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

Christopher E. Zwilling, Daniel R. Cavagnaro, Michel Regenwetter, Shiau Hong Lim, Bryanna Fields, Y. Zhang

Research output: Contribution to journalArticlepeer-review


This 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).

Original languageEnglish (US)
Pages (from-to)176-194
Number of pages19
JournalJournal of Mathematical Psychology
StatePublished - Aug 2019


  • Bayes factors
  • Gibbs sampler
  • Model selection
  • Order-constrained inference
  • Posterior model probability

ASJC Scopus subject areas

  • General Psychology
  • Applied Mathematics


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