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Higher-order factors of the Big Five.
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In this article, the authors analyzed factor correlations from 14 studies supporting the 5 factor, Big Five model of personality trait organization and found that two higher-order factors were clearly evident in all studies.Abstract:
Estimated factor correlations from 14 studies supporting the 5 factor, Big Five model of personality trait organization--5 studies based on children and adolescents, 9 on adults--were factor analyzed. Two higher-order factors were clearly evident in all studies. One was principally related to the Big Five trait dimensions Agreeableness, Conscientiousness, and Emotional Stability; the other, the dimensions Extraversion and Intellect. Two models, one for children and adolescents, the other for adults, were tested by confirmatory factor analysis with generally excellent results. Many personality theorists appear to have considered one or both of these 2 metatraits, provisionally labeled alpha and beta.read more
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Handbook of positive psychology
C. R. Snyder,Shane J. Lopez +1 more
TL;DR: The Handbook of Positive Psychology as mentioned in this paper provides a forum for a more positive view of the human condition and provides an analysis of what the foremost experts believe to be the fundamental strengths of humankind.
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Linking "big" personality traits to anxiety, depressive, and substance use disorders: a meta-analysis.
TL;DR: It is found that common mental disorders are strongly linked to personality and have similar trait profiles, and greater attention to these constructs can significantly benefit psychopathology research and clinical practice.
Journal ArticleDOI
A meta-analysis of the five-factor model of personality and academic performance.
TL;DR: A meta-analysis of personality-academic performance relationships, based on the 5-factor model, in which cumulative sample sizes ranged to over 70,000, found academic performance was found to correlate significantly with Agreeableness, Conscientiousness, and Openness.
Journal ArticleDOI
The Hierarchical Taxonomy of Psychopathology (HiTOP): A Dimensional Alternative to Traditional Nosologies
Roman Kotov,Robert F. Krueger,David Watson,Thomas M. Achenbach,Robert R. Althoff,R. Michael Bagby,Timothy A. Brown,William T. Carpenter,Avshalom Caspi,Lee Anna Clark,Nicholas R. Eaton,Miriam K. Forbes,Kelsie T. Forbush,David Goldberg,Deborah S. Hasin,Steven E. Hyman,Masha Y. Ivanova,Donald R. Lynam,Kristian E. Markon,Joshua D. Miller,Terrie E. Moffitt,Leslie C. Morey,Stephanie N. Mullins-Sweatt,Johan Ormel,Christopher J. Patrick,Darrel A. Regier,Leslie Rescorla,Camilo J. Ruggero,Douglas B. Samuel,Martin Sellbom,Leonard J. Simms,Andrew E. Skodol,Tim Slade,Susan C. South,Jennifer L. Tackett,Irwin D. Waldman,Monika A. Waszczuk,Thomas A. Widiger,Aidan G. C. Wright,Mark Zimmerman +39 more
TL;DR: The HiTOP promises to improve research and clinical practice by addressing the aforementioned shortcomings of traditional nosologies and provides an effective way to summarize and convey information on risk factors, etiology, pathophysiology, phenomenology, illness course, and treatment response.
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Journal ArticleDOI
Comparative fit indexes in structural models
TL;DR: A new coefficient is proposed to summarize the relative reduction in the noncentrality parameters of two nested models and two estimators of the coefficient yield new normed (CFI) and nonnormed (FI) fit indexes.
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Significance tests and goodness of fit in the analysis of covariance structures
TL;DR: In this article, a general null model based on modified independence among variables is proposed to provide an additional reference point for the statistical and scientific evaluation of covariance structure models, and the importance of supplementing statistical evaluation with incremental fit indices associated with the comparison of hierarchical models.
Book
Science and human behavior
TL;DR: The psychology classic "Walden Two" as mentioned in this paper is a detailed study of scientific theories of human nature and the possible ways in which human behavior can be predicted and controlled from one of the most influential behaviorists of the twentieth century.
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A rationale and test for the number of factors in factor analysis.
TL;DR: It is suggested that if Guttman's latent-root-one lower bound estimate for the rank of a correlation matrix is accepted as a psychometric upper bound, then the rank for a sample matrix should be estimated by subtracting out the component in the latent roots which can be attributed to sampling error.