Testing Measurement Invariance with Ordinal Missing Data: A Comparison of Estimators and Missing Data Techniques

Abstract

This paper is NOT THE PUBLISHED VERSION; but the author's final, peer-reviewed manuscript. The published version may be accessed by following the link in th citation below.

Addressing missing data in specification search in measurement invariance testing with Likert-type scale variables: A comparison of two approaches

“…This could also explain why the type I error rates from rFIML were conservative. Similar findings have been reported in previous research (e.g., Chen et al, 2019). Fourth, rFIML showed higher perfect recovery rates and power than WLSMV_PD in detecting non-uniform noninvariance (i.e., directions of non-invariance are different across non-invariant items) when missing data present.…”

“…These methods include full information maximum likelihood (FIML), robust full information maximum likelihood (rFIML), and the mean and variance adjusted weighted least-squared method paired with pairwise deletion (WLSMV_PD) (e.g., Beam, Marcus, Turkheimer & Emery, 2018;Bou Malham & Saucier, 2014;Fokkema, Smits, Kelderman, & Cuijpers, 2013;Sommer, et al, 2019). Given that previous research has shown that rFIML outperformed FIML for ordinal data (e.g., Chen, Wu, Garnier-Villarreal, Kite & Jia, 2019), we only consider rFIML and WLSMV_PD in the current study.…”

Why Ordinal Variables Can (Almost) Always Be Treated as Continuous Variables: Clarifying Assumptions of Robust Continuous and Ordinal Factor Analysis Estimation Methods

“…In factor analysis, the investigation of measurement invariance (Millsap, 2011) is heavily discussed. It has been recommended that invariance analysis for ordinal variables should also treat variables as ordinal (Chen et al, 2020;Svetina et al, 2020). By continuing our arguments, we think that the assessment of invariance can be equally defended by treating ordinal variables as continuous.…”

Why Ordinal Variables Can (Almost) Always be Treated as Continuous Variables: Clarifying Assumptions of Robust Continuous and Ordinal Factor Analysis Estimation Methods

“…In the modeling of factor structures, the investigation of measurement invariance (Millsap, 2011) is heavily discussed. It has been recommended that invariance analysis for ordinal variables should also treat variables as ordinal (Chen, Wu, Garnier-Villarreal, Kite, & Jia, 2020). By continuing our arguments, we think that the assessment of invariance can be equally defended by treating ordinal variables as continuous.…”