Abstract: Many Labs projects have become the gold standard for assessing the replicability of key findings in psychological science. The Many Labs 4 project recently failed to replicate the mortality salience effect where being reminded of one’s own death strengthens the own cultural identity. Here, we provide a Bayesian reanalysis of Many Labs 4 using meta-analytic and hierarchical modeling approaches and model comparison with Bayes factors. In a multiverse analysis we assess the robustness of the results with varying data inclusion criteria and prior settings. Bayesian model comparison results largely converge to a common conclusion: We find evidence against a mortality salience effect across the majority of our analyses. Even when ignoring the Bayesian model comparison results we estimate overall effect sizes so small (between d = 0.03 and d = 0.18) that it renders the entire field of mortality salience studies as uninformative.
Saturday, April 25, 2020
A Bayesian Multiverse Analysis of Many Labs 4: Quantifying the Evidence Against Mortality Salience
Haaf, Julia M., Suzanne Hoogeveen, Sophie Berkhout, Quentin F. Gronau, and Eric-Jan Wagenmakers. 2020. “A Bayesian Multiverse Analysis of Many Labs 4: Quantifying the Evidence Against Mortality Salience.” PsyArXiv. April 14. doi:10.31234/osf.io/cb9er
Abstract: Many Labs projects have become the gold standard for assessing the replicability of key findings in psychological science. The Many Labs 4 project recently failed to replicate the mortality salience effect where being reminded of one’s own death strengthens the own cultural identity. Here, we provide a Bayesian reanalysis of Many Labs 4 using meta-analytic and hierarchical modeling approaches and model comparison with Bayes factors. In a multiverse analysis we assess the robustness of the results with varying data inclusion criteria and prior settings. Bayesian model comparison results largely converge to a common conclusion: We find evidence against a mortality salience effect across the majority of our analyses. Even when ignoring the Bayesian model comparison results we estimate overall effect sizes so small (between d = 0.03 and d = 0.18) that it renders the entire field of mortality salience studies as uninformative.
Abstract: Many Labs projects have become the gold standard for assessing the replicability of key findings in psychological science. The Many Labs 4 project recently failed to replicate the mortality salience effect where being reminded of one’s own death strengthens the own cultural identity. Here, we provide a Bayesian reanalysis of Many Labs 4 using meta-analytic and hierarchical modeling approaches and model comparison with Bayes factors. In a multiverse analysis we assess the robustness of the results with varying data inclusion criteria and prior settings. Bayesian model comparison results largely converge to a common conclusion: We find evidence against a mortality salience effect across the majority of our analyses. Even when ignoring the Bayesian model comparison results we estimate overall effect sizes so small (between d = 0.03 and d = 0.18) that it renders the entire field of mortality salience studies as uninformative.
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