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| Efficient Identification of Mediation Effects Using Exhaustive Meta-analysis |
| Yang Yang1, Lin Weijie2, Zhou Wenjie1,3,5, Wei Zhipeng4,5, Yang Kehu4,5 |
1.School of Management, Northwest Normal University, Lanzhou 730070
2.Joint Service College, National Defense University PLA China, Beijing 100858
3.School of Information Resource Management, Renmin University of China, Beijing 100872
4.Evidence-based Medical Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou 730030
5.Cross-innovation Laboratory of Evidence-based Social Science, Lanzhou University, Lanzhou 730030 |
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Abstract Quantitative analysis methods in social science research do not adequately evaluate mediation mechanisms between variables. From an evidence-based perspective, this study integrates the difference-in-coefficients (c-c′) approach to mediation identification using an exhaustive meta-analytic framework to address the effect size comparability and endogeneity inherent in conventional mediation tests and their meta-analyses. We propose a complete framework that generates standardized, comparable contextual data by exhaustively combining control variables based on the mathematical logic that the total effect is the sum of the direct and indirect effects and identifies the mediation effects by conducting a meta-regression on the difference (c-c′). The empirical results obtained using information poverty and financial analytics as the contexts for verifying robustness demonstrate that this method enhances the internal validity by avoiding the endogeneity risks associated with conventional estimates of the path coefficients (a, b). Additionally, it enhances the external validity by simulating diverse control settings to evaluate the stability of the mediation effects across the contexts. By bridging these two dimensions, this method improves the internal and external validity of the identified mediation effects and enriches the methodological toolbox for detecting and testing complex mechanisms in the social sciences.
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Received: 12 November 2024
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