{"title":"Two IRT Characteristic Curve Linking Methods Weighted by Information","authors":"Shaojie Wang, Minqiang Zhang, Won-Chan Lee, Feifei Huang, Zonglong Li, Yixing Li, Sufang Yu","doi":"10.1111/jedm.12315","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":47871,"journal":{"name":"Journal of Educational Measurement","volume":null,"pages":null},"PeriodicalIF":1.4000,"publicationDate":"2022-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Educational Measurement","FirstCategoryId":"102","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/jedm.12315","RegionNum":4,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"PSYCHOLOGY, APPLIED","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍:
The Journal of Educational Measurement (JEM) publishes original measurement research, provides reviews of measurement publications, and reports on innovative measurement applications. The topics addressed will interest those concerned with the practice of measurement in field settings, as well as be of interest to measurement theorists. In addition to presenting new contributions to measurement theory and practice, JEM also serves as a vehicle for improving educational measurement applications in a variety of settings.