Vocational Rehabilitation Service Patterns: An Application of Social Network Analysis to Examine Employment Outcomes of Transition-Age Individuals With Autism

Abstract

Young adults with autism spectrum disorder (ASD) face poor employment outcomes following transition from school to adult life. Social network analysis is a useful approach for examining service patterns associated with employment success for this population. An advantage of this approach is its focus on the interdependence of variables rather than individual predictors. This study applies network methodology to examine the relations between vocational rehabilitation services and young adults with ASD to predict employment status. Using the Rehabilitation Services Administration (RSA-911) data set, participants included 2,219 individuals with ASD between the ages of 16 and 24 served by the public vocational rehabilitation system and closed as either competitively employed or not employed. A two-mode network was constructed such that a relation was defined for each service an individual received. Results from a core-periphery analysis indicated that of the 22 services available, core services included assessment, counseling/guidance, job placement, on-the-job support, job search support, and transportation services. Follow-up analyses suggested that the greater number of these six core services an individual received, the better the employment outcome. Findings highlight that these services should be viewed as interconnected and suggest a set of six core services that may be particularly beneficial for this population.

Keywords

vocational rehabilitation autism transition employment social networks two-mode networks core-periphery model

The prevalence of autism spectrum disorder (ASD) has been steadily on the rise, with recent estimates suggesting as many as one in 45 children have ASD in the United States (Zablotsky, Black, Maenner, Schieve, & Blumberg, 2015). Many individuals diagnosed with ASD may have difficulties communicating their wants and needs, have complex sensory processing needs, exhibit strange behaviors (e.g., clapping, rocking, avoiding eye contact), and present as having flat affect (National Institute of Health [NIH], 2009). In addition, a substantial portion of individuals with ASD also have coexisting intellectual disabilities (Bradley, Summers, Wood, & Bryson, 2004). These challenges coupled with societal stigma and limited supports and services impact community participation and quality of life outcomes for this population. In particular, youth and young adults with ASD often face specific challenges related to employment, including unemployment, underemployment, and disparities in compensation (Shattuck et al., 2012). For example, findings from the National Longitudinal Transition Study–2 indicate that a little over one third (37.2%) of individuals with ASD ages 21 through 25 work for pay, compared with approximately 60% of individuals with other disabilities (Newman et al., 2011). Further, employment and postsecondary education outcomes of youth with ASD are considerably weaker than those of youth with other disabilities (Barnhill, 2007). Thus, it is not surprising that greater numbers of youth with ASD are seeking rehabilitation services designed to promote and support gainful employment for people with disabilities (McDonough & Revell, 2010).

The state-federal vocational rehabilitation (VR) system has been increasingly used as a source of assistance for individuals with ASD. Case closure reports show that the number of individuals with ASD seeking these services has tripled between 2003 and 2008 (Butterworth, Migliore, & Timmons, 2010). The state-federal VR system is the oldest and most successful public program designed to support the employment goals of people with disabilities. On an annual basis, it serves more than one million individuals with agencies in all 50 states (Martin, West-Evans, & Connelly, 2010). Eligible individuals for this program have a physical or mental impairment that presents a substantial impediment to employment and can receive a range of rehabilitation services based on their personal preferences and goals to assist with obtaining or maintaining competitive employment. Services can be provided directly by the VR counselor or contracted out to other agencies or professionals. Types of services offered include assessment, on-the-job training, job search assistance, and counseling (see U.S. Department of Education, Office of Special Education and Rehabilitation Services, Rehabilitation Services Administration, 2008, pp. 20–29, for a complete list and description of services). Successful rehabilitation closure occurs after the services provided have led to at least 90 days of competitive employment (Ditchman et al., 2013).

After a client’s case is closed, the types of services received, demographic information, and employment outcome are recorded in the Rehabilitation Services Case Administration Report (RSA-911) administrative database. Using this data set, a number of studies have examined the extent to which specific services are associated with employment outcomes for a range of disability populations (e.g., Catalano, Pereira, Wu, Ho, & Chan, 2006; Dutta, Gervey, Chan, Chou, & Ditchman, 2009; Marini, Lee, Chan, Chapin, & Romero, 2008). These studies generally support VR services as better predictors of employment outcomes than demographic characteristics (e.g., Bolton, Bellini, & Brookings, 2000), with a number of studies to date specifically examining VR services as predictors of employment outcomes for individuals with ASD (e.g., Chen, Sung, & Pi, 2015; Migliore, Timmons, Butterworth, & Lugas, 2012; Sung, Sánchez, Kuo, Wang, & Leahy, 2015). Findings from studies with adults and youth with ASD suggest that job placement, job search support, and on-the-job supports are pivotal predictors of employment success (Migliore et al., 2012; Schaller & Yang, 2005; Sung et al., 2015).

Although these findings are indeed useful for identifying predictors of successful case closure, these studies are limited in their data analytic approach, which primarily examines the unique contribution of individual VR services on employment outcomes, while controlling for the impact of other services that may have been received. However, in reality, these services do not work in isolation, and clients generally receive a range of services. In fact, it is fairly uncommon for individuals to receive only one service through VR. Thus, we argue it may be more useful to conceptualize VR services as a network rather than as independent contributors—that is, the whole is greater than sum of parts. Social network analysis can address the limitations of conventional analytic approaches by quantitatively examining the structure of relational data sets. However, the extent to which network-related information can be used to understand service patterns’ association with employment using the RSA-911 data set has not been examined to date.

Social Network Analysis

Social network analysis is a set of statistical methods that examine the structural relations among entities (i.e., individuals, groups, or organizations). While other quantitative and qualitative methods generally focus on the individual behavior of an entity, the network perspective focuses on patterns of relations among entities. A guiding principle is that relations in a network can affect behavior, decisions, and actions, and by understanding the relations in a network, we can begin to understand the origins and mechanisms of behavior (Knoke & Yang, 2008). The main objectives of social network analysis are to (a) measure relations, (b) explain relations, and (c) understand the consequences of relations. In particular, there has been an increased interest in understanding the role of social networks in the employment of individuals with a disability (e.g., Bates & Davis, 2004; Potts, 2005). For example, Carey, Potts, Bryen, and Shankar (2004) found that individuals with severe communication disabilities utilized their job contact networks to search and obtain employment.

A network is defined by a set of actors and the relations between the actors. Actors may be individuals or collectivities and may be referred to as nodes. Examples of individual actors include children playing on a playground, birds in a flock, employees in an organization, or actors in a particular film. Examples of collective actors include businesses of a certain industry, flocks of birds, movies of a certain genre, and political parties. A relation is defined as a specific connection or tie between actors; commonly, these connections are measured between two actors. Relations may be undirected, where mutuality occurs (e.g., friendship), or directed, where one actor initiates and is received by the other actor (e.g., advice). Examples of relations among individuals as actors may include individuals talking with one another, sharing information, or sitting next to each other.

Networks can be classified as either one- or two-mode networks. Two-mode networks, or affiliation networks, represent the association between actors and another entity such as groups, events, or activities for which the actor is affiliated (i.e., each link represents an individual’s affiliation to a particular group, event or activity). In two-mode networks, two distinct entities represent the nodes (actors and events) and the lines connecting the actors (a tie represents the participation of an actor in an event). The best-known example of a two-mode network analysis is the study of race and social class in a southern community in the United States. Researchers collected data on 18 women and their participation in 14 social events (Davis, Gardner, & Gardner, 1941). To understand the social life among women, the researchers examined the presence/absence at social events. Davis and colleagues (1941) found that women organized into two main groups across three “levels” of participation (Freeman & White, 1993). Other examples include company board networks, where the tie represents the director sitting on the company board (Robins & Alexander, 2004); committee networks, where the tie represents the House of Representative assignment to a committee or subcommittee (Porter, Mucha, Newman, & Warmbrand, 2005); and employment networks, where the tie represents the individual and employment preferences (Kilduff, 1990). In all of these examples, the networks consist of a set of actors and a collection of groups, events, or activities, and the concept of affiliation is broadly defined, including group membership or participation in events. In this paper, we use a two-mode network, defined as transition-age individuals with ASD and their connections to VR services.

Study Purpose

The increasing prevalence of individuals with ASD coupled with data reflecting dismal employment and community integration outcomes pose a significant challenge for schools, state VR agencies, and communities. The purpose of the present study was to apply network methodology to examine structural regularities between VR services and young adults with ASD to predict employment status. In this study, we used the RSA-911 data set to construct a two-mode network, with one set of actors defined as young adults with ASD and the other set of actors defined as VR services. An undirected tie between a young adult with ASD and a VR service indicates that an individual received that service. We addressed the broad research question:

Research Question 1: Can network-related information be used to understand service patterns associated with employment outcomes for young adults with ASD served by the state-federal VR system?

Method

Participants

Data used in this study were extracted from the RSA-911 database for fiscal year 2009. This database contains information related to consumers served by the state-federal VR system after their case has been closed, as well as demographic variables, disability characteristics, and data reflecting number, duration, and type of services received. Individuals were included in this study if they (a) were between the ages of 16 and 24 at the time of application, (b) had a primary disability category of autism, (c) received an individualized education plan (IEP) while in school, (d) were not working at time of application for services, and (e) exited the VR system as either competitively employed for 90 days (status 26) or closed unsuccessfully (status 28).

A total of 2,129 individuals with ASD (male = 1,794, female = 335) met study inclusion criteria. Most (82.2%) of the individuals were White, followed by African American/Black (10.2%), Hispanic (4.0%), and Asian /Pacific Islander (2.8%). The mean age was 18.55 (SD = 1.92), with over half (60.2%) falling between 16 and 18 years old. Only 15.1% reported any postsecondary education. Less than one third (29.9%) were receiving social supplementary income (SSI) at the time of application. The only application status difference found between those who were employed following services and those who were not was receipt of SSI, with a greater proportion of those not working having received SSI at time of application. Specifically, 298 (26.1%) of individuals closed successfully had reported receiving SSI at application compared with 339 (34.4%) of those closed unemployed, χ²(1, N = 2,129) = 17.64, p < .001. No differences based on age, sex, race/ethnicity, or education level were found between those closed successfully and those who were not.

Study Variables

Employment outcome

A primary variable of interest in this study was employment status at closure. Individuals successfully rehabilitated were engaged in competitive employment at the time of closure for at least 90 days (status 26). Competitive employment is defined as part-time or full-time employment in an integrated setting (or self-employment) with compensation at or above the minimum wage (U.S. Department of Education, Office of Special Education and Rehabilitation Services, Rehabilitation Service Administration, 2008). Unsuccessful closure refers to individuals who had initiated an employment plan but were not working after exit from VR services (status 28).

Service variables

There were 22 different service types that could be offered through the state-federal VR system. These services included Assessment; Diagnosis and Treatment of Impairments; Counseling and Guidance; College or University Training; Occupational/Vocational Training; On-the-Job Training; Basic Academic Remedial or Literacy Training; Job Readiness Training; Disability-Related, Augmentative Skills Training; Miscellaneous Training; Job Search Assistance; Job Placement Assistance; On-the-Job Supports; Transportation Services; Maintenance; Rehabilitation Technology; Reader Services; Interpreter Services; Personal Attendant Services; Technical Assistance Services; Information and Referral Services; and Other Services (see U.S. Department of Education, Office of Special Education and Rehabilitation Services, Rehabilitation Service Administration, 2008, pp. 20–29, for a complete description of each service).

Demographic variables

Demographic characteristics included gender, race, ethnicity, age, education level, and receiving public support at application (e.g., SSI). These variables were used to characterize the study sample. Only SSI at time of application was found to significantly differ between those who were closed employed and those who were not.

Network variables

A number of 2-mode network-related variables were examined. Specifically, we examined degree centrality, density, and core-periphery structure, which are discussed in more detail in the “Data Analysis” section.

Data Analysis

We analyzed the following network measures using UCInet version 6.04 (Borgatti, Everett, & Freeman, 2002): density and degree centrality, and we conducted a two-mode categorical/core periphery model. Density is the number of ties present in the network and is normalized by dividing the raw number of ties by the maximum number of ties that are possible for the same size graph (Borgatti & Everett, 1997; Wasserman & Faust, 1994). If a two-mode network has sets of size n_a and n_b, then this would equal n_individuals n_services edges. Thus, for the current two-mode network, the number of ties present was divided by n_individuals n_services edges where n_individuals = 2129 and n_services = 22 (Borgatti & Everett, 1997). Density can range from 0 (no ties present) to 1 (all possible ties present). A low density indicates there are individuals receiving few services, while a high density indicates individuals are receiving many services.

Degree centrality was also calculated and is defined as the number of lines incident with the actor. Put simply, degree of an individual is based on the number of services he or she received, and the degree of the service reflects the number of individuals who received the service. For this study, we followed Freeman’s (1979) suggestion to report the normalization of degree, which divides by the number of actors in the network minus one.

Finally, we conducted a two-mode core-periphery analysis, which divides the matrix, including the rows (i.e., individuals) and columns (i.e., services), into two classes. The model aims to identify two components: (a) core—individuals who share many services together and, as a result, have a high density of ties among themselves—and (b) periphery—individuals who share few services and, as a result, have a low density of ties among themselves (Borgatti & Everett, 1999; Everett & Borgatti, 2000). Thus, the core consists of actors that are closely connected to each of the services and can be thought of as frequently co-occurring individuals and services. On the other hand, the periphery consists of individuals and services that are less connected. The core-periphery analysis was used to determine the core services that are grouped together across individuals, and these were then examined as a predictor for employment status. Follow-up hierarchical logistic regression analysis was used to examine the impact of core services that emerged from the core-periphery analysis and individual’s degree centrality on employment outcome, controlling for SSI receipt at application. For this analysis, SPSS 21.0 was used.

Results

Overall Network Description

Figure 1 displays a visualization of the network using Gephi, applying the Fruchterman-Reingold layout (Fruchterman & Reingold, 1991). Each point represents an actor included in the study, with services labeled. (Note: individuals receiving 0 services are not shown.) The overall density of the services in the network was .195, suggesting that individuals receive only a few services in general (M = 4.28, SD = 2.37, range = 0–14). The degree centrality was calculated for each of the 22 services (presented in Table 1). Assessment, counseling, job placement, on-the-job support, job search, and other services had the highest degree centrality, while interpreter and reader services had the lowest. Degree centrality was also calculated for each individual. Individuals who were closed successfully had a significantly higher mean degree centrality (M = 0.221, SD = 0.11, range = 0.045–0.636) than those who were closed unsuccessful (M = 0.164, SD = 0.10, range = 0–0.591); t = 12.63, p < .001. Individuals who were employed on average received 4.9 services compared with 3.6 for those unsuccessfully closed.

Figure 1.

Network visualization depicting the connections between individuals and services received through the vocational rehabilitation system.

Table 1.

Services’ Degree and Percentage Received Based on Closure Status (N = 2,129).

		% received
Service	Degree centrality	All (N = 2,129)	Employed (n = 1,143)	Unemployed (n = 918)
Assessment	0.706	70.6	73.3	67.5
Counseling and guidance	0.630	63.0	66.8	58.7
Job placement assistance	0.461	46.1	61.2	28.6
On-the-job supports	0.406	40.6	52.9	26.3
Job search assistance	0.319	31.9	39.9	22.7
Other services	0.278	27.8	30.2	25.1
Transportation services	0.240	24.0	23.9	24.0
Information and referral services	0.218	21.8	23.6	19.7
Job readiness	0.217	21.7	25.5	17.4
Diagnosis and treatment	0.205	20.5	21.9	19.0
Miscellaneous training	0.194	19.4	21.0	17.5
College or university training	0.097	9.7	8.8	10.7
Maintenance services	0.083	8.3	9.4	7.0
Vocational training	0.081	8.1	9.1	6.9
On the job training	0.061	6.1	8.7	3.1
Remedial training	0.030	3.0	3.2	2.7
Augmentative skills training	0.023	2.3	2.8	1.6
Rehabilitation technology	0.020	2.0	2.4	1.6
Technical assistance	0.006	0.6	1.0	0.2
Personal attendant services	0.003	0.3	0.2	0.4
Interpreter services	0.001	0.1	0.1	0.1
Reader services	0.000	0	0	0

Note. Normalized degree centrality was calculated based on the number of individuals who received the service divided by the number of total actors in the network minus one.

Core-Periphery Model

To examine the structure of the network, we used core-periphery analysis. Results revealed that six services made up the core of the network—these included assessment, job placement assistance, counseling, job search assistance, on-the-job support, and transportation. All other services made up the periphery group. An independent samples t-test was used to see if there were differences in the number of the six core services received based on employment outcome. Those who were closed in competitive employment received a significantly higher number of core services than those who were not working at closure (employed: M = 3.18, SD = 1.48; not employed: M = 2.28, SD = 1.37); t(2116.21) = 14.61, p < .001. To further examine the extent to which the number of core services aligned with employment outcome, we examined the successful closure rates of individuals who received zero through all six core services. As depicted in Figure 2, as more core services were received, higher rates of successful case closure were observed. For example, of the 250 individuals receiving 5 of the 6 core services, over 70% were closed successfully. Further, of the 74 individuals who received all six core services, over 85% were closed successfully. On the other hand, of the 98 individuals who did not receive any of the core services, only about 20% were employed successfully.

Figure 2.

Employment rates by number of core services received.

Logistic Regression Analysis

To examine the extent to which the number of core services and periphery services predicted employment closure status, a hierarchical logistic regression analysis was computed. In the first step, SSI at application was entered as a control variable. In the second step, the number core services from the core-periphery analysis was entered (range = 0 to 6), and in the final step, the number of periphery services received by each individual was entered (range = 0 to 16). The results of the analysis are presented in Table 2. The greater number of core services received increased the odds of successful employment closure, controlling for other variables. In the final model, SSI at application decreased the odds of successful closure (OR = 0.62), while the number of core services received increased the odds (OR = 1.54). In other words, for every additional core service received, the odds of successful employment was about 1.54 times greater, while the number of periphery services did not significantly contribute to the model.

Table 2.

Logistic Regression Model for Employment Closure Status (N = 2,129).

Step	Predictor variable	B	SE	Wald	Odds ratio	95% CI
Step 1^a	SSI	−.40	.10	17.55	0.67***	[0.56, 0.81]
Step 2^b	SSI	−0.49	.10	23.37	0.62***	[0.51, 0.75]
Step 2^b	Core services (0–6)	0.44	.03	181.04	1.56***	[1.46, 1.66]
Step 3^c	SSI	−0.48	.10	22.63	0.62***	[0.51, 0.76]
	Core services (0–6)	0.43	.03	158.18	1.54***	[1.44, 1.65]
	Periphery services (0–16)	0.35	.04	1.01	1.03	[0.97, 1.11]

Note. CI = confidence interval; SSI = received social supplementary income at time of application.

2 log-likelihood = 2912.33, model χ²(1) = 17.61, p < .001, Nagelkerke R² = .01. ^b2 log-likelihood = 2708.72, step χ²(1) = 203.61, p < .001, Nagelkerke R² = .13. ^c2 log-likelihood = 2707.71, step χ²(1) = 1.01, p = .315; final model χ²(3) = 22.23, p < .001, Nagelkerke R² = .13.

***

p < .001.

Discussion

The purpose of this study was to apply network analysis to the RSA-911 case services database to investigate the extent to which the structure of VR services was associated with employment outcomes for transition-age youth with ASD. The RSA-911 database constitutes an important source of information regarding the rehabilitation closure rates for people with disabilities served by the state-federal VR system. However, previous research with the data set has primarily used logistic regression analyses to determine individual service predictors that independently increase the odds of successful case closure for different disability populations. Network analysis provides an alternative to these traditional data analytic approaches and takes into consideration the combination of services received by consumers. This is particularly applicable to the VR system because consumers generally receive more than one service, and network analysis offers a more accurate representation of the complex relations among these services that exist in reality. This study is novel in that it is the first to apply network analysis to examine service characteristics of the RSA-911 data.

The density findings from this study revealed that the VR service network was sparse. That is, young adults with ASD generally received only a few services. This makes sense given that VR counselors are trained to respond to the unique employment needs of the individuals they serve to promote successful employment. In fact, core-periphery analysis revealed that the network is comprised of a core set of six services (counseling/guidance, assessment, job placement, on-the-job support, job search assistance, and transportation), with the remaining services representing periphery services. These six core services are among the more frequently used services. Moreover, our findings suggest that the proportion of consumers with successful employment outcomes increased as a greater number of these six core services were received. Findings from the follow-up analysis further support the impact of the number of core services on increasing the odds of employment outcome, even when controlling for SSI status at application.

Findings from this study highlight the importance of considering relational patterns and structure of VR services as opposed to merely looking at each service as an independent predictor of employment outcome. However, the individual benefits of a number of the core services that emerged in our analysis, such as job placement, job search support, and on-the-job support, are not surprising in light of the existing research highlighting these as individual predictors of rehabilitation success (Migliore et al., 2012; Schaller & Yang, 2005; Sung et al., 2015). It is not surprising that strategies that focus on matching individuals with jobs that highlight their unique interests and strengths are instrumental in supporting job retention (Hendricks, 2010; Smith, 1990). There has also been some emerging evidence that counseling and guidance may be a particularly relevant service for young adult males with ASD (Sung et al., 2015). On the other hand, assessment and transportation have generally not been found to be independent predictors of employment outcomes for this population in the existing literature. Assessment is a service that is received by the majority (around two thirds) of all consumers (Schaller & Yang, 2005; Sung et al., 2015), so it may not independently differentiate consumers based on employment outcome. However, our findings suggest that assessment and transportation may be important to consider as part of the collective core set of services promoting employment, especially given that transportation is often a frequent barrier for youth with ASD (Hendricks & Wehman, 2009).

Of the individuals who received all six core services, the vast majority (87.8%) were closed successfully, as compared with only 40.0% of those who received only one of the core services. Yet, these findings cannot be explained by the notion that “more is better.” The follow-up logistic analysis indicated that entering the number of periphery services after controlling for the number of core services received did not help explain employment outcomes. This study suggests that the “core services” represent a unique set of services that appear to collectively impact the employment outcomes of transition-age individuals with ASD. In other words, quantity of services does seem to matter, but only to a limited extent and when related to this distinct set of services. Instead of reflecting on individual service predictors, findings underscore that services should be viewed as interconnected, and our analysis extends the current literature on service predictors for individuals with ASD by identifying this distinct collective set of services that appear to be particularly beneficial for this population.

These findings align with current job placement models and vocational support programs that promote a “place and train” approach (rapid placement in real-world competitive employment settings with the necessary support and training to successfully maintain these placements) and emphasize personal preferences, job matching, development of job search skills, and access to follow-up support. For example, supported employment programs designed specifically for individuals with ASD, such as the Treatment and Education of Autistic and Related Communication-Handicapped Children (TEACCH; Keel, Mesibov, & Woods, 1997) and Prospects (Howlin, Alcock, & Burkin, 2005), emphasize the need for job placement to be individualized and based on the individual’s strengths, skills, and abilities. Current job placement models and vocational support programs also take into consideration the work environment and required tasks when assessing a strong job fit (Hendricks, 2010; Hillier et al., 2007; Keel et al., 1997). For people with ASD, oftentimes, an appropriate job is one that provides sufficient time for learning and skill development with minimal distractions and structured schedules and tasks (Hendricks, 2010; Lee & Carter, 2012). Work environments with limited sensory stimulation that do not require complex interpersonal and social skills have also been described as more appropriate for many individuals with ASD (Hendricks, 2010; Müller, Schuler, Burton, & Yates, 2003). In addition to job matching, other job placement skills addressed in these models include job search strategies, preparation of resumes and applications, and interviewing (Hillier et al., 2007; Howlin et al., 2005; Keel et al., 1997).

On-the job support is also an effective strategy for promoting job retention, especially for individuals with ASD who may require more individualized training to learn job tasks or other employment-related skills. Research suggests that individuals benefit more from training on job-related tasks when provided on the actual job site where learning takes place in a natural setting (Wehman, 2001). Supported employment models generally stress the importance of individually tailoring job training to promote the acquisition of required job skills and also social integration in the workplace. Often this support and training takes the form of a job coach who typically provides intensive training and support at the beginning of the placement and gradually fades away as the individual better understands the job tasks. Ideally, natural supports (e.g., co-workers) should be cultivated early on in the work setting as they are more sustainable and less stigmatizing than the presence of professional supports (Hagner & Cooney, 2005; Lee & Carter, 2012). On-the-job support for people with ASD is comprised of a diverse set of strategies depending on the individual’s unique level of cognitive, social, and behavioral functioning and the work environment. Although research on instructional strategies with transition-age youth with ASD in the context of employment is relatively scarce, there is evidence to suggest that strategies such as behavior management and modeling (Burt, Fuller, & Lewis, 1991; Kemp & Carr, 1995), positive behavior support plans and ecological modifications (Schall, 2010), visual supports (Foley & Staples, 2003; Riffel et al., 2005), and augmentative and alternative communication interventions (Foley & Staples, 2003) can promote workplace success for this population.

Implications for Research and Practice

Findings from this study suggest several areas to enhance the employment success of transition-age youth with ASD. Counselors working with youth with ASD should recognize the six core services identified in our analysis as a unique, collective package that may be particularly beneficial for this population. At the same time, we recognize that counselors should adhere to a person-centered, empowerment approach in their work with consumers as opposed to a one-size-fits-all model. For example, our findings show that around one fifth of the consumers who had not received any of the core services were still closed successfully. This suggests that there is likely a minority of individuals with ASD who come with a very specific set of needs that could be addressed by one or more of the other services. However, the remaining four fifths of individuals who did not receive any of the six core services were ultimately closed unsuccessfully. VR counselors should be aware of existing vocational intervention programs designed (or adapted) specifically for transition-age youth with ASD that incorporate these core job-related services associated with employment outcomes. For instance TEACCH (Keel et al., 1997), Project SEARCH (Wehman et al., 2013), and the Individual Placement and Support (IPS) model (Becker, Swanson, Bond, & Merrens, 2008; Drake & Bond, 2008; McLaren, Lichtenstein, Lynch, Becker, & Drake, 2017) have a strong emphasis on job placement assistance, job search assistance, and on-the-job supports.

It is also important to consider services that may be underutilized with this population. For example, rehabilitation technology was a service seldom used by consumers in this study, yet there is increasing research documenting the benefits of technological support strategies (e.g., video modeling, personal digital assistants) with this population, especially in employment contexts (Allen, Wallace, Renes, Bowen, & Burke, 2010; Hill, Belcher, Brigman, Renner, & Stephens, 2013). Still, the majority of individuals appeared to benefit from the core services, suggesting that these six core services should be given particular consideration when working with young adults with ASD—however, this should not be at the cost of discounting the relevance of other potentially useful services. A VR counselor working closely with a consumer should evaluate the extent to which any of these six services would be beneficial based on the client’s unique situation. This set of services may be particularly useful as a starting point during the application and plan development process, perhaps in the form of an initial screening checklist.

Future research should determine whether this same core set of services emerges with other disability or demographic subgroup populations. It will also be important to include longitudinal design strategies to assess the true causal effect of services on employment outcomes given the retrospective and cross-sectional nature of the RSA-911 data set. Perhaps most important is incorporating strategies to better document the quality of the specific types of services provided. A major concern regarding the state-federal VR system is that considerable variability in the quality of the VR services exists across states, counties, and even within local offices (Ditchman et al., 2013). Fidelity and quality indicator checks will prove a valuable contribution to future research. For example, the extent to which on-the-job supports were based on strict adherence to an empirically based supported employment model such as the IPS model (Becker et al., 2008; Drake & Bond, 2008) is not known. Thus, it is impossible to assume that all individuals receiving this service type can really be grouped together as having received the same service, impacting the validity of the conclusions that can be drawn. The growing development of initiatives to assess the fidelity of IPS in VR settings offers promise for capturing quality of at least these services. Future research is also needed to examine more thoroughly the quality of employment outcomes. Job satisfaction and long-term retention are not currently assessed in the current research using RSA-911 data sets, which categorizes successful rehabilitation closure as competitive employment for 90 days. Future research should extend beyond the traditional 90-day mark to include information about the long-term supports and outcomes of young adults with ASD who are employed. This is particularly relevant in light of recent changes authorized by the Workforce Innovation and Opportunities Act (WIOA) requiring “competitive integrated employment” outcomes based on criteria related to income, integration, and opportunities for advancement (WIOA, 2014).

Finally, this study demonstrates that network analysis can be a useful methodological approach for rehabilitation research, yet it is seldom used in the field to date. Although this study applied network analysis to the RSA-911 data set, there are a number of additional contexts and research questions relevant to rehabilitation that could benefit from this approach. Despite growing interest in the social networks of people with disabilities (e.g., Asselt-Goverts, Embregts, Hendriks, Wegman, & Teunisse, 2015; Kamstra, Putten, Post, & Vlaskamp, 2015), network analysis methodology has rarely been applied in this area. Community inclusion is an important aspect of rehabilitation, and network analysis describing individuals’ connections with others or movements within the community setting could be informative for evaluating current challenges as well as providing guidance for facilitating social connectedness and embeddedness in a community. For example, the inclusiveness of school or work settings could be assessed through reported ties of individuals based on interactions or friendships. This could allow researchers to more fully examine the extent to which individuals with disabilities are truly embedded and integrated in these settings as well as how these social connections relate to job performance outcomes (Pearce & Randel, 2004). In terms of VR, examination of employment service providers’ employer and informal personal networks could yield information about how to best allocate time and resources (Cross, Borgatti, & Parker, 2002). Network analysis has also been applied to social media contexts, such as Twitter and Facebook feeds. One interesting study specifically examined the Twitter networks of a small group of adults with severe communication disabilities (Hemsley, Dann, Palmer, Allan, & Balandin, 2015), with findings suggesting that the interconnections of participant Twitter networks might be part of a larger, emergent community of people using augmentative and alternative communication online. Further research in this area would likely be beneficial for understanding social networking among individuals with disabilities as well as monitoring indicators of stigma and attitudes toward specific disability groups. It may also be useful to apply network analysis to examine publication and citation networks within rehabilitation to assess the structure of influence on the field, as has been done in other disciplines (e.g., Barnett, Huh, Kim, & Park, 2011). In sum, there are myriad opportunities for the application of social network analysis to address rehabilitation research questions.

Study Limitations

This study presents some limitations that are important to note. First, data validity and missing data may threaten the generalizability of the results. The RSA-911 data set is based on retrospective information recorded by VR counselors, and despite the cross-checks in place to reduce potential errors, unexpected entry or memory mistakes may still have occurred. In addition, the case data are based on consumer cases that have been closed; thus, individuals with actively open cases were not included. The data set used in this study was from fiscal year 2009 because this was the data set that the researchers have available at the time of the analysis. Although many of the participant and service variable relationships aligned with more current data sets, this is still an important consideration when generalizing findings to the present economic conditions. Second, the dichotomous nature of the service and outcome variables used is not an accurate representation of the true variability that exists. With regard to employment outcome, successful closure was based on 90 days of competitive employment, which fails to capture longer term employment outcomes or consider levels of job satisfaction, job match, or compensation. Perhaps more concerning, the service variables were coded merely as having received the service or not. This disregards the substantial variability in the quality and manner in which these services were provided both across and within states and even agencies. Third, we did not control for other service variables, such as cost and length of services, which may have impacted our analyses. Additional research using this approach to examine service network patterns for specific subgroups of the transition-age ASD population would be beneficial. Finally, we did not include a temporal component in our network models, and the follow-up analyses were based on cross-sectional design, making causality of relationships impossible to determine.

Conclusion

Network analysis can be a useful methodological approach for rehabilitation research in addition to conventional linear statistical approaches (e.g., regression analyses) because it takes into account relational patterns between individuals and services. Findings from the present study highlight the importance of considering the relational structure of VR services as opposed to merely looking at each service as an independent predictor of employment outcomes for young adults with ASD. However, there is a need for further research using network analysis techniques to better understand VR service patterns for specific disability and demographic subgroups as well as to apply these approaches to other rehabilitation research contexts to advance the field.

Footnotes

Acknowledgements

We would like to acknowledge and thank Dr. Fong Chan for his help preparing the data set for analysis.

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) received no financial support for the research, authorship, and/or publication of this article.

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