首页    期刊浏览 2024年11月08日 星期五
登录注册

文章基本信息

  • 标题:Multilevel Generalized Mantel-Haenszel For Differential Item Functioning Detection
  • 本地全文:下载
  • 作者:French, Brian F ; Finch, Holmes ; Immekus, Jason C
  • 期刊名称:Frontiers in Education
  • 电子版ISSN:2504-284X
  • 出版年度:2019
  • 卷号:4
  • 页码:1-10
  • DOI:10.3389/feduc.2019.00047
  • 出版社:Frontiers Media S.A.
  • 摘要:Research has demonstrated that when data are collected in a multilevel framework, standard single level differential item functioning (DIF) analyses can yield incorrect results, particularly inflated Type I error rates. Prior research in this area has focused almost exclusively on dichotomous items. Thus, the purpose of this simulation study was to examine the performance of the Generalized Mantel-Haenszel (GMH) procedure and a Multilevel GMH (MGMH) procedure for the detection of uniform differential item functioning (DIF) in the presence of multilevel data with polytomous items. Multilevel data were generated with manipulated factors (e.g., intraclass correction, subjects per cluster) to examine Type I error rates and statistical power to detect DIF. Results highlight the differences in DIF detection when the analytic strategy matches the data structure. Specifically, the GMH had an inflated Type I error rate across conditions, and thus an artificially high power rate. Alternatively, the MGMH had good power rates while maintaining control of the Type I error rate. Directions for future research are provided.
  • 关键词:validity; Differential Item Functioning; Test and Item Development ; Multilevel Models; Generalized
国家哲学社会科学文献中心版权所有