  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.
