Incremental criterion prediction of personality facets over factors: Obtaining unbiased estimates and confidence intervals

被引:38
作者
Anglim, Jeromy [1 ]
Grant, Sharon L. [2 ]
机构
[1] Deakin Univ, Sch Psychol, Burwood, Vic 3125, Australia
[2] Swinburne Univ Technol, Fac Hlth Arts & Design, Hawthorn, Vic 3122, Australia
关键词
Personality; Big Five traits; Facets; Multiple regression; Bootstrapping; JOB-PERFORMANCE; MULTIPLE-REGRESSION; LIFE SATISFACTION; 5-FACTOR MODEL; NARROW; LEVEL; BROAD; VALIDITY; CONSCIENTIOUSNESS; DISORDERS;
D O I
10.1016/j.jrp.2014.10.005
中图分类号
B84 [心理学];
学科分类号
04 ; 0402 ;
摘要
Many researchers have argued that higher order models of personality such as the Five Factor Model are insufficient, and that facet-level analysis is required to better understand criteria such as well-being, job performance, and personality disorders. However, common methods in the extant literature used to estimate the incremental prediction of facets over factors have several shortcomings. This paper delineates these shortcomings by evaluating alternative methods using statistical theory, simulation, and an empirical example. We recommend using differences between Olkin-Pratt adjusted r-squared for factor versus facet regression models to estimate the incremental prediction of facets and present a method for obtaining confidence intervals for such estimates using double adjusted-r-squared bootstrapping. We also provide an R package that implements the proposed methods. (C) 2014 Elsevier Inc. All rights reserved.
引用
收藏
页码:148 / 157
页数:10
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