Introducing the OCEAN.20: A 20-Item Five-Factor Personality Measure Based on the Trait Self-Descriptive Inventory

O’Keefe, Damian F.; Kelloway, E. Kevin; Francis, Robbie E.

doi:10.1080/08995605.2012.716265

Cited by 13 publications

(7 citation statements)

References 32 publications

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“…The revised five-factor model of personality component (FF-B) that consists of 13 items for the theoretically defined five factors has good model fit. Previous studies have reported similar findings on the five-factor personality traits (34)(35)(36). In addition, this finding provides further evidence to support the finding of a previous study that was done by Yusoff (37) on the psychometric properties of USMaP-i in the medical student population.…”

Section: Discussionsupporting

confidence: 89%

A Confirmatory Factor Analysis of USM Personality Inventory (USMaP-i) among Medical Degree Program Applicants in Universiti Sains Malaysia

Farliza¹,

Nor²,

Yusoff³

et al. 2016

EIMJ

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Introduction:USMaP-i is an English, 66-item self-administered inventory, consisting of personality (60 items, 5 factors) and faking (one factor) components, which was mainly developed to measure personality traits among Malaysian students based on local cultures and values. The personality component was based on the Big Five dimensions as suggested by numerous personality researchers. Previous exploratory studies showed promising validity, reliability and stability of USMaP-i. Objective: To provide further validity evidence of USMaP-i for use among medical degree program applicants by confirmatory factor analysis (CFA). Methods: Data were collected as a part of screening of medical degree program applicants for year 2010-2013 intakes in Universiti Sains Malaysia (USM), of which 657 cases were suitable for analyses following a data screening measures. CFA was performed by bootstrap maximum likelihood estimation due to non-normality of items at multivariate level. Results: Although the revised five-factor model of personality showed good model fit (X2(df) = 144.36(55), P-value < 0.001; CFI = .944, TLI = .921; RMSEA = .050; SRMR = .032, Bollen-Stine bootstrap P-value = 0.004), the reliability of the factors is very poor (composite reliabilities (CR) = .483 to .650). In contrast, the unidimensional faking component exhibited good model fit (X2(df) = 14.15(5), P-value = 0.015; CFI = .984, TLI = .968; RMSEA = .053; SRMR = .011, Bollen-Stine bootstrap P-value = 0.068) and factor reliability (CR = 0.731). Conclusion: The personality component should be revised and revalidated due to poor reliability, despite showing good model fit. In contrast, the faking component showed good model fit and reliability. Further validation studies are recommended before its use among medical degree program applicants.

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Section: Discussionsupporting

confidence: 89%

A Confirmatory Factor Analysis of USM Personality Inventory (USMaP-i) among Medical Degree Program Applicants in Universiti Sains Malaysia

Farliza¹,

Nor²,

Yusoff³

et al. 2016

EIMJ

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“…These approaches identify personality traits that in- or decrease the likelihood of disinhibited online communication. One of the two personality models that are predominantly used in this context is the Big 5 model of personality, which spans the five personality factors of openness, conscientiousness, extraversion, agreeableness, and neuroticism (O'Keefe et al, 2012 ). For example, Kokkinos et al ( 2013 ) found that high extraversion and conscientiousness lower the likelihood that an individual engages in cyberbullying.…”

Section: Introductionmentioning

confidence: 99%

Self-control in Online Discussions: Disinhibited Online Behavior as a Failure to Recognize Social Cues

2018

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In an online experiment we examined the role of self-control in recognizing social cues in the context of disinhibited online behavior (e.g., flaming and trolling). We temporarily lowered participants' self-control capacity with an ego depletion paradigm (i.e., color Stroop task). Next, we measured participants' sensitivity to social cues with an emotional Stroop task containing neutral, negative, and taboo words. Sensitivity to social cues is represented by the increase in reaction time to negative and especially taboo words compared to neutral words. As expected, undepleted participants were slower to process the color of negative and taboo words. By contrast, depleted participants (i.e., those with lowered self-control capacity) did not react differently to taboo or negative words than they did to neutral words. The experiment illustrates that self-control failure may manifest itself in a failure to recognize social cues. The finding underlines the importance of self-control in understanding disinhibited online behavior: Many instances of disinhibited online behavior may occur not because people are unable to control themselves, but because they do not realize that a situation calls for self-control in the first place.

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“…Participants were excluded if they were currently taking any medication. Participants completed the OCEAN.20 personality inventory 49 and the Situational Vertigo Questionnaire (SVQ) questionnaire 50 . The OCEAN.20 inventory includes twenty items, five of which are each dedicated to each of the Big Five personality traits 51 , e.g., Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism.…”

Section: Methodsmentioning

confidence: 99%

“…Facial electromyography (fEMG) was recorded at 1000 Hz from the corrugator supercilii and zygomaticus major via the Bionomadix ambulatory psychophysiology system (BIOPAC). fEMG data were processed as follows: (1) bandpass filter applied at 49-51 Hz to remove 50 Hz noise, (2) filtered between 20 and 400 Hz, (3) rectified and smoothed via linear envelope (9 Hz filter), and (4) subjected to root mean square transformation 49 , 50 . Skin Conductance Level (SCL) was also recorded at 1000 Hz via the Bionomadix system.…”

Section: Methodsmentioning

confidence: 99%

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Assessment of threat and negativity bias in virtual reality

Baker

Pawling

Fairclough

2020

Sci Rep

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Negativity bias, i.e., tendency to respond strongly to negative stimuli, can be captured via behavioural and psychophysiological responses to potential threat. A virtual environment (VE) was created at room-scale wherein participants traversed a grid of ice blocks placed 200 m above the ground. Threat was manipulated by increasing the probability of encountering ice blocks that disintegrated and led to a virtual fall. Participants interacted with the ice blocks via sensors placed on their feet. Thirty-four people were recruited for the study, who were divided into High (HN) and Low (LN) Neuroticism groups. Movement data were recorded alongside skin conductance level and facial electromyography from the corrugator supercilii and zygomaticus major. Risk-averse behaviours, such as standing on ‘safe’ blocks and testing blocks prior to movement, increased when threat was highest. HN individuals exhibited more risk-averse behaviour than the LN group, especially in the presence of high threat. In addition, activation of the corrugator muscle was higher for HN individuals in the period following a movement to an ice block. These findings are discussed with respect to the use of room-scale VE as a protocol for emotion induction and measuring trait differences in negativity bias within VR.

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Introducing the OCEAN.20: A 20-Item Five-Factor Personality Measure Based on the Trait Self-Descriptive Inventory

Cited by 13 publications

References 32 publications

A Confirmatory Factor Analysis of USM Personality Inventory (USMaP-i) among Medical Degree Program Applicants in Universiti Sains Malaysia

A Confirmatory Factor Analysis of USM Personality Inventory (USMaP-i) among Medical Degree Program Applicants in Universiti Sains Malaysia

Self-control in Online Discussions: Disinhibited Online Behavior as a Failure to Recognize Social Cues

Assessment of threat and negativity bias in virtual reality

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