For the purposes of study integration, this fixed-effects approach to modeling between-sample heterogeneity accomplishes a primary goal, to control for differences among participants (in this case based on study membership) so that we may obtain findings and draw inferences about associations of theoretical interest that are maximally valid (e.g., Shadish, Cook & Campbell, 2002). However, in IDA we may have a second goal of identifying sources of between-study heterogeneity as a means of testing the generalizability of our findings. Whether for purposes of control or exploration, identifying important sources of between-study heterogeneity is a critical aspect of IDA. As we will describe later, not only can between-study heterogeneity be directly factored into many IDA applications, but some of these study-to-study differences may be of substantive interest in their own right. Indeed, this latter point is what makes IDA such an intriguing endeavor.
