A Bias-Corrected RMSD Item Fit Statistic: An Evaluation and Comparison to Alternatives
A Bias-Corrected RMSD Item Fit Statistic: An Evaluation and Comparison to Alternatives
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DOI：
10.3102/1076998619890566
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发表时间：
2019-12-19
影响因子：
2.4
通讯作者：
Hartig, Johannes
中科院分区：
文献类型：
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作者：
Kohler, Carmen;Robitzsch, Alexander;Hartig, Johannes
测试项目是否符合项目反应理论模型的假设是评估测试的重要步骤。在文献中，存在大量的项目拟合统计，其中许多显示出严重的局限性。本研究研究了均方根偏差（RMSD）项目拟合统计量，该统计量在各种大规模评估研究中用于评估项目拟合。本研究的三个研究问题是：(1)经验RMSD是否为总体RMSD的无偏估计量；(2)如果不是这种情况，是否可以纠正这种偏见；(3)检验统计量是否提供了足够的显著性检验来检测不拟合项目。通过模拟研究，发现经验RMSD不是总体RMSD的无偏估计，非参数自启动不能完全消除这种偏差。然而，使用参数引导，RMSD可以用作测试统计量，在I型错误率和功率方面优于其他方法（infit和outfit， S - x -2）。实证应用表明，RMSD的参数自举导致项目拟合决策相当保守，这表明更宽松的截止标准。
Testing whether items fit the assumptions of an item response theory model is an important step in evaluating a test. In the literature, numerous item fit statistics exist, many of which show severe limitations. The current study investigates the root mean squared deviation (RMSD) item fit statistic, which is used for evaluating item fit in various large-scale assessment studies. The three research questions of this study are (1) whether the empirical RMSD is an unbiased estimator of the population RMSD; (2) if this is not the case, whether this bias can be corrected; and (3) whether the test statistic provides an adequate significance test to detect misfitting items. Using simulation studies, it was found that the empirical RMSD is not an unbiased estimator of the population RMSD, and nonparametric bootstrapping falls short of entirely eliminating this bias. Using parametric bootstrapping, however, the RMSD can be used as a test statistic that outperforms the other approaches-infit and outfit, S - X-2-with respect to both Type I error rate and power. The empirical application showed that parametric bootstrapping of the RMSD results in rather conservative item fit decisions, which suggests more lenient cut-off criteria.