A Comparison of Missing-Data Imputation Techniques in Exploratory Factor Analysis

Background and Purpose: To compare the effects of missing-data imputation techniques, mean imputation, group mean imputation, regression imputation, and multiple imputation (MI), on the results of exploratory factor analysis under different missing assumptions. Methods: Missing data with different missing assumptions were generated from true data. The quality of imputed data was examined by correlation coefficients. Factor structures were compared indirectly by coefficients of congruence and directly by factor structures. Results: MI had the best quality and matching factor structure to the true data for all missing assumptions with different missing rates. Mean imputation had the least favorable results in factor analysis. The imputation techniques revealed no important differences with 10% of data missing. Conclusion: MI showed the best results, especially with larger proportions of missing data.