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Given the continued changes in demographic diversity of students in the United States, it is important to ensure that participants included in special education research reflect the diversity of the classroom. We examined 16 years of intervention research across 12 special education journals to evaluate the extent to which diverse student populations (e.g., race, ethnicity, disability, sexual orientation, English language learner status) were included in published intervention research. We analyzed 495 intervention articles (9.6%) out of 5,180 total articles. Results revealed that progress has been made in the inclusion of diverse participants in special education intervention research compared with previously conducted reviews, yet some racial and ethnic populations are still underrepresented. We discuss strategies for recruitment and retention of underrepresented diverse populations.

Changes in legislation have led to an increased push for children with autism spectrum disorder to be educated in classrooms with typically developing peers. This systematic review of the literature between the years of 2005 and 2012 aimed to identify effective interventions to support the children with autism spectrum disorder in the inclusive preschool classroom. Single-subject studies concerning the improvement of social communication skills for children with autism spectrum disorder in inclusive preschools were identified through systematic searches of electronic databases using key terms, journal hand searches, and ancestral searches of identified articles. Data were extracted from the resulting 16 articles to (a) examine rigor of design, (b) evaluate intervention effectiveness using visual analysis and percentage nonoverlapping data (PND), and (c) determine evidence-based practices. Results suggest a range of effective interventions to improve the social communication skills for children with autism spectrum disorder in inclusive preschool classrooms. Suggestions for future research are discussed.

Measurement of language atypicalities in Autism Spectrum Disorder (ASD) is cumbersome and costly. Better language outcome measures are needed. Using language transcripts, we generated Automated Language Measures (ALMs) and tested their validity. 169 participants (96 ASD, 28 TD, 45 ADHD) ages 7 to 17 were evaluated with the Autism Diagnostic Observation Schedule. Transcripts of one task were analyzed to generate seven ALMs: mean length of utterance in morphemes, number of different word roots (NDWR), um proportion, content maze proportion, unintelligible proportion, c-units per minute, and repetition proportion. With the exception of repetition proportion (p $$= .07$$ = . 07 ), nonparametric ANOVAs showed significant group differences (p$$< 0.01$$ < 0.01 ). The TD and ADHD groups did not differ from each other in post-hoc analyses. With the exception of NDWR, the ASD group showed significantly (p$$< 0.01$$ < 0.01 ) lower scores than both comparison groups. The ALMs were correlated with standardized clinical and language evaluations of ASD. In age- and IQ-adjusted logistic regression analyses, four ALMs significantly predicted ASD status with satisfactory accuracy (67.9–75.5%). When ALMs were combined together, accuracy improved to 82.4%. These ALMs offer a promising approach for generating novel outcome measures.

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