2020
DOI: 10.3758/s13423-020-01798-5
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The JASP guidelines for conducting and reporting a Bayesian analysis

Abstract: Despite the increasing popularity of Bayesian inference in empirical research, few practical guidelines provide detailed recommendations for how to apply Bayesian procedures and interpret the results. Here we offer specific guidelines for four different stages of Bayesian statistical reasoning in a research setting: planning the analysis, executing the analysis, interpreting the results, and reporting the results. The guidelines for each stage are illustrated with a running example. Although the guidelines are… Show more

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Cited by 537 publications
(293 citation statements)
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“…Bayesian statistics using the Bayesian information criteria proposed by Wagenmakers ( 2007 ) were further applied to estimate the likelihood of the existence of the interaction (H 0 ). In accordance Bayes factors classifications by van Doorn et al ( 2019 ), the interaction term shows moderate evidence for H 0 , BF 10 = .278.…”
Section: Resultssupporting
confidence: 87%
“…Bayesian statistics using the Bayesian information criteria proposed by Wagenmakers ( 2007 ) were further applied to estimate the likelihood of the existence of the interaction (H 0 ). In accordance Bayes factors classifications by van Doorn et al ( 2019 ), the interaction term shows moderate evidence for H 0 , BF 10 = .278.…”
Section: Resultssupporting
confidence: 87%
“…Here we regard any relative evidence greater than "anecdotal" in favor of H1 (BF > 3) or in favor of H0 (BF < 1/3) as meaningful. These procedures follow the JASP guidelines for conducting and reporting a Bayesian analysis (van Doorn et al, 2020). For Student's paired t-tests, we used an alpha of 0.05.…”
Section: Discussionmentioning
confidence: 99%
“…Additionally, we performed Bayesian Wilcoxon signed-rank tests to determine the relative support for alternative and null hypotheses in each tactile condition. Given that we lacked proper justification for informed prior specification, we used the default Cauchy prior with width r set to 0.707 (1/ p 2) [41]. Bayes factor robustness was further validated with analyses using narrow (r = 0.354), wide (r = 1) and ultrawide (r = p 2) priors.…”
Section: Discussionmentioning
confidence: 99%
“…Bayes factor robustness was further validated with analyses using narrow (r = 0.354), wide (r = 1) and ultrawide (r = p 2) priors. BF analyses were reported and interpreted in accordance with the recent guidelines [41] As common non-parametric methods do not permit the conclusive examination of interaction effects, linear quantile mixed models (LQMM) were used in the complementary analysis [42]. This statistical approach to dependent data makes no assumptions regarding the distribution of the outcome variable.…”
Section: Discussionmentioning
confidence: 99%