Differences in Self-Determination Across Disability Categories: Findings From National Longitudinal Transition Study 2012

Abstract

Researchers have established variability in self-determination scores across disability groups, but most nationally representative research has used data collected over a decade ago from the National Longitudinal Transition Study 2 (NLTS2). To provide an updated analysis of differences in characteristics of self-determination (i.e., autonomy, psychological empowerment, self-realization) across disability groups, this study analyzed data from the recently completed National Longitudinal Transition Study 2012 (NLTS2012). The authors tested measurement equivalence across seven disability groups: high-incidence disabilities (learning disabilities, emotional disturbances, speech or language impairments, and other health impairments), sensory disabilities (visual and hearing impairment), multiple disabilities (multiple disabilities and deaf-blindness), intellectual disability, traumatic brain injury, orthopedic impairments, and autism spectrum disorder (ASD). Students in the multiple disabilities, intellectual disability, and ASD groups showed lower self-determination scores compared with other disability groups. Greater variability was also found in scores among these groups. Implications for assessment research practice, and policy are highlighted.

Keywords

self-determination autism developmental disabilities

Enhancing the self-determination and transition outcomes of students with disabilities has received significant attention since the introduction of the transition mandates in Individuals with Disabilities Education Act (IDEA) in 1990; however, students with disabilities continue to face significant challenges in accessing postsecondary education and community-based employment. Young adults with disabilities are less likely to have completed a bachelor’s degree than those with no disability (Newman et al., 2011). According to recent data from the U.S. Bureau of Labor Statistics (2019), in 2018, approximately eight in 10 persons with a disability were not in the labor force, compared with about three in 10 of those without a disability. Even more problematic is that little change has occurred over the past decade in the employment rate for adults with disabilities ages 18 to 64 years (Kraus et al., 2018).

Given these ongoing negative outcomes, researchers continue to work to identify evidence-based predictors and practices associated with enhanced transition outcomes (Haber et al., 2016; Test et al., 2009). Enhancing student self-determination has been identified as a predictor of post-school outcomes, including having a paid job (Powers et al., 2012; Shogren et al., 2015) and accessing postsecondary education (Berry et al., 2012; Field et al., 2003). Interventions to promote self-determination implemented during secondary school have been shown to impact in-school outcomes such as access to the general education curriculum (Shogren et al., 2012), transition planning–related goal attainment (Shogren, Shaw, et al., 2018), and engagement and self-direction in planning for the transition from high school to adult life (Griffin et al., 2014). These findings are not surprising given that self-determination is defined by acting as the causal agent over one’s life and self-determination interventions target self-regulated goal setting and attainment skills, with an emphasis in secondary transition on building skills needed to set goals and develop action plans, including recruiting needed supports, for secondary and post-school goals (Wehmeyer et al., 2017).

Interventions to promote self-determination, however, may not be consistently implemented in schools, despite such interventions being identified as being an evidence-based practice (National Technical Assistance Center on Transition [NTACT], 2016). As a result, previous research has shown significant variability in self-determination outcomes for students with varying disability labels in secondary schools. For example, researchers have found differences in self-reported, self-determination based on disability label, with students with intellectual disability and emotional disturbance (ED) reporting lower levels of self-determination than peers with learning and other disabilities (Carter et al., 2006). In addition, emerging research suggests that students with autism spectrum disorder (ASD) tend to report some of the lowest levels of self-determination, perhaps even lower than their peers with intellectual disability only (Chou et al., 2017). Researchers have also found differences based on race, ethnicity and interactions between disability label, race/ethnicity, and self-determination outcomes (Shogren, Burke, et al., 2018; Shogren, Kennedy, et al., 2014).

One limitation of studies that have examined differences in self-determination across disability categories is the use of a convenience sample (Carter et al., 2006); however, a line of work using data from the National Longitudinal Transition Study 2 (NLTS2) provided nationally representative information about self-determination in adolescents with diverse disability labels. NLTS2 collected data on in-school and post-school outcomes over a 10-year period with a nationally representative sample of students across all 12 IDEA disability categories recognized by IDEA at the secondary level. NLTS2 directly assessed the self-determination of students with disabilities while they were still in school using a subset of items from The Arc’s Self-Determination Scale (SDS; Wehmeyer & Kelchner, 1995), a self-determination measure commonly used in the disability field. Because a subset of items was selected for the direct assessment, representing only three of the four subscales on the SDS, analyses of NLTS2 data have focused on examining differences in autonomy, psychological empowerment, and self-realization, three characteristics of self-determination. In the first analysis of NLTS2 self-determination data, Wagner et al. (2007) summed items from each subscale and categorized students into high, medium, and low score groups for autonomy, psychological empowerment, and self-realization. Key findings included that most youth tended to rate themselves high in self-realization and students with ASD showed the lowest levels of autonomy and psychological empowerment among the 12 disability groups.

Another study that used NLTS2 to investigate self-determination across disability groups was conducted by Shogren, Kennedy, et al. (2014). The authors used multi-group confirmatory factor analysis (CFA) to both examine the degree to which the included items from the SDS adequately represented each of the three characteristics of self-determination assessed (autonomy, self-realization, and psychological empowerment) and then to test for measurement invariance (i.e., are the same constructs being measured across disability groups) and latent differences in means, variances, and correlations across disability groups. The authors hypothesized that there would be conceptual disability groupings that would emerge based on disability-related characteristics, and initially tested three conceptual disability groupings: students with high-incidence disabilities (learning disabilities, emotional disturbances, speech or language impairments, and other health impairments); cognitive disabilities (ASD, intellectual disability, multiple disabilities, and traumatic brain injury, deaf-blindness); and sensory and physical disability (visual impairment, hearing impairment, and orthopedic impairment). Results indicated that students with intellectual disability, traumatic brain injury, and orthopedic impairments could not be collapsed with any other group; thus, the findings suggested six distinct groups that reflected different mean levels of autonomy, psychological empowerment, and self-realization. Students with intellectual disability and cognitive disabilities (ASD, multiple disabilities, deaf-blindness) scored significantly lower than all other groups in their psychological empowerment. For latent variances, all groups differed significantly from the reference group—high-incidence disabilities—except for the traumatic brain injury group, suggesting variability within and across disability groups that had implications for research and practices related to promoting self-determination in secondary transition planning.

Rationale for the Present Study

Evidence indicates ongoing negative outcomes experienced by students with disabilities in the transition from school to adult life (Newman et al., 2011). Enhancing self-determination has been shown to promote more positive outcomes (Berry et al., 2012; Shogren et al., 2015); but there are established disparities in self-determination outcomes across disability groups. Existing data, however, informing differences across disability groups (i.e., NLTS2) are over a decade old. Since then, multiple federally funded initiatives, such as the NTACT, have provided educators with technical assistance and resources that can be used to promote student self-determination. In addition, several randomized controlled trials have produced evidence of effective interventions (e.g., Lee et al., 2008; Shogren et al., 2015, 2020) that enhance student self-determination. These initiatives and research efforts may have an impact on schools’ capacities to promote student self-determination and the resulting self-determination levels of students across multiple disability categories served in secondary special education, necessitating analysis of emerging data on self-determination outcomes. Further study is particularly important as research in other areas has suggested that despite efforts, there may actually be decreases in school’s engagement of students in transition planning; for example, high school students who reported meeting with school staff to discuss post-high school transition plans decreased from 79% in 2003 to 70% in 2012 (Liu et al., 2018). In addition, the characteristics of the population of students with disabilities receiving special education services have changed significantly since NLTS2 data were collected; there have been significant changes and growth in certain disability categories, like ASD (Office of Special Education Programs, 2018).

Given these issues, there is clear need to reexamine the autonomy, psychological empowerment, and self-realization of students with disabilities using contemporary data sources to inform needed research and ongoing practice initiatives. Recognizing the need for updated data on the secondary school experiences of students with disabilities, NLTS2012 was funded to provide an update on the data collected under NLTS2. It provides data on the experiences of a nationally representative sample of secondary students with and without disabilities ages 12 to 23 years. Like NLTS2, NLTS2012 collected direct assessment information from students on three characteristics associated with self-determination (i.e., autonomy, psychological empowerment, and self-realization), using a subset of items from The Arc’s Self-Determination Scale (SDS) (Wehmeyer & Kelchner, 1995), thus allowing for an updated analysis of the self-determination status of students with disabilities, which is the goal of this article.

The present study aims to (a) describe students’ autonomy, psychological empowerment, and self-realization using NLTS2012 data and to (b) replicate and extend previous studies that used NLTS2 data. As will be further described, we built on the Shogren, Kennedy, et al. (2014) analytic model, using multi-group CFA to examine differences across seven groups, including the six disability groups established by Shogren et al., with the separation of the ASD group given emerging research suggesting the unique aspects of self-determination development in this population (Chou et al., 2017). These seven disability groups are as follows: high-incidence disabilities (learning disabilities, emotional disturbances, speech or language impairments, and other health impairments), sensory disabilities (visual and hearing impairment), intellectual disability, traumatic brain injury, orthopedic impairments, ASD, and multiple disabilities, which encompasses multiple disabilities and deaf-blindness. Questions addressed by this study were as follows:

Research Question 1 (RQ1): Can measurement invariance be established among the seven disability groups using NLTS2012 data?

Research Question 2 (RQ2): If so, what are the latent differences (i.e., means, variances, and correlation) in autonomy, psychological empowerment, and self-realization in youth across these seven disability groups?

Method

NLTS2012 Sample

The purpose of the NLTS2012, funded by the U.S. Department of Education, was to obtain nationally representative information on the characteristics, expectations, as well as the special education and transition services students with disabilities received in secondary school (Lipscomb et al., 2017). Unlike NLTS2, NLTS2012 is a cross-sectional study (data were collected once from each participant who ranged in age from 12 to 23 years between 2011 and 2013) and does not include data on post-school outcomes (e.g., employment or postsecondary education). Although NLTS2012 did include a sample of youth without disabilities, to replicate previous research, our focus was on the data collected to be representative of students with disabilities. Like NLTS2, NLTS2012 was structured to be representative of the 12 disability groups under IDEA (i.e., ASD, deaf-blindness, emotional disturbance, hearing impairment, specific learning disability, intellectual disability, multiple disabilities, orthopedic impairment, other health impairment, speech or language impairment, traumatic brain injury, and visual impairment).

Youth were selected for inclusion in NLTS2012 in a two-stage sampling process. First, a stratified random sample of local education agencies was selected, with stratification based on geographic region (i.e., Northeast, Southeast, Midwest, West), district size (enrollment in Grades 7–12), and community wealth (i.e., the proportion of district living below poverty level). Second, youth were randomly selected from each of the special education disability categories until sampling targets were met. Data from sampled youth were weighted to create a nationally representative sample by disability category and type of school district. Following this sampling procedure, approximately 12,980 parent surveys and 11,120 youth surveys were completed between 2011 and 2013, representing 57% and 48% weighted response rates for parent and the full youth survey, respectively (see Burghardt et al., 2017 for more information).

Data Source

Two questionnaires were administered as part of NLTS2012, one directed to youth and one to their parents. The youth questionnaire covered topics such as youth perceptions about schools, participation in individualized education program and transition-planning meetings, extracurricular and social activities, and expectations for the future. The self-determination items (n = 21) were included in the youth questionnaire, and as such, this was the data source for our analyses.

Although proxy responses were allowed for some items in NLTS2012 when the youth was unable to respond, this was not the case for the 21 self-determination items, as previous research has suggested significant differences between self- and proxy-report. Approximately 84% of students in the NLTS2012 sample were able to complete surveys without proxy responses (Burghardt et al., 2017), and thus, this 84% of the sample contributed data to the present analyses.

Sample Used in the Present Study

As noted, the data used here were students with an identified disability included in NLTS2012 who were (a) eligible for special education services and supports and (b) able to answer self-report questions, without proxy support (n = 7,140). Table 1 lists the sample broken down the by 12 disability categories and weighted percentages for each disability group. The average age of the targeted sample was 16 years of age, ranging from 12 to 23 years. The sample was 35% female and 65% male. About half of the students were White, Asian, or Other race (58%) and 19% were Black. Around 22% were Hispanic students. The parent reported annual income was US$0 to US$40,000 per year for 51% of households; US$40,001 to US$80,000 per year for 23% of households; US$80,001 to US$120,000 per year for 11% of households; and US$120,000 or more per year for 8% of households.

Table 1.

Percentage of Students by Disability Category Who Completed or Partially Completed the Youth Questionnaire (n = 10,380).

Disability category	n (Unweighted)	Weighted percentage
ASD	1,080	6.3
Deaf-blindness	140	0
Emotional disturbance	1,230	8.9
Hearing impairment	570	1.3
Intellectual disability	1,330	10.2
Multiple disabilities	1,000	2.7
Orthopedic impairments	510	1.0
Other health impairments	1,270	14.8
Specific learning disability	1,610	49.3
Speech or language impairment	1,080	4.4
Traumatic brain injury	290	.6
Visual impairment	260	.5

Source. U.S. Department of Education, National Center for Education Statistics, National Longitudinal Transition Study 2012 (NLTS2012).

Note. The unweighted sample size is rounded to the nearest 10 per the requirement of the restricted data use agreement. ASD = autism spectrum disorder.

Self-Determination Assessment

NLTS2012 included a subset of questions from The Arc’s Self-Determination Scale (SDS) (Wehmeyer & Kelchner, 1995). The full SDS includes 72 items that assess four essential characteristics of self-determination (i.e., autonomy, self-regulation, psychological empowerment, and self-realization) defined by the functional theory of self-determination (Wehmeyer, 1996). Psychometric studies of the SDS have been conducted in several studies with diverse disability populations, including students with intellectual disability, learning disabilities, physical disabilities, emotional disturbances, speech impairment, other health impairments, and ASD. Researchers have verified the theoretical structure of SDS and found that autonomy, self-regulation, psychological empowerment, and self-realization are four related but distinct latent constructs (Shogren et al., 2008; Shogren et al., 2012). A total of 21 items were selected from three of the four subscales of SDS: autonomy (seven of 32 items), psychological empowerment (seven of 16 items), and self-realization (seven of 15 items). These items differed from NLTS2, given more recent research that examined psychometric properties of a short form of the SDS (Wehmeyer et al., 2011), necessitating reexamination of measurement invariance across the disability groups in this study as the items differed. Furthermore, given that one subscale of the SDS in NLTS2012 (as in NLTS2) was fully excluded given its open-ended response format, it remained necessary to focus on analyzing three of the four characteristics of self-determination measured on the SDS (autonomy, psychological empowerment, and self-realization), not overall self-determination. See Appendix A of the Online Supplemental Material for descriptions of the 21 items and response options included in the current analysis.

Analytic Procedures

Missing data

Of the total sample of students with disabilities identified in NLTS2012, 48.6% of the youth did not have data on the youth survey. This included 40% (n = 6,900) who were missing both caregiver and youth survey (i.e., complete missing data) and 8.6% (n=1,490) who only have data from the parent survey (i.e., only proxy respondents). We excluded these 48.6% of students in our analysis. Therefore, some data from both parent and youth surveys were available for approximately 51% (n = 8,880) of the total NLTS2012 sample. Of these students, an additional 1,740 respondents were missing data on all self-determination items but had responses on other items on the youth survey, so they were not excluded on the previous step. After excluding this group, the final sample size was 7,140. About 80 students had missing data on one or more of the self-determination items, but so long as they had a response for one item, these data were treated as missing and handled using FIML.

Measurement invariance

For Research Question 1, we first conducted an initial CFA using the entire sample (12 disability groups collapsed) to confirm that the overall model fits well and to explore initial factor and correlation structures. Prior to conducting the CFA, we recoded the seven self-realization items to be consistent with other SD items, so that a higher score indicated more positive ratings. Next, we developed a parceling scheme by counterbalancing on the basis of factor loadings in the initial model that was utilized in all subsequent multiple group comparisons (Little et al., 2002, 2013).

To be consistent with Shogren, Kennedy, et al. (2014), we first examined an unparcelled model, then the parceled model. This approach was to ensure that parameter estimates were similar for both models. The preliminary non-parceled CFA involved ordinal items; therefore, we used the means and variances–adjusted weighted least squares estimator (WLSMV). For the final models using parceled items, we used maximum-likelihood (ML) estimation. We conducted all analyses in Mplus, Version 8.1 (Muthén & Muthén, 2017).

Establishing measurement invariance, also known as factorial invariance, is the process of determining if the latent constructs in our common factor analysis model measure the same constructs in each group. In other words, it assesses whether participants across disability groups conceptualize autonomy, psychological empowerment, and self-realization, and those constructs’ relationships, in the same way across groups. This is essential in assessing the fundamental similarities of the constructs across groups of people. Measurement equivalence among the groups was tested in three phases (Meredith, 1993). First, we tested configural invariance by constraining all groups to be equal in terms of their fixed and freed parameters. Specifically, construct variances and means were held constant across groups. Next, we tested weak factorial invariance by further constraining the model to have equal factor loadings across all groups but freeing construct variances in all groups but the first. Third, we tested for strong factorial invariance in which, among the other constraints, we constrained the indicator means to be equal across groups. We then evaluated each step of invariance using a relative change in comparative fit index (CFI). Each step of invariance was evaluated for a change in CFI less than .01 (Cheung & Rensvold, 2002). If this standard was met between each nested model, invariance was supported (Little et al., 2013).

Latent construct differences

We used structural equation modeling (SEM), in particular, multiple-group CFA, based on the means and covariance structures (MACS) model (Little, 1997). After establishing measurement invariance in Research Question 1, we conducted an omnibus test to investigate the homogeneity of the variance—covariance matrix across groups. We then tested equality of variances, covariances, and means, across groups. If these tests showed inequality, we further examined all parameters. We strategically examined bands of latent similarities and differences in the means, standard deviations, and correlations among the constructs and across groups. Because we adopted the effects coding method of scale identification and rescaled items, all means are on the original scale and the variances and covariances are estimated as standard deviation and correlations. In other words, effects coding with rescaled constructs estimates latent mean values, latent standard deviations, and latent correlations in the observed metric of the indicator, weighed by the degree of contribution each indicator makes to a construct and corrected for measurement error. This method is advantageous over the marker-variable method because estimated latent means and variances will not vary depending on the indicator chosen as the marker variable. In addition, with the MACS model, direct comparisons on latent parameters across groups can be made using nested chi-square tests. All model fit information is identical regardless of the scaling method used.

Results

Measurement Invariance

CFA using unparcelled items yield good model fit (root mean square error of approximation [RMSEA] = 0.020, CI 95% = [0.019, 0.022], CFI = 0.958; Tucker–Lewis Index [TLI] = 0.952, χ² = 727.99, df = 186, p < .001). Fit indices using unparceled items are comparable with those using parceled items (RMSEA = 0.026, 95% CI = [0.022, 0.030], CFI = 0.969, TLI = 0.954, χ² = 136.54, df = 24, p < .001).

To explore measurement invariance across the seven disability groups constructed from the NLTS2012, we followed the procedures described in the methods section. Table 2 shows the freely estimated model fit the data well: $(χ^{2} (168) = 520.85, RMSEA = 0.045, CFI = 0.971) .$ To test measurement invariance, we applied constraints across loadings and intercepts and did not find significant differences across the seven disability groups. At each stage of invariance testing, CFI changes were less than 0.01 and/or nested models fell within 90% CI of RMSEA for each nested model comparison, showing invariance. This means that the same constructs were measured across groups. Once measurement invariance was established, we were then able to examine differences in the latent means, variances, and covariance.

Table 2.

Model Fits Statistics for Evaluation of Measurement Invariance Using FIML.

Invariance/equality test	$χ^{2}$	$d f$	$R M S E A$	$R M S E A$ $90 % C I$	$C F I$	ΔCFI
Measurement invariance
Configural (seven groups)	520.846	170	0.045	0.041–0.050	0.971	–
Metric (seven groups)	650.947	200	0.046	0.042–0.050	0.963	0.008
Scalar (seven groups)	796.707	240	0.048	0.044–0.051	0.954	0.009
Latent parameter test					$Δ χ^{2}$	Δ $p - value$
Homogeneity of Var/Cov	946.851	280	0.049	0.045–0.052	138.56	<.001
SD	862.179	260	0.049	0.045–0.052	65.472	<.001
Correlation	905.349	260	0.050	0.046–0.053	97.058	<.001
Homogeneity of means	938.897	260	0.051	0.047–0.054	142.190	<.001
Band constraints
Latent means
AUTO	800.409	243	0.047	0.044–0.051	3.702	0.295
PSYE	796.960	243	0.047	0.044–0.051	0.253	0.969
SREAL	800.847	243	0.047	0.044–0.051	4.140	0.247
Latent stds
AUTO	801.285	244	0.047	0.044–0.051	4.578	0.333
PSYE	803.565	246	0.047	0.044–0.051	6.858	0.334
SREAL	803.018	244	0.047	0.044–0.051	6.311	0.177
Latent corrs
AUTO × PSYE	801.838	246	0.047	0.043–0.051	5.131	0.527
PSYE × SREAL	798.205	244	0.047	0.044–0.051	1.495	0.828
SREAL × AUTO	801.493	245	0.047	0.044–0.051	4.786	0.443
Overall
Latent means	806.533	249	0.047	0.043–0.050	9.826	0.364
Latent stds	810.134	255	0.047	0.043–0.050	13.427	0.569
Latent corrs	809.815	255	0.046	0.043–0.050	13.108	0.594

Source. U.S. Department of Education, National Center for Education Statistics, National Longitudinal Transition Study 2012 (NLTS2012).

Note. RMSEA = root mean square error of approximation; CFI = comparative fit index; AUTO = autonomy; PSYE = psychological empowerment; SREAL = self-realization.

Latent Construct Differences

Tests for homogeneity of latent variance, covariances, and equality of means revealed significant differences across groups (p < .001) that needed to be further explored. To understand the pattern of differences among the latent means and variances among the seven groups (i.e., high-incidence disabilities group [learning disabilities, emotional disturbances, speech or language impairments, and other health impairments], sensory disabilities [visual and hearing impairment], multiple disabilities [multiple disabilities and deaf-blindness], intellectual disability, traumatic brain injury, orthopedic impairments, and ASD), we systematically looked at the impact of adding or freeing latent constraints across the hypothesized seven group structure. We established a structural model to test the differences in autonomy, psychological empowerment, and self-realization.

Table 3 shows the latent means of the seven groups for autonomy, psychological empowerment, and self-realization. When investigating each construct within and across groups, significant differences were found. Across all constructs’ latent means, a consistent pattern was found. In the orthopedic impairments group as well as the ASD group, both had significantly different latent mean levels from each other and all remaining groups. Specifically, students with orthopedic impairments had latent means that were higher than all other groups in the constructs of autonomy, psychological empowerment, and self-realization. Students with ASD had latent means that were lower than all other groups for all three SD constructs.

Table 3.

Latent Means and Standard Deviations Based on Effects Coding.

Construct	Latent M (SD)	High incidence	Sensory disability	Intellectual disability	Orthopedic impairments	Multiple disabilities	Traumatic brain injury	ASD
AUTO	Latent M (Latent SD)	2.643(0.563)	2.651(0.518)	2.566(0.607)	2.717(0.621)	2.599(0.606)	2.664(0.587)	2.435(0.543)
PSYE	Latent M (Latent SD)	1.911(0.113)	1.911(0.115)	1.899(0.122)	1.923(0.104)	1.895(0.122)	1.897(0.126)	1.847(0.123)
SREAL	Latent M (Latent SD)	1.935(0.105)	1.939(0.092)	1.911(0.127)	1.951(0.085)	1.922(0.120)	1.932(0.123)	1.910(0.120)

Source. U.S. Department of Education, National Center for Education Statistics, National Longitudinal Transition Study 2012 (NLTS2012).

Note. High incidence group consists of learning disabilities, emotional disturbances, speech or language impairments, and other health impairments; multiple disabilities consists of multiple disabilities and deaf-blindness groups. ASD = autism spectrum disorder; AUTO = autonomy; PSYE = psychological empowerment; SREAL = self-realization.

In determining the patterns of differences among the remaining disability groups, the data, model parsimony, and chi-square difference tests informed the identification of patterns, which were the same for each latent construct. Specifically, the high incidence and the sensory disability group had statistically equivalent latent mean levels across the three constructs. The intellectual disability, multiple disabilities, and traumatic brain injury groups had also had equivalent latent means across the three constructs.

To further explore differences, we examined effect sizes for the seven disability groups, which are presented in Table 4. Cohen (1992) suggests 0.20 is indicative of a small effect, 0.50 a medium effect, and 0.80 a large effect. The effect size is the standardized difference in latent means between two groups. Medium effect sizes were found when comparing the orthopedic impairment group and the ASD group for all three subscales (i.e., autonomy, psychological empowerment, and self-realization).

Table 4.

Effect Size for Group Differences.

Construct	Effect size	A vs. B	A vs. Ortho	A vs. ASD	B vs. Ortho	B vs. ASD	Ortho vs. ASD
AUTO	Effect size	0.088	0.131	0.376	0.203	0.269	0.493
AUTO	[95% CI]	[0.029, 0.147]	[0.13, 0.248]	[0.293, 0.459]	[0.078, 0.328]	[0175, 0.362]	[0.353, 0.632]
PSYE	Effect size	0.122	0.104	0.557	0.226	0.435	0.661
PSYE	[95% CI]	[0.063, 0.18]	[–0.013, 0.222]	[0.473, 0.64]	[ 0.101, 0.351]	[0.341, 0.529]	[0.52, 0.802]
SREAL	Effect size	0.164	0.144	0.258	0.273	0.074	0.363
SREAL	[95% CI]	[0.105, 0.223]	[0.027, 0.261]	[0.175, 0.341]	[0.148, 0.398]	[–0.019, 0.167]	[0.225, 0.502]

Source. U.S. Department of Education, National Center for Education Statistics, National Longitudinal Transition Study 2012 (NLTS2012).

Note. Group A: high-incidence, sensory disability; Group B: intellectual disability, multiple disabilities, traumatic brain injury, orthopedic impairments, and ASD. ASD = autism spectrum disorder; AUTO = autonomy; PSYE = psychological empowerment; SREAL = self-realization.

All latent standard deviations across groups were similar for the psychological empowerment construct, but for the self-realization construct, there were higher latent standard deviations in the intellectual disability, multiple disabilities, traumatic brain injury, and ASD groups, indicating greater variability in self-realization outcomes. The sensory disability and orthopedic impairment group had lower, but similar standard deviations, indicating less variability in self-realization. The latent standard deviation of the high incidence disability group differed from all other groups, indicating a unique pattern. Regarding the autonomy construct, there were higher latent standard deviations in the intellectual disability, multiple disabilities, traumatic brain injury, and orthopedic impairments groups.

The latent correlations between self-determination constructs were also estimated using the MACS model with standardization constructs (aka, phantom constructs; Little et al., 2013). The common correlations among the constructs for all disability groups were autonomy and self-realization (r = .20), autonomy and psychological empowerment (r = .29), and psychological empowerment and self-realization (r = .79). Across groups, there was no significant difference between the latent correlations for autonomy and psychological empowerment (see Table 5). The freely estimated correlations ranged from .24 to .37, and the constrained, common correlation was .27. For psychological empowerment with self-realization, the high incidence, orthopedic impairments, multiple disabilities, and ASD groups had statistically equivalent correlations whereas the intellectual disability and traumatic brain injury groups differed but were equal to each other. The sensory disability group was treated as its own group because it differed from other groups. Testing of the latent correlation between self-realization and autonomy showed a significant difference between two distinct groups. The high incidence disability and traumatic brain injury groups had very low construct correlations, and the remaining five groups (sensory, intellectual, orthopedic, multiple disabilities, and ASD) showed a common, higher correlations (see Table 5).

Table 5.

Latent Correlations by the Seven Groups Based on Effects Coding.

Construct	Latent correlation	High incidence	Sensory disability	Intellectual disability	Orthopedic impairments	Multiple disabilities	Traumatic brain injury	ASD
AUT × PSYE	Latent corr	.247	.274	.263	.269	.366	.348	.321
	(constrained)	(.265)	(.265)	(.265)	(.265)	(.265)	(.265)	(.265)
	[r²]	[.070]	[.070]	[.070]	[.070]	[.070]	[.070]	[.070]
PSYE × SREAL	Latent corr	.859	.645	.585	.919	.890	.571	.809
	(constrained)	(.860)	(.644)	(.587)	(.860)	(.860)	(.587)	(.860)
	[r²]	[.740]	[.415]	[.344]	[.740]	[.740]	[.344]	[.740]
SREAL × AUT	Latent corr	.197	.260	.245	.212	.325	.026	.254
	(constrained)	(.198)	(.248)	(.248)	(.248)	(.248)	(.198)	(.248)
	[r²]	[.039]	[.062]	[.062]	[.062]	[.062]	[.039]	[.062]

Source. U.S. Department of Education, National Center for Education Statistics, National Longitudinal Transition Study 2012 (NLTS2012).

Note. Corr = correlation; AUT = autonomy; PSYE = psychological empowerment; SREAL = self-realization.

Discussion

Despite more than two decades of federal legislation and research focused on implementing evidence-based practices to enhance transition outcomes, including interventions to promote self-determination, disparities in self-determination outcomes continue to exist among secondary students with disabilities (Chou et al., 2017; Shogren, Little, et al., 2018). The purpose of this study was to (a) replicate findings reported in a previous, nationally representative study that examined the impact of disability label on the autonomy, psychological empowerment, and self-realization of students with disabilities in secondary school (Shogren, Kennedy, et al., 2014) and to (b) provide an updated analysis using a more recent sample from the NLTS2012 data. Our findings show that measurement invariance can be established using the new set of indicators of autonomy, psychological empowerment, and self-realization adopted in NLTS2012 among seven disability groups: high incidence (learning disabilities, emotional disturbances, speech or language impairments, and other health impairments), sensory disabilities (visual and hearing impairment), multiple disabilities (multiple disabilities and deaf-blindness), intellectual disability, traumatic brain injury, orthopedic impairments, and ASD. These results suggest that latent constructs for autonomy, psychological empowerment, and self-realization can be created and utilized using NLTS2012 data to explore characteristics of self-determination and relations between other student reported experiences and variables from the parent survey, which has implications for ongoing research exploring the relationship between self-determination and other outcomes.

Measurement Invariance Across Disability Groups

In examining measurement invariance, we extended the previous analyses with NTSL2 data, by separating the group of students with ASD. Research conducted since NLTS2 data collection suggests that students with ASD have significantly lower levels of autonomy than students with intellectual disability and learning disabilities (Chou et al., 2017). We were able to establish measurement invariance among the seven groups, including the distinct ASD group, suggesting that the students from these seven groups interpreted the self-determination items in a conceptually similar way. Few studies have compared students with ASD with other disability groups on autonomy, psychological empowerment, and self-realization using a nationally representative sample. Given that there has been a 189% increase in the number of high school students with ASD under IDEA from 2006 to 2015, it is critical to establish that the same set of items can be utilized to measure self-determination in students with ASD allowing comparisons in outcomes across disability groups.

Latent Means Differences Across Disability Groups

Consistent with previous findings using the NLTS2 data (Shogren, Kennedy, et al., 2014), our results show students with ASD, intellectual disability, and multiple disabilities continue to lag behind their peers with high incidence disabilities in their autonomy, psychological empowerment, and self-realization. This disparity is concerning given that it has been over a decade since the NLTS2 data were collected. Over the past decade, there have been a growing number of studies devoted to promoting self-determination skills in students with disabilities (Burke et al., 2019; Shogren et al., 2012; Wehmeyer et al., 2011), as well as national and state level initiatives that promote self-determination in individuals with disabilities, specifically with students with intellectual disability (Shogren, Shaw, et al., 2018).

One strategy to address this disparity is to provide implementation supports to schools to ensure high fidelity of implementation of evidence-based programs to enhance student’s self-determination (Shogren et al., 2015). Evidence from the field of implementation science shows that evidence-based intervention alone is not sufficient to result in desired outcome. Rather, improved outcomes are a result of effective intervention, effective implementation, and enabling context (National Implementation Research Network, 2016). Results from a recent statewide implementation of the Self-Determined Learning Model of Instruction (SDLMI), an evidence-based intervention to promote self-determination and post-school outcomes, suggest providing implementations supports is critical for program implementation fidelity and sustainability and teachers need to be provided with coaching and instructional materials to promote self-determination for all students, particularly for those with significant support needs (Burke et al., 2019).

One specific finding of note is that, consistent with findings reported using convenience samples, students with ASD showed lower levels of self-determination compared with their peers with intellectual disabilities and multiple disabilities across all three aspects of self-determination (autonomy, psychological empowerment, and self-realization). These results suggest lower levels of self-determination may not be related to intellectual functioning alone because only about 30% of children with ASD are also diagnosed with an intellectual disability (Baio et al., 2018). Researchers have proposed that the difficulties in social interaction, social skills, and related executive functioning skills may be related to self-determination (Carter et al., 2006; Chou et al., 2017). Future studies need to elucidate the relation between social skills and self-determination and examine whether interventions addressing social skills may results in improved self-determination skills.

These findings confirm the role that contextual factors (i.e., personal and environmental factors; see Shogren, Luckasson, et al., 2014) likely play in explaining variabilities in self-determination outcomes among students with disabilities. Factors discussed in the literature related to self-determination include students’ characteristics, such as disability label, race, ethnicity, gender, and socioeconomic status, as well as environmental factors including expectations and supports available at school, home, and in the community (see Mumbardó-Adam et al., 2017 for a review). Access to inclusive opportunities may be one factor that influenced self-determination. For example, Carter and colleagues (2013) studied 627 students with disabilities in one of the four settings: mostly general education, general and special education, mostly special education, and other setting and found educational placement was the most robust predictor of self-determination skills.

Despite the evidence supporting the positive effect of inclusive educational settings on self-determination, unfortunately, students with ASD, intellectual disability, and multiple disabilities are more likely to be in self-contained classrooms and less likely to receive supports from school to access inclusive opportunities (Kurth et al., 2016). Consequently, these students may experience fewer opportunities and less support to learn and practice self-determination skills, such as goal setting and problem-solving, if their environments are characterized by low expectations and lack of supports/accommodations. Future studies are needed to delineate the relation among educational placement, teachers’ expectation, and self-determination skills. Findings from such work could help elucidate the need for specific supports related to supporting self-determination in inclusive settings.

Differences in Latent Variance

The seven groups had similar standard deviations for scores on the psychological empowerment construct. The groups differed, however, on the autonomy and self-realization construct, with greatest variabilities in the autonomy subscale. These results are consistent with previous findings using NLTS2 (Shogren, Kennedy, et al., 2014), suggesting the ongoing importance of documenting variability in self-determination outcomes within disability groups and beginning to work to understand the variety of factors related to disability, other personal factors, and environment experiences in shaping outcomes. Only when we comprehensively understand factors contributing to such variability among students with disabilities, can we then target effective, individualized interventions that address how students with different characteristics respond to self-determination interventions.

Differences in Latent Correlation

Consistent with previous findings using NLTS2 data (Shogren, Kennedy, et al., 2014), psychological empowerment and self-realization were highly correlated, whereas weaker correlations are found between autonomy and psychological empowerment, as well as between autonomy and self-realization. These findings align with the recent re-conceptualization of self-determination under Causal Agency Theory. This theory builds on work related to autonomy, psychological empowerment, and self-realization and defined broader characteristics that contribute to self-determination, including volitional action, agentic action, and action-control belief (Shogren et al., 2015; Wehmeyer et al., 2017). Autonomy is one of the two components under volitional action, whereas psychological empowerment and self-realization are components of action-control beliefs, supporting the correlational findings in this study. Volitional action are self-initiated actions based on one’s interests, preferences, and goals and may include key elements such as choice-making, decision-make, and goal setting (Shogren et al., 2015). Different from volitional action, action-control beliefs focus on the belief that one’s actions, through specific means, lead to desired outcomes, and self-determined people are empowered and striving for self-realization during the process of setting and attaining goals. Given the newness of Causal Agency Theory and the recent introduction of the Self-Determination Inventory (SDI), a new measure of self-determination aligned with Causal Agency Theory (Shogren, Little, et al., 2018), future research using the SDI and comparing findings to the SDS may be useful to further identify specific factors that influence the development of self-determination and its specific characteristics.

Limitations and Future Research and Practice Recommendations

There are several limitations to note as well as future directions to advance the understanding of factors related to self-determination in research and practice. First, as we have discussed earlier, only a subset of items from the SDS were included. These items do not capture all four constructs described in the functional self-determination theory. Furthermore, the SDS is no longer the most current assessment tool available, and future work should consider examining the use of newly developed SDI. Second, the NLTS2012 study did not include students with significant support needs for whom self-report is not feasible. As a result, findings from this study may not generalize to students with significant support needs. More work is needed in the field to determine how to assess self-determination in students with significant support needs, so as not to leave this group out of research and practice. Furthermore, in this study we only assessed the impact of disability categories on self-determination and did not assess other contextual factors discussed in the literature (Mumbardó-Adam et al., 2017). Our results, however, do provide directions for future research as described below. Finally, future studies may consider using NLTS2012 data to investigate whether students with ED score lower on self-determination compared with other disability groups, as suggested in a previous study (Carter et al., 2006).

Research is needed to understand contextual factors associated with self-determination in students with disabilities, particularly students with intellectual disability, multiple disabilities, and ASD given the variability within these disability groups. Understanding how these factors interact with each other and how they impact self-determination in students will facilitate the development of individualized interventions. A series of studies have explored these relationships using NLTS2 data (Shogren, Kennedy, et al., 2014); however, replication studies using NLTS2012 are needed given changes in student demographics and national and state initiatives promoting self-determination since the NLTS2 data were collected. Another ongoing challenge is to assess self-determination in students with significant support needs for whom self-report may be difficult. Future research is needed to develop self-determination observational measures to assess students with complex communication needs and other significant support needs.

Conclusion

Although many efforts have been undertaken to promote self-determination in students with disabilities in secondary schools in the past 10 years, students with ASD, intellectual disability, and multiple disabilities continue to show lower levels of self-determination compared with students in other disability groups a finding that has not changed significantly. Thus, further understanding of contextual factors that contribute to the variability in student self-determination is critical to guide ongoing efforts to devise individualized interventions to enhance both self-determination and post-school transition outcomes. In addition, there is an urgent need to support implementation of evidence-based self-determination interventions in schools to reduce the disparity in self-determination among students with disabilities.

Footnotes

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.

ORCID iDs

Xueqin Qian

Karrie Shogren

Supplemental Material

Supplemental material for this article is available on the Journal of Disability Policy Studies website with the online version of this article.

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