Two IRT Characteristic Curve Linking Methods Weighted by Information. Issue 4 (17th April 2022)

• Record Type: Journal Article
• Authors: Wang, Shaojie; Zhang, Minqiang; Lee, Won‐Chan; Huang, Feifei; Li, Zonglong; Li, Yixing; Yu, Sufang
• Is Part Of: Journal of educational measurement. Volume 59:Issue 4(2022)
• Page Start: 423
• Page End: 441
• Publication Date: 2022-04-17
• Subjects: Educational tests and measurements -- Periodicals; 371.2605
• DOI: 10.1111/jedm.12315
• Languages: English
• ISSNs: 0022-0655

Abstract

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.