Evaluating Close Fit in Ordinal Factor Analysis Models With Multiply Imputed Data

Dexin Shi, Bo Zhang, Ren Liu, Zhehan Jiang

Research output: Contribution to journalArticlepeer-review


Multiple imputation (MI) is one of the recommended techniques for handling missing data in ordinal factor analysis models. However, methods for computing MI-based fit indices under ordinal factor analysis models have yet to be developed. In this short note, we introduced the methods of using the standardized root mean squared residual (SRMR) and the root mean square error of approximation (RMSEA) to assess the fit of ordinal factor analysis models with multiply imputed data. Specifically, we described the procedure for computing the MI-based sample estimates and constructing the confidence intervals. Simulation results showed that the proposed methods could yield sufficiently accurate point and interval estimates for both SRMR and RMSEA, especially in conditions with larger sample sizes, less missing data, more response categories, and higher degrees of misfit. Based on the findings, implications and recommendations were discussed.

Original languageEnglish (US)
Pages (from-to)171-189
Number of pages19
JournalEducational and Psychological Measurement
Issue number1
StatePublished - Feb 2024


  • SRMR
  • missing data
  • model fit
  • multiple imputation
  • ordinal factor analysis model

ASJC Scopus subject areas

  • Education
  • Developmental and Educational Psychology
  • Applied Mathematics
  • Applied Psychology


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