Abstract
Autism spectrum disorder (ASD) is by definition a complex and heterogeneous disorder. Variation in factors such as developmental level, language ability and IQ further complicate the presentation of symptoms. Clinical research and basic science must continue to inform each other's questions to help address the heterogeneity inherent to the disorder. This review uses a clinical perspective to outline the common tools and best practices for diagnosing and characterizing ASD in a research setting. We discuss considerations for classifying research populations, including language ability and IQ and examine the advantages and disadvantages of different psychometric measurements. Ultimately, the contribution of multiple sources of data representing different perspectives is crucial for interpreting and understanding the ASD phenotype.