Academic Journal
Customizable Bayesian adaptive testing with Python - The adaptivetesting package.
| Τίτλος: | Customizable Bayesian adaptive testing with Python - The adaptivetesting package. |
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| Συγγραφείς: | Engicht J; Institute of Psychology, Friedrich-Schiller University Jena, Am Steiger 3, Haus 1, 07743, Jena, Germany. jonas-engicht@uni-jena.de., Bee RM; Institute of Psychology, Friedrich-Schiller University Jena, Am Steiger 3, Haus 1, 07743, Jena, Germany., Koch T; Institute of Psychology, Friedrich-Schiller University Jena, Am Steiger 3, Haus 1, 07743, Jena, Germany. |
| Πηγή: | Behavior research methods [Behav Res Methods] 2026 Jul 24; Vol. 58 (9). Date of Electronic Publication: 2026 Jul 24. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Springer Country of Publication: United States NLM ID: 101244316 Publication Model: Electronic Cited Medium: Internet ISSN: 1554-3528 (Electronic) Linking ISSN: 1554351X NLM ISO Abbreviation: Behav Res Methods Subsets: MEDLINE |
| Imprint Name(s): | Publication: 2010- : New York : Springer Original Publication: Austin, Tex. : Psychonomic Society, c2005- |
| Ιατρικοί όροι (MeSH): | Software* , Programming Languages*, Bayes Theorem ; Humans ; Algorithms ; Computer Simulation |
| Περίληψη: | This paper introduces an open-source Python package for simplified, customizable computerized adaptive testing (CAT) using Bayesian methods for ability estimation. It addresses the lack of sophisticated packages for CAT in the Python programming language. Moreover, it bridges the gap between the construction and simulation of adaptive tests and their practical application by providing a dedicated API for integration with experiment software. Thereby, it eliminates the need for major code rewrites when transitioning from simulated to real-world adaptive testing. By leveraging Python's object-oriented programming approach, such as abstract classes, protocols, and inheritance, the package allows for easy extension and customization of its functionality. For example, Bayesian estimators can be modified to incorporate custom priors. This paper outlines the relevance and practical use of the adaptivetesting package through a walkthrough example. The package is fully documented, and its source code is published on GitHub. It is also available on the Python Package Index (PyPi) and conda-forge thus it can easily be installed using Python's package manager pip or conda. Leveraging R's reticulate package, adaptivetesting can also be accessed from within RStudio. (© 2026. The Author(s).) |
| Competing Interests: | Declarations. Funding: This paper is the result of a research project within the framework of the “Honours Programme for Future Researchers” at the Friedrich Schiller University Jena (Germany), funded by the Excellence Strategy of the German State Government and the Länder. Conflicts of interests/Competing interests: We have no known conflict of interest to disclose. Ethics approval: Not applicable. Consent to participate: Not applicable. Consent for publication: Not applicable. Open practices statement: The package’s source code, as well as the here presented code examples and analyses are available in the following GitHub repository: https://github.com/condecon/adaptivetesting. The dataset used in the application examples is obtained from Bohn et al. (2025) and is available in the following GitHub repository under the Creative Commons Attribution 4.0 International License (CC BY 4.0): https://github.com/manuelbohn/previc . No changes were made to the original dataset. |
| References: | Behav Res Methods. 2019 Feb;51(1):195-203. (PMID: 30734206) Appl Psychol Meas. 2009 Sep 1;33(6):419-440. (PMID: 20011456) Behav Res Methods. 2025 Feb 17;57(3):95. (PMID: 39961954) IEEE Trans Pattern Anal Mach Intell. 2026 Aug;48(8):8744-8763. (PMID: 41805521) Br J Math Stat Psychol. 2009 May;62(Pt 2):369-83. (PMID: 18534047) Behav Res Methods. 2025 Mar 26;57(5):126. (PMID: 40140187) Psychometrika. 2009 Jun;74(2):273-296. (PMID: 20119511) |
| Contributed Indexing: | Keywords: Bayesian statistics; Computerized adaptive testing; Item response theory; Python package |
| Entry Date(s): | Date Created: 20260724 Date Completed: 20260724 Latest Revision: 20260813 |
| Update Code: | 20260814 |
| PubMed Central ID: | PMC13400684 |
| DOI: | 10.3758/s13428-026-03079-w |
| PMID: | 42498892 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1554-3528 |
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| DOI: | 10.3758/s13428-026-03079-w |