students and interventions that can be implemented to improve their outcomes. However, before considering implications for VR practice and future research, several limitations of the research must be noted. The first limitation is that data collected in the BPS survey is over ten years old and may not be representative of the characteristics and postsecondary experiences of contemporary students. However, a new cohort was surveyed in 2012 and will be surveyed again in 2017 as part of the BPS:12/17 study (NCES, 2017) and will enable researchers to compare the status of more contemporary students to those who were surveyed in the BPS:04/09 study. Furthermore, in more current studies, researchers have established that students with these conditions still represent the largest group of students with disabilities on U.S. college and university campuses, continue to experience many barriers to postsecondary success, and still fail to persist in postsecondary education at the same rate as their academic peers (Boutin, 2008; DaDeppo, 2009; Getzel, 2008; NCES, 2011; Wessel, Jones, Markle, & Westfall, 2009). A second limitation was that data were all based on self-report, thus, the presence of a learning disability, ADHD, or a psychiatric disability was not determined by diagnoses from qualified clinicians. A third limitation is that the BPS survey only queried students about disability during their first year of enrollment at a postsecondary institution. This limitation is problematic because the onset of a psychiatric disability may not occur until after the first year of enrollment. Likewise, LD or ADHD, although typically diagnosed at a younger age, may not be diagnosed until after students experience academic struggles. Relatedly, a fourth limitation is that some participants may have chosen not to disclose their disability, and failure to disclose could result in an inaccurate estimation of the number of students with disabilities in the dataset. Indeed, other research has reported higher enrollment rates of students with these disabling conditions (NCES, 2013).

A fifth limitation of the dataset is that it does not include variables such as academic self-efficacy, functional limitations, and academic challenges that would provide researchers with the ability to examine the degree to which these variables affect social and academic integration as well as persistence. Additionally, ADD and LD were combined into one category as were all psychiatric disabilities. Furthermore, participants were only able to report one disability on the BPS survey, and these populations often have co-occurring disorders (e.g., substance use disorders, ADHD and depression) that may present greater barriers to persistence than their reported disabilities. Finally, because we only analyzed data from the first three years of the longitudinal survey, we cannot draw any conclusions about rates of persistence to degree completion. However, higher education researchers have documented that students are most likely to drop out of college during their first year, and those who persist beyond their first year have a strong likelihood of persisting to degree completion (Horn & Carroll, 1998). The same is true for those who persist beyond the second year and complete their third year of four- year college programs.

5.2 VR practice implications

Several findings of this study warrant attention in terms of their implications for VR practice. First, in comparison to students without disabilities, students with disabilities had fewer additional at-risk characteristics. Students with LD/ADD and psychiatric disabilities were more likely to have parents who attended college, and they also came from a higher socioeconomic status than their counterparts without disabilities. Being a first-generation college student and having a lower socioeconomic status are variables that have been demonstrated to put students at risk of non-persistence (Engle & Tinto, 2008; Horn & Carroll, 1998; Ishitani, 2003; Lohfink & Paulsen, 2005; McCarron & Inkelas, 2006; Tinto, 2004; Warburton, Bugarin, & Nunez, 2001). Students with ADD, LD, and psychiatric disabilities in the data set were also more socially and academically integrated than students without disabilities. One interpretation of this finding is that students with parents who attended college and who are from a higher socioeconomic status are more likely than first generation students and those with a lower socioeconomic status to successfully navigate postsecondary environments, thus increasing their likelihood of interacting with faculty members outside of the classroom, participating in study groups, and engaging in extracurricular activities and university-sponsored social events.

Despite the fact that participants in this investigation possessed demographic characteristics that have been demonstrated to facilitate persistence, and they were more likely than participants without disabilities to be academically and socially integrated (which are also facilitators of persistence), they were still more at risk of non-persistence than students without disabilities. In other words, the results showed that when these demographic and in-college variables were held constant, disability still emerged as a significant predictor of nonpersistence. Considering the functional limitations associated with these conditions (e.g., difficulty concentrating, being easily distracted, poor problem-solving, sleep disturbances, challenges with time management, difficulty meeting deadlines, poor attendance), many of these students’ elementary and secondary school and home environments were highly structured by parents and teachers to foster student success. However, postsecondary environments require that students provide their own structures; therefore, it is not surprising that these students would have difficulties managing the demands of college life and achieving their postsecondary goals (Belch, 2011; Boutin & Accordino, 2011; Hartley, 2013).

We are in agreement with Boutin and Accordino (2011) that students with mental health diagnoses (and those with ADHD and LD) should be encouraged by VR professionals to pursue postsecondary education as a means to increase their employability and to prepare for occupations that provide liveable wages and opportunities for advancement. However, our research findings and the results of other research studies suggest that these students may be (a) in need of supports above and beyond what postsecondary institutions typically provide or (b) failing to utilize already available academic supports (e.g., classroom accommodations, writing labs, library tutorials, study skills and time management workshops, counseling for stress management, crisis services, peer support groups, tutors). Other researchers have established that many of these students do not utilize academic support services because of the belief that they can succeed without these services or because they are unaware of the availability of these services on their college and university campuses (Rehfuss & Quillin, 2005). VR professionals have an obligation to familiarize themselves with the various services on college campuses attended by clients with these disabling conditions so they can educate them about and encourage their use of these services. In particular, VR professionals should encourage and assist these clients to proactively seek out these services rather than waiting until a crisis occurs (e.g., a failing grade, academic probation, academic dismissal, exacerbation of symptoms). In fact, proactive planning can be implemented prior to the transition to postsecondary settings and should include (a) preparing students in advance for the demands of postsecondary education and (b) developing an individualized list of strategies and resources that these students can seek out in advance of their enrollment. VR professionals should also develop relationships with college representatives at their local institutions such as disability service providers, counseling center personnel, and student affairs personnel to collaboratively coordinate, deliver, monitor, and evaluate the effectivensess of these services.

In the VR process, VR counselors and case managers are likely to have more contact with clients during the assessment and development phases of the Individualized Plan for Employment (IPE) planning process, and contact during the implementation phase of the IPE is likely to wane, with client follow-up only being required at three month intervals. However, results from this study and related research indicate that contacts with postsecondary students with LD, ADHD, and psychiatric disabilities should occur more frequently to closely monitor their progress and to provide early intervention before a crisis arises. In addition, VR professionals and postsecondary personnel should enhance efforts to provide additional resources and interventions for those students who may be in need of supports that are above and beyond what are typically provided. Examples of evidence-based and emerging interventions include supported education for students with psychiatric disabilities (Mowbry, Collins, & Bybee, 1999; Ringeisen et al., 2017), which could also be beneficial for students with LD and ADHD, and ADHD coaching (Prevatt, 2016; Prevatt & Yelland, 2013).

5.3 Implications for future research

Several implications for future research can be gleaned from this investigation. First, we recommend that this study be replicated with participants in the BPS:12/17 survey to provide an indication of the demographic, in-college, and persistence characteristics of more contemporary students and to determine if these have changed over time. Second, research is needed to query a nationally representative sample of non-persisters about their reasons for dropping out of colleges and universities and to determine factors that contributed to their non-persistence. Such research would enable researchers, VR professionals, and postsecondary institutions to implement and evaluate targeted interventions for remediating barriers and facilitating academic persistence to degree completion. Third, continuing research is suggested to investigate the effectiveness of evidence-based interventions as well as innovative and emerging strategies to respond to the service and support needs of contemporary postsecondary students with learning disabilities, ADHD, and psychiatric disabilities.

6 Conclusion

Results from this investigation of the effect of having a learning disability, ADHD, or a psychiatric disability on students’ three-year persistence outcomes at 4-year higher education institutions indicate that despite the fact that these students did not have many of the typical at-risk demographic characteristics (e.g., low-income, first-generation, student of color) and seemed to be more socially and academically integrated, they still had lower persistence rates than students who did not report having a disability. This study showed that having a disability contributed to non-persistence above and beyond the students’ demographic characteristics and in-college experiences. Even when demographic and in-college characteristics were held constant, the students with LD, ADHD, or a psychiatric disability were still more likely to leave college both at the end of the first and second years compared with students who did not have a disability. The findings of this study signify the importance of support services and interventions targeted for students with these disabilities to ensure their college success.

Because a postsecondary education can lead to better employment prospects, it behooves VR professionals to encourage these students to attend colleges and universities. However, VR professionals must be astute at assessing these students’ academic challenges and needs and ensuring they have adequate supports to achieve their academic goals. Likewise, as four-year colleges and universities in the U.S. continue to invest in initiatives to retain students, they must ensure that these initiatives are also directed at the unique needs of students with learning disabilities, ADHD, and psychiatric disabilities who are expected to continue to represent a substantial proportion of the student populations at these institutions.

Conflict of interest

None to report.

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