In sum, our approach to hypothesis testing in the IDA framework uses guidelines for statistical analysis common in single-study analysis, with the goals of controlling for sources of between-study heterogeneity and, when possible, trying to understand how such sources impact study differences. The core principles are to identify and model such sources of between-study heterogeneity at all plausible points in the analysis while using strategies to reduce model complexity and increase interpretability (i.e., model building and trimming strategies). Because these guidelines may be applied using a variety of statistical techniques, IDA offers an incredibly flexible approach to testing hypotheses regarding a wide range of problems of interest in clinical psychology.
