Identifying Moderation Effects via Meta-Analysis of Big Data:Basic Model and Empirical Testing
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 anal-yses 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.Fi-nally,taking research on information poverty as an example,this study demonstrates in detail the entire process of identify-ing 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 siz-es from an extensive compilation of original research findings,thereby augmenting the external validity of moderating ef-fects and enhancing the dependability of causal inference.
big data meta-analysismoderation effectevidence-based social sciencecausal relationship
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