{"title":"Bayesian Logistic Regression: A New Method to Calibrate Pretest Items in Multistage Adaptive Testing","authors":"TsungHan Ho","doi":"10.1080/08957347.2023.2274572","DOIUrl":null,"url":null,"abstract":"ABSTRACTAn operational multistage adaptive test (MST) requires the development of a large item bank and the effort to continuously replenish the item bank due to concerns about test security and validity over the long term. New items should be pretested and linked to the item bank before being used operationally. The linking item volume fluctuations in MST, however, bring into question the quality of the link to the reference scale. In this study, various calibration/linking methods along with a newly proposed Bayesian logistic regression (BLR) method were evaluated by comparison with the test characteristic curve method through simulated MST response data in terms of item parameter recovery. Results generated by the BLR method were promising due to its estimation stability and robustness across studied conditions. The findings of the present study should help inform practitioners of the utilities of implementing the pretest item calibration method in MST. Disclosure statementNo potential conflict of interest was reported by the author(s).","PeriodicalId":51609,"journal":{"name":"Applied Measurement in Education","volume":"134 4‐6","pages":"0"},"PeriodicalIF":1.1000,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Measurement in Education","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/08957347.2023.2274572","RegionNum":4,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"EDUCATION & EDUCATIONAL RESEARCH","Score":null,"Total":0}
引用次数: 0
Abstract
ABSTRACTAn operational multistage adaptive test (MST) requires the development of a large item bank and the effort to continuously replenish the item bank due to concerns about test security and validity over the long term. New items should be pretested and linked to the item bank before being used operationally. The linking item volume fluctuations in MST, however, bring into question the quality of the link to the reference scale. In this study, various calibration/linking methods along with a newly proposed Bayesian logistic regression (BLR) method were evaluated by comparison with the test characteristic curve method through simulated MST response data in terms of item parameter recovery. Results generated by the BLR method were promising due to its estimation stability and robustness across studied conditions. The findings of the present study should help inform practitioners of the utilities of implementing the pretest item calibration method in MST. Disclosure statementNo potential conflict of interest was reported by the author(s).
期刊介绍:
Because interaction between the domains of research and application is critical to the evaluation and improvement of new educational measurement practices, Applied Measurement in Education" prime objective is to improve communication between academicians and practitioners. To help bridge the gap between theory and practice, articles in this journal describe original research studies, innovative strategies for solving educational measurement problems, and integrative reviews of current approaches to contemporary measurement issues. Peer Review Policy: All review papers in this journal have undergone editorial screening and peer review.