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Evaluating Close Fit in Ordinal Factor Analysis Models With Multiply Imputed Data.
- Source :
-
Educational & Psychological Measurement . Feb2024, Vol. 84 Issue 1, p171-189. 19p. - Publication Year :
- 2024
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
- Subjects :
- *STATISTICS
*STRUCTURAL equation modeling
*COMPUTER simulation
*SAMPLE size (Statistics)
*CONFIDENCE intervals
*DATABASE management
*CONCEPTUAL structures
*SURVEYS
*FACTOR analysis
*DESCRIPTIVE statistics
*CHI-squared test
*DATA analysis
*STATISTICAL models
*DATA analysis software
*LONGITUDINAL method
Subjects
Details
- Language :
- English
- ISSN :
- 00131644
- Volume :
- 84
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Educational & Psychological Measurement
- Publication Type :
- Academic Journal
- Accession number :
- 174837564
- Full Text :
- https://doi.org/10.1177/00131644231158854