Self-determination,social skills,job search,and transportation: Is there a relationship with employment of young adults with autism?

Abstract

BACKGROUND: Job seekers’ personal traits like self-determination and social skills are often highlighted as key for achieving employment. However, a growing literature suggests that external factors should not be overlooked either, including job search strategies and transportation.

OBJECTIVE: The purpose of this study was to increase knowledge about the relationships between these factors and employment of young adults with autism, within the same analytical context provided by the National Longitudinal Transition Study-2 (NLTS2).

METHODS: Chi-Square and One-Way-Anova were used to investigate the relationships between the four factors and employment of young adults with autism.

RESULTS: Contrary to the prevailing literature, our findings did not confirm a relationship between employment and most of these factors. Only a subscale of self-determination—psychological empowerment—and transportation independence were associated with employment outcomes of young adults with autism.

CONCLUSIONS: Based on the findings, we recommend that transportation independence be a component of educational programs for students with autism in preparation for their professional careers. Moreover, we recommend improving instruments and data collection practices to accurately gauge self-determination, social skills, and job search practices before concluding that these factors are not associated with employment outcomes.

Keywords

Employment postsecondary outcomes autism social skills self-determination job search transportation

1 Introduction

Employment has been long recognized as one of the most important social determinants of health and well-being in adult life (Bartley, Ferrie, & Montgomery, 2006; Garcia-Villamisar & Hughes, 2007). However, research shows that people with disabilities—including young adults with autism—often report poor employment outcomes (Barnhill, 2007; Cederlund, Hagberg, Billstedt, Gillberg, & Gillberg, 2008; Sanford et al., 2011; Taylor & Seltzer, 2011). For example, Newman et al. (2011) found that only 60% of youth with any disability and 37.2% of youth with autism were employed eight years after high school. Another study documented that of the youth with autism who were working, abut 50% reported earning less than minimum wage, and 42% worked in settings where most of their coworkers had a disability, rather than working in the general labor market (Migliore & Zalewska, 2012). A study by Cimera and Cowan (2009) analyzing vocational rehabilitation (VR) systems’ outcomes found that while individuals with autism were hired at a higher rate than people with other disabilities, they worked significantly fewer hours and earned lower wages.

These bleak employment outcomes call for improving our understanding about factors that can increase the employment outcomes of youth with autism (Cimera & Cowan, 2009; Dew & Alan, 2007; Graetz, 2010; Mauch, Pfefferle, Booker, Pustell, & Levin, 2011). Among key factors promoting employment and quality of life, often the literature highlights job seekers’ personal traits like self-determination and social skills (Matson, Dempsey, & LoVullo, 2009; Nota, Ferrari, Soresi, & Wehmeyer, 2007). However, a growing literature suggests that external factors should not be overlooked either, including job search strategies (Liu, Huang, & Wang, 2014; Butterworth, Migliore, Nord, & Gelb, 2012; Manroop & Richardson, 2015) and transportation (Conley, 2003; Migliore et al., 2008).

1.1 Self-determination

Self-determination is defined as the ability to take initiative over one’s own life, and the capacity to make decisions while not allowing outside influences to interfere (Wehmeyer, 2005). Research to date suggests that self-determination is likely an important predictor of positive outcomes, including better academic results, employment, and quality of life (Konrad, Fowler, Walker, Test, & Wood, 2007; LaChapelle et al., 2005; Martorell, Gutierrez-Recacha, Pereda, & Ayuso-Mateos, 2008; Shogren, Wehmeyer, Palmer, Rifenbark, & Little, in press; Test et al., 2009).

However, despite a large body of literature on self-determination, researchers recommend that research about self-determination and strategies for improving it be expanded (Shogren, Wehmeyer, Palmer, Rifenbark, & Little, 2015; Wehmeyer, Shogren, Zager, Smith, & Simpson, 2010). Research on autism and self-determination is particularly important since individuals with autism often struggle with social interactions, which are usually the context in which self-determination is exercised and demonstrated (Lee & Carter, 2012).

Studies suggest that youth with autism may have a reduced opportunity to practice self-determination skills. For instance, research has shown that students with autism were less likely than students with other disabilities to take a leadership role or to actively participate in their transition planning meetings (Cameto et al., 2004; Shogren & Plotner, 2012). Students with autism were also more likely to be absent during transition planning meetings (Wagner, Newman, Cameto, Javitz, & Valdes, 2012). At the same time, significant correlations were established between self-determination components and proactive participation in transition planning. Based on the National Longitudinal Transition Study-2 (NLTS2) analysis, students with high scores on personal autonomy (a subscale of the self-determination scale) were likely to take more of a leadership role during their transition planning process (U.S. Office of Special Education Programs [OSEP], 2005).

1.2 Social skills

Social skills are often listed among the most important personal traits leading to successful transition and employment outcomes (Carter, Austin, & Trainor, 2012; Henricsson & Rydell, 2006; Milsom & Glanville, 2010; World Health Organization [WHO], 2011, as cited by Liptak, Kennedy, & Dosa, 2011). However, few studies specifically explored the effect of social skills on employment of youth with autism. One of the exceptions is a NLTS2 analysis done by Roux et al. (2013), which revealed that better conversational skills of youth with autism predicted their employment.

Social skills may be particularly challenging for youth with autism due to greater communication challenges associated with this condition (American Psychiatric Association, 2013). Studies consistently report that individuals with autism are more often socially isolated and may experience challenges with social behaviors (Billstedt, Gillberg, & Gillberg, 2005; Cederlund, Hagberg, Billstedt, Gillberg, & Gillberg, 2008; Liptak et al., 2011). Findings from the NLTS2 demonstrated that adolescents with autism engage in social participation less often than their peers with other disabilities, e.g., seeing friends outside of school, talking on the phone, and getting invited to social activities (Shattuck, Orsmond, Wagner, & Cooper, 2011). Communication challenges appear to be a key factor, particularly for people with autism pursuing employment (Dew & Alan, 2007; Hurlbutt & Chalmers, 2004; Muller et al., 2003).

1.3 Job search

Despite intuitively being a key factor for successful employment, job search strategies have not been extensively researched (Manroop & Richardson, 2015; Saks, 2005). A 2011 NLTS2 report points out gaps in the job search process for youth with autism. For example, these youth were significantly less likely to find their current or most recent job on their own compared to almost all other disability groups (Newman et al., 2011). While literature has explored the importance of early work experience during high school and its impact on future employment outcomes (Carter, Austin, & Trainor, 2011; Chiang, Cheung, Hickson, Xiang, & Tsai, 2012), limited research exists on the impact of job search methods used when finding a job for young adults with disabilities, especially in context of autism. Job search activities can be either initiated by individuals and their families, or by service entities, such as schools. Examples of strategies undertaken by the individual include searching for and responding to job offers, approaching employers, and networking. Examples of job search supports received from a service entity include the school contacting employers or employment support agencies when youth are about to graduate from high school (Migliore, Cohen, Butterworth, & Winsor, 2010).

General research on job search suggests that some techniques may be more effective than others. Networking has been often cited as a particularly effective job search strategy (Carey, Potts, Bryen, & Shankar, 2004; Fesko & Temelini, 1997; Timmons, Hall, Bose, Wolfe, & Winsor, 2011; Van Hoye, van Hooft, & Lievens, 2009). At the same time, networking may be especially challenging for individuals with autism due to communication difficulties (Müller et al., 2003). Once a job opening is found, in some cases hire does not follow or employment does not hold simply because of the lack of transportation to and from a workplace.

1.4 Transportation

Without reliable transportation to and from a workplace, employment remains unattainable or unsustainable (Hernandez et al., 2007; Fuller, 2010; Trainor, Carter, Owens, & Swedeen, 2008). Even when transportation services exist, using these services can be challenging (Precin, Otto, Popalzai, & Samuel, 2012). For example, there are situations where disability-specific transportation services are available, but they are perceived as unreliable, inflexible, or not easy to use (Darrah, Maggil-Evans, & Galambos, 2010; Fuller, 2010).

Availability of transportation was a predictor of employment outcomes in a study by Carter et al. (2011). A study involving adults with developmental disabilities in facility-based programs, their family members, and staff in the programs documented that between 66% and 69% of participants across these three groups reported transportation as an important or very important factor influencing their choices in favor of facility-based programs (Migliore, Grossi, Mank, & Rogan, 2008). Also, a survey of service providers funded by the Maryland vocational rehabilitation program documented that transportation was the most important employment barrier, creating difficulties for 75% of the individuals seeking vocational support (Conley, 2003). Transportation, as well as community mobility in a broader sense, may be particularly crucial for youth with autism (Precin, Otto, Popalzai, & Samuel, 2012). For instance, based on the analysis of NLTS2 Wave 1, 34% of students with autism identified transportation assistance as a support need, a figure only exceeded by youth with multiple disabilities (Cameto, Levine, & Wagner, 2004).

The literature reviewed in this introduction emphasized relationships between employment outcomes and personal traits like self-determination and social skills, as well as external factors like job-search strategies and transportation. The purpose of this study was to examine these relationships with a focus on young adults with autism and using secondary data from the NLTS2. Although several other factors may influence employment, we focused on these four factors because in our experience they emerged as the most critical among the variables available within the NLTS2 dataset. Our goal was to contribute to the literature by providing an analysis of these relationships within the same analytical context, using NLTS2 data. This paper addresses the following research question: What is the relationship between the employment outcomes of young adults with autism and self-determination, social skills, job search practices, and transportation independence? Our hypothesis was that a greater presence of these factors was associated with greater employment outcomes of young adults with autism.

2 Methods

The research design of this study was secondary data analysis. We used data from the NLTS2, a dataset that includes about 10,000 variables measured through five waves of data collection between 2000 and 2010. These variables describe the characteristics of high school students and their households with a focus on school experiences, extracurricular activities, post-high school experiences, and transition outcomes in the domains of education, employment, leisure, and living situation. Students were 13–16 years old at the onset of the study and 22–25 years old by the end of the study. NLTS2 was funded by the National Center for Special Education Research at the Institute of Education Sciences, U.S. Department of Education, and carried out by SRIInternational.

2.1 Sampling and data collection

At the onset of the study in 2000, the NLTS2 researchers recruited a nationally representative sample of youth with any types of disabilities who – in December 2000 – were between the ages of 13 and 16 and were receiving special education services. The NLTS2 researchers sampled the participants in two phases. First, researchers selected a stratified random sample of 3,630 local education agencies and 70 state-supported special schools. A total of 500 local education agencies and 30 special schools agreed to participate and provided rosters of their students. Next, researchers stratified these rosters by disability category and other socio-economic characteristics, and then randomly sampled about 12,000 students. This sample size was deemed to yield adequate power for generalizing findings at each disability group level, taking into account the likelihood of attrition due to the longitudinal nature of the data collection (NLTS2, 2012).

Data were obtained from students, their parents, and school staff through questionnaires or computer-assisted telephone interviews at multiple points in time – or waves – between 2000 and 2010. For example, data were collected through four surveys of youth, five surveys of parents, two surveys of school staff, one assessment of youth, youth’s transcripts, and a survey about school characteristics. Response rates of the parents/youth survey – the survey with the most data – ranged from 82.1% in wave one to 48% in wave five.

2.2 Participants

In our study, we focused on students with autism as a primary disability as defined by local education agencies, and based on the Individuals with Disabilities Education Act (P.L. 105-17; Knoblauch & Sorenson, 1998). However, we included descriptive analyses about students with intellectual disability (ID) and students with other disabilities to provide some context. After attrition, the NLTS2 dataset in 2010 included about 700 students with autism, about 450 students with ID, and about 3,900 students with other disabilities. Because our interest was in employment outcomes, we included only data from youth who were out of high school and not in postsecondary education at the time of the wave 5 survey. As a result, the final sample for our data analyses included about 570 youth with autism, 400 youth with ID, and 3,140 students with other disabilities.

2.3 Variables

The dependent variable examined in this study was employment outcomes of young adults with autism. The independent variables were personal traits of self-determination and social skills, as well as the job search and transportation.

2.3.1 Employment outcomes

measured whether or not the young adults had ever worked for pay since leaving high school. Moreover, we reported some other descriptive outcomes including employment status at the time of the interview, earnings, work hours, fringe benefits, and job satisfaction. Data were obtained from wave 5, at the end of the NLTS2 study.

2.3.2 Self-determination

All self-determination items in the NLTS2 dataset were from the Arc’s self-determination scale, although the dataset did not include all items from the original scale (Wehmeyer, 2000). Self-determination was measured through a 26-item scale organized into four clusters: personal autonomy (ten items), autonomy in career planning (five items), self-realization (five items), and psychological empowerment (six items). The questions asked youth to report about the extent to which their behavior mirrored aspects of these four self-determination domains.

Responses were organized in a four-point Likert-type scale ranging from “not even when I have the chance” to “every time I have the chance.” Youth responded to these items during the direct assessment interview in wave 1 or wave 2, depending on their age at the time of the interview. Youth with higher support needs, however, did not participate in this direct assessment interview; therefore, their self-determination scores were notavailable.

2.3.3 Social skills

The variables related to social skills investigated 11 aspects of social interactions grouped into three main domains: assertion (four items: joins group activities, makes friends easily, is confident in social situations, and starts conversations), self-control (four items: ends disagreement, stays out of trouble, receives criticism well, and controls temper), and cooperation skills (three items: keeps working until finished, speaks in appropriate tone, and cooperates with family members). Parents addressed these questions in wave 1. Responses were organized in a three-point Likert-type scale including “never,” “sometimes,” or “always” to report youth’s engagement in the social skills listed. Most of the items were from the Social Skills Rating System, Parent Form, from Gresham and Elliott (1990).

2.3.4 Job search

The job search variables were from six question items from the youth/parent survey and four items from the school programs survey. The six questions from the youth/parent survey only were asked of youth who were unemployed at the time of the interview and were looking for jobs. The four questions from the school surveys were asked of youth who had been involved in transition planning. Examples of question topics included applying for jobs, schools contacting employment support programs, and schools contacting the vocational rehabilitation program (see Table 2).

2.3.5 Transportation

Finally, the transportation variable investigated five question items from the youth/parent survey and one item from the school programs surveys. Examples include job seekers’ holding driving licenses; riding with family members or disability-specialized services; and getting around independently (e.g., walk, bike, scooter, drive, or public transportation, Table 2).

2.4 Data analysis

In preparation for data analysis, we created a new dataset by drawing the needed variables from the various datasets including parent/youth dataset, teachers dataset, and school programs dataset, from all five waves. Next we recoded and created variables as needed to perform the data analyses.

Descriptive analysis was performed through computing frequencies of occurrences for the categorical variables (e.g., percentages of employed versus not employed), computing means and ranges for the continuous variables (e.g., average earnings in dollars), and comparing the findings across youth with autism, youth with ID, and youth with other disabilities. To test the relationship between predictors and employment outcomes, we ran Chi-Square analyses for categorical outcomes and one-way ANOVA for continuous outcomes.

All data analyses were conducted after applying the weights to correct for the clustered sampling design of the NLTS2. This sampling method purposely oversampled small population groups (for example, certain disability groups) that a random sampling otherwise could have been missed. Applying the weights during data analysis allows us to re-establish the correct representativeness of each population group, based on their actual “weight” within the general population. Moreover, we used the complex sample feature in SPSS v.17 to adjust the standard errors of measures (SEM) to take into account weights. All estimates were reported only when based on a sample of at least 30 valid cases to ensure accuracy; missing data were handled with list-wise deletion. Finally, in compliance with Institute of Education Sciences (IES) guidelines for protecting the confidentiality of participants, all figures were rounded down to the closest decimal, we did not report un-weighted data, and we obtained clearance for dissemination from the Security Office at the IES.

3 Results

Tables 1 and 2 show the descriptive findings for the independent and dependent variables, as well as comparisons with data from peers with intellectual and developmental disabilities and peers with other disabilities for context.

3.1 Self-determination

As Table 1 shows, the summative score for the self-determination scale for youth with autism was 62.2 (out of 86 points), a score slightly lower than for youth with ID (66.2) and other disabilities (67.9). Youth with autism reported slightly lower scores in the self-determination subscales of personal autonomy and autonomy in career planning, compared to youth with ID and youth with other disabilities. Differences across disability groups in regard to self-realization and psychological empowerment were negligible.

3.2 Social skills

Similarly, young adults with autism scored slightly lower on the social skills scale compared to their peers with other disabilities. The average summative score in social skills was 10.1 (out of 22 top achievable score), while youth with ID and youth with other disabilities reported scores of 11.9 and 12.4. Youth with autism reported a noticeably lower average score for the social skills subscale of assertion (3 out of 8 top achievable score), compared with 4.5 and 5.1 for youth with ID and other disabilities. This means that respondents with autism were more likely to respond “never” than “always” to the four questions on the assertion subscale. A score of 8 means that all participants responded “always” to all four question items in the subscale of assertion; a score of 0 means that all respondents responded “never” to all four questions. There were not noticeable differences across disability groups in regard to the social skills subscales of self-control and cooperation.

3.3 Job search

As Table 2 shows, young adults in every disability group reported low rates of proactive job-search activities, including talking to employment services, employers, or family and friends (3% to 14%) and placing or answering ads (23% to 44%). Applying for a job was the most popular proactive job search activity for all three groups: 61% of youth with autism, 35% of youth with ID, and 59% of youth with other disabilities applied for jobs. For all three groups, schools reported low rates of actively supporting a job search by contacting employers (35% for autism and ID and 46% for other) or job placement agencies (33% for autism and 46% for other; ID was the exception, with 62% of students having their school contact a job placement agency). However, schools reported higher rates of contacting job services such as VR (80% for autism, 66% for ID, and 47% for other). Youth with autism and ID were less likely to check with employment services, employers, or family and friends about jobs than youth with otherdisabilities.

There were no substantial differences across disability groups in job search methods that involved individuals’ initiative: almost identical proportions of youth with autism and youth with other disabilities applied for jobs (61% and 59%, respectively; however, only 35% of youth with ID did the same) or did something else to find a job (12% for youth with autism and 13% for youth with ID as well as for youth with other disabilities).

Schools were more likely to contact employers or job placement agencies for students with other disabilities than for their peers with autism and ID, but more likely to contact supported employment programs and VR for students with autism and ID than for their peers with other disabilities. Schools contacted VR more often for youth with autism (for 80% of these students), and contacted supported employment programs more often for youth with ID (for 61% of these students). Overall, schools tended to contact VR and employment support agencies for a larger number of students compared to contacting outside employment entities. However, for youth with autism, there was the biggest gap between how often schools contacted VR vs. how often schools contacted employers. For youth with other disabilities, the frequency of contacting VR was almost identical to frequency of contacting employers (47% vs. 46%). These data were limited to youth for whom transition planning had been developed (n = 140).

3.4 Transportation

As Table 2 shows, transportation was often a challenge for youth with autism and ID: 26% of youth with autism and 31% of youth with ID reported that their transportation needs were very or somewhat difficult, compared to 17% of youth with other disabilities. Moreover, 39% of youth with autism and 46% of youth with ID identified transportation assistance needs in their IEP, compared to only 6% of youth with other disabilities. Only 41% of youth with autism and 20% of youth with ID reported having a driver’s license or permit, compared to 74% of their peers with other disabilities. This was reflected in transportation use patterns, as youth with autism also reported using rides from family/agency/dial-a-van services to get to their current or most recent jobs more often than youth with other disabilities (55% for youth with autism and 53% for youth with ID, vs. 20% for youth with other disabilities).

3.5 Outcomes

Employment outcomes were generally poorer for youth with autism and youth with ID than for youth with other disabilities. As Table 2 shows, 67% of youth with autism and 53% of youth with ID have worked for pay since leaving high school, compared with 86% of youth with other disabilities. Only 45% of youth with autism and 37% of youth with ID had a job at the time of the wave 5 interview, compared to 61% of youth with other disabilities. Of those who work, 41% of young adults with autism and 40% of youth with ID work in a setting where a majority of their coworkers have disabilities, compared with only 3% of youth with other disabilities.

As Table 1 shows, youth with autism worked on average slightly fewer months than other disability groups: 25.6 months, compared to 27.4 months for youth with ID and 30.7 months for youth with other disabilities. Mean hourly earnings at their current or most recent job was $7.70, and mean number of weekly work hours was 23.7 (compared to $6.90 and 25.2 hours for the ID group, and $10.50 and 36.6 hours for youth with other disabilities). As Table 2 shows, youth with autism or ID were less likely to make at least $5.15/hour, the federal minimum wage at the time of data collection (65% and 74%, respectively, vs. 95% for youth with other disabilities).

A smaller proportion of youth with autism received benefits such as paid vacation, sick leave, health insurance, and retirement benefits, compared to both the ID and other disabilities groups. Finally, youth with autism were the least likely to report liking their current or most recent job very much (34%, compared with 64% for youth with ID and 44% for youth with other disabilities).

3.6 Demographics

The sample was predominantly white (71% for youth with autism, 63% for youth with ID, and 70% for youth with other disabilities) and male (84% for youth with autism, 58% for youth with ID, and 65% for youth with other disabilities). The majority of the sample lived in a household with income below $50,000, which falls below the median household income in the United States in 2012, according to 2013 U.S. Census Bureau report (DeNavas-Walt, Proctor, & Smith, 2013). A bigger proportion of youth with autism lived in a household with income over $50,000 (44%, as compared to 37% of youth with ID and 38% of youth with other disabilities).

3.7 Most factors were not associated with employment

The self-determination summative score, social skills, and job search were not associated with employment outcomes. Only a subscale of self-determination—psychological empowerment—and transportation independence were associated with employment outcomes of young adults with autism.

3.7.1 Self-determination

As Table 3 shows, we did not find a statistically significant correlation between the self-determination summative score and youth’s employment. Although the mean summative score of youth who were employed was slightly higher than the corresponding figure for youth who were not employed, the difference was not statistically significant, and the explained variance of the relationship at 2.6% was negligible (R Square 0.026). However, when looking at individual subscales of self-determination, we found that youth who gained employment reported a statistically significant higher score on psychological empowerment, compared to youth who were not employed (p < 0.05), with psychological empowerment explaining 16.3% of the variance.

3.7.2 Social skills

No statistically significant relationships were found between social skills and employment. Although youth who were employed reported slightly higher social skills scores compared to their peers who were not employed, the differences were not statistically significant and the explained variance was limited to 3.3% at most (R Square 0.033).

3.7.3 Job search

Similarly, as Table 4 shows, there was no statistically significant correlation between job search variables and employment outcomes. For example, whereas 59% of youth who applied for jobs found employment, 50% of youth who did not apply for jobs were also employed, and the difference was not statistically significant.

3.7.4 Transportation

We found a relationship between some transportation variables and employment. For example, youth with autism who had a driver’s license or learner’s permit had over five times higher odds of being employed compared to their peers who did not have a driver’s license or learner’s permit, and the difference was statistically significant (p < 0.01). As Table 4 shows, while 61% of the youth who had a driver’s license or learner’s permit were employed, only 23% of their peers without these items were employed.

Means of transportation also were associated with employment. Youth who used independent means of transportation (e.g., walking, driving, biking, or taking public transportation) had almost five times higher odds of being employed (84% were employed) compared to their peers who did not report independence in their means of transportation (53% were employed). These relationships were statistically significant (p < 0.05). Finally, we did not find a relationship between perceived difficulties in transportation and employment, or between identification of transportation assistance needs on the IEP and employment.

4 Discussion

The purpose of this study was to investigate the relationship between employment of young adults with autism and self-determination, social skills, job search strategies, and transportation, using the NLTS2 dataset. We were surprised to find that, contrary to prevailing literature, self-determination, social skills, and job search were not correlated with employment outcomes of young adults with autism. Only psychological empowerment, a subscale of self-determination, and transportation independence were positively associated with employment outcomes. Next we discuss these findings.

4.1 Self-determination

Contrary to existing literature (Martorell, Gutierrez-Recacha, Pereda, & Ayuso-Mateos, 2008; Test et al., 2009; Shogren et al., in press; Wehmeyer & Palmer, 2003), our findings did not support claims that self-determination is associated with greater employment outcomes. There may be numerous reasons for this discrepancy. One reason could be that young adults with autism tend to report lower self-determination skills compared to peers with other types of disabilities. For example, the literature shows that students with autism were less likely than students with other disabilities to take a leadership role or to actively participate in their transition planning meetings (Cameto et al., 2004; Shogren & Plotner, 2012). Moreover, they were more likely to be absent during transition planning meetings (Wagner, Newman, Cameto, Javitz, & Valdes, 2012).

Another reason has to do with the challenges of accurately gauging the construct of self-determination. As Wehmeyer (2005) noted, self-determination findings often are based on self-reported data, as in the case of the NLTS2 dataset. Also, the questionnaire used in the NLTS2 to gauge self-determination was based on a limited number of questions from the full Arc’s Self-Determination Scale instrument. This means that this measurement tool was not a complete scale, and thus may not have captured the whole story behind this multifaceted construct.

Finally, self-determination was assessed in wave 1 and 2, when participants were 16 to 19 years old. It is possible that such measures were not reflecting the self-determination skills of the participants when they were out of high school and looking for employment.

4.2 Social skills

Another unexpected finding was that social skills were not correlated with employment outcomes. This finding was not consistent with the prevailing literature in support of social skills as a key factor promoting employment outcomes (Carter et al., 2012; Martorell et al., 2008; Test et al., 2009). Yet, before dismissing social skills as critical factor for employment, we are inclined to think that measurement and data collection method may have played a role.A limitation of the instrument used in the NLTS2 to measure participants’ social skills is that the response items to each question included only three options: “never,” “sometimes,” and “always” (Gresham & Elliott, 1990). The small number of response items may limit the variability of the data and thus the possibility of detecting correlation.

Finally, social skills were assessed in wave 1, when participants were 14 to 17 years old. It is possible that by the time students exited high school and were looking for jobs, their social skills had changed due to their educational experiences as well as personal maturation.

4.3 Job search

Contrary to the literature in support of job search as a critical factor for improving employment outcomes (Manroop & Richardson, 2015; Saks, 2005), our findings did not confirm this hypothesis. The job search is a complex process, and the available variables provided limited data. Youth with autism showed low engagement in job-search methods requiring personal initiative, including contacting an employer, contacting friends and family, looking for job listings, and applying for jobs. A lower proactivity in the job search domain is consistent with existing literature, as lack of proactivity can be also observed in other transition activities such as the participation of students with autism in their transition planning meetings (Cameto et al., 2004; Shogren & Plotner, 2012).

One possible explanation is that students with autism are less inclined to use proactive job-search strategies because their transition goals include supported and sheltered employment more often than competitive employment, and techniques to find these two types of employment may be different. Shogren and Plonter (2012) reported that 39% of youth with autism had supported employment and sheltered employment listed as their transition goals, while for youth with other disabilities, 4% had supported employment, and only 2% had sheltered employment listed as transition planning goals. Moreover, according to the same study, transition goals of students with autism prioritize maximizing functional independence and enhancing social relationships, which potentially takes away the focus from employment and from using proactive techniques for the job search.

For non-proactive job-search practices, specifically school referrals, VR was contacted most frequently regarding students’ future jobs, for a reported 80% of youth, compared with employers and other agencies. This finding is in line with previous research demonstrating that, in general, during transition planning, VR counselors are more present than workers of other agencies (Cameto, 2005).

On the other hand, schools contacted employers less frequently when students with autism were involved, compared to students with other disabilities. According to Trainor et al. (2008), transition staff members tend to believe that students’ disability status can be a barrier to employment due to employers’ bias. Consequently, school staff may be more reluctant to contact employers for students with higher support needs, such as students with autism.

Another finding of this study demonstrates that youth with autism tend to use networking less frequently than youth with other disabilities when engaging in the job search. One explanation could be that youth with autism have smaller social circles, which can be in part due to limited social skills (Potts, 2005). Confirming this assumption, Langford, Lengnick-Hall, and Mukta Kulkarni (2003) concluded that the social networks of people with disabilities often are not diversified and mostly include individuals who are unemployed or underemployed, which is not as conducive for the job search as a diversified social network would be. Moreover, people with disability tend to underuse their social networks when searching for jobs. All of these factors combined may influence employment outcomes for this group negatively (Langford et al., 2013).

4.4 Transportation

We found that transportation independence was correlated with employment outcomes: Youth who had a driver’s license or who used independent transportation means, e.g., biking, public transit, walking, had five times higher odds of being employed. These findings echoed some earlier NLTS2-based studies, in which independent means of transportation and ability to get places without assistance were associated with better employment outcomes (Carter et al., 2011; McDonnall, 2011). These findings are to be expected, since it is intuitive to assume that higher mobility and transportation independence allows youth access to their job site (Carter et al., 2011).

A study by Magill-Evans et al. (2008), based on questionnaires of 76 people with disabilities, reported that many participants indicated that not having a license prevented them from accepting certain jobs. Having a driver’s license likely eliminates barriers caused by lack of access to public transportation (Trainor et al., 2008). However, McDonnall (2011) suggested not only that travel skills may be at play when higher employment rates are predicted by independent transportation skills, but also that a “sense of independence in general” may be empowering for students in regards to obtaining a job.

Support needs could have been an intervening variable explaining the relationship between independent transportation means and better employment outcomes. For example, it is reasonable to assume that people who face greater challenges in obtaining a driving license may also lack critical work skills needed to obtain employment. In some cases, however, as Magill-Evans et al. (2008) reported, sometimes the cost of obtaining a driver’s license was the main hindrance for individuals not driving. This suggests that there may be other reasons for which people do not drive, other than higher support needs.

4.5 Limitations and strengths

This article has both limitations and strengths. One limitation is the use of secondary data. Because the NLTS2 is an existing dataset, this study was limited in the types and number of variables available for investigation. Another limitation was that the self-determination data were not available for youth who had higher support needs. While overall the sample size of the NLTS2 is relatively large, it was challenging for controlling for intervening variables that might have had effects on the employment outcomes. Examples of intervening variables include the level of young adults’ support needs, socio-economic and education characteristics of the young adults’ parents, employment rates of the locations where the young adults lived, and rural versus urban settings. Finally, the research design was correlational and, thus, unable to uncover causal relationships. Therefore, any relationships should be interpreted with caution.

Despite these limitations, this article also has strengths. For example, using the NLTS2 dataset was a strength because its longitudinal design allows for drawing data over a period of about 10 years, thus increasing accuracy (Wagner, Kutash, Duchnowski, & Epstein, 2005). Another strength of this study is that the NLTS2 dataset is nationally representative. To date, not many studies exist with data about employment predictors of young adults with autism and with national representativeness (Lindsay, 2011; Lee & Carter, 2012). Also, predictors such as transportation and job search practices have not been often explored for this population.

4.6 Implications for research, policy, and practice

Based on the findings from this study, we recommend that researchers pay closer attention to perfecting measurement tools and practices to gauge students’ self-determination and social skills. Using the full self-determination scale rather than only a portion of it, as was the case in the NLTS2, would be a first step toward a more accurate measurement of the construct. For the social skills instrument, expanding the number of response items beyond three would allow for more variability of responses and, thus, greater precision in correlation analysis.

Moreover, it would be helpful to assess self-determination and social skills closer to when students exit high school. The NLTS2 dataset includes measures of these two personal traits when participants were 14 to 17 years old. More recent data would be more meaningful both to gauge students’ improvement during education and to gauge the relationships between these personal traits and employment outcomes after high school. Also, schools could do more to promote proactive job search activities, including talking to employment services, employers, or family and friends, and reviewing job listings. All these job search activities were fairly limited, making harder for correlation analysis to detect any relationship with employment outcomes.

Given the importance of transportation for obtaining and sustaining employment, schools would be of great help by ensuring that young adults have greater awareness of the available transportation options, how to use transportation independently, and ways to creatively identify alternatives when transportation options are scarce. Our findings also support recommendations for policies that support flexible, accessible, and affordable transportation services as a necessary infrastructure for the successful employment for young adults who lack transportation independence.

Finally, we recommend funding more experimental research to increase the validity of the findings around improving the employment outcomes of young adults with autism.

5 Conclusion

The purpose of this article was to investigate the relationship between employment outcomes of young adults with autism and their personal traits: self-determination and social skills, as well as external factors, job search and transportation. Contrary to the prevailing literature, our findings did not reveal a relationship between employment and most of these factors. We only found a positive relationship between employment and a subscale of self-determination – psychological empowerment – andtransportation independence.

If employment of young adults with autism is to improve, more needs to be done to improve our understanding about key factors associated with employment. Our study points to enhancing transportation independence as an important factor. However, overall the findings point to the importance of improving measurement and data-collection techniques for accurately gauging the role of key factors such as self-determination, social skills, and job search practices.

Author contributions

Agnes Zalewska contributed to identifying variables from the NLTS2 dictionaries for this study, assisted in interpreting the findings, and wrote the introduction, results, and discussion sections of the manuscript. Alberto Migliore developed the study proposal submitted for funding, executed the data analyses, wrote the method section, and edited the final manuscript. John Butterworth supervised all phases of the project, from the development of the proposal submitted for funding to manuscript development, by providing valuable advice.

Conflict of interest

The authors have no conflict of interest to report.

Footnotes

Acknowledgments

The authors would like to thank Stephanie Wallace for formatting this manuscript in APA style and consistent with the authors’ guidelines, as well as Anya Weber for copyediting.

This project was funded by Grant R40MC22646 through the U.S. Department of Health and Human Services, Health Resources and Services Administration, Maternal and Child Health Research Program, and in part by the Administration on Developmental Disabilities, U.S. Department of Health and Human Services (#90DN0216).

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