QTEST: Quantitative testing of theories of binary choice

Michel Regenwetter, Clintin P. Davis-Stober, Shiau Hong Lim, Ying Guo, Anna Popova, Chris Zwilling, Yun Shil Cha, William Messner

Research output: Contribution to journalArticlepeer-review


The goal of this paper is to make modeling and quantitative testing accessible to behavioral decision researchers interested in substantive questions. We provide a novel, rigorous, yet very general, quantitative diagnostic framework for testing theories of binary choice. This permits the nontechnical scholar to proceed far beyond traditionally rather superficial methods of analysis, and it permits the quantitatively savvy scholar to triage theoretical proposals before investing effort into complex and specialized quantitative analyses. Our theoretical framework links static algebraic decision theory with observed variability in behavioral binary choice data. The article is supplemented with a custom-designed public-domain statistical analysis package, the QTEST software. We illustrate our approach with a quantitative analysis using published laboratory data, including tests of novel versions of "Random Cumulative Prospect Theory." A major asset of the approach is the potential to distinguish decision makers who have a fixed preference and commit errors in observed choices from decision makers who waver in their preferences.

Original languageEnglish (US)
Pages (from-to)2-34
Number of pages33
Issue number1
StatePublished - 2014


  • Behavioral decision research
  • Luce's challenge
  • Order-constrained likelihood-based inference
  • Probabilistic specification
  • Theory testing

ASJC Scopus subject areas

  • Social Psychology
  • Neuropsychology and Physiological Psychology
  • Applied Psychology
  • Statistics, Probability and Uncertainty


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