Analysis with missing data in drug prevention research

J. W. Graham, S. M. Hofer, A. M. Piccinin

Research output: Contribution to journalArticlepeer-review

100 Scopus citations


Missing data problems have been a thorn in the side of prevention researchers for years. Although some solutions for these problems have been available in the statistical literature, these solutions have not found their way into mainstream prevention research. This chapter is meant to serve as an introduction to the systematic application of the missing data analysis solutions presented recently by Little and Rubin (1987) and others. The chapter does not describe a complete strategy, but it is relevant for (1) missing data analysis with continuous (but not categorical) data, (2) data that are reasonably normally distributed, and (3) solutions for missing data problems for analyses related to the general linear model, in particular, analyses that use (or can use) a covariance matrix as input. The examples in the chapter come from drug prevention research. The chapter discusses (1) the problem of wanting to ask respondents more questions than most individuals can answer; (2) the problem of attrition and some solutions; and (3) the problem of special measurement procedures that are too expensive or time consuming to obtain for all subjects.

Original languageEnglish (US)
Pages (from-to)13-63
Number of pages51
JournalNIDA Research Monograph Series
Issue number142
StatePublished - Dec 1 1994
Externally publishedYes

ASJC Scopus subject areas

  • Medicine (miscellaneous)


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