QTest 2.1: Quantitative testing of theories of binary choice using Bayesian inference

Publication date: August 2019Source: Journal of Mathematical Psychology, Volume 91Author(s): Christopher E. Zwilling, Daniel R. Cavagnaro, Michel Regenwetter, Shiau Hong Lim, Bryanna Fields, Yixin ZhangAbstractThis stand-alone tutorial gives an introduction to the QTest 2.1 public domain software package for the specification and statistical analysis of certain order-constrained probabilistic choice models. Like its predecessors, QTest 2.1 allows a user to specify a variety of probabilistic models of binary responses and to carry out state-of-the-art frequentist order-constrained hypothesis tests within a Graphical User Interface (GUI). QTest 2.1 automatizes the mathematical characterization of so-called “random preference models”, adds some parallel computing capabilities, and, most importantly, adds tools for Bayesian inference and model selection. In this tutorial, we provide an in-depth introduction to the Bayesian features: We review order-constrained Bayesian p-values, DIC and Bayes factors, building on the data, models, and prior QTest based frequentist data analyses of an earlier (frequentist) tutorial by Regenwetter et al. (2014).

Source: Journal of Mathematical Psychology - July 6, 2019 Category: Psychiatry & Psychology Source Type: research