Best practices for missing data management in counseling psychology

Area of Science:

• Psychology
• Statistics

Background:

• Accurate interpretation of research findings necessitates understanding how missing data are managed.
• Inconsistencies in reporting missing data handling can obscure study validity.

Purpose of the Study:

• To emphasize the importance of transparently reporting missing data strategies in counseling psychology research.
• To evaluate common methods for handling missing data and recommend best practices.

Main Methods:

• Review of missing data patterns and common handling strategies.
• Simulation of data to evaluate three methods: mean substitution, multiple imputation, and full information maximum likelihood.

Main Results:

• Mean substitution demonstrated poor performance in handling missing data.
• Multiple imputation and full information maximum likelihood proved to be effective and recommended alternatives.

Conclusions:

• Counseling psychology researchers should meticulously report the extent and patterns of missing data, along with the chosen handling strategy.
• Adoption of robust methods like multiple imputation or full information maximum likelihood is advised over simpler techniques.
• Journal editors should enforce clear reporting standards for missing data in submitted research.