Journal article
Estimating Classification Accuracy and Consistency Indices for Multiple Measures with the Simple Structure MIRT Model
Journal of educational measurement, Vol.60(1), pp.106-125
Spring 2023
DOI: 10.1111/jedm.12338
Abstract
Multiple measures, such as multiple content domains or multiple types of performance, are used in various testing programs to classify examinees for screening or selection. Despite the popular usages of multiple measures, there is little research on classification consistency and accuracy of multiple measures. Accordingly, this study introduces an approach to estimate classification consistency and accuracy indices for multiple measures under four possible decision rules: (1) complementary, (2) conjunctive, (3) compensatory, and (4) pairwise combinations of the three. The current study uses the IRT-recursive-based approach with the simple-structure multidimensional IRT model (SS-MIRT) to estimate the classification consistency and accuracy for multiple measures. Theoretical formulations of the four decision rules with a binary decision (Pass/Fail) are presented. The estimation procedures are illustrated using an empirical data example based on SS-MIRT. In addition, this study applies the estimation procedures to the unidimensional IRT (UIRT) context, considering that UIRT is practically used more. This application shows that the proposed procedure of classification consistency and accuracy could be used with a UIRT model for individual measures as an alternative method of SS-MIRT.
Details
- Title: Subtitle
- Estimating Classification Accuracy and Consistency Indices for Multiple Measures with the Simple Structure MIRT Model
- Creators
- Seohee Park - American Board of Internal MedicineKyung Yong Kim - Univ N Carolina, Dept Educ Res Methodol, 232 Sch Educ,130 Spring Garden St, Greensboro, NC 27402 USAWon-Chan Lee - Univ Iowa, Educ Measurement & Stat, 210E Lindquist Ctr, Iowa City, IA 52242 USA
- Resource Type
- Journal article
- Publication Details
- Journal of educational measurement, Vol.60(1), pp.106-125
- Publisher
- Wiley
- DOI
- 10.1111/jedm.12338
- ISSN
- 0022-0655
- eISSN
- 1745-3984
- Number of pages
- 20
- Language
- English
- Electronic publication date
- 06/20/2022
- Date published season
- Spring 2023
- Date published
- 2023
- Academic Unit
- Psychological and Quantitative Foundations
- Record Identifier
- 9984371116602771
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