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首页> 外文学位 >Loglinear model as a DIF detection method for dichotomous and polytomous items and its comparison with other observed score matching DIF methods.
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Loglinear model as a DIF detection method for dichotomous and polytomous items and its comparison with other observed score matching DIF methods.

机译：对数线性模型作为二分类和多分类项目的DIF检测方法，并与其他观察到的得分匹配DIF方法进行比较。

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摘要

DIF detection methods identify the difference between the performances of subgroups when the subgroups are matched by examinees' ability level or a proxy variable, such as total test score (Holland & Wainer, 1993). Log-linear Models (LLM) method is one of the DIF detection methods. This method was first introduced by Mellenbergh (1982) to investigate the relationship among item responses, subgroups, and categorized total test score in terms of DIF detection.;This study examined the performance of LLM as a DIF detection method for dichotomous items and polytomous items. LLM method was compared with Mantel-Haenszsel (MH) and logistic regression (LR) methods to detect uniform DIF and with LR to detect non-uniform DIF in dichotomous item response data. MH was not included in non-uniform DIF detection, because, the previous studies indicated that it is not able to detect non-uniform DIF (Narayanon & Swaminathan, 1996; Uttaro & Milsap, 1994). In addition, LLM was compared with Mantel, generalized Mantel-Haenszsel (GMH), ordinal logistic regression (OLR), logistic discriminate function analysis (LDFA) methods in polytomous item response data. For this purpose, both simulation study and empirical study were conducted under various sample sizes, ability mean differences (impact) and item parameters. Since the previous studies did not investigate the effect of ability mean differences on DIF detection with LLM, this study also focused on the effect of ability mean differences between subgroups. This study found that MH was better to detect uniform DIF when LR and LLM indicated equally well performance on uniform and non-uniform DIF detection. In Addition, GMH and LLM performed better than Mantel, OLR, and LDFA for the polytomous item response data.
机译：当子组与考生的能力水平或代理变量（例如总测试成绩）相匹配时，DIF检测方法可识别子组性能之间的差异（Holland＆Wainer，1993）。对数线性模型（LLM）方法是DIF检测方法之一。该方法由Mellenbergh（1982）首次引入，旨在研究项目响应，子组和分类总测试成绩之间在DIF检测方面的关系;该研究检验了LLM作为二分项目和多项目项目的DIF检测方法的性能。 。将LLM方法与Mantel-Haenszsel（MH）和Logistic回归（LR）方法在二分项目响应数据中检测均匀DIF的方法进行比较，并与LR检测不均匀DIF的方法进行比较。 MH不包括在非均匀DIF检测中，因为先前的研究表明，它不能检测非均匀DIF（Narayanon＆Swaminathan，1996； Uttaro＆Milsap，1994）。此外，在多项项目响应数据中，将LLM与Mantel，广义Mantel-Haenszsel（GMH），有序逻辑回归（OLR），逻辑区分函数分析（LDFA）方法进行了比较。为此，在各种样本量，能力平均差异（影响）和项目参数下进行了模拟研究和实证研究。由于先前的研究并未调查能力平均差异对LLM检测DIF的影响，因此本研究也侧重于亚组之间的能力平均差异的影响。这项研究发现，当LR和LLM在均匀和非均匀DIF检测上表现出同样好的性能时，MH更好地检测均匀DIF。此外，对于多件物品响应数据，GMH和LLM的性能优于Mantel，OLR和LDFA。

著录项

  • 作者

    Yesiltas, Gonca.;

  • 作者单位

    The Florida State University.;

  • 授予单位 The Florida State University.;
  • 学科 Educational psychology.
  • 学位 Ph.D.
  • 年度 2016
  • 页码 136 p.
  • 总页数 136
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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