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首页> 外文学位 >Detection and Classification of DIF Types Using Parametric and Nonparametric Methods: A comparison of the IRT-Likelihood Ratio Test, Crossing-SIBTEST, and Logistic Regression Procedures.
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Detection and Classification of DIF Types Using Parametric and Nonparametric Methods: A comparison of the IRT-Likelihood Ratio Test, Crossing-SIBTEST, and Logistic Regression Procedures.

机译：使用参数和非参数方法对DIF类型进行检测和分类：IRT似然比检验，Crossing-SIBTEST和Logistic回归程序的比较。

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摘要

The purpose of this investigation was to compare the efficacy of three methods for detecting differential item functioning (DIF). The performance of the crossing simultaneous item bias test (CSIBTEST), the item response theory likelihood ratio test (IRT-LR), and logistic regression (LOGREG) was examined across a range of experimental conditions including different test lengths, sample sizes, DIF and differential test functioning (DTF) magnitudes, and mean differences in the underlying trait distributions of comparison groups, herein referred to as the reference and focal groups. In addition, each procedure was implemented using both an all-other anchor approach, in which the IRT-LR baseline model, CSIBEST matching subtest, and LOGREG trait estimate were based on all test items except for the one under study, and a constant anchor approach, in which the baseline model, matching subtest, and trait estimate were based on a predefined subset of DIF-free items. Response data for the reference and focal groups were generated using known item parameters based on the three-parameter logistic item response theory model (3-PLM). Various types of DIF were simulated by shifting the generating item parameters of select items to achieve desired DIF and DTF magnitudes based on the area between the groups' item response functions. Power, Type I error, and Type III error rates were computed for each experimental condition based on 100 replications and effects analyzed via ANOVA. Results indicated that the procedures varied in efficacy, with LOGREG when implemented using an all-other approach providing the best balance of power and Type I error rate. However, none of the procedures were effective at identifying the type of DIF that was simulated.
机译：这项研究的目的是比较三种检测差异项功能（DIF）的方法的功效。在一系列不同的实验条件下，包括不同的测试长度，样本量，DIF和差异测试功能（DTF）大小，以及比较组（在此称为参考组和焦点组）的基础特征分布中的平均差异。此外，每个程序均使用其他所有锚定方法实施，其中IRT-LR基线模型，CSIBEST匹配子测试和LOGREG特征估计均基于除所研究的所有测试项目以外的所有测试项目，以及恒定锚定方法，其中基线模型，匹配子测试和性状估计基于无DIF项的预定义子集。参考和焦点小组的响应数据是根据三参数逻辑物流项目响应理论模型（3-PLM）使用已知的项目参数生成的。通过移动选定项目的生成项目参数以基于组的项目响应函数之间的面积来实现所需的DIF和DTF大小，可以模拟各种类型的DIF。基于100个重复和通过ANOVA分析的效应，针对每个实验条件计算功效，I型错误和III型错误率。结果表明，该程序的功效各不相同，使用其他所有方法实施LOGREG时，可提供最佳的功效和I型错误率平衡。但是，这些过程都不能有效地识别模拟的DIF类型。

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  • 作者

    Lopez Rivas, Gabriel E.;

  • 作者单位

    University of South Florida.;

  • 授予单位 University of South Florida.;
  • 学科 Psychology General.;Psychology Experimental.
  • 学位 Ph.D.
  • 年度 2012
  • 页码 154 p.
  • 总页数 154
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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