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A Comparison of Methods for Creating Multiple Imputations of Nominal Variables
1a Institute for Measurement, Methodology, Analysis, and Policy , Texas Tech University.
Multivariate Behavioral Research
|March 8, 2017
Summary
Multiple imputation (MI) methods for missing nominal data were compared. Multiple imputation with chained equations (MICE) using polytomous regression demonstrated the best performance in recovering analysis model parameters.
Area of Science:
- Social Sciences
- Statistics
- Data Analysis
Background:
- Many social science variables are nominal.
- Missing data on nominal variables complicates analysis.
- Multiple imputation (MI) is a common method for handling missing data.
Purpose of the Study:
- To evaluate five MI methods for incomplete nominal variables.
- To compare their performance under various conditions.
- To identify the most effective imputation technique.
Main Methods:
- Multiple imputation with chained equations (MICE) using polytomous regression.
- MICE using classification and regression trees (CART).
- MICE using nested logistic regressions.
- Allison's (2002) ranking procedure.
- Joint modeling with the general location model.
- Monte Carlo simulation study.
Main Results:
- MICE with polytomous regression was the strongest performer.
- Allison's (2002) ranking procedure performed poorly.
- MICE with CART also showed poor performance in most conditions.
Conclusions:
- MICE using polytomous regression is recommended for handling missing nominal data.
- Other methods like Allison's ranking and MICE-CART are less effective.
- The choice of imputation method impacts parameter recovery in social science research.
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