Samantha Estrada, Department of Psychology and Counseling, The University of Texas at Tyler, 3900 University Blvd., Tyler, TX 75799, USA
J Appl Meas. 2020;21(4):496-514.
To understand the role of fit statistics in Rasch measurement is simple: applied researchers can only benefit from the desirable properties of the Rasch model when the data fit the model. The purpose of the current study was to assess the Q-Index robustness (Ostini and Nering, 2006), and its performance was compared to the current popular fit statistics known as MSQ Infit, MSQ Outfit, and standardized Infit and Outfit (ZSTDs) under varying conditions of test length, sample size, item difficulty (normal and uniform), and dimensionality utilizing a Monte Carlo simulation. The Type I and Type II error rates are also examined across fit indices. This study provides applied researchers guidelines the robustness and appropriateness of the use of the Q-Index, which is an alternative to the currently available item fit statistics. The Q-Index was slightly more sensitive to the levels of multidimensionality set in the study while MSQ Infit, Outfit, and standardized Infit and Outfit (ZSTDs) failed to identify the multidimensional conditions. The Type I error rate of the Q-Index was lower than the rest of the fit indices; however, the Type II error rate was higher than the anticipated beta = .20 across all fit indices.
要理解拟合统计数据在 Rasch 测量中的作用很简单：应用研究人员只有在数据符合模型时，才能受益于 Rasch 模型的理想特性。本研究的目的是评估 Q-Index 的稳健性（Ostini 和 Nering，2006），并利用蒙特卡罗模拟，在不同的测试长度、样本大小、项目难度（正态和均匀）和维度条件下，将其性能与当前流行的拟合统计数据（称为 MSQ Infit、MSQ Outfit 和标准化 Infit 和 Outfit（ZSTDs））进行比较。还检查了拟合指标的 I 型和 II 型错误率。本研究为应用研究人员提供了使用 Q-Index 的稳健性和适当性的指导，Q-Index 是当前可用的项目拟合统计数据的替代方法。Q-Index 对研究中设定的多维性水平略为敏感，而 MSQ Infit、Outfit 和标准化 Infit 和 Outfit（ZSTDs）则无法识别多维条件。Q-Index 的 I 型错误率低于其他拟合指标；然而，在所有拟合指标中，II 型错误率都高于预期的β=0.20。
