Multiple-Group Invariance with Categorical Outcomes Using Updated Guidelines: An Illustration Using Mplus and the lavaan/semTools Packages. Issue 1 (2nd January 2020)

• Record Type: Journal Article
• Authors: Svetina, Dubravka; Rutkowski, Leslie; Rutkowski, David
• Is Part Of: Structural equation modeling. Volume 27:Issue 1(2020)
• Page Start: 111
• Page End: 130
• Publication Date: 2020-01-02
• Subjects: Measurement invariance -- model identification -- categorical variables -- Mplus -- lavaan -- semTools in R; Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2019.1602776
• Languages: English
• ISSNs: 1070-5511

Abstract

Abstract : Meaningful comparisons of means or relationships between latent constructs across groups require evidence that measurement is equivalent across the studied groups– a property known as measurement equivalence or invariance (ME/I). Methods typically involve an evaluation of increasingly stringent models via confirmatory factor analysis, a typical assumption of which is continuous observed variables. When that assumption is not met – as is often the case in many surveys – alternative methods that directly model the categorical nature of the data exist. Although well established, categorical ME/I models pose a number of complexities and various recommendations for their evaluation. To that end, we describe the current state of categorical ME/I and demonstrate an up-to-date method for model identification and invariance testing. In the tutorial, we exemplify a common approach to establishing ME/I via multiple-group confirmatory factor analysis using M plus and the lavaan and semTools packages in R.