Handling of missing data in psychological research:: Problems and solutions

Missing data are a pervasive problem in empirical psychological research. From the methodological perspective, traditional procedures such as Casewise and Pairwise Deletion, Regression Imputation, and Mean Imputation have distinct weaknesses. Yet modem statistical methods for the analysis of datasets with missing values that have been developed in the past three decades have not yet gained a significant foothold in research practice. We begin this article by introducing the basic concepts and terminology of missing data, as proposed by Rubin (1976). We then give an overview of the different approaches to handling missing data discussed in the literature, distinguishing between three types of procedures: traditional procedures (e.g., Listwise Deletion), imputation-based procedures, in which missing values are replaced by imputed values, and model-based procedures, in which models are estimated and missing data handled in a single step. In the empirical section of the article, we demonstrate the application of Multiple Imputation using a dataset from a large-scale educational assessment. Implications for research practice are discussed.