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Abstract

The Individualized Education Program (IEP) goal attainment is a key indicator of achievement in educational performance for students with disabilities. Yet, little is known of how well autistic high school students are meeting their IEP goals and factors affecting their attainment. This study examined the relationship between self-determination, a positive predictor of goal attainment and post-school success, and IEP goal attainment using secondary data from the center on Secondary Education for Students with Autism Spectrum Disorder. Multilevel modeling was used to account for students nested within schools. Results suggest a positive link between self-determination and IEP goal attainment, while accounting for differences in demographics. However, the strength of this relationship varied across goal types (academic, independence/behavior, social, transition). Further research is needed to explore how self-determination interacts with individual, family, and school-level factors that influence IEP goal attainment, as well as to uncover the mechanisms through which self-determination supports the achievement of these goals.

Keywords

autism self-determination IEP goal attainment high school

The Individualized Education Program (IEP) is an educational roadmap required for children with disabilities. Its primary purpose is to ensure access to the most appropriate educational program within the least restrictive environment for the disabled student. A key component of a student’s IEP is measurable annual goals and objectives (Yell et al., 2003), which students can set and shape during their IEP meetings throughout their schooling years and transition planning meetings once they enter high school (Cameto et al., 2004; Shogren & Plotner, 2012). As part of best practices in transition planning (Shogren & Plotner, 2012), there is an increased emphasis on student participation in their IEP meetings in high school (Wagner et al., 2012). Unlike the structured curriculum and clear goals and benchmarks of earlier schooling, post-school life requires students to navigate less defined paths. Students must make informed decisions about their next life phase and identify their major goal for post high school graduation, which would then set the direction for setting of smaller, related goals in their IEP to work toward this major objective. The presence of goals and objectives in the IEP helps structure what students should be taught as part of their educational program and provides a direction for students to strive to make reasonable progress toward skills acquisition within a specific timeframe. Thus, IEP goal attainment is a key indicator of achievement in educational outcomes for students with disabilities and a way for schools to illustrate provision of a free, appropriate public education (Yell et al., 2020). However, despite the importance that the IEP holds, based on the current literature, we do not currently have a clear understanding of how well autistic high school students are achieving their IEP goals, as well as factors that influence goal attainment for this age group (Findley et al., 2022; Ruble & McGrew, 2013).

Researchers have investigated various personal, family, and school factors that influence IEP goal attainment for young students on the autism spectrum below the age of 12 (Love et al., 2020; Ruble & McGrew, 2013; Wong et al., 2017). They often found that teacher characteristics and behavior (burnout, stress, and self-efficacy) are significantly related to student IEP goal attainment. For personal characteristics, Ruble and McGrew (2013) found that while higher IQ, increased adaptive behavior, and decreased autism severity were significant predictors of positive goal attainment change, IEP quality and child engagement were stronger predictors of children’s progress toward IEP goals. Studies with samples of high school autistic students were mostly done in the context of specific intervention studies (e.g. COMPASS, Ruble et al., 2018), and goal attainment was used as a measure for effectiveness of intervention, rather than IEP goal attainment in the context of a regular high school programming. Goals were also presented only on a single outcome area (e.g. transition IEP goals, Ruble et al., 2019) in each study.

The transition period can be challenging for autistic adolescents, due to the additional difficulties they may face that can be attributed to characteristics of their disability and barriers in the environment (Hedges et al., 2014). For example, some students on the autism spectrum face challenges with executive functioning and adaptive behavior, which can translate into problems with planning, organization, future-oriented thinking, and processing information (Cribb et al., 2019). Their peers may struggle to understand their unique needs, resulting in poor impressions. These misconceptions then hinder social interactions and deplete avenues of social support and peer learning for autistic adolescents (Hedges et al., 2014; Huber & Carter, 2019). Autistic students also often have more limited leadership roles in the transition planning process (Shogren & Plotner, 2012), and we want to investigate ways in which we can best support students on the spectrum through this process, so they can better work toward achieving their desired post-school outcomes.

Self-determination has been recognized widely in the literature as a critical predictor of student goal attainment (Shogren et al., 2012, 2018, 2019), as well as subsequent post-school success (Mazzotti et al., 2021). Students’ active involvement in their IEP and transition planning meetings creates opportunities for learning and applying self-determined behavior (Field et al., 1998). Our current conceptualization of self-determination can be best understood using the Causal Agency Theory, which emphasizes how people define the actions and beliefs needed to engage in self-caused actions that allow them to fulfill basic psychological needs (Shogren et al., 2017). This means that self-determined people are those who tend to act and think in certain ways (i.e., taking self-determined actions) that will allow them to accomplish a specific end or achieve a goal they value. Researchers have identified ways to increase students’ overall level of self-determination through the implementation of educational interventions conducted in K-12 settings for students on the autism spectrum (Burke et al., 2020), suggesting that it is a malleable quality that can be nurtured. Yet, young adults on the autism spectrum have some of the lowest rates of self-determination of all young adults (Chou et al., 2017). In addition, compared with other disability groups, there are much fewer self-determination studies that focus on the autistic population. For example, there were seven times more participants with intellectual disability than autism in self-determination intervention literature (Burke et al., 2020). Given that self-determination is a malleable characteristic that can serve as a crucial contributor to effective involvement in transition planning and subsequent post-school success, it is a construct that requires further exploration and attention in this disability population.

Self-Determination and IEP Goal Attainment

Individuals with higher levels of self-determination are more likely to attain goals because skills critical to working toward goals map onto the same component skills leading to self-determination (Shogren & Wehmeyer, 2016). For example, individuals with greater self-monitoring skills (a component skill related to self-determination) can better evaluate their progress toward goals and help ensure consistent actions are taken toward desired goals (Harkin et al., 2016). Also, more self-determined individuals can take autonomous action and shape goals according to their preferences and interests. These preferred goals are more likely to be attained as these goals have personal meaning, and individuals are more motivated to work toward those goals (Hortop et al., 2013). The relationship between self-determination and goal attainment is also a reciprocal one. When individuals work to attain goals, their level of self-determination also increases (Raley et al., 2020). This is because fulfilling intrinsic and autonomous goals fulfills basic psychological needs related to self-determination (Shogren & Wehmeyer, 2017).

Within the education setting, students often strive to achieve mastery in various key domains supporting their development (Williams-Diehm et al., 2010) by working toward multiple self-set or teacher directed learning goals. These domains include academic content (for an example in mathematics learning, see Raley et al., 2020), and functional goals (e.g. gaining transition knowledge and skills to support transition to post-school; Wehmeyer et al., 2007) outlined in students’ IEPs. Past studies have found a positive impact of self-determination on academic achievement (Gaumer Erickson et al., 2015; Zheng et al., 2014). Researchers have also found interventions, which successfully led to improvements in students’ level of self-determination also significantly increased student goal attainment in academic and transition areas. These results show a positive relationship between self-determination and educational goal attainment (Lee et al., 2015; Shogren et al., 2012, 2018, 2019; Shogren, Hicks, et al., 2021; Wehmeyer et al., 2012), primarily for students with intellectual and learning disabilities.

Goal attainment in previous studies was commonly measured using student-set goals for the purpose of the study/intervention (Shogren et al., 2012, 2018), but was not examined in the context of students’ IEPs, nor in a natural school context without an intervention condition. The findings from a small-scale study (n = 6) by German and colleagues (2000) suggest that this same pattern of higher self-determination and higher goal attainment can also apply to the achievement of IEP goal attainment. Students who could better express their preferences and monitor progress toward goals (i.e., taking self-determined actions) had higher levels of IEP goal attainment. However, we do not know much about the impact of self-determination on IEP goal attainment for the autistic student population, as students with intellectual and learning disabilities composed most of the sample in previous studies that examined goal attainment. Less than 5% of the participants in the studies reviewed had autism in addition to other disability groups, and autistic students did not constitute the primary disability group examined (Shogren et al., 2012, 2018). In addition, although transition-aged youth have expressed a desire to work toward goals in other outcome areas as well (e.g., community living and social goals; Burke et al., 2020), researchers have mainly examined the impact of self-determination on goal attainment only on goals in academic and transition areas (Lee et al., 2015). This gap in the literature highlights areas for further exploration and study.

In addition, researchers have often found self-determination to be positively associated with post-school outcomes (Shogren et al., 2015). Yet there is currently no information on how increases in self-determination lead to increases in positive outcomes. And, the impact of self-determination instruction cannot only be measured in terms of post-school success, as it will be too late for high school educators to intervene post-graduation. Thus, establishing the relationship between self-determination and IEP goal attainment allows us to consider the use of goal attainment outcomes as yardsticks and proximal indicators that can speak both to the impact of self-determination interventions in improving high school goal attainment, and whether students are on track to achieving their desired post-school outcomes.

Aim of Study

Autistic adolescents often struggle during their transition planning (Chandroo et al., 2020; Snell-Rood et al., 2020) and notably fail to obtain their desired post-school outcomes (Gaona et al., 2019; Mills et al., 2022), often due to the additional difficulties they may face as a result of barriers in the environment that do not address their support needs (Hedges et al., 2014). Given that the goals in a student’s IEP drive their school experiences, as well as knowledge and skill acquisition, and in turn influence subsequent post-school outcomes (Alverson et al., 2019; Mazzotti et al., 2021), it is critical to identify factors that influence and support attainment of goals. Researchers have found that students with disabilities who are more self-determined tend to have higher levels of goal attainment, and being self-determined can also be beneficial in supporting adolescents in their transition to post-school (Mazzotti et al., 2021). Given that there are limited resources and limited time available in school to equip students with the necessary skills (Kucharczyk et al., 2015), we want to help schools focus their effort on the most critical and influential constructs and practices that can support the academic, functional, and post-school success of autistic students.

To address this gap in the literature, we used secondary data from the Center on Secondary Education for Students with Autism Spectrum Disorder (CSESA; Hume et al., 2022), the largest high school study conducted with autistic adolescents to date, and examined the following research question: Does autistic high school students’ self-determination predict their IEP goal attainment?

Method

Participants

This study drew data from the work of CSESA who investigated the implementation of a comprehensive school-based program focusing on the learning needs of autistic high school students. The original CSESA study was a cluster randomized control trial design, comparing student outcomes pre- and post-intervention across two groups (CSESA model and service-as-usual [SAU]) randomized at school level. Data were gathered across 2 years (2014–2016) and researchers found that the CSESA intervention significantly influences autistic student IEP goal attainment (see Hume et al., 2022 for a complete description of study). We used only the SAU subgroup for analysis, as the focus of our study was not to evaluate the CSESA intervention, so as not to have to account for treatment differences. This procedure is similar to that of other research groups that have used this data to examine other aspects of the dataset (Kraemer et al., 2022; Tomaszewski et al., 2019).

Sixty high schools participated from three states across the United States: North Carolina, Wisconsin, and California. For inclusion, schools needed to be public (not private or charter) and enroll students with and without disabilities. High schools were located in a mix of urban (40%), suburban (45%), and rural (15%) areas. Just over half had Title 1 eligibility (57%), and across schools, about 40% of the student population were eligible for free and reduced lunch (range: 4.9%–87.2%), as reported in Hume et al. (2022).

The Center on Secondary Education for Students with Autism Spectrum Disorder staff recruited adolescents with autism and their caregivers at high schools across the three states. Adolescents were enrolled in the study if they: (a) were between 13 and 22 years old, (b) had special educational eligibility of autism, (c) planned to remain in high school for 2 years after study enrollment, and (d) did not have a significant uncorrected vision/hearing impairment. All eligible participants received recruitment information, and all interested adolescents and caregivers voluntarily consented and assented to participate in the study. The study was conducted in full compliance with the university approved Institutional Review Board (IRB) protocols.

In total, 547 autistic high school students and their caregivers were enrolled in the CSESA study, with 244 students in the SAU group. Of these 244, 24 students were no longer enrolled in the SAU schools by Year 2 spring, the time point where outcome for this study was measured. Thus, 220 students across 30 high schools from the SAU group were included for analysis in this study. See Table 1 for a breakdown of student demographics.

Table 1.

Demographics and Student Characteristics Summary.

Characteristics	Overall (N = 220)
Gender
Male	188 (85.5%)
Female	32 (14.5%)
Race
American Indian/Alaskan Native	5 (2.3%)
Asian	13 (5.9%)
Black/African American	32 (14.5%)
White	127 (57.7%)
Multi-racial	15 (6.8%)
Other	12 (5.5%)
Missing	16 (7.3%)
Ethnicity
Hispanic	42 (19.1%)
Non-Hispanic	167 (75.9%)
Missing	11 (5.0%)
Family annual income
<$40,000	27 (12.3%)
$40,000–$79,000	55 (25.0%)
≥$80,000	82 (37.3%)
Missing	56 (25.5%)
State
North Carolina	85 (38.6%)
Wisconsin	58 (26.4%)
California	77 (35.0%)
Age
Mean (SD)	16.4 (1.48)
Median [Min, Max]	16.2 [13.8, 20.9]
IQ
Mean (SD)	85.0 (27.5)
Median [Min, Max]	90.0 [30.0, 139]
Missing	16 (7.3%)

Procedures

After recruitment, trained research staff administered a battery of assessments with the adolescents and had questionnaires completed by caregivers and teachers with direct knowledge of the autistic adolescent. The scores from this student-completed American Institutes for Research (AIR) self-determination scale (described in more detail below) were used from the start of the CSESA project (Year 1 Fall) and IEP goal attainment scores in Year 2 Spring to ensure temporal precedence of predictors to outcomes (MacKinnon, 2008).

Measures

IEP Goal Attainment

Goal Attainment Scaling (GAS; Kiresuk & Sherman, 1968; Ruble et al., 2012) is an individualized outcome measure that allows rating of students’ progress toward their identified goals. Based on an identified goal, GAS supports the creation of a 5-point rating scale with outcomes at each of the anchors being individually defined. In this study, the following rating scale was used: (a) 0–current performance, (b) 1–initial progress toward the goal, (c) 2–further progress toward goal, (d) 3–goal achieved, and (e) 4–progress beyond goal. The GAS has demonstrated validity and sensitivity for use with the autistic student population to monitor progress (Ruble et al., 2021).

To develop GAS rating scales, CSESA staff taught teachers how to scale students’ IEP goals using the psychometric equivalent testing format, where a rubric was used to standardize the process for writing each corresponding GAS goal (PET-GAS; Ruble et al., 2012), for students in SAU schools. Teachers identified goals from students’ IEPs in four component areas: academic (e.g. reading comprehension), social, independence and behavior, and transition outcomes. These areas were identified by CSESA investigators as high need domains for autistic high school students based on review of the literature. Teachers created GAS goals at the start of the CSESA study and provided ratings based on their student’s performance at the end. To establish reliability, CSESA staff conducted observations of students and collected agreement data on the teachers’ final rating of the GAS goals on 26.3% of the sample. Interrater agreement was 95%.

Goal Attainment Scaling goals were obtained from students’ existing IEP goals, and as such, not every student had a GAS goal in all component areas. Most students in the sample had GAS goals that in more than one area. See Supplemental Material for a breakdown of the number of goal areas covered in their GAS goals per student.

Also, each student may have more than one GAS goal in a component area (e.g. a student can have two academic-related IEP goals that were scaled using the GAS). For students with available GAS data on more than one goal in the same area, GAS scores across these goals were summed and averaged across the number of goals in that area. Students’ mean GAS scores in each of the four component areas, as well as an overall mean GAS score (tabulated across all goals) from Year 2 Spring semester were used for analysis in this study.

Self-Determination

The AIR self-determination scale (Wolman et al., 1994) measures an individual’s capacity and opportunities to be self-determined, forming the two subscales in the measure. To best align with the most up to date conceptualization of self-determination as presented in Causal Agency Theory, we used the capacity domain as measured in the AIR student form in Year 1 Fall to represent the self-determination construct in this study. Researchers have used the capacity subscale to represent self-determination in past studies (Biggs & Carter, 2016; Carter et al., 2013), although parents were the reporter on the scale. We used self-report in this study to present self-determination to spotlight and amplify the autistic perspective (Fletcher-Watson et al., 2019; Milton, 2014).

For the AIR student form, the capacity subscale comprises of 12 items. Each item is worded as a behavior, and respondents are required to rate the frequency that the behavior occurs on a scale of 1 (never) to 5 (always). Scores were summed and then averaged across all 12 items to derive a mean capacity score. The AIR student form has been validated in students on the autism spectrum (Tomaszewski et al., 2020), demonstrating high internal consistency (0.91) and adequate item reliability for the capacity subscale (Chou et al., 2017).

Demographics

Caregiver(s) filled out a demographic form with information on gender, race/ethnicity, and household income at the start of the project in Year 1 Fall. For analysis in this study, race was coded as 0 = White and 1 = All other races. Gender was coded as 0 = male and 1 = female, as those were the only options presented in the demographic form. Family annual income was coded as 1 = <$40,000, 2 = $40,000–$79,000, 3 = ≥ $80,000.

Analysis

Multilevel modeling (MLM; Snijders & Bosker, 1999) served as the primary analytic strategy because of the nested nature of the CSESA dataset, where students are nested within schools. Accounting the hierarchical structure of data prevents both underestimations of regression coefficient errors and overestimating statistical significance (Luke, 2004). The use of multilevel models also allows us to examine the influence of both individual and contextual variables on relationships of interest through the disaggregation of within and between-level effects (Luke, 2004).

There was an average of seven students in each school for the analysis sample (n = 220, SD = 2.4, range = 4–12). See Table 1 in Supplemental Materials for breakdown of student numbers at school level by goal categories. Covariates, such as demographic characteristics (gender, race, annual family income), were controlled for in all analysis models included as individual student (Level 1) predictors. State of residence was also controlled as a fixed effect as a contextual predictor at Level 2 (school level) to account for differences in state educational policies that may influence practices and student performance in each state (U.S. Department of Education et al., 2022).

All analyses were performed with Stata SE 17.0. Analysis code used in this study is publicly available via the Open Science Framework (OSF): https://osf.io/cej6h/. Prior to engaging in inferential analyses, we computed descriptive statistics, ran the Breusch-Pagan test, plotted histograms, scatterplots, and QQ-plots to examine the patterns of variability, normality, and heteroscedasticity (Ernst & Albers, 2017). All variance inflation factor (VIF; Thompson et al., 2017) values for each regression model were found to be below two, suggesting no significant multicollinearity.

Missing Data Determination and Handling

Three variables contained missing data—annual family income (25%), self-determination capacity mean score (14%), and race (7%). While assuming a conditionally Missing at Random (MAR) process, we used multivariate imputation using chained equations (MICE; Van Buuren & Groothuis-Oudshoorn, 2011) to treat missing values. The number of datasets imputed was 25 for all outcomes—one data set per greatest percent missingness (White et al., 2011). Each imputation model included all variables present in the full analysis model (Hardt et al., 2012; White et al., 2011), as well as one additional auxiliary variable (age). Goal Attainment Scaling data obtained was variable across students dependent on the types of goals students had in their IEP. Hence, the sample included for imputation and regression was dependent on the number of students with a corresponding goal in each respective goal area.

Regression Analysis

There was only a fair amount of clustering in schools for academic (ρ = 0.14) and transition goal attainment (ρ = 0.17). To account for the nested structure, we used one-way random effects ANCOVA models to estimate the effects of self-determination on outcomes. Self-determination was group-mean centered post-imputation. School mean, which is the group mean, refers to the average self-determination score by school. For social and independence/behavior goal attainment outcomes with no significant school clustering (ICC <0.05; Huta, 2014), we fit single-level linear models to estimate the effect of self-determination on outcomes. Pooled estimates across imputed datasets (Rubin, 1987) were derived using maximum likelihood estimation for multilevel models, and ordinary least squares (default) estimator was used for single level models.

For all models, self-determination was first entered as a predictor as the main variable of interest, followed by covariates in the second conditional model. Categories containing the greatest number of participants were used as the reference group for all categorical variables. For multilevel models, variance explained was determined through change in Level 1 variance (Level 1 R²) using the multivariate variance partitioning method (R²1(approx.); Bryk & Raudenbush, 1992), and change in total variance using the R² (S&B) formula (Snijders & Bosker, 1994). Level 1 and total R² values were derived by averaging across R² from each imputed dataset, as recommended by Van Ginkel (2019). For single-level models, variance explained in the model was determined based on adjusted R² values derived using the mibeta command (Marchenko, 2010) in Stata.

Results

Descriptive Analyses

Across all goal areas, on average, students had a score of 2 on the 5-point rating scale of the GAS, indicating that students had generally made further progress toward their IEP goal. See Table 2 in Supplemental Material for breakdown on individual goal areas. As assessed using visual inspection, and normality test for skewness and kurtosis (p < .05; Kim, 2013), no predictor or outcome variable was largely skewed or greatly violated the normality assumption. Assessment of the preliminary correlations of the continuous variables based on non-imputed data suggested that self-determination was positively correlated to overall IEP goal attainment, and academic, behavior, and transition goal attainment, each to varying degrees.

Mean IEP Goal Attainment

Student self-determination was likely to be positively associated with IEP goal attainment (across all goal categories; see Table 2), given that the 95% confidence interval associated with the regression coefficient does not contain zero, and p-value is small, even after controlling for covariates (b = 0.159, p = .039). A 1-point increase in a student’s mean self-determination capacity score was associated with, on average, a 0.16 increase in overall mean GAS score. Inclusion of self-determination in the model accounted for 3% of the variance observed in IEP goal attainment.

Table 2.

Multilevel Regression for Mean IEP Goal Attainment (n = 220).

	Unconditional model^a					Model conditional on self-determination					Model conditional on covariates and self-determination
	Estimate	SE	95% CI		p	Estimate	SE	95% CI		p	Estimate	SE	95% CI		p
Variables	Estimate	SE	LL	UL	p	Estimate	SE	LL	UL	p			LL	UL
Fixed effects
Intercept	2.085***	0.07	1.95	2.22	<.001	2.274*	1.06	0.20	4.35	.032	2.214*	1.07	0.12	4.31	.038
Self-determination
School mean centered						0.160*	0.08	0.01	0.31	.041	0.159*	0.08	0.01	0.31	.039
School mean						–0.051	0.28	–0.61	0.51	.857	0.001	0.28	–0.55	0.55	.997
Gender
Male											Reference
Female											–0.051	0.14	–0.33	0.22	.714
Race
White											Reference
All others											–0.006	0.12	–0.24	0.23	.961
Household income
<$40K											–0.300	0.18	–0.66	0.07	.108
$40,000–$79,000											–0.280*	0.12	–0.52	–0.04	.023
≥$80,000											Reference
State
NC											Reference
WI											–0.077	0.17	–0.40	0.25	.642
CA											0.140	0.16	–0.17	0.45	.373
Random effects
Between school variance	0.06					0.06					0.06
Within school variance	0.52					0.50					0.48
Total variance	0.58					0.56					0.54
R ²
Change in within school variance	-					0.03					0.04
Change in total variance	-					0.02					0.05
ICC	0.10

Note. CI = confidence interval; LL = lower limit; UL = upper limit; SE = Standard Error.

Unconditional model is the two-level HLM without any covariates.

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

IEP Goal Attainment in Various Domains

Self-determination may have a positive association with academic, transition and independence/goal attainment. However, none of the regression coefficients met statistical significance of p <.05 and the 95% confidence interval included zero and negative values (see Tables 3, 4 and 6 in Supplemental Material). While these null findings indicate uncertainty about the strength of the relationship between the variables based on the study sample, they do not necessarily mean that self-determination is unimportant for goal attainment for these areas. For social goal attainment, there was not a clear association with self-determination (see Table 5 in Supplemental Material), with a wide 95% confidence interval containing both negative and positive values (95% [CI] = [–0.26, 0.28]). The negative adjusted R² values also suggest that the sample size may be too small to determine an effect (Thompson, 2006).

Discussion

Previous studies on IEP goal attainment in autistic students receiving regular school programming have predominantly focused on elementary-aged children (Love et al., 2020; Ruble & McGrew, 2013; Wong et al., 2017). This research broadens the scope by investigating the achievement of IEP goals by transition-aged autistic students in high school across multiple key outcome areas over a 2-year period. In addition, it expands our understanding on factors influencing IEP goal attainment for the autistic population by examining self-determination, a factor found to be key in supporting goal attainment (Shogren et al., 2012, 2018) in other disability populations, yet underexplored in the autistic population (Morán et al., 2021).

Acknowledging the study’s exploratory nature, results from this study suggest initial evidence of a positive relationship between self-determination and student IEP goal attainment for autistic high school students, even after accounting for differences in demographic factors such as household income, gender, and race. This is one of the first investigations of self-determination’s influence on goal attainment of IEP goals, as previous work has investigated the effect of self-determination interventions on goal attainment for intervention-specific goals (German et al., 2000; Raley et al., 2020; Shogren et al., 2012, 2018, Shogren, Hicks, et al., 2021). Findings from this study also add to the small but growing literature base examining the impact of self-determination on in-school outcomes for autistic students, confirming positive impacts (Morán et al., 2021). While it may not be unexpected to find that autistic students with higher levels of self-determination tended to have higher levels of overall goal attainment, as there is a high overlap in the skills related to self-determination and skills required to attain goals (Shogren & Wehmeyer, 2016). This study provided an initial confirmation, informing priorities in practice.

While the study provides preliminary promising evidence of the positive association between self-determination and IEP goal attainment, we obtained mainly null results in looking at the relationships between self-determination and individual goal domain areas. The uneven cluster sizes between schools (ranging from 1 to 12) may have introduced bias, and smaller sample sizes for individual goal areas may have reduced the likelihood of detecting meaningful effects (Maas & Hox, 2005). The narrow range of scores available on the Goal Attainment Scale (0–4) might have also impacted our ability to observe a clear relationship between self-determination and IEP goal attainment due to too little variation in outcomes. Publishing null results is crucial in combating publication bias, as it promotes transparency and contributes to the Open Science movement by ensuring that the full spectrum of scientific findings, not just significant ones, is available for evaluation and replication (APA, 2023; Bah, 2024). Despite the lack of statistical significance, the positive trends observed suggest that self-determination may still be relevant to goal attainment, reinforcing the need for further research with larger samples, more sensitive measures, and more nuanced methodologies, such as subgroup analysis (e.g. groups based on levels of environmental supports and opportunities for self-determination).

Limitations

Goal Attainment Scaling has been widely used in educational research and is established as a key measure that assesses individualized goal attainment outcomes for students on the autism spectrum (Ruble et al., 2021; Shogren, Dean, et al., 2021). However, the ordinal nature of the rating scale meant scores across students tend to fall more into discrete categories than present as a continuous variable. Thus, the assumption of linear relationships between predictors and outcomes may be violated (Bishop & Herron, 2015), and may result in biased and inefficient estimates (Ernst & Albers, 2017). To address this, we ran regression analysis using other non-parametric models as well. However, the use of ordinal logistic models did not suggest differing conclusions from the linear models, and as linear regression is a more powerful procedure than its non-parametric counterparts, the use of linear regression was retained (Ernst & Albers, 2017), although additional research is needed.

We also acknowledge that the achievement of IEP goals is influenced not only by student effort but also by other variables, such as teacher and classroom characteristics (Love et al., 2020; Ruble & McGrew, 2013; Wong et al., 2017). However, due to constraints associated with secondary data analysis, such as the lack of classroom-level information in the CSESA study, we were unable to explore and account for the influence of classroom and teacher variables in this study. This should be further explored in future studies.

A limitation of this study is that findings are based on a subset of autistic students; that is, those who were able to independently complete a self-determination measure and are unlikely to have a co-occurring intellectual disability. This suggests that these participants may have higher cognitive performance or skills compared with the broader autistic student population, and caution should be exercised when generalizing these results to all students on the autism spectrum.

We were also not able to accurately evaluate the differential impact of self-determination on each area (i.e., whether self-determination supported attainment of goals in one area more than another), as a limited number of students (n = 13, 6%) had available GAS data in all four goal areas and thus may not represent the population well. This presents an interesting area for future study with a different sample. More work is needed to better understand the role of self-determination and its influence on goal attainment across individual goal areas, particularly to examine whether supports might be needed to ensure goals across domains are equally prioritized for autistic students.

Implications for Practice and Future Research

Self-determination is promoted as a critical and malleable construct that can support adolescents with disabilities in navigating their transition planning process and eventual achievement of desired post-school outcomes (Thoma & Getzel, 2005). However, there is limited existing information on the relationship between self-determination and outcomes that guide educational programs. These findings represent a key step toward understanding how self-determination, a strength-based construct, can support student goal attainment.

This study suggests that self-determination is a promising predictor of achievement of certain in-school outcomes, where students with higher levels of self-determination tend to make more progress in attaining the goals set in their IEP. This positive trend was observed across the overall mean attainment of IEP goals and multiple goal areas such as transition, independence/behavior, and academic. These areas have been identified as high-need domains for autistic adolescents (Hume et al., 2022), and this positive link between self-determination and goal attainment in these critical areas of need suggests that self-determination could be a malleable characteristic that educators can actively promote to better support students in meeting these needs. There are currently several evidence and research-based interventions and curricula that have been found to increase students’ level of self-determination (Burke et al., 2020; Raley et al., 2018). These programs provide structure and opportunities for students to hone crucial skills associated with the construct and were also well-received by students, teachers, and families (Burke et al., 2020). In particular, interventions, such as the Self-Determined Learning Model of Instruction (Shogren et al., 2019) and Whose Future is it Anyway (Wehmeyer et al., 2004), significantly increased autistic students’ self-determination (Morán et al., 2021). Teachers who work closely with students on the autism spectrum can consider adopting these interventions in their classrooms to support their outcomes, and researchers to examine the impact of these interventions of student IEP goal attainment.

While the study provides preliminary evidence that increased levels of self-determination are associated with higher levels of IEP goal attainment, the mechanism by which self-determination impacts IEP goal attainment has not been elucidated in this study. A probable hypothesis is that more self-determined students tend to be more involved in their IEP planning (Williams-Diehm et al., 2008), and this, in turn, increases the likelihood that a student’s needs or interests will be discussed during the IEP meeting (Martin et al., 2004). Hence, IEP goals will be more directly related to a student’s interest, and thus students are more likely to attain them, increasing the overall IEP goal attainment. To better build our understanding of the relationship between self-determination and IEP goal attainment, future studies should look to measure students’ level of participation in IEP meetings and their level of involvement in the IEP goal-setting process (e.g., Student IEP Behavior Scale, Student Input Scale; Sanderson & Goldman, 2022) and examine the relationship of these variables with self-determination and IEP goal attainment outcomes. Understanding the mechanism by which self-determination impacts IEP goal attainment can better inform educational interventions, as it allows us to identify specific targets or methods that are most likely to promote positive outcomes.

Conclusion

The current study represents a first step toward elucidating the relationship between self-determination, a key predictor for positive post-school outcomes for students with disabilities, and IEP goal attainment for autistic adolescents, a population who often struggle with their postsecondary transition (Chandroo et al., 2020) and has been understudied in the self-determination intervention literature. Findings provided preliminary evidence that increased levels of self-determination are associated with higher levels of IEP goal attainment for autistic students. However, the complexities in the relationship between self-determination and goal attainment across learning domains suggests that multiple factors can act to influence student goal attainment. Researchers have found various personal (e.g. input during IEP meetings; Lee & Kim, 2022), family, and school factors that influence IEP goal attainment for students on the autism spectrum (see Love et al., 2020; Ruble & McGrew, 2013; Wong et al., 2017). To obtain a more holistic understanding of self-determination’s impact on IEP goal attainment for autistic students, future studies should seek to examine the influence of self-determination in conjunction with these other factors to guide instructional practice.

Supplemental Material

sj-docx-1-cde-10.1177_21651434251333405 – Supplemental material for Relationship Between Self-Determination and IEP Goal Attainment for Autistic Adolescents

Supplemental material, sj-docx-1-cde-10.1177_21651434251333405 for Relationship Between Self-Determination and IEP Goal Attainment for Autistic Adolescents by Delia D. D. Kan, Kara A. Hume, Karrie A. Shogren, Brianne Tomaszewski and Jian Ming Ng in Career Development and Transition for Exceptional Individuals

Footnotes

Acknowledgements

The authors wish to thank all stakeholders who participated in the CSESA study, as well as Dr. Eugenia Conde for her statistical advice.

Authors’ Note

To respect views surrounding language use, we used both person-first (student on the autism spectrum) and identify-first (autistic student) descriptors interchangeably throughout the manuscript.

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.

Open Practices

Analysis code used in this study is publicly available via the Open Science Framework (OSF): .

ORCID iDs

Delia D. D. Kan

Karrie A. Shogren

Jian Ming Ng

Supplemental Material

Supplementary material for this article is available on the Career Development and Transition for Exceptional Individuals website at

References

Alverson

C. Y.

Lindstrom

L. E.

Hirano

K. A.

(2019). High school to college: Transition experiences of young adults with autism. Focus on Autism and Other Developmental Disabilities, 34(1), 52–64. https://doi.org/10.1177/1088357615611880

American Psychological Association. (2023, November). Open science at APA. https://www.apa.org/pubs/journals/resources/open-science#:~:text=All%20APA%20core%20titles%20encourage,contribute%20to%20advancing%20the%20literature

Bah

(2024, August 9). The importance of publishing null findings in research. Open Universal Science. https://opusproject.eu/openscience-news/the-importance-of-publishing-null-findings-inresearch/#:~:text=Publishing%20null%20findings%20is%20crucial,been%20tested%20and%20found%20lacking

Biggs

E. E.

Carter

E. W.

(2016). Quality of life for transition-age youth with autism or intellectual disability. Journal of Autism and Developmental Disorders, 46, 190–204. https://doi.org/10.1007/s10803-015-2563-x

Bishop

P. A.

Herron

R. L.

(2015). Use and misuse of the likert item responses and other ordinal measures. International Journal of Exercise Science, 8(3), 297–302. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4833473/

Bryk

A. S.

Raudenbush

S. W.

(1992). Hierarchical linear models for social and behavioural research: Applications and data analysis methods. Sage.

Burke

K. M.

Raley

S. K.

Shogren

K. A.

Hagiwara

Mumbardó-Adam

Uyanik

Behrens

(2020). A meta-analysis of interventions to promote self-determination for students with disabilities. Remedial and Special Education, 41(3), 176–188. https://doi.org/10.1177/0741932518802274

Cameto

Levine

Wagner

(2004). Transition planning for students with disabilities: A special topic report of findings from the National Longitudinal Transition Study-2 (NLTS2). National Center for Special Education Research. https://eric.ed.gov/?id=ED496547

Carter

E. W.

Lane

K. L.

Cooney

Weir

Moss

C. K.

Machalicek

(2013). Self-determination among transition-age youth with autism or intellectual disability: Parent perspectives. Research and Practice for Persons with Severe Disabilities, 38(3), 129–138. https://doi.org/10.1177/154079691303800301

10.

Chandroo

Strnadová

Cumming

T. M.

(2020). Is it really student-focused planning? Perspectives of students with autism. Research in Developmental Disabilities, 107, Article 103783. https://doi.org/10.1016/j.ridd.2020.103783

11.

Chou

Y.-C.

Wehmeyer

M. L.

Shogren

K. A.

Palmer

S. B.

Lee

(2017). Autism and self-determination: Factor analysis of two measures of self-determination. Focus on Autism and Other Developmental Disabilities, 32(3), 163–175. https://doi.org/10.1177/1088357615611391

12.

Cribb

Kenny

Pellicano

(2019). “I definitely feel more in control of my life”: The perspectives of young autistic people and their parents on emerging adulthood. Autism, 23(7), 1765–1781. https://doi.org/10.1177/1362361319830029

13.

Ernst

A. F.

Albers

C. J.

(2017). Regression assumptions in clinical psychology research practice—A systematic review of common misconceptions. PeerJ, 5, Article e3323. https://doi.org/10.7717/peerj.3323

14.

Field

Martin

Miller

Ward

Wehmeyer

(1998). Self-determination for persons with disabilities: A position statement of the division on career development and transition. Career Development for Exceptional Individuals, 21(2), 113–128. https://doi.org/10.1177/088572889802100202

15.

Findley

J. A.

Ruble

L. A.

McGrew

J. H.

(2022). Individualized education program quality for transition age students with autism. Research in Autism Spectrum Disorders, 91, Article 101900. https://doi.org/10.1016/j.rasd.2021.101900

16.

Fletcher-Watson

Adams

Brook

Charman

Crane

Cusack

Leekam

Milton

Parr

J. R.

Pellicano

(2019). Making the future together: Shaping autism research through meaningful participation. Autism, 23(4), 943–953. https://doi.org/10.1177/1362361318786721

17.

Gaona

Palikara

Castro

(2019). “I’m ready for a new chapter”: The voices of young people with autism spectrum disorder in transition to post-16 education and employment. British Educational Research Journal, 45(2), 340–355. https://doi.org/10.1002/berj.3497

18.

Gaumer Erickson

A. S.

Noonan

P. M.

Zheng

Brussow

J. A

. (2015). The relationship between self-determination and academic achievement for adolescents with intellectual disabilities. Research in Developmental Disabilities, 36, 45–54. https://doi.org/10.1016/j.ridd.2014.09.008

19.

German

S. L.

Martin

J. E.

Huber Marshall

Sale

R. P.

(2000). Promoting self-determination: Using take action to teach goal attainment. Career Development for Exceptional Individuals, 23(1), 27–38. https://doi.org/10.1177/088572880002300103

20.

Hardt

Herke

Leonhart

(2012). Auxiliary variables in multiple imputation in regression with missing X: A warning against including too many in small sample research. BMC Medical Research Methodology, 12(1), Article 184. https://doi.org/10.1186/1471-2288-12-184

21.

Harkin

Webb

T. L.

Chang

B. P. I.

Prestwich

Conner

Kellar

Benn

Sheeran

(2016). Does monitoring goal progress promote goal attainment? A meta-analysis of the experimental evidence. Psychological Bulletin, 142(2), 198–229. https://doi.org/10.1037/bul0000025

22.

Hedges

S. H.

Kirby

A. V.

Sreckovic

M. A.

Kucharczyk

Kucharcyzk

Hume

Pace

(2014). “Falling through the Cracks”: Challenges for high school students with autism spectrum disorder. The High School Journal, 98(1), 64–82. https://doi.org/10.1353/hsj.2014.0014

23.

Hortop

E. G.

Wrosch

Gagné

(2013). The why and how of goal pursuits: Effects of global autonomous motivation and perceived control on emotional well-being. Motivation and Emotion, 37(4), 675–687. https://doi.org/10.1007/s11031-013-9349-2

24.

Huber

Carter

(2019). Fostering peer relationships and shared learning for students with autism spectrum disorders. In Jordan

Roberts

J. M.

Hume

(Eds.), The SAGE handbook of autism and education (pp. 265–275). Sage. https://doi.org/10.4135/9781526470409

25.

Hume

Odom

S. L.

Steinbrenner

J. R.

Smith DaWalt

Hall

L. J.

Kraemer

Tomaszewski

Brum

Szidon

Bolt

D. M.

(2022). Efficacy of a school-based comprehensive intervention program for adolescents with autism. Exceptional Children, 88(2), 223–240. https://doi.org/10.1177/00144029211062589

26.

Huta

(2014). When to use hierarchical linear modeling. The Quantitative Methods for Psychology, 10(1), 13–28. https://doi.org/10.20982/tqmp.10.1.p013

27.

Kim

H.-Y.

(2013). Statistical notes for clinical researchers: Assessing normal distribution using skewness and kurtosis. Restorative Dentistry & Endodontics, 38(1), 52–54. https://doi.org/10.5395/rde.2013.38.1.52

28.

Kiresuk

T. J.

Sherman

R. E.

(1968). Goal attainment scaling: A general method for evaluating comprehensive community mental health programs. Community Mental Health Journal, 4, 443–453. https://doi.org/10.1007/BF01530764

29.

Kraemer

B. R.

Tomaszewski

Rentschler

L. F.

Steinbrenner

J. R.

Hume

K. A.

McDaniel

Dawalt

Brum

Szidon

(2022). Quality of the transition component of the IEP for high school students with autism. Career Development and Transition for Exceptional Individuals, 45(4), 200–212. https://doi.org/10.1177/21651434221079743

30.

Kucharczyk

Reutebuch

C. K.

Carter

E. W.

Hedges

El Zein

Fan

Gustafson

J. R.

(2015). Addressing the needs of adolescents with autism spectrum disorder: Considerations and complexities for high school interventions. Exceptional Children, 81(3), 329–349. https://doi.org/10.1177/0014402914563703

31.

Lee

C. E.

Kim

J. G.

(2022). Person-centered transition planning for youth on the autism spectrum: What are we still missing? Exceptionality, 30(3), 173–186. https://doi.org/10.1080/09362835.2021.1938065

32.

Lee

S.-H.

Wehmeyer

M. L.

Shogren

K. A.

(2015). Effect of instruction with the self-determined learning model of instruction on students with disabilities: A Meta-Analysis. Education and Training in Autism and Developmental Disabilities, 50(2), 237–247. http://www.jstor.org/stable/24827538

33.

Love

A. M. A.

Findley

J. A.

Ruble

L. A.

McGrew

J. H.

(2020). Teacher self-efficacy for teaching students with autism spectrum disorder: Associations with stress, teacher engagement, and student IEP outcomes following COMPASS consultation. Focus on Autism and Other Developmental Disabilities, 35(1), 47–54. https://doi.org/10.1177/1088357619836767

34.

Luke

(2004). Multilevel modeling. Sage. https://doi.org/10.4135/9781412985147

35.

Maas

C. J.

Hox

J. J.

(2005). Sufficient sample sizes for multilevel modeling. Methodology, 1(3), 86–92. https://doi.org/10.1027/1614-2241.1.3.86

36.

MacKinnon

(2008). Introduction to statistical mediation analysis. Taylor & Francis Group. https://doi.org/10.4324/9780203809556

37.

Marchenko

(2010). Mibeta: Standardized coefficients for multiply imputed data. UCLA: Statistical Consulting Group. https://stats.oarc.ucla.edu/stata/faq/how-can-i-estimate-r-squared-for-a-model-estimated-with-multiply-imputed-data/

38.

Martin

J. E.

Marshall

L. H.

Sale

(2004). A 3-year study of middle, junior high, and high school IEP meetings. Exceptional Children, 70(3), 285–297. https://doi.org/10.1177/001440290407000302

39.

Mazzotti

V. L.

Rowe

D. A.

Kwiatek

Voggt

Chang

W.-H.

Fowler

C. H.

Poppen

Sinclair

Test

D. W.

(2021). Secondary transition predictors of postschool success: An update to the research base. Career Development and Transition for Exceptional Individuals, 44(1), 47–64. https://doi.org/10.1177/2165143420959793

40.

Mills

Sharp

N. L.

Triandafilidis

Tracey

(2022). Thrown out into the world: Transition to post schooling for autistic young people. Western Sydney University. https://doi.org/10.26183/3gsv-mv41

41.

Milton

D. E.

(2014). Autistic expertise: A critical reflection on the production of knowledge in autism studies. Autism, 18(7), 794–802. https://doi.org/10.1177/1362361314525281

42.

Morán

M. L.

Hagiwara

Raley

S. K.

Alsaeed

A. H.

Shogren

K. A.

Qian

Gómez

L. E.

Alcedo

M. Á

. (2021). Self-determination of students with autism spectrum disorder: A systematic review. Journal of Developmental and Physical Disabilities, 33, 887–908. https://doi.org/10.1007/s10882-020-09779-1

43.

Raley

S. K.

Shogren

K. A.

McDonald

(2018). Whole-class implementation of the self-determined learning model of instruction in inclusive high school mathematics classes. Inclusion, 6(3), 164–174. https://doi.org/10.1352/2326-6988-6.3.164

44.

Raley

S. K.

Shogren

K. A.

Rifenbark

G. G.

Thomas

McDonald

A. F.

Burke

K. M.

(2020). Enhancing secondary students’ goal attainment and self-determination in general education mathematics classes using the self-determined learning model of instruction. Advances in Neurodevelopmental Disorders, 4(2), 155–167. https://doi.org/10.1007/s41252-020-00152-z

45.

Rubin

D. B.

(1987). Multiple imputation for nonresponse in surveys. John Wiley & Sons.

46.

Ruble

McGrew

Dale

Yee

(2021). Goal attainment scaling: An idiographic measure sensitive to parent and teacher report of IEP goal outcome assessment for students with ASD. Journal of Autism and Developmental Disorders, 52, 3344–3352. https://doi.org/10.1007/s10803-021-05213-3

47.

Ruble

McGrew

J. H.

(2013). Teacher and child predictors of achieving IEP goals of children with autism. Journal of Autism and Developmental Disorders, 43(12), 2748–2763. https://doi.org/10.1007/s10803-013-1884-x

48.

Ruble

McGrew

J. H.

Toland

M. D.

(2012). Goal attainment scaling as an outcome measure in randomized controlled trials of psychosocial interventions in autism. Journal of Autism and Developmental Disorders, 42(9), 1974–1983. https://doi.org/10.1007/s10803-012-1446-7

49.

Ruble

McGrew

J. H.

Wong

Adams

(2019). A preliminary study of parent activation, parent-teacher alliance, transition planning quality, and IEP and postsecondary goal attainment of students with ASD. Journal of Autism and Developmental Disorders, 49(8), 3231–3243. https://doi.org/10.1007/s10803-019-04047-4

50.

Ruble

L. A.

McGrew

J. H.

Toland

Dalrymple

Adams

Snell-Rood

(2018). Randomized control trial of COMPASS for improving transition outcomes of students with autism spectrum disorder. Journal of Autism and Developmental Disorders, 48(10), 3586–3595. https://doi.org/10.1007/s10803-018-3623-9

51.

Sanderson

K. A.

Goldman

S. E.

(2022). Understanding the characteristics and predictors of student involvement in IEP meetings. The Journal of Special Education, 56(1), 3–13. https://doi.org/10.1177/00224669211008259

52.

Shogren

K. A.

Burke

K. M.

Anderson

M. H.

Antosh

A. A.

Wehmeyer

M. L.

LaPlante

Shaw

L. A.

(2018). Evaluating the differential impact of interventions to promote self-determination and goal attainment for transition-age youth with intellectual disability. Research and Practice for Persons with Severe Disabilities, 43(3), 165–180. https://doi.org/10.1177/1540796918779775

53.

Shogren

K. A.

Burke

K. M.

Antosh

Wehmeyer

M. L.

LaPlante

Shaw

L. A.

Raley

(2019). Impact of the Self-Determined Learning Model of Instruction on self-determination and goal attainment in adolescents with intellectual disability. Journal of Disability Policy Studies, 30(1), 22–34. https://doi.org/10.1177/1044207318792178

54.

Shogren

K. A.

Dean

E. E.

Burke

K. M.

Raley

S. K.

Taylor

J. L.

(2021). Goal Attainment Scaling: A framework for research and practice in the intellectual and developmental disabilities field. Intellectual and Developmental Disabilities, 59(1), 7–21. https://doi.org/10.1352/1934-9556-59.1.7

55.

Shogren

K. A.

Hicks

T. A.

Raley

S. K.

Pace

J. R.

Rifenbark

G. G.

Lane

K. L.

(2021). Student and teacher perceptions of goal attainment during intervention with the self-determined learning model of instruction. The Journal of Special Education, 55(2), 101–112. https://doi.org/10.1177/0022466920950264

56.

Shogren

K. A.

Little

T. D.

Wehmeyer

M. L.

(2017). Human agentic theories and the development of self-determination. In Wehmeyer

M. L.

Shogren

K. A.

Little

T. D.

Lopez

S. J.

(Eds.), Development of self-determination through the life-course (pp. 17–26). Springer. https://doi.org/10.1007/978-94-024-1042-6_2

57.

Shogren

K. A.

Palmer

S. B.

Wehmeyer

M. L.

Williams-Diehm

Little

T. D.

(2012). Effect of intervention with the self-determined learning model of instruction on access and goal attainment. Remedial and Special Education, 33(5), 320–330. https://doi.org/10.1177/0741932511410072

58.

Shogren

K. A.

Plotner

A. J.

(2012). Transition planning for students with intellectual disability, autism, or other disabilities: Data from the National Longitudinal Transition Study-2. Intellectual and Developmental Disabilities, 50(1), 16–30. https://doi.org/10.1352/1934-9556-50.1.16

59.

Shogren

K. A.

Wehmeyer

M. L.

(2016). Self-determination and goal attainment. In Wehmeyer

M. L.

Shogren

K. A.

(Eds.), Handbook of research-based practices for educating students with intellectual disability (pp. 255–273). Taylor & Francis. https://doi.org/10.4324/9781315736198

60.

Shogren

K. A.

Wehmeyer

M. L.

(2017). Goal setting and attainment. In Wehmeyer

M. L.

Shogren

K. A.

Little

T. D.

Lopez

S. J

(Eds.), Development of self-determination through the life-course (pp. 237–250). Springer. https://doi.org/10.1007/978-94-024-1042-6

61.

Shogren

K. A.

Wehmeyer

M. L.

Palmer

S. B.

Rifenbark

G. G.

Little

T. D.

(2015). Relationships between self-determination and postschool outcomes for youth with disabilities. The Journal of Special Education, 48(4), 256–267. https://doi.org/10.1177/0022466913489733

62.

Snell-Rood

Ruble

Kleinert

McGrew

J. H.

Adams

Rodgers

Odom

Wong

W. H.

(2020). Stakeholder perspectives on transition planning, implementation, and outcomes for students with autism spectrum disorder. Autism, 24(5), 1164–1176. https://doi.org/10.1177/1362361319894827

63.

Snijders

Bosker

(1994). Modeled variance in two-level models. Sociological Methods & Research, 22(3), 342–363. https://doi.org/10.1177/0049124194022003004

64.

Snijders

Bosker

(1999). Multilevel analysis: An introduction to basic and advanced multilevel modeling. Sage.

65.

Thoma

C. A.

Getzel

E. E.

(2005). “Self-determination is what it’s all about”: What post-secondary students with disabilities tell us are important considerations for success. Education and Training in Developmental Disabilities, 40(3), 234–242. https://www.jstor.org/stable/23879718

66.

Thompson

(2006). Foundations of Behavioral Statistics: An Insight-Based Approach. Guilford Press.

67.

Thompson

C. G.

Kim

R. S.

Aloe

A. M.

Becker

B. J.

(2017). Extracting the Variance Inflation Factor and other multicollinearity diagnostics from typical regression results. Basic and Applied Social Psychology, 39(2), 81–90. https://doi.org/10.1080/01973533.2016.1277529

68.

Tomaszewski

Kraemer

Steinbrenner

J. R.

Smith DaWalt

Hall

L. J.

Hume

Odom

(2020). Student, educator, and parent perspectives of self-determination in high school students with autism spectrum disorder. Autism Research, 13(12), 2164–2176. https://doi.org/10.1002/aur.2337

69.

Tomaszewski

Smith DaWalt

Odom

S. L.

(2019). Growth mixture models of adaptive behavior in adolescents with autism spectrum disorder. Autism, 23(6), 1472–1484. https://doi.org/10.1177/1362361318815645

70.

U.S. Department of Education, Office of Special Education and Rehabilitative Services, & Office of Special Education Programs. (2022). 43rd annual report to congress on the implementation of the individuals with disabilities education act, 2021. https://sites.ed.gov/idea/2021-individuals-with-disabilities-education-act-annual-report-to-congress/

71.

Van Buuren

Groothuis-Oudshoorn

. (2011). Mice: Multivariate imputation by chained equations in R. Journal of Statistical Software, 45(3), 1–67. https://doi.org/10.18637/jss.v045.i03

72.

Van Ginkel

J. R

. (2019). Significance tests and estimates for R2 for multiple regression in multiply imputed datasets: A cautionary note on earlier findings, and alternative solutions. Multivariate Behavioral Research, 54(4), 514–529. https://doi.org/10.1080/00273171.2018.1540967

73.

Wagner

Newman

Cameto

Javitz

Valdes

(2012). A national picture of parent and youth participation in IEP and transition planning meetings. Journal of Disability Policy Studies, 23(3), 140–155. https://doi.org/10.1177/1044207311425384

74.

Wehmeyer

Lawrence

Kelchner

Palmer

Garner

Soukup

(2004). Whose future is it anyway? A student directed transition planning process (2nd ed.). Beach Center on Disability.

75.

Wehmeyer

M. L.

Palmer

S. B.

Soukup

J. H.

Garner

N. W.

Lawrence

(2007). Self-determination and student transition planning knowledge and skills: Predicting involvement. Exceptionality, 15(1), 31–44. https://doi.org/10.1080/09362830709336924

76.

Wehmeyer

M. L.

Shogren

K. A.

Palmer

S. B.

Williams-Diehm

K. L.

Little

T. D.

Boulton

(2012). The impact of the self-determined learning model of instruction on student self-determination. Exceptional Children, 78(2), 135–153. https://doi.org/10.1177/001440291207800201

77.

White

I. R.

Royston

Wood

A. M.

(2011). Multiple imputation using chained equations: Issues and guidance for practice. Statistics in Medicine, 30(4), 377–399. https://doi.org/10.1002/sim.4067

78.

Williams-Diehm

Palmer

Lee

Schroer

(2010). Goal content analysis for middle and high school students with disabilities. Career Development for Exceptional Individuals, 33(3), 132–142. https://doi.org/10.1177/0885728810380230

79.

Williams-Diehm

Wehmeyer

M. L.

Palmer

Soukup

J. H.

Garner

(2008). Self-determination and student involvement in transition planning: A multivariate analysis. Journal on Developmental Disabilities, 14(1), 25–36. https://oadd.org/wp-content/uploads/2008/01/williamsdiehmEtAl.pdf

80.

Wolman

Campeau

Dubois

Mithaug

Stolarski

(1994). AIR self-determination scale and user guide. American Institute for Research.

81.

Wong

V. W.

Ruble

L. A.

McGrew

J. H.

(2017). Too stressed to teach? Teaching quality, student engagement, and IEP outcomes. Exceptional Children, 83(4), 412–427. https://doi.org/10.1177/0014402917690729

82.

Yell

M. L.

Bateman

Shriner

(2020). Developing and implementing educationally meaningful and legally sound IEPs: Bringing it all together. TEACHING Exceptional Children, 52(5), 344–347. https://doi.org/10.1177/0040059920919087

83.

Yell

M. L.

Katsiyannis

Drasgow

Herbst

(2003). Developing legally correct and educationally appropriate programs for students with autism spectrum disorders. Focus on Autism and Other Developmental Disabilities, 18(3), 182–191. https://doi.org/10.1177/10883576030180030601

84.

Zheng

Gaumer Erickson

Kingston

N. M.

Noonan

P. M.

(2014). The relationship among self-determination, self-concept, and academic achievement for students with learning disabilities. Journal of Learning Disabilities, 47(5), 462–474. https://doi.org/10.1177/0022219412469688

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