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  3. Evaluating Close Fit In Ordinal Factor Analysis Models With Multiply Imputed Data.
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  3. Evaluating Close Fit In Ordinal Factor Analysis Models With Multiply Imputed Data.

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

Dexin Shi1, Bo Zhang2, Ren Liu3

  • 1University of South Carolina, Columbia, USA.

Educational and Psychological Measurement
|January 22, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces methods for assessing ordinal factor analysis model fit using multiple imputation (MI) with fit indices like SRMR and RMSEA. The proposed techniques provide accurate estimates for missing data analysis.

Keywords:
RMSEASRMRmissing datamodel fitmultiple imputationordinal factor analysis model

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Area of Science:

  • Statistics
  • Psychometrics
  • Data Analysis

Background:

  • Missing data is a common challenge in statistical modeling.
  • Multiple imputation (MI) is a recommended technique for handling missing data.
  • Fit indices for ordinal factor analysis with MI are not well-established.

Purpose of the Study:

  • To introduce methods for computing MI-based fit indices in ordinal factor analysis.
  • To assess the fit of ordinal factor analysis models using multiply imputed data.
  • To provide accurate point and interval estimates for SRMR and RMSEA.

Main Methods:

  • Utilized multiple imputation (MI) for handling missing data.
  • Developed procedures for computing standardized root mean squared residual (SRMR) and root mean square error of approximation (RMSEA) with MI data.
  • Constructed confidence intervals for the MI-based fit indices.

    • Main Results:

    • Proposed methods yielded accurate point and interval estimates for SRMR and RMSEA.
    • Accuracy improved with larger sample sizes, less missing data, more response categories, and higher misfit.
    • Simulation results supported the validity of the developed techniques.

    Conclusions:

    • The introduced methods effectively assess ordinal factor analysis model fit with multiply imputed data.
    • These techniques offer reliable fit index estimation in the presence of missing data.
    • Recommendations for practical application and future research were discussed.