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Is personality a key predictor of missing study data? An analysis from a randomized controlled trial.
Anthony Jerant1, Benjamin P Chapman, Paul Duberstein
1Department of Family and Community Medicine, University of California Davis School of Medicine, Sacramento, California 95817, USA. afjerant@ucdavis.edu
Annals of Family Medicine
|March 11, 2009
Summary
Personality traits significantly predict missing data in research. Higher Openness, Agreeableness, and Conscientiousness correlate with more complete data collection in studies.
Area of Science:
- Psychology
- Clinical Research
- Data Science
Background:
- Psychological factors' impact on research data collection is understudied.
- The Five Factor Model (FFM) of personality includes Neuroticism, Extraversion, Openness, Agreeableness, and Conscientiousness.
Purpose of the Study:
- To investigate if FFM personality traits predict missing data in a randomized controlled trial (RCT).
- To determine the association between personality factors and data completion rates.
Main Methods:
- A 6-week RCT involving 415 adults (≥40 years) with chronic conditions.
- Random effects logistic regression analyzed FFM predictors of missing self-efficacy data at multiple follow-ups.
- Controlled for disease burden, study arm, and sociodemographics.
Main Results:
- The overall missing data rate was 4.5%.
- Higher Openness, Agreeableness, and Conscientiousness were linked to significantly less missing data.
- Adding personality variables improved the prediction model's accuracy (AUC increased from 0.71 to 0.77).
Conclusions:
- Personality traits are strong predictors of missing data in clinical research.
- Assessing personality can enhance data completion and help adjust for potential biases.
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