scholarly journals Are Trait–Outcome Associations Caused by Scales Or Particular Items? Example Analysis of Personality Facets and Bmi

2015 ◽  
Vol 29 (6) ◽  
pp. 622-634 ◽  
Author(s):  
Uku Vainik ◽  
René Mõttus ◽  
Jüri Allik ◽  
Tõnu Esko ◽  
Anu Realo
Keyword(s):  
European Association ◽  
Large Population ◽  
Population Based ◽  
Simulation Design ◽  
Personality Psychology ◽  
Informant Ratings ◽  
Neo Personality Inventory ◽  
Personality Research ◽  
Personality Facets ◽  
Sum Scores

In personality research, trait–outcome associations are often studied by correlating scale sum scores with an outcome. For example, an association between the NEO Impulsiveness scale and body mass index (BMI) is often interpreted to pertain to underlying trait Impulsiveness. We propose that this expectation can be corroborated by testing for Spearman's theorem of indifference of indicator. Namely, an underlying trait–outcome association should not depend on the specific items (i.e. indicators) used to measure the trait. To test this theorem, we outline an indicator exclusion procedure and demonstrate its viability using a simulation design. We then apply this procedure to test personality–BMI associations for indifference of indicator in a large population–based sample of adult Estonians (N = 2581) using self–ratings and informant ratings obtained with the NEO Personality Inventory–3. Our results show that the N5: Impulsiveness–BMI association mostly depends on two eating–related items, suggesting that the trait associated with BMI may be narrower than the trait the N5: Impulsiveness scale is supposed to measure. Associations between BMI, E3: Assertiveness and C2: Order seem to pertain to the trait. In sum, testing for indifference of indicator provides a potentially useful method to clarify trait–outcome relationships. R scripts are provided that implement the indicator exclusion procedure. Copyright © 2015 European Association of Personality Psychology

Objections to the HEXACO Model of Personality Structure—and why those Objections Fail

2020 ◽  
Vol 34 (4) ◽  
pp. 492-510 ◽  
Author(s):  
Michael C. Ashton ◽  
Kibeom Lee
Keyword(s):  
Social Media ◽  
European Association ◽  
Personality Structure ◽  
Personality Psychology ◽  
Personality Research ◽  
Target Article ◽  
Social Media Platforms ◽  
Dimensional Models ◽  
Hexaco Model

The six–dimensional HEXACO model of personality structure and its associated inventory have increasingly been used in personality research. But in spite of the evidence supporting this structure and demonstrating its advantages over five–dimensional models, some researchers continue to use and promote the latter. Although there has been little overt, organized argument against the adoption of the HEXACO model, we do hear sporadic offerings of reasons for retaining the five–dimensional systems, usually in informal conversations, in manuscript reviews, on social media platforms, and occasionally in published works. In this target article, we list all of the objections to the HEXACO model that we have heard of, and we then explain why each objection fails. © 2020 European Association of Personality Psychology

A Framework for Testing Causality in Personality Research

2018 ◽  
Vol 32 (3) ◽  
pp. 254-268 ◽  
Author(s):  
Giulio Costantini ◽  
Marco Perugini
Keyword(s):  
Theoretical Model ◽  
European Association ◽  
Laboratory Research ◽  
Personality Psychology ◽  
Causal Explanations ◽  
Personality Research ◽  
Conceptual Clarity ◽  
Candidate Target ◽  
Ecological Momentary ◽  
Over Time

Causal explanations in personality require conceptual clarity about alternative causal conditions that could, even in principle, affect personality. These causal conditions crucially depend on the theoretical model of personality, each model constraining the possibility of planning and performing causal research in different ways. We discuss how some prominent models of personality allow for specific types of causal research and impede others. We then discuss causality from a network perspective, which sees personality as a phenomenon that emerges from a network of behaviours and environments over time. From a methodological perspective, we propose a three–step strategy to investigate causality: (1) identify a candidate target for manipulation (e.g. using network analysis), (2) identify and test a manipulation (e.g. using laboratory research), and (3) deliver the manipulation repeatedly for a congruous amount of time (e.g. using ecological momentary interventions) and evaluate its ability to generate trait change. We discuss how a part of these steps was implemented for trait conscientiousness and present a detailed plan for implementing the remaining steps. Copyright © 2018 European Association of Personality Psychology

Using Big Data and Machine Learning in Personality Measurement: Opportunities and Challenges

2020 ◽  
Vol 34 (5) ◽  
pp. 632-648
Author(s):  
Leo Alexander ◽  
Evan Mulfinger ◽  
Frederick L. Oswald
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
European Association ◽  
Reliability And Validity ◽  
Personality Measurement ◽  
Personality Psychology ◽  
Personality Research ◽  
The Future ◽  
Challenges And Opportunities ◽  
Conceptual Paper

This conceptual paper examines the promises and critical challenges posed by contemporary personality measurement using big data. More specifically, the paper provides (i) an introduction to the type of technologies that give rise to big data, (ii) an overview of how big data is used in personality research and how it might be used in the future, (iii) a framework for approaching big data in personality science, (iv) an exploration of ideas that connect psychometric reliability and validity, as well as principles of fairness and privacy, to measures of personality that use big data, (v) a discussion emphasizing the importance of collaboration with other disciplines for personality psychologists seeking to adopt big data methods, and finally, (vi) a list of practical considerations for researchers seeking to move forward with big data personality measurement and research. It is expected that this paper will provide insights, guidance, and inspiration that helps personality researchers navigate the challenges and opportunities posed by using big data methods in personality measurement. © 2020 European Association of Personality Psychology

scholarly journals Towards More Rigorous Personality Trait–Outcome Research

2016 ◽  
Vol 30 (4) ◽  
pp. 292-303 ◽  
Author(s):  
René Mõttus
Keyword(s):  
Personality Traits ◽  
Personality Trait ◽  
Outcome Research ◽  
European Association ◽  
Causal Inferences ◽  
Personality Psychology ◽  
Factor Loadings ◽  
Personality Research ◽  
Trait Level

Much of personality research attempts to identify causal links between personality traits and various types of outcomes. I argue that causal interpretations require traits to be seen as existentially and holistically real and the associations to be independent of specific ways of operationalizing the traits. Among other things, this means that, to the extents that causality is to be ascribed to such holistic traits, items and facets of those traits should be similarly associated with specific outcomes, except for variability in the degrees to which they reflect the traits (i.e. factor loadings). I argue that, before drawing causal inferences about personality trait–outcome associations, the presence of this condition should be routinely tested by, for example, systematically comparing the outcome associations of individual items or facets, or sampling different indicators for measuring the same purported traits. Existing evidence suggests that observed associations between personality traits and outcomes at least sometimes depend on which particular items or facets have been included in trait operationalizations, calling trait–level causal interpretations into question. However, this has rarely been considered in the literature. I argue that when outcome associations are specific to facets, they should not be generalized to traits. Furthermore, when the associations are specific to particular items, they should not even be generalized to facets. Copyright © 2016 European Association of Personality Psychology

scholarly journals Assessing Personality–Situation Interplay in Personnel Selection: Toward More Integration into Personality Research

2017 ◽  
Vol 31 (5) ◽  
pp. 424-440 ◽  
Author(s):  
Filip Lievens ◽  
Wendy Johnson
Keyword(s):  
Personnel Selection ◽  
European Association ◽  
Personality Psychology ◽  
Situational Judgement Tests ◽  
Personality Research ◽  
Research Themes ◽  
Job Candidates ◽  
Assessment Centre ◽  
Display Behaviour ◽  
Situational Judgement

Over the years, the personnel selection field has developed methods to assess trait expression in particular situations, but these approaches have evolved mostly outside the field of personality psychology. In this article, I review available personnel selection evidence regarding two such approaches: (i) situational judgement tests that present short scenarios and ask job candidates how they would handle the situations and (ii) assessment centre exercises requiring candidates to display behaviour in specified interactive situations. I describe these approaches and discuss their relations with personality research. I posit that adapting these approaches to personality research creates methodological diversity to address key research themes related to within–person variability, trait–behaviour links, personality disorders, and personality expression and perception. Copyright © 2017 European Association of Personality Psychology

Associations between Body Composition and Mortality: A Large Population-Based Cohort Study

2020 ◽  
Author(s):  
Fu-Rong Li ◽  
Pei-Liang Chen ◽  
Xin Cheng ◽  
Hai-Lian Yang ◽  
Wen-Fang Zhong ◽  
...  
Keyword(s):  
Body Composition ◽  
Cohort Study ◽  
Large Population ◽  
Population Based

Fracture Risk in Patients with Myasthenia Gravis: A Population-Based Cohort Study

2021 ◽  
pp. 1-8
Author(s):  
Charles Kassardjian ◽  
Jessica Widdifield ◽  
J. Michael Paterson ◽  
Alexander Kopp ◽  
Chenthila Nagamuthu ◽  
...  
Keyword(s):  
Myasthenia Gravis ◽  
Lower Risk ◽  
Cox Regression ◽  
Large Population ◽  
Population Based ◽  
Osteoporosis Prevention ◽  
Inception Cohort ◽  
Hazard Ratios ◽  
Multiple Covariates ◽  
Region Of Residence

Background: Prednisone is a common treatment for myasthenia gravis (MG), and osteoporosis is a known potential risk of chronic prednisone therapy. Objective: Our aim was to evaluate the risk of serious fractures in a population-based cohort of MG patients. Methods: An inception cohort of patients with MG was identified from administrative health data in Ontario, Canada between April 1, 2002 and December 31, 2015. For each MG patient, we matched 4 general population comparators based on age, sex, and region of residence. Fractures were identified through emergency department and hospitalization data. Crude overall rates and sex-specific rates of fractures were calculated for the MG and comparator groups, as well as rates of specific fractures. Adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using Cox regression. Results: Among 3,823 incident MG patients (followed for a mean of 5 years), 188 (4.9%) experienced a fracture compared with 741 (4.8%) fractures amongst 15,292 matched comparators. Crude fracture rates were not different between the MG cohort and matched comparators (8.71 vs. 7.98 per 1000 patient years), overall and in men and women separately. After controlling for multiple covariates, MG patients had a significantly lower risk of fracture than comparators (HR 0.74, 95% CI 0.63–0.88). Conclusions: In this large, population-based cohort of incident MG patients, MG patients were at lower risk of a major fracture than comparators. The reasons for this finding are unclear but may highlight the importance osteoporosis prevention.

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