A Review of Applications of the Bayes Factor in Psychological Research

Heck, Daniel W.; Hoijtink, Herbert; Fu, Qianrao; Gu, Xin; Pálfi, Bence; Leenders, R.Th.A.J.; Derks, Koen

doi:10.31234/osf.io/cu43g

Cited by 17 publications

(26 citation statements)

References 82 publications

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“…On the other hand, a Bayes factor in favor of 0 of 10 ( 01 = 1 10 = 10) indicates that the sample outcomes are 10 times more likely to be observed under the null hypothesis 0 than under the alternative hypothesis 1 . Because of the ease of interpretation of the Bayes factor, it is rapidly being adopted in many areas of business and science such as Psychology (Heck et al 2020;Ly, Verhagen, and Wagenmakers 2016), Sociology (Bollen et al 2012;Lynch and Barlett 2019), and Economy (Cipriani, Constantini, and Guarino 2012;Richard and Vecer 2021). Furthermore, Bayes factor calculations have been made very easy in many standard situations such as the (partial) correlation test , the t-test (Rouder et al 2009;Wetzels et al 2009), or the ANOVA (Rouder et al 2012;Wetzels, Grasman, and Wagenmakers 2012) and implementation in easy-to-use software such as JASP (JASP Team 2020; Love et al 2019).…”

Section: Bayesian Hypothesis Testingmentioning

confidence: 99%

The Bayesian Approach to Audit Evidence: Quantifying Statistical Evidence using the Bayes Factor

Derks¹,

Swart²,

Wagenmakers³

et al. 2021

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The impact of statistical methods on the audit practice is growing because of the increasing availability of audit data and the statistical methods to analyze these data. A key aspect in the statistical approach to auditing is assessing the strength of evidence for or against a hypothesis. Unfortunately, the often-used frequentist statistical methods cannot provide the statistical evidence that audit standards demand directly nor easily. In this article we discuss an alternative approach that can provide this evidence: Bayesian inference. Firstly, we explore the philosophical differences between frequentist and Bayesian inference. Secondly, we discuss misconceptions in the interpretation of frequentist statistical evidence, and finally we discuss how Bayesian inference allows the auditor to obtain and interpret statistical evidence in line with audit standards via its alternative to the p value, the Bayes factor.

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Section: Bayesian Hypothesis Testingmentioning

confidence: 99%

The Bayesian Approach to Audit Evidence: Quantifying Statistical Evidence using the Bayes Factor

Derks¹,

Swart²,

Wagenmakers³

et al. 2021

Preprint

Self Cite

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“…Inference can then proceed by selecting the model that best accounts for the data. Bayes factors are a classical method for model selection that appropriately penalizes for model complexity (Heck et al, 2020;Kass & Raftery, 1995). However, it may be undesirable to base inference on a single model due to model uncertainty.…”

Section: Statistical Challenges For Cognitive Modelingmentioning

confidence: 99%

Inclusion Bayes Factors for Mixed Hierarchical Diffusion Decision Models

Boehm¹,

Nj²,

Qf³

et al. 2021

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Cognitive models provide a substantively meaningful quantitative description of latent cognitive processes. The quantitative formulation of these models supports cumulative theory building and enables strong empirical tests. However, the non-linearity of these models and pervasive correlations among model parameters pose special challenges when applying cognitive models to data. Firstly, estimating cognitive models typically requires large hierarchical data sets that need to be accommodated by an appropriate statistical structure within the model. Secondly, statistical inference needs to appropriately account for model uncertainty to avoid overconfidence and biased parameter estimates. In the present work we show how these challenges can be addressed through a combination of Bayesian hierarchical modelling and Bayesian model averaging. To illustrate these techniques, we apply the popular diffusion decision model to data from a collaborative selective influence study.

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“…Bayes factors can range from 0 to ∞, where BF 10 > 1 indicates evidence in favor of M 1 and BF 10 < 1 evidence in favor of M 0 (see, e.g., Etz & Vandekerckhove, 2018;Heck et al, 2020;Jeffreys, 1961;Kass & Raftery, 1995;van Ravenzwaaij & Etz, 2021).…”

Section: Introductionmentioning

confidence: 99%

Bayes Factor Model Comparisons Across Parameter Values for Mixed Models

Linde¹,

Ravenzwaaij²

2021

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Nested data structures, in which conditions include multiple trials, are often analyzed using repeated-measures analysis of variance or mixed effects models. Typically, researchers are interested in determining whether there is an effect of the experimental manipulation. Unfortunately, these kinds of analyses have different appropriate specifications for the null and alternative models, and a discussion on which is to be preferred and when is sorely lacking. van Doorn et al. (2021) performed three types of Bayes factor model comparisons on a simulated data set in order to examine which model comparison is most suitable for quantifying evidence for or against the presence of an effect of the experimental manipulation. Here we extend their results by simulating multiple data sets for various scenarios and by using different prior specifications. We demonstrate how three different Bayes factor model comparison types behave under changes in different parameters, and we make concrete recommendations on which model comparison is most appropriate for different scenarios.

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A Review of Applications of the Bayes Factor in Psychological Research

Cited by 17 publications

References 82 publications

The Bayesian Approach to Audit Evidence: Quantifying Statistical Evidence using the Bayes Factor

The Bayesian Approach to Audit Evidence: Quantifying Statistical Evidence using the Bayes Factor

Inclusion Bayes Factors for Mixed Hierarchical Diffusion Decision Models

Bayes Factor Model Comparisons Across Parameter Values for Mixed Models

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