Best Practices for Missing Data Management in Counseling Psychology

Gabriel L. Schlomer, Sheri Bauman, Noel A. Card

Research output: Contribution to journalArticlepeer-review

1315 Scopus citations


This article urges counseling psychology researchers to recognize and report how missing data are handled, because consumers of research cannot accurately interpret findings without knowing the amount and pattern of missing data or the strategies that were used to handle those data. Patterns of missing data are reviewed, and some of the common strategies for dealing with them are described. The authors provide an illustration in which data were simulated and evaluate 3 methods of handling missing data: mean substitution, multiple imputation, and full information maximum likelihood. Results suggest that mean substitution is a poor method for handling missing data, whereas both multiple imputation and full information maximum likelihood are recommended alternatives to this approach. The authors suggest that researchers fully consider and report the amount and pattern of missing data and the strategy for handling those data in counseling psychology research and that editors advise researchers of this expectation.

Original languageEnglish (US)
Pages (from-to)1-10
Number of pages10
JournalJournal of Counseling Psychology
Issue number1
StatePublished - Jan 2010


  • best practices
  • counseling psychology
  • full information maximum likelihood
  • missing data
  • multiple imputation

ASJC Scopus subject areas

  • Social Psychology
  • Clinical Psychology
  • Psychiatry and Mental health


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