Principled missing data treatments

K.M. Lang, Todd D. Little

Research output: Contribution to journalArticleScientificpeer-review

50 Citations (Scopus)

Abstract

We review a number of issues regarding missing data treatments for intervention and prevention researchers. Many of the common missing data practices in prevention research are still, unfortunately, ill-advised (e.g., use of listwise and pairwise deletion, insufficient use of auxiliary variables). Our goal is to promote better practice in the handling of missing data. We review the current state of missing data methodology and recent missing data reporting in prevention research. We describe antiquated, ad hoc missing data treatments and discuss their limitations. We discuss two modern, principled missing data treatments: multiple imputation and full information maximum likelihood, and we offer practical tips on how to best employ these methods in prevention research. The principled missing data treatments that we discuss are couched in terms of how they improve causal and statistical inference in the prevention sciences. Our recommendations are firmly grounded in missing data theory and well-validated statistical principles for handling the missing data issues that are ubiquitous in biosocial and prevention research. We augment our broad survey of missing data analysis with references to more exhaustive resources.

Original languageEnglish
Pages (from-to)284-294
JournalPrevention Science
Volume19
Issue number3
DOIs
Publication statusPublished - 2018

Keywords

  • Missing data
  • Multiple imputation
  • Full information maximum likelihood
  • Auxiliary variables
  • Intent-to-treat
  • Statistical inference
  • INFORMATION MAXIMUM-LIKELIHOOD
  • MULTIPLE IMPUTATION
  • MULTIVARIATE IMPUTATION
  • REPORTING PRACTICES
  • SAMPLE SELECTION
  • DROP-OUT
  • MODELS
  • VARIABLES
  • SPECIFICATION
  • NONRESPONSE

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