A model-based test for treatment effects with probabilistic classifications

Psychological Methods
Daniel R Cavagnaro, Clintin P Davis-Stober

Abstract

Within modern psychology, computational and statistical models play an important role in describing a wide variety of human behavior. Model selection analyses are typically used to classify individuals according to the model(s) that best describe their behavior. These classifications are inherently probabilistic, which presents challenges for performing group-level analyses, such as quantifying the effect of an experimental manipulation. We answer this challenge by presenting a method for quantifying treatment effects in terms of distributional changes in model-based (i.e., probabilistic) classifications across treatment conditions. The method uses hierarchical Bayesian mixture modeling to incorporate classification uncertainty at the individual level into the test for a treatment effect at the group level. We illustrate the method with several worked examples, including a reanalysis of the data from Kellen, Mata, and Davis-Stober (2017), and analyze its performance more generally through simulation studies. Our simulations show that the method is both more powerful and less prone to type-1 errors than Fisher's exact test when classifications are uncertain. In the special case where classifications are deterministic, we find a ...Continue Reading

Citations

Dec 24, 2019·The Spanish Journal of Psychology·Sanghyuk ParkMichel Regenwetter
Apr 9, 2019·Journal of Mathematical Psychology·Daniel W Heck, Clintin P Davis-Stober

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