Special issue on meta-analytic structural equation modeling: introduction from the guest editors

A Primer on Meta-Analytic Structural Equation Modeling: the Case of Depression

In this paper, we show how the methods of systematic reviewing and meta-analysis can be used in conjunction with structural equation modeling to summarize the results of studies in a way that will facilitate the theory development and testing needed to advance prevention science. We begin with a high-level overview of the considerations that researchers need to address when using meta-analytic structural equation modeling (MASEM) and then discuss a research project that brings together theoretically important cognitive constructs related to depression to (a) show how these constructs are related, (b) test the direct and indirect effects of dysfunctional attitudes on depression, and (c) test the effects of study-level moderating variables. Our results suggest that the indirect effect of dysfunctional attitudes (via negative automatic thinking) on depression is two and a half times larger than the direct effect of dysfunctional attitudes on depression. Of the three study-level moderators tested, only sample recruitment method (clinical vs general vs mixed) yielded different patterns of results. The primary difference observed was that the dysfunctional attitudes → automatic thoughts path was less strong for clinical samples than it was for general and mixed samples. These results illustrate how MASEM can be used to compare theoretically derived models and predictions resulting in a richer understanding of both the empirical results and the theories underlying them.

Synthesizing Indirect Effects in Mediation Models With Meta-Analytic Methods

AIMS

A mediator is a variable that explains the underlying mechanism between an independent variable and a dependent variable. The indirect effect indicates the effect from the predictor to the outcome variable via the mediator. In contrast, the direct effect represents the predictor's effort on the outcome variable after controlling for the mediator.

METHODS

A single study rarely provides enough evidence to answer research questions in a particular domain. Replications are generally recommended as the gold standard to conduct scientific research. When a sufficient number of studies have been conducted addressing similar research questions, a meta-analysis can be used to synthesize those studies' findings.

RESULTS

The main objective of this paper is to introduce two frameworks to integrating studies using mediation analysis. The first framework involves calculating standardized indirect effects and direct effects and conducting a multivariate meta-analysis on those effect sizes. The second one uses meta-analytic structural equation modeling to synthesize correlation matrices and fit mediation models on the average correlation matrix. We illustrate these procedures on a real dataset using the R statistical platform.

CONCLUSION

This paper closes with some further directions for future studies.

Dealing With Artificially Dichotomized Variables in Meta-Analytic Structural Equation Modeling

Abstract. Meta-analytic structural equation modeling (MASEM) is a relatively new method in which effect sizes of different independent studies between multiple variables are typically first pooled into a matrix and next analyzed using structural equation modeling. While its popularity is increasing, there are issues still to be resolved, such as how to deal with primary studies in which variables have been artificially dichotomized. To be able to advise researchers who apply MASEM and need to deal with this issue, we performed two simulation studies using random-effects two stage structural equation modeling. We simulated data according to a full and partial mediation model and systematically varied the size of one (standardized) path coefficient (β MX = .16, β MX = .23, β MX = .33), the percentage of dichotomization (25%, 75%, 100%), and the cut-off point of dichotomization (.5, .1). We analyzed the simulated datasets in two different ways, namely, by using (1) the point-biserial and (2) the biserial correlation as effect size between the artificially dichotomized predictor and continuous variables. The results of these simulation studies indicate that the biserial correlation is the most appropriate effect size to use, as it provides unbiased estimates of the path coefficients in the population.

Beyond Meta-Analysis: Secondary Uses of Meta-Analytic Data

Secondary uses of meta-analytic data (SUMAD) represent advanced analyses and applications of first-order meta-analytic results for theoretical (e.g., theory testing) and practical (e.g., evidence-based practice) purposes to produce novel knowledge that cannot be directly obtained from the input meta-analytic results. First-order meta-analytic results in the form of bivariate effect sizes have been used as input to such secondary analyses and applications. Given the increasing popularity of SUMAD in human resource management (HRM) and organizational behavior (OB), there is a need for a systematic review on this topic. This article has two primary goals. First, it reviews essential works regarding SUMAD in the fields of HRM/OB and provides taxonomies of SUMAD in theoretical and practical domains. Second, it introduces recent SUMAD and discusses future directions that encourage more innovative and rigorous research endeavors along this line.

Synthesising quantitative evidence in systematic reviews of complex health interventions

Public health and health service interventions are typically complex: they are multifaceted, with impacts at multiple levels and on multiple stakeholders. Systematic reviews evaluating the effects of complex health interventions can be challenging to conduct. This paper is part of a special series of papers considering these challenges particularly in the context of WHO guideline development. We outline established and innovative methods for synthesising quantitative evidence within a systematic review of a complex intervention, including considerations of the complexity of the system into which the intervention is introduced. We describe methods in three broad areas: non-quantitative approaches, including tabulation, narrative and graphical approaches; standard meta-analysis methods, including meta-regression to investigate study-level moderators of effect; and advanced synthesis methods, in which models allow exploration of intervention components, investigation of both moderators and mediators, examination of mechanisms, and exploration of complexities of the system. We offer guidance on the choice of approach that might be taken by people collating evidence in support of guideline development, and emphasise that the appropriate methods will depend on the purpose of the synthesis, the similarity of the studies included in the review, the level of detail available from the studies, the nature of the results reported in the studies, the expertise of the synthesis team and the resources available.

Some reflections on combining meta-analysis and structural equation modeling

Meta-analysis and structural equation modeling (SEM) are 2 of the most prominent statistical techniques employed in the behavioral, medical, and social sciences. They each have their own well-established research communities, terminologies, statistical models, software packages, and journals (Research Synthesis Methods and Structural Equation Modeling: A Multidisciplinary Journal). In this paper, I will provide some personal reflections on combining meta-analysis and SEM in the forms of meta-analytic SEM and SEM-based meta-analysis. The critical contributions of Becker (1992), Shadish (1992), and Viswesvaran and Ones (1995) in the early development of meta-analytic SEM are highlighted. Another goal of the paper is to illustrate how meta-analysis can be extended and integrated with other techniques to address new research questions such as the analysis of Big Data. I hope that this paper may stimulate more research development in the area of combining meta-analysis and SEM.

Accounting for Missing Correlation Coefficients in Fixed-Effects MASEM

Meta-analytic structural equation modeling (MASEM) is increasingly applied to advance theories by synthesizing existing findings. MASEM essentially consists of two stages. In Stage 1, a pooled correlation matrix is estimated based on the reported correlation coefficients in the individual studies. In Stage 2, a structural model (such as a path model) is fitted to explain the pooled correlations. Frequently, the individual studies do not provide all the correlation coefficients between the research variables. In this study, we modify the currently optimal MASEM-method to deal with missing correlation coefficients, and compare its performance with existing methods. This study is the first to evaluate the performance of fixed-effects MASEM methods under different levels of missing correlation coefficients. We found that the often used univariate methods performed very poorly, while the multivariate methods performed well overall.