Citation

Alcázar-Córcoles MA, Verdejo-García A, Bouso-Sáiz JC, Revuelta-Menéndez J, Ramírez-Lira E. Actas Esp. Psiquiatr. 2017; 45(3): 89-97.

Affiliation

Departamento de cultura, arte y desarrollo humano, Centro Universitario del Sur (CUSUR), Universidad de Guadalajara. Jalisco, México.

Copyright

(Copyright © 2017, STM Editores)

DOI

unavailable

PMID

28594054

Abstract

INTRODUCTION: There is a renewed interest in incorporating personality variables in criminology theories in order to build models able to integrate personality variables and biological factors with psychosocial and sociocultural factors. The aim of this article is the assessment of personality dimensions that contribute to the prediction of antisocial behavior in adolescents.

METHODS: For this purpose, a sample of adolescents from El Salvador, Mexico, and Spain was obtained. The sample consisted of 1035 participants with a mean age of 16.2. There were 450 adolescents from a forensic population (those who committed a crime) and 585 adolescents from the normal population (no crime committed). All of participants answered personality tests about neuroticism, extraversion, psychoticism, sensation seeking, impulsivity, and violence risk.

RESULTS: Principal component analysis of the data identified two independent factors: (i) the disinhibited behavior pattern (PDC), formed by the dimensions of neuroticism, psychoticism, impulsivity and risk of violence; and (ii) the extrovert behavior pattern (PEC), formed by the dimensions of sensation risk and extraversion. Both patterns significantly contributed to the prediction of adolescent antisocial behavior in a logistic regression model which properly classifies a global percentage of 81.9%, 86.8% for non-offense and 72.5% for offense behavior.

CONCLUSIONS: The classification power of regression equations allows making very satisfactory predictions about adolescent offense commission. Educational level has been classified as a protective factor, while age and gender (male) have been classified as risk factors.

Language: en