Journal article
Two IRT Characteristic Curve Linking Methods Weighted by Information
Journal of educational measurement, Vol.59(4), pp.423-441
12/01/2022
DOI: 10.1111/jedm.12315
Abstract
Traditional IRT characteristic curve linking methods ignore parameter estimation errors, which may undermine the accuracy of estimated linking constants. Two new linking methods are proposed that take into account parameter estimation errors. The item- (IWCC) and test-information-weighted characteristic curve (TWCC) methods employ weighting components in the loss function from traditional methods by their corresponding item and test information, respectively. Monte Carlo simulation was conducted to evaluate the performances of the new linking methods and compare them with traditional ones. Ability difference between linking groups, sample size, and test length were manipulated under the common-item nonequivalent groups design. Results showed that the two information-weighted characteristic curve methods outperformed traditional methods, in general. TWCC was found to be more accurate and stable than IWCC. A pseudo-form pseudo-group analysis was also performed, and similar results were observed. Finally, guidelines for practice and future directions are discussed.
Details
- Title: Subtitle
- Two IRT Characteristic Curve Linking Methods Weighted by Information
- Creators
- Shaojie Wang - South China Normal UniversityMinqiang Zhang - South China Normal UniversityWon‐Chan Lee - University of IowaFeifei Huang - South China Normal UniversityZonglong Li - South China Normal UniversityYixing Li - South China Normal UniversitySufang Yu - South China Normal University
- Resource Type
- Journal article
- Publication Details
- Journal of educational measurement, Vol.59(4), pp.423-441
- Publisher
- Wiley
- DOI
- 10.1111/jedm.12315
- ISSN
- 0022-0655
- eISSN
- 1745-3984
- Language
- English
- Date published
- 12/01/2022
- Academic Unit
- Psychological and Quantitative Foundations
- Record Identifier
- 9984371086602771
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