Correct Point Estimator and Confidence Interval for RMSEA Given Categorical Data. Issue 5 (2nd September 2020)

• Record Type: Journal Article
• Authors: Lai, Keke
• Is Part Of: Structural equation modeling. Volume 27:Issue 5(2020)
• Page Start: 678
• Page End: 695
• Publication Date: 2020-09-02
• Subjects: Categorical data -- the root mean square error of approximation -- confidence interval -- weighted least squares; Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2019.1687302
• Languages: English
• ISSNs: 1070-5511

Abstract

Abstract : RMSEA estimation given nonnormal continuous data is usually based on the mean-adjusted ( T M ) or mean-variance-adjusted ( T M V ) chi-square statistic, but a plain application of these statistics has poor performance. Savalei and colleagues gave a better way (the BSL method) to infer RMSEA using T M or T M V . However, the BSL method is applicable to continuous data only. For categorical data, currently RMSEA inference is still based on a plain application of T M or T M V, but such practice is already problematic under continuous data. In this paper, we first show that it is more meaningful to define RMSEA under unweighted least squares (ULS) than under weighted least squares (WLS) or diagonally weighted least squares (DWLS). Then, we propose a correct point estimator and confidence interval for RMSEA given categorical data and ULS. Simulation results show our methods perform well while all the traditional methods break down.