Scholars increasingly recognize the potential of meta-analytic structural equation modeling (MASEM) as a way to build and test theory (Bergh et al., 2016). Yet, 1 of the greatest challenges facing MASEM researchers is how to incorporate and model meaningful effect size heterogeneity identified in the bivariate meta-analysis into MASEM. Unfortunately, common MASEM approaches in applied psychology (i.e., Viswesvaran & Ones, 1995) fail to account for effect size heterogeneity. This means that MASEM effect sizes, path estimates, and overall fit values may only generalize to a small segment of the population. In this research, we quantify this problem and introduce a set of techniques that retain both the true score relationships and the variability surrounding those relationships in estimating model parameters and fit indices. We report our findings from simulated data as well as from a reanalysis of published MASEM studies. Results demonstrate that both path estimates and overall model fit indices are less representative of the population than existing MASEM research would suggest. We suggest 2 extension MASEM techniques that can be conducted using online software or in R, to quantify the stability of model estimates across the population and allow researchers to better build and test theory. (PsycINFO Database Record

This article presents a comprehensive synthesis and quantitative review of studies examining relationships between team design characteristics and team performance. Across 398 primary studies, the present study meta-analytically investigated the effects of team composition, team task design, and team leadership characteristics on team performance. The study further investigates how the effects of these team design characteristics differ according to the industry context within which the team is embedded (high technology, manufacturing, service, or student). Overall, this review emphasizes the importance of continued inquiries focusing on team design while also discussing the implications of this review for theory, practice, and future research.

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Yu, Downes, Carter, and O'Boyle (2016) introduce a new technique to incorporate effect size heterogeneity into meta-analytic structural equation modeling (MASEM) labeled full information meta-analytical structural equation modeling (FIMASEM). Cheung's (2018) commentary raises concerns about the viability of FIMASEM and provides its initial validation. In this reply, we briefly respond to those concerns noting how they relate to Yu et al.'s original conclusions, general MASEM practices, and operational decisions within the FIMASEM procedure. We synthesize Cheung's criticisms and build on his findings to lay out a research agenda for the future of MASEM and the role that our technique might play in it. In doing so, we clarify the conceptual nature of FIMASEM, identity inferential mistakes that current MASEM studies are likely to make, and offer specific and actionable recommendations in terms of the types of research questions FIMASEM is best suited to address and how FIMASEM results can best be interpreted and reported. (PsycINFO Database Record