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Modelling the Incremental Value of Personality Facets: The Domains‐Incremental Facets‐Acquiescence Bifactor Model
European Journal of Personality ( IF 7.000 ) Pub Date : 2020-06-01 , DOI: 10.1002/per.2268
Daniel Danner 1 , Clemens M. Lechner 2 , Christopher J. Soto 3 , Oliver P. John 4
Affiliation  

Personality can be described at different levels of abstraction. Whereas the Big Five domains are the dominant level of analysis, several researchers have called for more fine‐grained approaches, such as facet‐level analysis. Personality facets allow more comprehensive descriptions, more accurate predictions of outcomes, and a better understanding of the mechanisms underlying trait–outcome relationships. However, several methodological issues plague existing evidence on the added value of facet‐level descriptions: Manifest facet scale scores differ with respect to their reliability, domain‐level variance (variance that is due to the domain factor) and incremental facet‐level variance (variance that is specific to a facet and not shared with the other facets). Moreover, manifest scale scores overlap substantially, which affects associations with criterion variables. We suggest a structural equation modelling approach that allows domain‐level variance to be separated from incremental facet‐level variance. We analysed data from a heterogeneous sample of adults in the USA (N = 1193) who completed the 60‐item Big Five Inventory‐2. The results illustrate how the variance of manifest personality items and scale scores can be decomposed into domain‐level and incremental facet‐level variance. The association with criterion variables (educational attainment, income, health, and life satisfaction) further demonstrates the incremental predictive power of personality facets. © 2020 The Authors. European Journal of Personality published by John Wiley & Sons Ltd on behalf of European Association of Personality Psychology

中文翻译:

对人格方面的增量值进行建模:域-方面-默认双因素模型

个性可以用不同的抽象层次来描述。尽管五大领域是分析的主导水平,但一些研究人员呼吁采用更细粒度的方法,例如构面分析。人格方面允许更全面的描述,更准确的结果预测,以及对特质与结果关系的潜在机制有更好的理解。但是,一些方法学问题困扰着有关方面级别描述附加值的现有证据:清单方面级别得分在可靠性,域级别方差(归因于域因子)和增量方面级别方差(特定于某个方面且未与其他方面共享的差异)。而且,清单量表分数明显重叠,这会影响与标准变量的关联。我们建议一种结构方程建模方法,该方法允许将域级方差与增量方面级方差分开。我们分析了来自美国成年人异质样本的数据(N  = 1193)完成了60个项目的大五库存2。结果说明了明显的个性项和量表分数的方差如何分解为领域级和方面方面的方差。与标准变量(教育程度,收入,健康和生活满意度)的关联进一步证明了人格方面的递增预测能力。©2020作者。John Wiley&Sons Ltd代表欧洲人格心理学协会出版的《欧洲人格杂志》
更新日期:2020-06-01
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