Bayesian evaluation of informative hypotheses in SEM using Mplus: a black bear story

Abstract

Half in jest we use a story about a black bear to illustrate that there are some discrepancies between the formal use of the p-value and the way it is often used in practice. We argue that more can be learned from data by evaluating informative hypotheses, than by testing the traditional null hypothesis. All criticisms of classical null hypothesis testing aside, the best argument for evaluating informative hypotheses is that many researchers want to evaluate their expectations directly, but have been unable to do so because the statistical tools were not yet available. It will be shown that a Bayesian model selection procedure can be used to evaluate informative hypotheses in structural equation models using the software Mplus. In the current paper we introduce the methodology using a real-life example taken from the field of developmental psychology about depressive symptoms in adolescence and provide a step-by-step description so that the procedure becomes more comprehensible for applied researchers. As this paper illustrates, this methodology is ready to be used by any researcher within the social sciences.