Dealing With Missing Data in Developmental Research

Craig K. Enders

Research output: Contribution to journalArticlepeer-review

247 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)27-31
Number of pages5
JournalChild Development Perspectives
Issue number1
StatePublished - Mar 2013


  • Attrition
  • Imputation
  • Maximum likelihood
  • Missing data
  • Multiple imputation

ASJC Scopus subject areas

  • Pediatrics, Perinatology, and Child Health
  • Developmental and Educational Psychology
  • Life-span and Life-course Studies


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