QTEST is a custom-designed public-domain statistical analysis package for order-constrained inference.
The goal of QTEST 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.
QTEST Software
We provide installation for Mac OS and Windows. We also provide the QTEST source code for users who would like to use the functions directly in Matlab. Please note that this download is data intensive and benefits from a fast internet pipeline. Your machine will need approximately 2GB of storage to complete the installation process.
To install the QTEST software to use with a graphical user interface (GUI), follow the instructions at the link below. Note that there are different installation files for Mac vs. Windows. You will first need to install Matlab runtime R2025b. This stage is included in the GitHub steps.
Source code can be used directly in MATLAB and requires a valid MATLAB license to run. Use version R2025b for best performance. Before running the source code, install the following toolboxes in MatLab:
Optimization ToolboxParallel Computing ToolboxStatistics and Machine Learning Toolbox
QTEST 2.1 Tutorial
A tutorial following the analyses in Zwilling et al., 2019 can be found here. The corresponding tutorial files referenced throughout this document can be found here. Please note that this tutorial refers to the earlier QTEST 2.1 version of the software. Because the current version maintains a very similar user interface, most of the tutorial still applies. A user should be able to navigate small changes in the graphical user interface. We plan to write a new tutorial in a new future release. Contact Mike Regenwetter at regenwet@illinois.edu if you need assistance.
Release Notes
If publishing results generated by QTEST, please include the following citations:
Zwilling, C., Cavagnaro, D. R., Regenwetter, M., Lim, S.H., Fields, B., & Zhang, Y. (2019) QTEST 2.1: Quantitative testing of theories of binary choice using Bayesian inference. Journal of Mathematical Psychology, 91, 176-194.
Regenwetter, M., Davis-Stober, C. P., Lim, S. H., Guo, Y., Popova, A., Zwilling, C., Cha, Y. S., & Messner, W. (2014). QTEST: Quantitative testing of theories of binary choice. Decision, 1(1), 2-34.
and please acknowledge that:
QTEST was developed with support by the National Science Foundation grants SES 10-62045 and SES 14-59699 (PI: M. Regenwetter) as well as by the Humboldt Foundation (Co-PIs: J. Stevens and M. Regenwetter).