Dealing With Missing Data in Developmental Research

Abstract Approaches to handling missing data have improved dramatically in recent years and researchers can now choose from a variety of sophisticated analysis options. The methodological literature favors maximum likelihood and multiple imputation because these approaches offer substantial improvements over older approaches, including a strong theoretical foundation, less restrictive assumptions, and the potential for bias reduction and greater power. These benefits are especially important for developmental research where attrition is a pervasive problem. This article provides a brief introduction to modern methods for handling missing data and their application to developmental research.