An introduction to modern missing data analyses

Amanda N. Baraldi, Craig K. Enders

Research output: Contribution to journalArticlepeer-review

778 Scopus citations


A great deal of recent methodological research has focused on two modern missing data analysis methods: maximum likelihood and multiple imputation. These approaches are advantageous to traditional techniques (e.g. deletion and mean imputation techniques) because they require less stringent assumptions and mitigate the pitfalls of traditional techniques. This article explains the theoretical underpinnings of missing data analyses, gives an overview of traditional missing data techniques, and provides accessible descriptions of maximum likelihood and multiple imputation. In particular, this article focuses on maximum likelihood estimation and presents two analysis examples from the Longitudinal Study of American Youth data. One of these examples includes a description of the use of auxiliary variables. Finally, the paper illustrates ways that researchers can use intentional, or planned, missing data to enhance their research designs.

Original languageEnglish (US)
Pages (from-to)5-37
Number of pages33
JournalJournal of School Psychology
Issue number1
StatePublished - Feb 1 2010


  • Maximum likelihood
  • Missing data
  • Multiple imputation
  • Planned missingness

ASJC Scopus subject areas

  • Education
  • Developmental and Educational Psychology


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