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| Identifying Moderation Effects via Meta-Analysis of Big Data: Basic Model and Empirical Testing |
| Lin Weijie1, Zhou Wenjie2,3,5, Wei Zhipeng4,5, Yang Kehu4,5 |
1.School of Economics and Management, Beijing Jiaotong University, Beijing 100044
2.School of Information Resource Management, Renmin University of China, Beijing 100080
3.Business School of Northwest Normal University, Lanzhou 730070
4.Evidence-Based Medical Center of School of Basic Medical Sciences of Lanzhou University, Lanzhou 730030
5.Cross-Innovation Laboratory of Evidence-Based Social Science of Lanzhou University, Lanzhou 730030 |
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Abstract Moderation effect testing, as an important method for identifying causal relationships in empirical research, helps uncover underlying relationships between independent and dependent variables. However, this method suffers from issues such as the inability to obtain true effect values and low external validity. Owing to the limitations imposed by the inherent flaws of primary studies, the evidence-based field urgently needs new models for identifying moderation effects. In this study, we adopt the concept of big data evidence and use a recursive method to systematically arrange and combine control variables, thereby simulating the “exhaustion” of all possible original research designs. We conduct regression analyses and record all effect values for all possible variable relationships, and then use meta-analysis to comprehensively merge all original effect sizes to obtain true effect values and enhance the external validity of moderation effect results. Finally, taking research on information poverty as an example, this study demonstrates in detail the entire process of identifying moderation effects from a big data evidence perspective. The main contribution of this paper is the enhancement of the meta-analysis framework within the realm of big data evidence-based approach. This involves distilling authentic effect sizes from an extensive compilation of original research findings, thereby augmenting the external validity of moderating effects and enhancing the dependability of causal inference.
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Received: 29 July 2023
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