Abstract

We applied three Bayesian methods to reanalyse the preregistered contributions to the Social Psychology special issue ‘Replications of Important Results in Social Psychology’ (Nosek & Lakens. 2014 Registered reports: a method to increase the credibility of published results. Soc. Psychol. 45, 137–141. (doi:10.1027/1864-9335/a000192)). First, individual-experiment Bayesian parameter estimation revealed that for directed effect size measures, only three out of 44 central 95% credible intervals did not overlap with zero and fell in the expected direction. For undirected effect size measures, only four out of 59 credible intervals contained values greater than (10% of variance explained) and only 19 intervals contained values larger than . Second, a Bayesian random-effects meta-analysis for all 38 t-tests showed that only one out of the 38 hierarchically estimated credible intervals did not overlap with zero and fell in the expected direction. Third, a Bayes factor hypothesis test was used to quantify the evidence for the null hypothesis against a default one-sided alternative. Only seven out of 60 Bayes factors indicated non-anecdotal support in favour of the alternative hypothesis (), whereas 51 Bayes factors indicated at least some support for the null hypothesis. We hope that future analyses of replication success will embrace a more inclusive statistical approach by adopting a wider range of complementary techniques.

Highlights

  • Conducted replication studies can greatly influence researchers’ confidence in the presence, impact and general nature of a hypothesized effect

  • We report the results from a Bayesian reanalysis of the main results across the replication studies in the Social Psychology special issue7 with the exception of the ManyLabs project [54]

  • This section summarizes the results for individual-study parameter estimation of the contributions to the Social Psychology special issue

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Summary

Introduction

Conducted replication studies can greatly influence researchers’ confidence in the presence, impact and general nature of a hypothesized effect. The trinity of replication guidelines was to collaborate with original authors, to use preregistration and to conduct high-powered studies (see [3,4,5]). A hypothesis testing approach quantifies evidence for the point null hypotheses versus a specific one-sided alternative hypothesis This cannot be accomplished by Neyman–Pearson’s style hypothesis testing, whose explicit goal it is to control error rate in repeated use. We analyse the special issue data using JASP ([10], jasp-stats.org), Stan [11,12,13] and R ([14], especially the BayesFactor package) Armed with these software programs, Bayesian methods can be applied to a series of common analyses such as the t-test, contingency tables, regression and analysis of variance (ANOVA)

Brief Bayesian background
Bayesian parameter estimation: the basic concepts
Bayesian parameter estimation: hierarchical models
Bayesian hypothesis testing
Single study example
Results for all studies
Results from Bayesian parameter estimation: individual studies
Directed effect sizes
Undirected effect sizes
Results from Bayesian parameter estimation: hierarchical analysis
Results from Bayesian hypothesis testing
General discussion
Dangers of generalizing the results beyond the special issue
Alternative statistical analyses
74. Nosek BA et al 2015 Promoting an open research
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