Investigating measurement invariance for multiple covariates in organizational research using exploratory factor analysis and confirmatory factor analysis trees.

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).