A Tutorial on Conducting and Interpreting a Bayesian ANOVA in JASP

Don van den Bergh; Johnny Van Doorn; Maarten Marsman; Tim Draws; Erik-Jan Van Kesteren; Koen Derks; Fabian Dablander; Quentin F. Gronau; Simon Kucharsk; Akash R. Komarlu Narendra Gupta; Alexandra Sarafoglou; Jan G. Voelkel; Angelika Stefan; Alexander Ly; Max Hinne; Dora Matzke; Eric-Jan Wagenmakers

A Tutorial on Conducting and Interpreting a Bayesian ANOVA in JASP

Don van den Bergh^*, Johnny Van Doorn, Maarten Marsman, Tim Draws, Erik-Jan Van Kesteren, Koen Derks, Fabian Dablander, Quentin F. Gronau, Simon Kucharsk, Akash R. Komarlu Narendra Gupta, Alexandra Sarafoglou, Jan G. Voelkel, Angelika Stefan, Alexander Ly, Max Hinne, Dora Matzke, Eric-Jan Wagenmakers

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

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.

Original language	English
Pages (from-to)	73-96
Number of pages	24
Journal	Annee Psychologique
Volume	120
Issue number	1
Publication status	Published - Mar 2020

Keywords

Analysis of Variance
Bayes Factor
Hypothesis Test
Jasp
Posterior distribution
Tutorial

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van den Bergh, D., Van Doorn, J., Marsman, M., Draws, T., Van Kesteren, E.-J., Derks, K., Dablander, F., Gronau, Q. F., Kucharsk, S., Gupta, A. R. K. N., Sarafoglou, A., Voelkel, J. G., Stefan, A., Ly, A., Hinne, M., Matzke, D., & Wagenmakers, E.-J. (2020). A Tutorial on Conducting and Interpreting a Bayesian ANOVA in JASP. Annee Psychologique, 120(1), 73-96. https://shs.cairn.info/journal-l-annee-psychologique-2020-1-page-73?lang=en

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title = "A Tutorial on Conducting and Interpreting a Bayesian ANOVA in JASP",

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.",

keywords = "Analysis of Variance, Bayes Factor, Hypothesis Test, Jasp, Posterior distribution, Tutorial",

author = "\{van den Bergh\}, Don and \{Van Doorn\}, Johnny and Maarten Marsman and Tim Draws and \{Van Kesteren\}, Erik-Jan and Koen Derks and Fabian Dablander and Gronau, \{Quentin F.\} and Simon Kucharsk and Gupta, \{Akash R. Komarlu Narendra\} and Alexandra Sarafoglou and Voelkel, \{Jan G.\} and Angelika Stefan and Alexander Ly and Max Hinne and Dora Matzke and Eric-Jan Wagenmakers",

year = "2020",

month = mar,

language = "English",

volume = "120",

pages = "73--96",

journal = "Annee Psychologique",

issn = "0003-5033",

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van den Bergh, D, Van Doorn, J, Marsman, M, Draws, T, Van Kesteren, E-J, Derks, K, Dablander, F, Gronau, QF, Kucharsk, S, Gupta, ARKN, Sarafoglou, A, Voelkel, JG, Stefan, A, Ly, A, Hinne, M, Matzke, D & Wagenmakers, E-J 2020, 'A Tutorial on Conducting and Interpreting a Bayesian ANOVA in JASP', Annee Psychologique, vol. 120, no. 1, pp. 73-96. <https://shs.cairn.info/journal-l-annee-psychologique-2020-1-page-73?lang=en>

TY - JOUR

T1 - A Tutorial on Conducting and Interpreting a Bayesian ANOVA in JASP

AU - van den Bergh, Don

AU - Van Doorn, Johnny

AU - Marsman, Maarten

AU - Draws, Tim

AU - Van Kesteren, Erik-Jan

AU - Derks, Koen

AU - Dablander, Fabian

AU - Gronau, Quentin F.

AU - Kucharsk, Simon

AU - Gupta, Akash R. Komarlu Narendra

AU - Sarafoglou, Alexandra

AU - Voelkel, Jan G.

AU - Stefan, Angelika

AU - Ly, Alexander

AU - Hinne, Max

AU - Matzke, Dora

AU - Wagenmakers, Eric-Jan

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N2 - 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.

AB - 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.

KW - Analysis of Variance

KW - Bayes Factor

KW - Hypothesis Test

KW - Jasp

KW - Posterior distribution

KW - Tutorial

M3 - Article

SN - 0003-5033

VL - 120

SP - 73

EP - 96

JO - Annee Psychologique

JF - Annee Psychologique

IS - 1

ER -

A Tutorial on Conducting and Interpreting a Bayesian ANOVA in JASP

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Keywords

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