The Impact of Noninvariant Intercepts in Latent Means Models

Abstract

Latent means methods such as multiple-indicator multiple-cause (MIMIC) and structured means modeling (SMM) allow researchers to determine whether or not a significant difference exists between groups' factor means. Strong invariance is typically recommended when interpreting latent mean differences. The extent of the impact of noninvariant intercepts on conclusions made when implementing both MIMIC and SMM methods was the main purpose of this study. The impact of intercept noninvariance on Type I error rates, power, and two model fit indices when using MIMIC and SMM approaches under various conditions were examined. Type I error and power were adversely affected by intercept noninvariance. Although the fit indices did not detect small misspecifications in the form of noninvariant intercepts, one did perform more optimally.

Keywords:

• latent mean comparisons
• measurement invariance
• multiple-indicator multiple-cause models
• structured means models

Notes

¹Because the data from each group are combined in the MIMIC approach, it is also assumed that the observed covariance matrix in each group is equivalent.

²It is important to note that when using raw data in Mplus, the intercepts are estimated for the MIMIC model. This is not standard, however, for all SEM software.

³Tables of results are not included when noninvariance is modeled, but these tables are available from the author on request.