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1. The JASP guidelines for conducting and reporting a Bayesian analysis

Author

Doorn, J. (Johnny) van, Bergh, D. (Don) van den, Böhm, U. (Udo), Dablander, F. (Fabian), Derks, K. (Koen), Draws, T. (Tim), Etz, A. (Alexander), Evans, N.J. (Nathan), Gronau, Q. F. (Quentin), Haaf, J.M. (Julia), Hinne, M. (Max), Kucharský, Š. (Šimon), Ly, A. (Alexander), Marsman, M. (Maarten), Matzke, D. (Dora), Komarlu Narendra Gupta, A.R. (Akash), Sarafoglou, A. (Alexandra), Stefan, A. (Angelika), Voelkel, J.G. (Jan), Wagenmakers, E.-J. (Eric-Jan), Doorn, J. (Johnny) van, Bergh, D. (Don) van den, Böhm, U. (Udo), Dablander, F. (Fabian), Derks, K. (Koen), Draws, T. (Tim), Etz, A. (Alexander), Evans, N.J. (Nathan), Gronau, Q. F. (Quentin), Haaf, J.M. (Julia), Hinne, M. (Max), Kucharský, Š. (Šimon), Ly, A. (Alexander), Marsman, M. (Maarten), Matzke, D. (Dora), Komarlu Narendra Gupta, A.R. (Akash), Sarafoglou, A. (Alexandra), Stefan, A. (Angelika), Voelkel, J.G. (Jan), and Wagenmakers, E.-J. (Eric-Jan)

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 geared towards analyses performed with the open-source statistical software JASP, most guidelines extend to Bayesian inference in general.

Published
2021
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2. A tutorial on conducting and interpreting a bayesian ANOVA in JASP Tutoriel pour réaliser et interpréter une analyse de variance bayésienne dans JASP

Author

Bergh, D. (Don) van den, Doorn, J. (Johnny) van, Marsman, M. (Maarten), Draws, T. (Tim), Kesteren, E.-J. (Erik-Jan), Derks, K. (Koen), Dablander, F. (Fabian), Gronau, Q. F. (Quentin), Kucharský, Š. (Šimon), Komarlu Narendra Gupta, A.R. (Akash), Sarafoglou, A. (Alexandra), Voelkel, J.G. (Jan), Stefan, A. (Angelika), Ly, A. (Alexander), Hinne, M. (Max), Matzke, D. (Dora), Wagenmakers, E.-J. (Eric-Jan), Bergh, D. (Don) van den, Doorn, J. (Johnny) van, Marsman, M. (Maarten), Draws, T. (Tim), Kesteren, E.-J. (Erik-Jan), Derks, K. (Koen), Dablander, F. (Fabian), Gronau, Q. F. (Quentin), Kucharský, Š. (Šimon), Komarlu Narendra Gupta, A.R. (Akash), Sarafoglou, A. (Alexandra), Voelkel, J.G. (Jan), Stefan, A. (Angelika), Ly, A. (Alexander), Hinne, M. (Max), Matzke, D. (Dora), and Wagenmakers, E.-J. (Eric-Jan)

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.

Published
2020
Full Text
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3. The JASP guidelines for conducting and reporting a Bayesian analysis

Author

Doorn, J. (Johnny) van, Bergh, D. (Don) van den, Böhm, U. (Udo), Dablander, F. (Fabian), Derks, K. (Koen), Draws, T. (Tim), Etz, A. (Alexander), Evans, N.J. (Nathan), Gronau, Q. F. (Quentin), Haaf, J.M. (Julia), Hinne, M. (Max), Kucharský, Š. (Šimon), Ly, A. (Alexander), Marsman, M. (Maarten), Matzke, D. (Dora), Komarlu Narendra Gupta, A.R. (Akash), Sarafoglou, A. (Alexandra), Stefan, A. (Angelika), Voelkel, J.G. (Jan), Wagenmakers, E.-J. (Eric-Jan), Doorn, J. (Johnny) van, Bergh, D. (Don) van den, Böhm, U. (Udo), Dablander, F. (Fabian), Derks, K. (Koen), Draws, T. (Tim), Etz, A. (Alexander), Evans, N.J. (Nathan), Gronau, Q. F. (Quentin), Haaf, J.M. (Julia), Hinne, M. (Max), Kucharský, Š. (Šimon), Ly, A. (Alexander), Marsman, M. (Maarten), Matzke, D. (Dora), Komarlu Narendra Gupta, A.R. (Akash), Sarafoglou, A. (Alexandra), Stefan, A. (Angelika), Voelkel, J.G. (Jan), and Wagenmakers, E.-J. (Eric-Jan)

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 geared towards analyses performed with the open-source statistical software JASP, most guidelines extend to Bayesian inference in general.

Published
2020
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4. Author Correction: A consensus-based transparency checklist.

Author

Aczel, B, Szaszi, B, Sarafoglou, A, Kekecs, Z, Kucharský, Š, Benjamin, D, Chambers, CD, Fisher, A, Gelman, A, Gernsbacher, MA, Ioannidis, JP, Johnson, E, Jonas, K, Kousta, S, Lilienfeld, SO, Lindsay, DS, Morey, CC, Munafò, M, Newell, BR, Pashler, H, Shanks, DR, Simons, DJ, Wicherts, JM, Albarracin, D, Anderson, ND, Antonakis, J, Arkes, HR, Back, MD, Banks, GC, Beevers, C, Bennett, AA, Bleidorn, W, Boyer, TW, Cacciari, C, Carter, AS, Cesario, J, Clifton, C, Conroy, RM, Cortese, M, Cosci, F, Cowan, N, Crawford, J, Crone, EA, Curtin, J, Engle, R, Farrell, S, Fearon, P, Fichman, M, Frankenhuis, W, Freund, AM, Gaskell, MG, Giner-Sorolla, R, Green, DP, Greene, RL, Harlow, LL, de la Guardia, FH, Isaacowitz, D, Kolodner, J, Lieberman, D, Logan, GD, Mendes, WB, Moersdorf, L, Nyhan, B, Pollack, J, Sullivan, C, Vazire, S, Wagenmakers, E-J, Aczel, B, Szaszi, B, Sarafoglou, A, Kekecs, Z, Kucharský, Š, Benjamin, D, Chambers, CD, Fisher, A, Gelman, A, Gernsbacher, MA, Ioannidis, JP, Johnson, E, Jonas, K, Kousta, S, Lilienfeld, SO, Lindsay, DS, Morey, CC, Munafò, M, Newell, BR, Pashler, H, Shanks, DR, Simons, DJ, Wicherts, JM, Albarracin, D, Anderson, ND, Antonakis, J, Arkes, HR, Back, MD, Banks, GC, Beevers, C, Bennett, AA, Bleidorn, W, Boyer, TW, Cacciari, C, Carter, AS, Cesario, J, Clifton, C, Conroy, RM, Cortese, M, Cosci, F, Cowan, N, Crawford, J, Crone, EA, Curtin, J, Engle, R, Farrell, S, Fearon, P, Fichman, M, Frankenhuis, W, Freund, AM, Gaskell, MG, Giner-Sorolla, R, Green, DP, Greene, RL, Harlow, LL, de la Guardia, FH, Isaacowitz, D, Kolodner, J, Lieberman, D, Logan, GD, Mendes, WB, Moersdorf, L, Nyhan, B, Pollack, J, Sullivan, C, Vazire, S, and Wagenmakers, E-J

Abstract

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Published
2020

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

Author

Bergh, D. (Don) van den, Doorn, J. (Johnny) van, Marsman, M. (Maarten), Draws, T. (Tim), Kesteren, E.-J. (Erik-Jan), Derks, K. (Koen), Dablander, F. (Fabian), Gronau, Q. F. (Quentin), Kucharský, Š. (Šimon), Komarlu Narendra Gupta, A.R. (Akash), Sarafoglou, A. (Alexandra), Voelkel, J.G. (Jan), Stefan, A. (Angelika), Ly, A. (Alexander), Hinne, M. (Max), Matzke, D. (Dora), Wagenmakers, E.-J. (Eric-Jan), Bergh, D. (Don) van den, Doorn, J. (Johnny) van, Marsman, M. (Maarten), Draws, T. (Tim), Kesteren, E.-J. (Erik-Jan), Derks, K. (Koen), Dablander, F. (Fabian), Gronau, Q. F. (Quentin), Kucharský, Š. (Šimon), Komarlu Narendra Gupta, A.R. (Akash), Sarafoglou, A. (Alexandra), Voelkel, J.G. (Jan), Stefan, A. (Angelika), Ly, A. (Alexander), Hinne, M. (Max), Matzke, D. (Dora), and Wagenmakers, E.-J. (Eric-Jan)

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.

Published
2020
Full Text
View/download PDF

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