Best practices for missing data management in counseling psychology.

TLDR

The article urges counseling psychology researchers to explicitly report missing data amounts, patterns, and handling strategies to ensure accurate interpretation of findings. The authors review missing data patterns, describe common handling strategies, and illustrate their evaluation of mean substitution, multiple imputation, and full information maximum likelihood using simulated data. Mean substitution performs poorly, whereas multiple imputation and full information maximum likelihood are recommended; researchers should fully report missing data details and editors should enforce this expectation.

Abstract

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.