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The MIMIC Method With Scale Purification for Detecting Differential Item Functioning

机译：带有规模纯化的MIMIC方法用于检测差异项功能

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摘要

This study implements a scale purification procedure onto the standard MIMIC method for differential item functioning (DIF) detection and assesses its performance through a series of simulations. It is found that the MIMIC method with scale purification (denoted as M-SP) outperforms the standard MIMIC method ( denoted as M-ST) in controlling false-positive rates and yielding higher true-positive rates. Only when the DIF pattern is balanced between groups or when there is a small percentage of DIF items in the test does M-ST perform as appropriately as M-SP. Moreover, both methods yield a higher true-positive rate under the two-parameter logistic model than under the three-parameter model. M-SP is preferable to M-ST, because DIF patterns in real tests are unlikely to be perfectly balanced and the percentages of DIF items may not be small.
机译：这项研究在标准MIMIC方法上实施了一种规模纯化程序，用于差异项功能（DIF）检测，并通过一系列模拟评估了其性能。发现在控制假阳性率和产生更高的真阳性率方面，具有规模纯化的MIMIC方法（表示为M-SP）优​​于标准MIMIC方法（表示为M-ST）。只有当DIF模式在组之间达到平衡或测试中DIF项的百分比很小时，M-ST的性能才与M-SP相同。而且，两种方法在两参数逻辑模型下均比在三参数模型下产生更高的真阳性率。 M-SP比M-ST更可取，因为实际测试中的DIF模式不太可能完美平衡，并且DIF项的百分比可能不小。

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