BACKGROUND: Postsecondary education (PSE) programs for students with intellectual disability (ID) have been increasing in recent years. Career development and skills for independent living are frequently cited objectives of PSE programs (Grigal, Hart, & Weir, 2012) yet evidence for the immediate effects of these programs is sparse. OBJECTIVE: In this study we conducted an initial investigation to monitor changes in independence during a one year period for six students with intellectual disability (ID) participating in an inclusive postsecondary education program. METHODS: Adaptive behaviors and support needs were measured using the Scale of Independent Behaviors-Revised (SIB-R; Bruininks Woodcock, Weatherman, & Hill, 1996), the Support Intensity Scale (SIS, Thompson et al., 2004), and weekly hours of support provided to the students were directly measured. A single group, pre-post design was used to compare measures of independence from the beginning and end of the academic year. RESULTS: Initial results, in the form of descriptive statistics, show evidence that students, living on a college campus and participating in a PSE program, learn to function in ways that reduce the needs for support without limiting participation in inclusive activities. CONCLUSION: Recommendations for improving impact assessments of PSE programs are also discussed.

Although postsecondary education (PSE) programs for students with intellectual disability (ID) have grown in recent years, there is little information about the social status of these students on an inclusive college campus. Because many college students without disability play a significant and enduring role in the functioning of PSE programs for students with ID, they provide an opportunity for insight into this question. This study used 3 peer-support focus groups (n = 15) to capture the observed social experiences of PSE students living and learning in a college community. The supports identified 4 foundational elements that determined whether students were more likely to be socially included or excluded. These elements were the campus environment, support, individual skills for developing and maintaining social relationships, and social self-determination. Recommendations are included which may assist postsecondary education programs in reducing social barriers and contribute to students' social inclusion in the college environment.

In this article, we demonstrate the potential of machine learning approaches as inductive analytic tools for expanding our current evidence base for policy making and practice that affects people with intellectual and developmental disabilities (IDD). Using data from the National Core Indicators In-Person Survey (NCI-IPS), a nationally validated annual survey of more than 20,000 nationally representative people with IDD, we fit a series of classification tree and random forest models to predict individuals' employment status and day activity participation as a function of their responses to all other items on the 2017–2018 NCI-IPS. The most accurate model, a random forest classifier, predicted employment outcomes of adults with IDD with an accuracy of 89 percent on the testing sample, and 80 percent on the holdout sample. The most important variable in this prediction was whether or not community employment was a goal in this person's service plan. These results suggest the potential machine learning tools to examine other valued outcomes used in evidence-based policy making to support people with IDD.

This study tests an empirically derived model for measuring personal opportunities for people with intellectual and developmental disabilities (IDD) using National Core Indicators In-Person Survey (NCI-IPS) state and national datasets. The four personal opportunities measured, (a) privacy rights, (b) everyday choice, (c) community participation, and (d) expanded friendships, were informed by existing conceptualizations of service as well as NCI-IPS measures. Analyses confirmed the fit of a four-factor model and demonstrated that factors were significantly and positively correlated. To demonstrate the relationships between personal opportunities and personal and environmental characteristics, we estimated a structural equation model that regressed personal opportunities on age, gender, place of residence, and level of intellectual disability. Implications for using personal opportunities for evaluating service quality of IDD systems are discussed.