Control groups are an essential part of autism research, providing a benchmark against which to assess those with autism. Finding, for instance, that participants with autism score an average of 68 percent on a test is meaningless if you don’t know how people who don’t have autism do on the same test.
A control group can also be used to try and rule out alternative and perhaps uninteresting explanations for group differences. The logic is simple: If two groups are matched on one measure, such as intelligence or age, then this can’t explain differences on another measure, such as performance on an emotion recognition test, that is under investigation.
Despite its widespread use, there are many issues to consider when designing an experiment with matched controls or when reading and attempting to evaluate such a study. Who should be in the control group? On what measures should they be matched? And how do we decide if the groups are truly matched?The post focuses on this last question and a recent paper by Sara Kover and Amy Atwood, which I think makes some pretty sensible recommendations.
Be warned, it involves statistics and made-up data.
Reference:s
Kover ST, & Atwoo AK (2013). Establishing equivalence: methodological progress in group-matching design and analysis. American journal on intellectual and developmental disabilities, 118 (1), 3-15 PMID: 23301899
Mervis, C. B., & Klein-Tasman, B. (2004). Methodological Issues in Group-Matching Designs: α Levels for Control Variable Comparisons and Measurement Characteristics of Control and Target Variables. Journal of Autism and Developmental Disorders, 24, 7-17. PDF
