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A tutorial on conducting and interpreting a bayesian ANOVA in JASP Tutoriel pour réaliser et interpréter une analyse de variance bayésienne dans JASP

Authors :
Bergh, Don
Doorn, Johnny
Marsman, Maarten
Draws, Tim
Kesteren, Erik-Jan
Derks, Koen
Dablander, Fabian
Gronau, Quentin
Kucharský, Šimon
Komarlu Narendra Gupta, Akash
Sarafoglou, Alexandra
Voelkel, Jan
Stefan, Angelika
Ly, Alexander
Hinne, Max
Matzke, Dora
Wagenmakers, Eric-Jan
Centrum Wiskunde & Informatica, Amsterdam (CWI), The Netherlands
Source :
Annee Psychologique, 120(1), 73-96
Publication Year :
2020

Abstract

Analysis of variance (ANOVA) is the standard procedure for statistical inference in factorial designs. Typically, ANOVAs are executed using frequentist statistics, where p-values determine statistical significance in an all-or-none fashion. In recent years, the Bayesian approach to statistics is increasingly viewed as a legitimate alternative to the p-value. However, the broad adoption of Bayesian statistics-and Bayesian ANOVA in particular-is frustrated by the fact that Bayesian concepts are rarely taught in applied statistics courses. Consequently, practitioners may be unsure how to conduct a Bayesian ANOVA and interpret the results. Here we provide a guide for executing and interpreting a Bayesian ANOVA with JASP, an open-source statistical software program with a graphical user interface. We explain the key concepts of the Bayesian ANOVA using two empirical examples.

Details

Language :
English
ISSN :
00035033
Database :
OpenAIRE
Journal :
Annee Psychologique, 120(1), 73-96
Accession number :
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