ArticlePrevention science : the official journal of the Society for Prevention Research2022
A Primer on Meta-Analytic Structural Equation Modeling: the Case of Depression.
Article in Prevention science : the official journal of the Society for Prevention Research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- SHAP-based explainable machine learning analysis of reward-related neural connectivity to predict preadolescent irritability.Neuroimage. Reports · 2026Article
- Next-Generation Meta-analysis for Next-Generation Questions: Introducing the Prevention Science Special Issue on Modern Meta-analytic Methods.Prevention science : the official journal of the Society for Prevention Research · 2022Article
- Leveraging Research Synthesis Methods to Support Evidence-Based Policy- and Decision-Making.Prevention science : the official journal of the Society for Prevention Research · 2022Article
- Modern Meta-Analytic Methods in Prevention Science: Introduction to the Special Issue.Prevention science : the official journal of the Society for Prevention Research · 2022Article
- To join the rebuild or not? An exploration of the factors influencing the public's intention to participate in urban renewal.Science progressArticle
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Authors and funding
10 authors.
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Abstract
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.
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