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首页> 外文期刊>Educational and Psychological Measurement >Binary Logistic Regression Analysis for Detecting Differential Item Functioning: Effectiveness of R-2 and Delta Log Odds Ratio Effect Size Measures
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Binary Logistic Regression Analysis for Detecting Differential Item Functioning: Effectiveness of R-2 and Delta Log Odds Ratio Effect Size Measures

机译：用于检测差异项功能的二进制Logistic回归分析：R-2和Delta对数比值效应大小度量的有效性

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摘要

The authors analyze the effectiveness of the R-2 and delta log odds ratio effect size measures when using logistic regression analysis to detect differential item functioning (DIF) in dichotomous items. A simulation study was carried out, and the Type I error rate and power estimates under conditions in which only statistical testing was used were compared with the rejection rates obtained when statistical testing was combined with an effect size measure based on recommended cutoff criteria. The manipulated variables were sample size, impact between groups, percentage of DIF items in the test, and amount of DIF. The results showed that false-positive rates were higher when applying only the statistical test than when an effect size decision rule was used in combination with a statistical test. Type I error rates were affected by the number of test items with DIF, as well as by the magnitude of the DIF. With respect to power, when a statistical test was used in conjunction with effect size criteria to determine whether an item exhibited a meaningful magnitude of DIF, the delta log odds ratio effect size measure performed better than R-2. Power was affected by the percentage of DIF items in the test and also by sample size. The study highlights the importance of using an effect size measure to avoid false identification.
机译：作者在使用逻辑回归分析检测二分项目中的差异项功能（DIF）时，分析了R-2和增量对数比值比效应大小量度的有效性。进行了仿真研究，将仅使用统计测试的条件下的I类错误率和功率估计值与将统计测试与根据推荐的截止标准进行的效应量度测量相结合时获得的拒绝率进行了比较。受控变量是样本量，组之间的影响，测试中DIF项目的百分比以及DIF的数量。结果表明，仅应用统计检验时的假阳性率要高于将效应大小决定规则与统计检验结合使用时的假阳性率。 I型错误率受带有DIF的测试项目的数量以及DIF的大小影响。关于功效，当将统计检验与效应大小标准结合使用以确定某项物品是否表现出有意义的DIF值时，对数对数比比率效应大小的度量要优于R-2。功率受测试中DIF项目的百分比以及样本量的影响。这项研究强调了使用效应大小量度来避免错误识别的重要性。

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