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Missing data: An update on the state of the art
1Department of Psychology, University of California Los Angeles.
Psychological Methods
|March 17, 2023
Summary
This review updates the state of the art in missing data methods, highlighting innovations in statistical theory and software over the past two decades. It covers advancements in full information maximum likelihood, Bayesian estimation, and multiple imputation techniques.
Area of Science:
- Statistics
- Psychological Methods
- Data Science
Background:
- The 2002 paper "Missing data: Our view of the state of the art" remains highly cited.
- Significant advancements in missing data methodologies have occurred over the past two decades.
- Modern techniques offer a broader range of applications and improved software options.
Purpose of the Study:
- To provide an updated review of the state of the art in missing data research.
- To catalog key innovations in missing data methodologies from the past 20 years.
- To discuss practical considerations and the current software landscape for handling missing data.
Main Methods:
- Review of developments in missing data theory.
- Examination of advancements in full information maximum likelihood (FIML) and Bayesian estimation.
- Analysis of progress in multiple imputation (MI) and models for missing not at random (MNAR) processes.
Main Results:
- Significant evolution in missing data theory and estimation techniques.
- Introduction of newer methods like factored regression specifications.
- Enhanced handling of missing data in multilevel models.
- Substantial improvements in available software for missing data analysis.
Conclusions:
- The field of missing data analysis has seen dramatic progress in theory, methods, and software.
- Current techniques provide more robust and versatile solutions for researchers.
- Practical implementation is facilitated by advanced software and ongoing methodological development.
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