An Improved Inferential Procedure to Evaluate Item Discriminations in a Conditional Maximum Likelihood Framework

Clemens Draxler, Andreas Kurz, Can Gürer and Jan Philipp Nolte
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Clemens Draxler: UMIT TIROL Private University for Health Sciences and Technology
Andreas Kurz: University of Salzburg
Jan Philipp Nolte: UMIT TIROL Private University for Health Sciences and Technology

Journal of Educational and Behavioral Statistics, 2024, vol. 49, issue 3, 403-430

Abstract: A modified and improved inductive inferential approach to evaluate item discriminations in a conditional maximum likelihood and Rasch modeling framework is suggested. The new approach involves the derivation of four hypothesis tests. It implies a linear restriction of the assumed set of probability distributions in the classical approach that represents scenarios of different item discriminations in a straightforward and efficient manner. Its improvement is discussed, compared to classical procedures (tests and information criteria), and illustrated in Monte Carlo experiments as well as real data examples from educational research. The results show an improvement of power of the modified tests of up to 0.3.

Keywords: item discrimination; conditional maximum likelihood; hypothesis tests; Rasch model (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:49:y:2024:i:3:p:403-430

DOI: 10.3102/10769986231183335

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