A Mega-Analysis of Personality Prediction: Robustness and Boundary Conditions

Decades of studies identify personality traits as an important predictor of life outcomes. However, previous investigations of personality-outcome associations have not taken a principled approach to covariate use or other sampling strategies to ensure the robustness of personality-outcome associations. The result is that it is unclear (1) whether personality predicts important outcomes after accounting for a range of background variables, (2) for whom and when personality predictions hold, and 3) which background variables are most important to account for. The present study examines the robustness and boundary conditions of personality prediction using the Big Five to predict 14 health, social, education/work, and societal outcomes across eight different person- and study-level moderators using individual participant data from 171,395 individuals across 10 longitudinal panel studies in a mega-analytic framework. Robustness and boundary conditions were systematically tested using two approaches: propensity score matching and specification curve analysis. Three findings emerged: First, personality traits remain a robust predictor of life outcomes. Second, the effects generalize, as there are few moderators of personality-outcome associations. Third, robustness was differential across covariate choice in nearly half of the tested models, with the inclusion or exclusion of some of these flipping the direction of association. In sum, personality is a powerful predictor of life outcomes with few moderated associations. However, researchers need to be careful in their choices of covariates. We discuss how these findings can inform personality prediction, as well as recommendations for covariate inclusion.

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A Mega-Analysis of Personality Prediction: Robustness and Boundary Conditions

10.31234/osf.io/7pg9b ◽

2020 ◽

Cited By ~ 1

Author(s):

Emorie D Beck ◽

Joshua James Jackson

Keyword(s):

Boundary Conditions ◽

Personality Traits ◽

Big Five ◽

Curve Analysis ◽

Sampling Strategies ◽

Life Outcomes ◽

Analytic Framework ◽

Powerful Predictor ◽

Individual Participant ◽

Personality Prediction

Decades of studies identify personality traits as an important predictor of life outcomes. However, previous investigations of personality-outcome associations have not taken a principled approach to covariate use or other sampling strategies to ensure the robustness of personality-outcome associations. The result is that it is unclear (1) whether personality predicts important outcomes after accounting for a range of background variables, (2) for whom and when personality predictions hold, and 3) which background variables are most important to account for. The present study examines the robustness and boundary conditions of personality prediction using the Big Five to predict 14 health, social, education/work, and societal outcomes across eight different person- and study-level moderators using individual participant data from 171,395 individuals across 10 longitudinal panel studies in a mega-analytic framework. Robustness and boundary conditions were systematically tested using two approaches: propensity score matching and specification curve analysis. Three findings emerged: First, personality traits remain a robust predictor of life outcomes. Second, the effects generalize, as there are few moderators of personality-outcome associations. Third, robustness was differential across covariate choice in nearly half of the tested models, with the inclusion or exclusion of some of these flipping the direction of association. In sum, personality is a powerful predictor of life outcomes with few moderated associations. However, researchers need to be careful in their choices of covariates. We discuss how these findings can inform personality prediction, as well as recommendations for covariate inclusion.

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The relationship between alcohol use and dementia: A combined analysis of prospective, individual-participant data from 15 international studies

10.31234/osf.io/7835k ◽

2021 ◽

Author(s):

Louise Mewton ◽

Rachel Visontay ◽

Nicholas Hoy ◽

Darren Lipnicki ◽

John D Crawford ◽

...

Keyword(s):

Alcohol Use ◽

Later Life ◽

Community Dwelling ◽

Competing Risk ◽

Individual Participant Data ◽

Risk Of Death ◽

Dementia Risk ◽

Individual Participant ◽

The Relationship

Objective: To synthesise international findings on the alcohol-dementia relationship and provide a cross-national comparison of the alcohol-dementia relationship with critical evidence for the relationship between alcohol use and dementia in under-studied populations. Design and setting: Individual participant data meta-analysis of 15 prospective epidemiological cohort studies from countries situated in five continents. Cox regression investigated the dementia risk associated with alcohol use. Sensitivity analyses compared lifetime abstainers with former drinkers, adjusted extensively for demographic and clinical characteristics, and assessed the competing risk of death. Participants: 24,472 community-dwelling individuals without a history of dementia at baseline and at least one follow-up dementia assessment. Main outcome measure: All-cause dementia as determined by clinical interview. Results: During 151,574 person-years of follow-up, there were 2,137 incident cases of dementia (14.1 per 1,000 person-years). In the combined sample, when compared with occasional drinkers (<1.3g/day), the risk for dementia was higher for current abstainers (HR: 1.29; 95% CI: 1.13, 1.48) and lower for moderate drinkers (25g/day-44.9g/day; HR: 0.79; 95% CI: 0.64, 0.98). When the combined sample was stratified by sex and gross domestic product, current abstainers had a greater risk of incident dementia when compared with light-to-moderate drinkers in both sexes and in the higher income countries. When comparing lifetime abstainers and former drinkers there were no consistent differences in dementia risk. Among current drinkers, there was no consistent evidence to suggest that the amount of alcohol consumed in later life was significantly associated with dementia risk. Adjusting for additional demographic and clinical covariates, and accounting for competing risk of death, did not substantially affect results. When analysed at the cohort level, there was considerable heterogeneity in the alcohol-dementia relationship. Conclusions: In a large and diverse international sample of older adults, the current study found that abstinence from alcohol is associated with an increased risk for all-cause dementia. Among current drinkers, there was no consistent evidence to suggest that the amount of alcohol consumed in later life was significantly associated with dementia risk.

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Response to physical rehabilitation and recovery trajectories following critical illness: individual participant data meta-analysis protocol

BMJ Open ◽

10.1136/bmjopen-2019-035613 ◽

2020 ◽

Vol 10 (5) ◽

pp. e035613

Author(s):

Jennifer R A Jones ◽

Sue Berney ◽

Michael J Berry ◽

D Clark Files ◽

David M Griffith ◽

...

Keyword(s):

Critical Illness ◽

Meta Analysis ◽

Physical Rehabilitation ◽

Individual Participant Data ◽

Life Outcomes ◽

Individual Participant ◽

Randomised Controlled ◽

Patient Subgroups ◽

Analysis Protocol

IntroductionThe number of inconclusive physical rehabilitation randomised controlled trials for patients with critical illness is increasing. Evidence suggests critical illness patient subgroups may exist that benefit from targeted physical rehabilitation interventions that could improve their recovery trajectory. We aim to identify critical illness patient subgroups that respond to physical rehabilitation and map recovery trajectories according to physical function and quality of life outcomes. Additionally, the utilisation of healthcare resources will be examined for subgroups identified.Methods and analysisThis is an individual participant data meta-analysis protocol. A systematic literature review was conducted for randomised controlled trials that delivered additional physical rehabilitation for patients with critical illness during their acute hospital stay, assessed chronic disease burden, with a minimum follow-up period of 3 months measuring performance-based physical function and health-related quality of life outcomes. From 2178 records retrieved in the systematic literature review, four eligible trials were identified by two independent reviewers. Principal investigators of eligible trials were invited to contribute their data to this individual participant data meta-analysis. Risk of bias will be assessed (Cochrane risk of bias tool for randomised trials). Participant and trial characteristics, interventions and outcomes data of included studies will be summarised. Meta-analyses will entail a one-stage model, which will account for the heterogeneity across and the clustering between studies. Multiple imputation using chained equations will be used to account for the missing data.Ethics and disseminationThis individual participant data meta-analysis does not require ethical review as anonymised participant data will be used and no new data collected. Additionally, eligible trials were granted approval by institutional review boards or research ethics committees and informed consent was provided for participants. Data sharing agreements are in place permitting contribution of data. The study findings will be disseminated at conferences and through peer-reviewed publications.PROSPERO registration numberCRD42019152526.

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How do academics assess the results of multiple experiments?

10.31234/osf.io/xyks4 ◽

2018 ◽

Cited By ~ 1

Author(s):

Olmo Van den Akker ◽

Linda Dominguez Alvarez ◽

Marjan Bakker ◽

Jelte M. Wicherts ◽

Marcel A. L. M. van Assen

Keyword(s):

Bayesian Inference ◽

Secondary Analysis ◽

Individual Participant Data ◽

Additional Experiment ◽

Underlying Theory ◽

Individual Participant ◽

Vote Counting

We studied how academics assess the results of a set of four experiments that all test a given theory. We found that participants’ belief in the theory increases with the number of significant results, and that direct replications were considered to be more important than conceptual replications. We found no difference between authors and reviewers in their propensity to submit or recommend to publish sets of results, but we did find that authors are generally more likely to desire an additional experiment. In a preregistered secondary analysis of individual participant data, we examined the heuristics academics use to assess the results of four experiments. Only 6 out of 312 (1.9%) participants we analyzed used the normative method of Bayesian inference, whereas the majority of participants used vote counting approaches that tend to undervalue the evidence for the underlying theory if two or more results are statistically significant.

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