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.

翻译：经验性论断通常依赖于单一总体、设计与分析。多分析师、多重宇宙及稳健性研究揭示了结果在合理分析选择中的可变性。然而，由于所有结果均基于同一数据集计算，综合这些结果并非易事。本文提出单数据集元分析，这是一种加权似然方法，确保数据集中的信息至多被纳入一次。该方法避免了若对数据应用标准元分析可能导致的过度自信推断。单数据集元分析能够提供跨分析方法的平均效应及分析师间异质性的元分析点估计与区间估计，并可结合经典与贝叶斯假设检验。该模型的固定效应与随机效应版本均可通过标准元分析软件进行估计，仅需微调输入参数。我们通过应用该方法于足球种族偏见的多分析师研究、婚姻状况与心血管疾病的多分析师研究，以及技术使用与幸福感的多重宇宙研究，展示了其有效性。结果表明，单数据集元分析能够补充多分析师与多重宇宙研究的定性评估。