Solving the "many variables" problem in MICE with principal component regression

TL;DR

This paper introduces a PCR-based method within MICE to automatically handle the challenge of many predictor variables in large social science datasets, showing competitive performance through simulations and case studies.

Contribution

It proposes using principal component regression as an automatic predictor selection method in MICE for high-dimensional data imputation.

Findings

PCR-based MICE performs best among tested methods.

PCR can match expert-designed imputation procedures.

Method is effective in large social science datasets.

Abstract

Multiple Imputation (MI) is one of the most popular approaches to addressing missing values in questionnaires and surveys. MI with multivariate imputation by chained equations (MICE) allows flexible imputation of many types of data. In MICE, for each variable under imputation, the imputer needs to specify which variables should act as predictors in the imputation model. The selection of these predictors is a difficult, but fundamental, step in the MI procedure, especially when there are many variables in a data set. In this project, we explore the use of principal component regression (PCR) as a univariate imputation method in the MICE algorithm to automatically address the "many variables" problem that arises when imputing large social science data. We compare different implementations of PCR-based MICE with a correlation-thresholding strategy by means of a Monte Carlo simulation study and a case study. We find the use of PCR on a variable-by-variable basis to perform best and that it can perform closely to expertly designed imputation procedures.