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

Po Yi Chen*, Wei Wu, Mauricio Garnier-Villarreal, Benjamin Arthur Kite, Fan Jia

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Ordinal missing data are common in measurement equivalence/invariance (ME/I) testing studies. However, there is a lack of guidance on the appropriate method to deal with ordinal missing data in ME/I testing. Five methods may be used to deal with ordinal missing data in ME/I testing, including the continuous full information maximum likelihood estimation method (FIML), continuous robust FIML (rFIML), FIML with probit links (pFIML), FIML with logit links (lFIML), and mean and variance adjusted weight least squared estimation method combined with pairwise deletion (WLSMV_PD). The current study evaluates the relative performance of these methods in producing valid chi-square difference tests (∆x2 ) and accurate parameter estimates. The result suggests that all methods except for WLSMV_PD can reasonably control the type I error rates of ∆x2 tests and maintain sufficient power to detect noninvariance in most conditions. Only pFIML and lFIML yield accurate factor loading estimates and standard errors across all the conditions. Recommendations are provided to researchers based on the results.

Original languageEnglish
Pages (from-to)87-101
Number of pages15
JournalMultivariate Behavioral Research
Volume55
Issue number1
DOIs
Publication statusPublished - 2 Jan 2020
Externally publishedYes

Keywords

  • Measurement invariance
  • missing data
  • ordinal data analysis

Fingerprint Dive into the research topics of 'Testing Measurement Invariance with Ordinal Missing Data: A Comparison of Estimators and Missing Data Techniques'. Together they form a unique fingerprint.

Cite this