Modern Alternatives for Dealing with Missing Data in Special Education Research

Craig Enders, Samantha Dietz, Marjorie Montague, Jennifer Dixon

Research output: Contribution to journalArticlepeer-review

23 Scopus citations


Missing data are a pervasive problem in special education research. The purpose of this chapter is to provide researchers with an overview of two "modern" alternatives for handling missing data, full information maximum likelihood (FIML) and multiple imputation (MI). These techniques are currently considered to be the methodological "state of the art", and generally provide more accurate parameter estimates than the traditional methods that are still common in published educational studies. The chapter begins with an overview of missing data theory, and provides brief descriptions of some traditional missing data techniques and their requisite assumptions. Detailed descriptions of FIML and MI are given, and the chapter concludes with an analytic example from a longitudinal study of depression.

Original languageEnglish (US)
Pages (from-to)101-129
Number of pages29
JournalAdvances in Learning and Behavioral Disabilities
StatePublished - 2005
Externally publishedYes

ASJC Scopus subject areas

  • Education


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