General-purpose imputation of planned missing data in social surveys: Different strategies and their effect on correlations

Abstract

Planned missing survey data, for example stemming from split questionnaire designs are becoming increasingly common in survey research, making imputation indispensable to obtain reasonably analyzable data. However, these data can be difficult to impute due to low correlations, many predictors, and limited sample sizes to support imputation models. This paper presents findings from a Monte Carlo simulation, in which we investigate the accuracy of correlations after multiple imputation using different imputation methods and predictor set specifications based on data from the German Internet Panel (GIP). The results show that strategies that simplify the imputation exercise (such as predictive mean matching with dimensionality reduction or restricted predictor sets, linear regression models, or the multivariate normal model without transformation) perform well, while especially generalized linear models for categorical data, classification trees, and imputation models with many predictor variables lead to strong biases.