有序分类变量的测量不变性

T. Gerosa
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引用次数: 2

摘要

多项目有序分类量表和结构方程建模方法在面板研究中经常用于分析潜在变量随时间的变化。这些模型的准确性取决于纵向测量不变性(LMI)的假设,即重复测量的潜在变量应该在每个时间点有效地表示相同度量中的相同结构。以往的研究对连续变量的LMI文献做出了广泛的贡献,但这些发现可能无法推广到有序分类数据。将有序分类数据视为连续的与多元正态性的假设相矛盾,并且可能在不变性检验结果和结构参数估计中产生不准确和扭曲。然而,对建立有序分类数据的LMI标准进行检验和比较的研究仍然很少。由于缺乏证据,本章详细描述了使用有序分类变量测试LMI的主要程序,并附有在对1,912名意大利中学教师进行的两波纵向调查中实际应用的示例。实证研究评估了不同的测试程序,当应用于有序分类数据时,是否会随着时间的推移导致关于模型拟合,不变性和结构参数的相似结论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Measurement Invariance with Ordered Categorical Variables
Multi-item ordered categorical scales and structural equation modelling approaches are often used in panel research for the analysis of latent variables over time. The accuracy of such models depends on the assumption of longitudinal measurement invariance (LMI), which states that repeatedly measured latent variables should effectively represent the same construct in the same metric at each time point. Previous research has widely contributed to the LMI literature for continuous variables, but these findings might not be generalized to ordered categorical data. Treating ordered categorical data as continuous contradicts the assumption of multivariate normality and could potentially produce inaccuracies and distortions in both invariance testing results and structural parameter estimates. However, there is still little research that examines and compares criteria for establishing LMI with ordinal categorical data. Drawing on this lack of evidence, the present chapter offers a detailed description of the main procedures used to test for LMI with ordered categorical variables, accompanied by examples of their practical application in a two-wave longitudinal survey administered to 1,912 Italian middle school teachers. The empirical study evaluates whether different testing procedures, when applied to ordered categorical data, lead to similar conclusions about model fit, invariance, and structural parameters over time.
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