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Fitting Ordinal Factor Analysis Models With Missing Data: A Comparison Between Pairwise Deletion and Multiple

Dexin Shi1, Taehun Lee2, Amanda J Fairchild1

  • 1University of South Carolina, Columbia, SC, USA.

Educational and Psychological Measurement
|January 15, 2020
PubMed
Summary
This summary is machine-generated.

Multiple imputation (MI) is recommended over pairwise deletion (PD) for ordinal factor analysis with missing data. MI provides accurate parameter estimates when data are missing at random (MAR), unlike biased PD results.

Keywords:
missing datamultiple imputationordinal factor analysispairwise deletion

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Area of Science:

  • Statistics
  • Psychometrics
  • Data Analysis

Background:

  • Missing data is common in statistical analyses.
  • Ordinal factor analysis is used for categorical data.
  • Pairwise deletion (PD) is a default method for handling missing data, but its performance is not always optimal.

Purpose of the Study:

  • To compare the performance of pairwise deletion (PD) and multiple imputation (MI) for handling missing data in ordinal factor analysis.
  • To determine which method yields parameter estimates and model fit indices closer to complete data results.
  • To evaluate performance under various conditions, including missing completely at random (MCAR) and missing at random (MAR) data.

Main Methods:

  • Ordinal factor analysis models were used.
  • Pairwise deletion (PD) and multiple imputation (MI) were compared.
  • Simulations were conducted across different conditions: response categories, sample size, missingness percentage, and model misfit.

Main Results:

  • Both PD and MI produced similar parameter estimates to complete data under MCAR conditions.
  • PD parameter estimates were severely biased under MAR conditions.
  • MI yielded comparable parameter estimates to complete data when missingness was below 50%, but fit indices (χ², RMSEA, WRMR) indicated poorer fit.

Conclusions:

  • Multiple imputation (MI) is recommended for applied researchers analyzing ordinal factor models with missing data.
  • Researchers should interpret model fit using TLI and CFI indices when using MI, as other indices may suggest worse fit than reality.