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Solving the many-variables problem in MICE with principal component regression.

Edoardo Costantini¹, Kyle M Lang², Klaas Sijtsma³

¹Department of Methodology and Statistics, Tilburg University, Tilburg, Netherlands. e.costantini@tilburguniversity.edu.

Behavior Research Methods

|August 4, 2023

Summary

This summary is machine-generated.

Principal component regression (PCR) effectively handles missing data in large datasets by automating predictor selection within Multiple Imputation by Chained Equations (MICE). This method performs comparably to expert-designed procedures in social science research.

Keywords:

High-dimensional data Missing data Multiple imputation Principal component regression

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Area of Science:

Social Sciences
Statistics
Data Science

Background:

Multiple Imputation (MI) is a standard technique for handling missing data in surveys and questionnaires.
Multivariate Imputation by Chained Equations (MICE) is a flexible MI method but requires careful predictor selection, which is challenging with many variables.

Purpose of the Study:

To investigate Principal Component Regression (PCR) as an automated method for predictor selection in MICE, addressing the 'many-variables' problem in large social science datasets.
To compare the performance of PCR-based MICE implementations against a correlation-thresholding strategy.

Main Methods:

Exploration of Principal Component Regression (PCR) as a univariate imputation method within the MICE framework.
Evaluation through two Monte Carlo simulation studies and one case study.
Comparison with a correlation-thresholding predictor selection strategy.

Main Results:

PCR applied on a variable-by-variable basis within MICE demonstrated superior performance.
PCR-based MICE achieved results comparable to imputation procedures designed by experts.
Automated predictor selection using PCR effectively addresses challenges in large datasets.

Conclusions:

Principal Component Regression offers a robust and efficient solution for automated predictor selection in MICE, particularly for large social science data.
PCR-based MICE provides a viable alternative to manual predictor selection, improving the practicality and performance of imputation.