Structural Equation Modeling, 2nd Edition

5 Multigroup modeling

5.1 Introduction

The models discussed in previous chapters are all based on a single group or a homogeneous population. Very often, a study may involve more than one group/population. For example, one may want to know whether a measuring instrument of interest is valid for studying different groups/populations. One may also want to know whether the mean values of the factors measured by instrument items or the relationships between the factors differ between different populations/groups. Multigroup CFA models are designed to address such issues. When causal relationships are tested in more than one group/population, one may wonder whether the same causal relationships hold across different groups/populations. Multigroup structural equation modeling (SEM) can be used to examine such population heterogeneity.

Multigroup modeling implements simultaneous analyses of multiple groups/populations. The “groups” can be different countries, regions, states, or culturally or socioeconomically different populations, or any mutually exclusive groups of individuals that can be defined using categorical variables such as gender, age, or ethnicity in one's data. Note that the groups involved in multigroup modeling are observed or measured. Unobserved sub‐populations/groups or unobserved population heterogeneity are considered latent classes, a topic of mixture modeling that we will discuss in Chapter 6. In addition, the number of groups in multi‐group modeling ...