Estimation of Conditional Standard Errors of Measurement for MLE Scores in MST

This paper proposes an information-based analytic method for calculating the conditional standard error of measurement (CSEM) in multistage testing (MST) using maximum likelihood estimation. The accuracy of the proposed method was evaluated by comparing CSEMs computed using the analytic method with those obtained from simulation across the same four MST designs. The results show that analytic and simulation-based CSEMs converge as test length increases, indicating that the proposed method provides a reliable approximation for longer tests. However, shorter tests and more complex MST designs require additional items to achieve comparable accuracy. The study also compared the proposed method with Park et al.'s analytic approach. Practical implications of the proposed method are discussed.

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Publication Details

Journal
Educational and Psychological Measurement
Published
2026-02-25
DOI
https://doi.org/10.1177/00131644261420391
Primary Topic
Psychometric Methodologies and Testing
Type
article
Field-Weighted Citation Impact
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article

Estimation of Conditional Standard Errors of Measurement for MLE Scores in MST

Won‐Chan Lee, Yuanyuan J. Stirn
Educational and Psychological Measurement
Psychometric Methodologies and Testing
article

Estimation of Conditional Standard Errors of Measurement for MLE Scores in MST

Won‐Chan Lee, Yuanyuan J. Stirn
article en

Abstract

This paper proposes an information-based analytic method for calculating the conditional standard error of measurement (CSEM) in multistage testing (MST) using maximum likelihood estimation. The accuracy of the proposed method was evaluated by comparing CSEMs computed using the analytic method with those obtained from simulation across the same four MST designs. The results show that analytic and simulation-based CSEMs converge as test length increases, indicating that the proposed method provides a reliable approximation for longer tests. However, shorter tests and more complex MST designs require additional items to achieve comparable accuracy. The study also compared the proposed method with Park et al.'s analytic approach. Practical implications of the proposed method are discussed.

Educational and Psychological Measurement
University of Iowa (US)
Openalex Percentile: Top 48%
Psychometric Methodologies and Testing
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