Estimating Diffusion-Based Item Response Theory Models: Exploring the Robustness of Three Old and Two New Estimators

Jochen Ranger and Jörg-Tobias Kuhn
Additional contact information
Jochen Ranger: Martin-Luther-University Halle-Wittenberg
Jörg-Tobias Kuhn: University of Dortmund

Journal of Educational and Behavioral Statistics, 2018, vol. 43, issue 6, 635-662

Abstract: Diffusion-based item response theory models for responses and response times in tests have attracted increased attention recently in psychometrics. Analyzing response time data, however, is delicate as response times are often contaminated by unusual observations. This can have serious effects on the validity of statistical inference. In this article, we compare three established and two new estimation approaches for diffusion-based item response theory models with respect to their robustness. The three established approaches are the marginal maximum likelihood (ML) estimator for continuous time, the marginal ML estimator for discrete time, and the weighted least squares (WLS) estimator. The new approaches are two modifications of the WLS estimator with better robustness properties. The performance of the estimators is compared in a simulation study. The simulation study illustrates that the new approaches are robust against some forms of random independent contamination. The marginal ML estimator for discrete time also performs well. The marginal ML estimator for continuous time is heavily affected by contamination.

Keywords: diffusion model; item response theory; contamination (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.3102/1076998618787791 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:43:y:2018:i:6:p:635-662

DOI: 10.3102/1076998618787791

Access Statistics for this article

More articles in Journal of Educational and Behavioral Statistics
Bibliographic data for series maintained by SAGE Publications ().