An Introduction to Calculating Bayes Factors in JASP for Speech, Language, and Hearing Research

Abstract

Purpose Evidence-based data analysis methods are important in clinical research fields, including speech-language pathology and audiology. Although commonly used, null hypothesis significance testing (NHST) has several limitations with regard to the conclusions that can be drawn from results, particularly nonsignificant findings. Bayes factors (BFs) can be used to complement NHST and quantify the strength of evidence in favor of 1 hypothesis over another, given the data: commonly, either the alternate hypothesis over the null or the null hypothesis over the alternate. This article provides an introduction to BFs through JASP, a free, open-source, graphics-based statistics package that allows researchers to easily conduct both NHST and Bayesian analyses in a clear and reproducible manner. Method and Results Both traditional NHST analyses and Bayesian equivalents for correlations, t tests, and analyses of variance were conducted in JASP using simulated data, with explanations of analysis options, statistical output, and figures provided. These examples also demonstrate what NHST and BFs can and cannot infer about a data set. Additionally, BFs were calculated from the summary statistics of published nonsignificant results to illustrate how JASP may be useful to consumers of research who only have access to statistics provided in a published study. Conclusions Bayesian analyses are underutilized in speech, language, and hearing research. By complementing traditional NHST analyses with BFs, researchers can directly test for and quantify the strength of evidence during hypothesis testing, thereby drawing stronger conclusions from their research and providing more relevant information for clinicians and researchers in the field.