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Out of Sight, Not Out of Mind: Strategies for Handling Missing Data

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Objective : To describe and illustrate missing data mechanisms (MCAR, MAR, NMAR) and missing data techniques (MDTs) and offer recommended best practices for addressing missingness.

Method : We simulated data sets and employed ad hoc MDTs (deletion techniques, mean substitution) and sophisticated MDTs (full information maximum likelihood, Bayesian estimation, multiple imputation) in linear regression analyses.

Results : MCAR data yielded unbiased parameter estimates across all MDTs, but loss of power with deletion methods. NMAR results were biased towards larger values and greater significance. Under MAR the sophisticated MDTs returned estimates closer to their original values.

Conclusion : State-of-theart, readily available MDTs outperform ad hoc techniques.

Keywords: Bayesian analysis; FIML; MDTs; health behavior research; imputation; missing data

Document Type: Research Article


Affiliations: 1 Assistant Professor, Department of Community and Family Health, College of Public Health, University of South Florida, Tampa, FL.

Publication date: 2008-01-01

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  • The American Journal of Health Behavior seeks to improve the quality of life through multidisciplinary health efforts in fostering a better understanding of the multidimensional nature of both individuals and social systems as they relate to health behaviors.

    The Journal aims to provide a comprehensive understanding of the impact of personal attributes, personality characteristics, behavior patterns, social structure, and processes on health maintenance, health restoration, and health improvement; to disseminate knowledge of holistic, multidisciplinary approaches to designing and implementing effective health programs; and to showcase health behavior analysis skills that have been proven to affect health improvement and recovery.

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