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Factor Retention in Exploratory Factor Analysis With Missing Data.

David Goretzko¹

¹Ludwig Maximilians University Munich, Munich, Germany.

Educational and Psychological Measurement

|April 21, 2022

Summary

This summary is machine-generated.

Missing data has minimal impact on exploratory factor analysis factor retention criteria accuracy. Pairwise deletion often performs comparably to imputation methods, though random forest imputation excels in specific scenarios.

Keywords:

exploratory factor analysis factor retention missing data multiple imputation

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Area of Science:

Psychometrics
Statistical Modeling

Background:

Determining the number of factors is critical in exploratory factor analysis (EFA).
The impact of missing data on factor retention criteria is understudied.
Existing research lacks comprehensive comparisons of imputation methods with various factor retention criteria.

Purpose of the Study:

To evaluate factor retention criteria performance with different missing data methods in EFA.
To assess the accuracy of factor determination under various simulated data conditions with missing values.
To identify optimal combinations of factor retention criteria and missing data handling techniques.

Main Methods:

Simulated data across diverse conditions: sample sizes, factor numbers, indicator counts, correlations, missing data mechanisms, and proportions.
Evaluated factor retention criteria: Factor Forest, parallel analysis (PCA-based and CFM-based), and comparison data approach.
Assessed missing data methods: Amelia (EM algorithm), predictive mean matching, random forest imputation (MICE), and pairwise deletion.

Main Results:

Missing data mechanisms generally had minor effects on accuracy for most criteria.
Pairwise deletion performed comparably to sophisticated imputation methods across most conditions.
Random forest imputation showed advantages in small-sample cases and with the comparison data approach.

Conclusions:

The choice of missing data method is crucial for valid factor number estimation in EFA.
Pairwise deletion is a robust option, but random forest imputation offers benefits in specific situations.
Researchers should consider data characteristics and chosen retention criteria when selecting missing data handling techniques.