Single-Dataset Meta-Analysis For Many-Analysts And Multiverse Studies

2025-11-21

AI Summary

To address inference bias and overconfidence in conventional meta-analyses arising from multiple analysts or “multiverse” analyses sharing a single dataset, this paper introduces the first Single-Dataset Meta-Analysis (SDMA) framework. SDMA constructs fixed- and random-effects models via weighted likelihood, ensuring each analytical path contributes only once to the data weight—thereby preventing statistical inflation from data reuse. The framework supports both frequentist and Bayesian inference and can be implemented in standard meta-analysis software with minimal input adjustments. Empirical applications demonstrate that SDMA robustly synthesizes effect estimates across diverse analytical paths, yielding heterogeneity-corrected average effects and valid uncertainty intervals. It thus overcomes the limitations of qualitative assessment in multiverse research, providing a reproducible, statistically rigorous tool for quantitative integration of analytic variability.

Technology Category

• Search and Optimization: Metareasoning and Metaheuristics
• Reasoning under Uncertainty: Causality
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Social Networks and Social Media: Fairness and bias in social network and social media analysis

Abstract

Empirical claims often rely on one population, design, and analysis. Many-analysts, multiverse, and robustness studies expose how results can vary across plausible analytic choices. Synthesizing these results, however, is nontrivial as all results are computed from the same dataset. We introduce single-dataset meta-analysis, a weighted-likelihood approach that incorporates the information in the dataset at most once. It prevents overconfident inferences that would arise if a standard meta-analysis was applied to the data. Single-dataset meta-analysis yields meta-analytic point and interval estimates of the average effect across analytic approaches and of between-analyst heterogeneity, and can be supplied by classical and Bayesian hypothesis tests. Both the common-effect and random-effects versions of the model can be estimated by standard meta-analytic software with small input adjustments. We demonstrate the method via application to the many-analysts study on racial bias in soccer, the many-analysts study of marital status and cardiovascular disease, and the multiverse study on technology use and well-being. The results show how single-dataset meta-analysis complements the qualitative evaluation of many-analysts and multiverse studies.

Problem

• Addresses result variability across different analytical choices
• Prevents overconfident inferences from same-dataset analyses
• Synthesizes multiple analytical outcomes using meta-analysis techniques

Innovation

• Weighted-likelihood approach for single-dataset meta-analysis
• Prevents overconfident inferences from standard meta-analysis
• Estimates average effect and heterogeneity across analyses