We consider IDA to a be an important addition to the toolkit for pooled data analysis, offering such advantages as economy (i.e., reuse of extant data), power (i.e., large combined sample sizes), the potential to address new questions not answerable by a single contributing study (e.g., combining longitudinal studies to cover a broader swath of the lifespan), and the opportunity to build a more cumulative science (i.e., examining the similarity of effects across studies and potential reasons for dissimilarities). We also recognize that IDA may not be appropriate for some questions and may be untenable for pooling studies that have no common items or overlap in variables that would support making important distinctions regarding the effect of between-study differences. Two significant sources of between-study heterogeneity to consider in embarking on an IDA are sampling and measurement, each of which may be addressed in many circumstances through the application of traditional analytic techniques to the unique problem of pooling raw data in IDA.
