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

Canhua Xiao1, Deborah W Bruner1, Tian Dai1

  • 1Emory University, Atlanta, Georgia.

Journal of Nursing Measurement
|September 13, 2019
PubMed
Summary
This summary is machine-generated.

Multiple imputation (MI) best handles missing data in exploratory factor analysis, especially with higher missing rates. Simpler methods like mean imputation performed poorly, though differences were minimal with only 10% missing data.

Keywords:
factor analysisimputation techniquesmissing datastatisticalstatistics as topic

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Area of Science:

  • Statistics
  • Psychometrics
  • Data Science

Background:

  • Missing data is a common challenge in statistical analyses, particularly in exploratory factor analysis (EFA).
  • Various imputation techniques exist to address missing data, each with potential impacts on analysis outcomes.
  • Understanding the performance of these techniques under different missing data scenarios is crucial for reliable EFA results.

Purpose of the Study:

  • To compare the efficacy of different missing-data imputation techniques in the context of exploratory factor analysis.
  • To evaluate the impact of mean imputation, group mean imputation, regression imputation, and multiple imputation (MI) on EFA outcomes.
  • To assess performance across varying missing data assumptions and rates.

Main Methods:

  • Generated missing data under diverse missing assumptions and rates from a true dataset.
  • Assessed the quality of imputed data using correlation coefficients.
  • Compared factor structures derived from imputed data to the true factor structure using coefficients of congruence and direct structure comparisons.

Main Results:

  • Multiple imputation (MI) demonstrated superior performance, yielding the highest data quality and most accurate factor structure recovery across all missing assumptions and rates.
  • Mean imputation consistently produced the least favorable results in exploratory factor analysis.
  • Imputation technique differences were negligible when only 10% of the data was missing.

Conclusions:

  • Multiple imputation (MI) is the recommended technique for handling missing data in exploratory factor analysis, particularly when dealing with substantial proportions of missing values.
  • The choice of imputation method significantly influences the accuracy of factor structure recovery, especially with higher missing data percentages.
  • For minimal missing data (around 10%), the impact of different imputation methods on EFA results is less pronounced.