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Cited by 29Personality Traits and PsychologySurvey Methodology and NonresponseStatistical Methods and Bayesian Inference

Is Personality a Key Predictor of Missing Study Data? An Analysis From a Randomized Controlled Trial

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Jerant, A., Chapman, B. P., Duberstein, P., Franks, P.·2009·The Annals of Family Medicine
DOI 10.1370/afm.920PMC PMC2653957
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Abstract

PURPOSE: Little is known regarding the effects of psychological factors on data collection in research studies. We examined whether Five Factor Model (FFM) personality factors-Neuroticism, Extraversion, Openness, Agreeableness, and Conscientiousness-predicted missing data in a randomized controlled trial (RCT). METHODS: Individuals (N = 415) aged 40 years and older with various chronic conditions, plus basic activity impairment, depressive symptoms, or both, were recruited from a primary care network and enrolled in a 6-week RCT of an illness self-management intervention, delivered by means of home visits or telephone calls or usual care. Random effects logistic regression modeling was used to examine whether FFM factors predicted missing illness management self-efficacy data at any scheduled follow-up (2, 4, and 6 weeks, and 6 and 12 months), controlling for disease burden, study arm, and sociodemographic characteristics. RESULTS: Across all follow-up points, the missing data rate was 4.5%. Higher levels of Openness (adjusted odds ratio [AOR] for 1-SD increase = 0.24; 95% CI, 0.12-0.46; P <.001), Agreeableness (AOR = 0.29; CI 0.14-0.60; P=.001), and Conscientiousness (AOR = 0.24; CI 0.15-0.50; P <.001) were independently associated with fewer missing data. Accuracy of the missing data prediction model increased when personality variables were added (change in area under the receiver operating characteristic curve from 0.71 to 0.77; chi(2)(1)=6.6; P=.01). CONCLUSIONS: Personality was a powerful predictor of missing study data in this RCT. Assessing personality could inform efforts to enhance data completion and adjust analyses for bias caused by missing data.

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Research works printed but not linked: 2

  1. Galluccio-Richardson RM. Predicting Adherence to a Weight Loss Regimen Using the NEO Personality Inventory-Revised [dissertation].
  2. Wilson ST. Personality, Insight, and Compliance: A Longitudinal Study of Treatment Adherence in Outpatients With Schizophrenia and Schizoaf- fective Disorder [dissertation]. New York, NY: Columbia University; 2003.

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Authors
Jerant, A., Chapman, B. P., Duberstein, P., & Franks, P.
Affiliations
  • University of California Davis Medical Center (US)
  • University of Rochester Medicine (US)
ORCID
Venue
The Annals of Family Medicine
Publisher
Annals of Family Medicine
Published
2009-03-01
Volume
7
Issue
2
Pages
148-156
ISSN
  • 1544-1709 (ISSN-L)
  • 1544-1717
DOI
10.1370/afm.920
PMC
PMC2653957
OpenAlex
W2136787969
Cited by
29
References
58
Citations by year
2026: 3 · 2025: 1 · 2023: 1 · 2022: 1 · 2017: 3 · 2016: 1 · 2015: 1 · 2014: 1 · 2013: 2 · 2012: 2
FWCI
4.96
Type
journal article
Language
en
Open access
Locations
  • The Annals of Family Medicine (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • Agency for Healthcare Research and Quality (R01HS013603)
  • National Institute on Aging (K08AG031328, L30 AG031021)
  • National Institute of Mental Health (K24MH072712, T32MH073452)
Topics
  • Social Sciences › Psychology › Clinical Psychology › Personality Traits and Psychology
  • Social Sciences › Sociology and Political Science › Survey Methodology and Nonresponse
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Keywords
missing data, personality, Big Five personality traits, openness to experience, agreeableness, conscientiousness, self-efficacy, data completion, chronic disease, depressive symptoms, primary care
MeSH terms
Adult, Aged, Aged, 80 and over, Data Interpretation, Statistical, Female, Humans, Male, Middle Aged, Patient Compliance, Patient Dropouts, Personality, Primary Health Care, Psychometrics, Self Care, Bias, Logistic Models, Randomized Controlled Trials as Topic

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