Validation of a Questionnaire for Personality Profiling using Cognitive Diagnostic Modeling

被引:6
作者
Revuelta, Javier [1 ]
Halty, Lucia [2 ]
Ximenez, Carmen [1 ]
机构
[1] Univ Autonoma Madrid, Madrid, Spain
[2] Univ Pontificia Comillas, Madrid, Spain
关键词
Cognitive Diagnostic Models; factor analysis; forensic profiling; personality profile; structural equation models; CONFIRMATORY FACTOR-ANALYSIS; BEHAVIORAL ACTIVATION; FEARFUL EXPRESSIONS; DINA MODEL; ANGER; VALIDITY; BIS; PSYCHOPATHY; RELIABILITY; INHIBITION;
D O I
10.1017/sjp.2018.62
中图分类号
B84 [心理学];
学科分类号
04 ; 0402 ;
摘要
This article describes the development of the ENCUIST (Extroversion, Neuroticism, Callous-Unemotional, Instability, Short-Test) questionnaire, which has been created to provide a personality profiling method based on a cognitive diagnostic modeling framework. The ENCUIST measures the attributes of extroversion, neuroticism, callous unemotionality and overt expressions of anger that are relevant in a forensic context. The scores provided by the ENCUIST are binary classifications of the individuals (high/low) in these attributes. The ENCUIST was developed using a sample of 516 subjects to study its validation through psychometric procedures, including factor analysis, cognitive diagnostic modeling and structural equation modeling. The results supported a four-factor structure. Linear regressions were used to evaluate the predictive validity of the scores provided by ENCUIST with respect to two external criteria that are relevant in the forensic context, namely behavioral activation and behavioral inhibition. The results showed that the extroversion dimension is positively related to behavioral activation, although the effect size is modest and the proportion of explained variance is only 11%. Moreover, the dimensions of neuroticism and anger expression are positively related to behavioral inhibition, with 7% of the variance explained. Together, these results suggest that cognitive diagnostic models are useful tools for the elaboration of personality profiles based on classifying subjects along binary attributes.
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页数:13
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