Using Bayes Factors to Test Hypotheses in Developmental Research
Research output: Contribution to journal › Article
This article discusses the concept of Bayes factors as inferential tools that can serve as an alternative to null hypothesis significance testing in the day-to-day work of developmental researchers. A Bayes factor indicates the degree to which data observed should increase (or decrease) the credibility of one hypothesis in comparison to another. Bayes factor analyses can be used to compare many types of models but are particularly helpful when comparing a point null hypothesis to a directional or nondirectional alternative hypothesis. A key advantage of this approach is that a Bayes factor analysis makes it clear when a set of observed data is more consistent with the null hypothesis than the alternative. Bayes factor alternatives to common tests used by developmental psychologists are available in easy-to-use software. However, we note that analysis using Bayes factors is a less general approach than Bayesian estimation/modeling, and is not the right tool for every research question.
|Research areas and keywords||
Subject classification (UKÄ) – MANDATORY
|Journal||Research in Human Development|
|Publication status||Published - 2017 Oct 4|