Maydeu-Olivares Alberto, Liu Yang
Faculty of Psychology.
Department of Psychology, University of North Carolina at Chapel Hill.
Psychol Methods. 2015 Jun;20(2):276-92. doi: 10.1037/a0039015. Epub 2015 Apr 13.
Researchers who evaluate the fit of psychometric models to binary or multinomial items often look at univariate and bivariate residuals to determine how a poorly fitting model can be improved. There is a class of z statistics and also a class of generalized X₂ statistics that can be used for examining these marginal fits. We describe these statistics and compare them with regard to the control of Type I error and statistical power. We show how the class of z statistics can be extended to accommodate items with multinomial response options. We provide guidelines for the use of these statistics, including how to control for multiple testing, and present 2 detailed examples. Using the root mean square error of approximation (RMSEA) for discrete data to adjudge fit, the examples illustrate how the use of these methods can dramatically improve the fit of item response theory models to widely used measures in personality and clinical psychology.
评估心理测量模型与二元或多项选择题拟合度的研究人员通常会查看单变量和双变量残差，以确定如何改进拟合不佳的模型。有一类z统计量和一类广义X₂统计量可用于检验这些边际拟合度。我们描述了这些统计量，并在控制I型错误和统计功效方面对它们进行了比较。我们展示了如何扩展z统计量类别以适应具有多项响应选项的题目。我们提供了使用这些统计量的指南，包括如何控制多重检验，并给出了两个详细示例。通过使用离散数据的近似均方根误差（RMSEA）来判断拟合度，这些示例说明了使用这些方法如何能显著提高项目反应理论模型与人格和临床心理学中广泛使用的测量方法的拟合度。
