Assumptions of Cross-Level Measurement and Structural Invariance in the Analysis of Multilevel Data: Problems and Solutions

Abstract

This article demonstrates assumptions of invariance that researchers often implicitly make when analyzing multilevel data. The first set of assumptions is measurement-based and corresponds to the fact that researchers often conduct single-level exploratory and confirmatory factor analyses, and reliability analyses, with multilevel data. The second assumption, that of structural invariance, is engineered into the common multilevel random coefficient model, in that such analyses impose structural invariance across multiple levels of analysis when lower-level relationships represent both between- and within-groups effects. The nature of these assumptions, and ways to address their tenability, are explored from a conceptual standpoint. Then an empirical example of these assumptions and ways to address them is provided. © 2008 American Psychological Association.