BANOVA: Bayesian Analysis of Experiments in Consumer Psychology

Abstract

This article introduces Bayesian extension of ANOVA for the analysis of experimental data in consumer psychology. The approach, called BANOVA, addresses some common challenges that consumer psychologists encounter in their experimental work, and is specifically suited for the analysis of repeated measures designs. There appears to be a recent surge in interest in those designs based on the recognition that they are sensitive to individual differences in the response to experimental treatments and that they offer advantages for assessing causal mediating mechanisms, even at the individual level. BANOVA enables the analysis of repeated measures data derived from mixed within-between-subjects experiments with Normal and non-Normal dependent variables and accommodates unobserved individual differences. It allows for the calculation of effect sizes, planned comparisons, simple effects, spotlight and floodlight analyses, and includes a wide range of mediation, moderation, and moderated mediation analyses. An R software package implements these analyses, and aims to provide a one-stop-shop for the analysis of experiments in consumer psychology. The package is illustrated through applications to a number of data sets from previously published studies.