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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

Structural equation modeling (SEM) serves as one of the most important advances in the social sciences in the past 40 years. Through a combination of factor analysis and path analysis, SEM allows organizational researchers to test causal models while accounting for random and nonrandom (bias) measurement error. SEM is now one of the most commonly used analytic techniques and its modern day ubiquity can be traced in large part to a series of intellectual contributions by Larry James. The current article focuses on the seminal work, James, Mulaik, and Brett (1982), and the unique contribution of the “conditions” required for appropriate confirmatory inference with the path and latent variable models. We discuss the importance of James et al.’s Condition 9 and 10 tests, systematically review 14 years of studies using SEM in leading management journals and reanalyze results based on new techniques that extend James et al. (1982), and conclude with suggestions for improved Condition 9 and 10 assessments.

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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