Large amounts of autism spectrum disorder (ASD) data is created through hospitals, therapy centers, and mobile applications; however, much of this rich data does not have pre-existing classes or labels. Large amounts of data--both genetic and behavioral--that are collected as part of scientific studies or a part of treatment can provide a deeper,…

Autism spectrum disorder (ASD) research has yet to leverage "big data" on the same scale as other fields; however, advancements in easy, affordable data collection and analysis may soon make this a reality. Indeed, there has been a notable increase in research literature evaluating the effectiveness of machine learning for diagnosing…

Drawing on a realist synthesis approach, this review identified qualitative data collection approaches that inform "first-person" lived experience in autism spectrum disorder (ASD) across phenotypic expression. It further drew upon methodologic approaches used in other conditions that similarly represent individuals with impaired verbal…

Guidelines to inform research evidence standards have acknowledged that there is currently no agreed-upon method for treatment effect size estimation in single-case research. This study has examined the application of treatment effect size calculations to supplement visual analysis in single-case research designs (SCD) for participants with autism…

Understanding neurocognitive mechanisms in children with autism spectrum disorder (ASD) is an essential goal of autism research. Studying young children with ASD or other neurodevelopmental conditions in demanding experimental settings, however, can pose many practical and ethical challenges. In this article, we present practical strategies that…