Evaluating comprehensiveness in personality systems: The California Q‐Set and the five‐factor model

McCrae, Robert R.; Costa, Paul T.; Busch, Catherine M.

doi:10.1111/j.1467-6494.1986.tb00403.x

Cited by 349 publications

(283 citation statements)

References 23 publications

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“…<…> Влияния на личность. Чтобы прояснить влияние личности на исход брака, 56 изучаемых в лонгитюдных исследованиях личностных характеристик были сначала сгруппированы в 5 факторов: нейротизм, экстравертивность, импульсивность, доброжелательность и добросовестность, -которые, как было показано, лежат в основе более специфических перечней личностных черт, используемых в различ-ных исследованиях [McCrae, Costa, Busch, 1986]. Из этих 5 личностных факторов, нейротизм (более широко, негативная аффективность) влияет на брак больше, чем другие факторы [Watson, Clark, 1984].…”

Section: результаты лонгитюдных исследований бракаunclassified

The Longitudinal Course of Marital Quality and Stability: A Review of Theory, Method and Research

Karney¹,

Bradbury²

2016

MONITORING OF PUBLIC OPINION

216

360

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Section: результаты лонгитюдных исследований бракаunclassified

The Longitudinal Course of Marital Quality and Stability: A Review of Theory, Method and Research

Karney¹,

Bradbury²

2016

MONITORING OF PUBLIC OPINION

216

360

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“…According to McCrae et al [62], the measurement of the five factors gives a complete characterization of a person only at a global level.…”

Section: Five Factor Modelmentioning

confidence: 99%

TwitPersonality: Computing Personality Traits from Tweets Using Word Embeddings and Supervised Learning

et al. 2018

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Abstract:We are what we do, like, and say. Numerous research efforts have been pushed towards the automatic assessment of personality dimensions relying on a set of information gathered from social media platforms such as list of friends, interests of musics and movies, endorsements and likes an individual has ever performed. Turning this information into signals and giving them as inputs to supervised learning approaches has resulted in being particularly effective and accurate in computing personality traits and types. Despite the demonstrated accuracy of these approaches, the sheer amount of information needed to put in place such a methodology and access restrictions make them unfeasible to be used in a real usage scenario. In this paper, we propose a supervised learning approach to compute personality traits by only relying on what an individual tweets about publicly. The approach segments tweets in tokens, then it learns word vector representations as embeddings that are then used to feed a supervised learner classifier. We demonstrate the effectiveness of the approach by measuring the mean squared error of the learned model using an international benchmark of Facebook status updates. We also test the transfer learning predictive power of this model with an in-house built benchmark created by twenty four panelists who performed a state-of-the-art psychological survey and we observe a good conversion of the model while analyzing their Twitter posts towards the personality traits extracted from the survey.

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“…later age at sexual debut). As described in detail in section 2.1.4, published analyses (McCrae, Costa, & Busch, 1986) provide a means to score any CAQ profile on each of the FFM dimensions. The relationships, across individuals, between (1) the correlation between each individual's CAQ profile and the SLH CAQ template (hereafter, CAQ-based slow LHS) and (2) the individual's CAQ-generated score on each FFM dimension, permit inferences about the relationships between the FFM dimensions and Life History Speed.…”

Section: Accepted M Manuscriptmentioning

confidence: 99%

Are extraversion and openness indicators of a slow life history strategy?

Manson

2017

Evolution and Human Behavior

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In both samples, individuals whose CAQ profile more closely resembled the SLH template were higher in Conscientiousness, Agreeableness, and Emotional Stability than those pursuing a faster LHS. Extraversion was unrelated to LHS, while Openness in the Block and Block data set was actually associated with a faster LHS. Analysis of individual CAQ items revealed some differences between the studies. Generally, Extraversion-loading items tapping excitementseeking and self-dramatization, and Openness-loading items tapping nonconformity, were associated with a faster LHS. Participants in the audio recording study also completed a HEXACO personality inventory and the Arizona Life History Battery. LHS as measured by the ALHB was uncorrelated with LHS as measured by the CAQ. Controlling for Extraversion yielded a significant positive relationship between the two LHS measures, suggesting that their poor convergent validity resulted from differences in their coverage of Extraversion-related content.

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Evaluating comprehensiveness in personality systems: The California Q‐Set and the five‐factor model

Cited by 349 publications

References 23 publications

The Longitudinal Course of Marital Quality and Stability: A Review of Theory, Method and Research

The Longitudinal Course of Marital Quality and Stability: A Review of Theory, Method and Research

TwitPersonality: Computing Personality Traits from Tweets Using Word Embeddings and Supervised Learning

Are extraversion and openness indicators of a slow life history strategy?

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