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This study explains multiple imputation, a powerful method for handling missing data in psychological research. It addresses practical challenges and demonstrates its application with free software, promoting wider adoption of advanced techniques.

Keywords:
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Area of Science:

  • Psychology
  • Statistics
  • Data Science

Background:

  • Research on missing data has increased significantly over the last 20 years.
  • Sophisticated methods like multiple imputation and maximum likelihood estimation are common in software.
  • Adoption of these advanced methods is inconsistent in psychology and related fields.

Purpose of the Study:

  • To describe and illustrate the application of multiple imputation for handling missing data.
  • To highlight the advantages of multiple imputation over maximum likelihood estimation for complex datasets.
  • To address practical issues encountered by clinical researchers using multiple imputation.

Main Methods:

  • Focuses on explaining and demonstrating multiple imputation techniques.
  • Illustrates handling of complex data structures like mixed categorical and continuous variables.
  • Provides practical examples using freely available software packages.

Main Results:

  • Multiple imputation offers superior statistical properties, including greater accuracy and power.
  • It effectively handles complex data structures that are challenging for maximum likelihood estimation.
  • The paper details solutions for common issues like item-level and multilevel missing data.

Conclusions:

  • Multiple imputation is a valuable and accessible tool for clinical researchers dealing with missing data.
  • Wider adoption of multiple imputation can improve the rigor and accuracy of psychological research.
  • Practical guidance and examples facilitate the implementation of multiple imputation in research settings.