title: The network structure of human personality according to the NEO-PI-R: matching network community structure to factor structure.
abstract: BACKGROUND
  Human personality is described preferentially in terms of factors (dimensions) found using factor analysis. An alternative and highly related method is network analysis, which may have several advantages over factor analytic methods.
  
  
  OBJECTIVE
  To directly compare the ability of network community detection (NCD) and principal component factor analysis (PCA) to examine modularity in multidimensional datasets such as the neuroticism-extraversion-openness personality inventory revised (NEO-PI-R).
  
  
  METHODS
  434 healthy subjects were tested on the NEO-PI-R. PCA was performed to extract factor structures (FS) of the current dataset using both item scores and facet scores. Correlational network graphs were constructed from univariate correlation matrices of interactions between both items and facets. These networks were pruned in a link-by-link fashion while calculating the network community structure (NCS) of each resulting network using the Wakita Tsurumi clustering algorithm. NCSs were matched against FS and networks of best matches were kept for further analysis.
  
  
  RESULTS
  At facet level, NCS showed a best match (96.2%) with a 'confirmatory' 5-FS. At item level, NCS showed a best match (80%) with the standard 5-FS and involved a total of 6 network clusters. Lesser matches were found with 'confirmatory' 5-FS and 'exploratory' 6-FS of the current dataset. Network analysis did not identify facets as a separate level of organization in between items and clusters. A small-world network structure was found in both item- and facet level networks.
  
  
  CONCLUSIONS
  We present the first optimized network graph of personality traits according to the NEO-PI-R: a 'Personality Web'. Such a web may represent the possible routes that subjects can take during personality development. NCD outperforms PCA by producing plausible modularity at item level in non-standard datasets, and can identify the key roles of individual items and clusters in the network.
authors.affiliation: Department of Mood Disorders, PsyQ Psychomedical Programs, The Hague, The Netherlands. R.Goekoop@PsyQ.nl
authors.forename: Rutger
authors.initials: R
authors.lastname: Goekoop
authors.forename: Jaap G
authors.initials: JG
authors.lastname: Goekoop
authors.forename: H Steven
authors.initials: HS
authors.lastname: Scholte
mesh_terms: D000293:Adolescent; D000328:Adult; D019058:Community Networks; D005120:Extraversion, Psychological; D005163:Factor Analysis, Statistical; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D009497:Neurotic Disorders / Q000175:diagnosis*; D010554:Personality Disorders / Q000175:diagnosis*; D010555:Personality Inventory / Q000706:statistics & numerical data*; D025341:Principal Component Analysis; D055815:Young Adult