Measurement Invariance Via Multigroup SEM: Issues and Solutions with Chi-square-difference Tests

Multigroup structural equation modeling (SEM) plays a key role in studying measurement invariance and in group comparison. When population covariance matrices are deemed not equal across groups, the next step to substantiate measurement invariance is to see whether the sample covariance matrices in all the groups can be adequately fitted by the same factor model, called configural invariance. After configural invariance is established, cross-group equalities of factor loadings, error variances, and factor variances-covariances are then examined in sequence. With mean structures, cross-group equalities of intercepts and factor means are also examined. The established rule is that if the statistic at the current model is not significant at the level of .05, one then moves on to testing the next more restricted model using a chi-square-difference statistic. This article argues that such an established rule is unable to control either Type I or Type II errors. Analysis, an example, and Monte Carlo results show why and how chi-square-difference tests are easily misused. The fundamental issue is that chi-square-difference tests are developed under the assumption that the base model is sufficiently close to the population, and a nonsignificant chi-square statistic tells little about how good the model is. To overcome this issue, this article further proposes that null hypothesis testing in multigroup SEM be replaced by equivalence testing, which allows researchers to effectively control the size of misspecification before moving on to testing a more restricted model. R code is also provided to facilitate the applications of equivalence testing for multigroup SEM. (PsycINFO Database Record