State of the aRt personality research: A tutorial on network analysis of personality data in R

被引：627

作者：

Costantini, Giulio ^{[1
]}

Epskamp, Sacha ^{[2
]}

Borsboom, Denny ^{[2
]}

Perugini, Marco ^{[1
]}

Mottus, Rene ^{[3
,4
]}

Waldorp, Lourens J. ^{[2
]}

Cramer, Angelique O. J. ^{[2
]}

机构：

[1] Univ Milano Bicocca, Dept Psychol, I-20126 Milan, Italy

[2] Univ Amsterdam, Dept Psychol Methods, NL-1018 XA Amsterdam, Netherlands

[3] Univ Edinburgh, Dept Psychol, Edinburgh EH8 9JZ, Midlothian, Scotland

[4] Univ Tartu, Dept Psychol, EE-50409 Tartu, Estonia

来源：

JOURNAL OF RESEARCH IN PERSONALITY | 2015年 / 54卷

关键词：

Network analysis; Psychometrics; Latent variables; Centrality; Clustering; Personality traits; HEXACO; GRAPHICAL MODEL SELECTION; COVARIANCE ESTIMATION; WEIGHTED NETWORKS; COMPLEX NETWORKS; ADAPTIVE LASSO; CENTRALITY; TRAITS; ARCHITECTURE; ASSOCIATION; MULTIMETHOD;

D O I：

10.1016/j.jrp.2014.07.003

中图分类号：

B84 [心理学];

学科分类号：

04 ; 0402 ;

摘要：

Network analysis represents a novel theoretical approach to personality. Network approaches motivate alternative ways of analyzing data, and suggest new ways of modeling and simulating personality processes. In the present paper, we provide an overview of network analysis strategies as they apply to personality data. We discuss different ways to construct networks from typical personality data, show how to compute and interpret important measures of centrality and clustering, and illustrate how one can simulate on networks to mimic personality processes. All analyses are illustrated using a data set on the commonly used HEXACO questionnaire using elementary R-code that readers may easily adapt to apply to their own data. (C) 2014 Elsevier Inc. All rights reserved.

引用

页码：13 / 29

页数：17

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