Methods for Handling Missing Data

Abstract

This chapter describes a general approach to handling missing data in psychological research. It provides a theoretical background in readable, nontechnical fashion. Our overall goal was to give practical, usable advice, rather than to give a detailed statistical treatment of issues surrounding analysis of incomplete data. We give an overview of the older, unacceptable methods for handling incomplete data, so that readers will know what approaches to avoid; although analysis of complete cases is sometimes an acceptable solution, we argue that pairwise deletion and mean substitution should be avoided. With respect to newer, acceptable methods, we give a general overview, including a brief discussion of the full information maximum likelihood structural equation modeling procedures (such as Amos, Mx, LISREL 8.5, and Mplus), but focus primarily on multiple imputation as a general solution. We give specific guidelines for making use of state‐of‐the‐art multiple imputation software and step‐by‐step instructions for using multiple imputation with Schafer's (1997) NORM program. Empirical examples of exploratory and data quality analyses and a substantive illustration involving multiple linear regression demonstrate the use of multiple imputation in practice. The chapter concludes with a discussion of some practical issues that often arise in connection with the analysis of incomplete data.