Investigating measurement invariance for multiple covariates in organizational research using exploratory factor analysis and confirmatory factor analysis trees.
介绍了探索性因子分析树和验证性因子分析树这两种新方法，用于在测量模型开发阶段和面对多个连续协变量时，有效检验测量不变性，帮助组织研究者比较潜变量（如工作满意度）在不同群体间的可比性。
Organizational research often deals with unobservable (latent) variables such as, for example, job satisfaction or leadership styles. When comparing these latent variables across groups, a comparability of the measurements is important-so-called measurement invariance (MI) considered a prerequisite. Common methodology to test whether MI holds or to explore noninvariance can only be used with established measurement models and specific hypotheses about potential violations of MI in mind. Therefore, exploratory factor analysis trees and confirmatory factor analysis trees have recently been developed. They promise to be an effective tool for early investigations of MI during the development of measurement models (e.g., scale development) and with many (continuous) covariates defining countless groups for which MI may be violated. (PsycInfo Database Record (c) 2026 APA, all rights reserved).