Exploratory and confirmatory factorial structure of the MCMI-III personality disorders: overlapping versus non-overlapping scales
Background and Objectives: The aim of this study was to explore the factorial structure of the 14 Personality Disorder (PD's) scales of the MCMI-III for the overlapping and non-overlapping scales, independently. Previous exploratory studies using different factor extraction procedures inform th...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2008 |
| País: | España |
| Institución: | Universidad Complutense de Madrid (UCM) |
| Repositorio: | Docta Complutense |
| Idioma: | inglés |
| OAI Identifier: | oai:docta.ucm.es:20.500.14352/131892 |
| Acceso en línea: | https://hdl.handle.net/20.500.14352/131892 |
| Access Level: | acceso abierto |
| Palabra clave: | MCMI-III Exploratory and confirmatory factorial analysis Personality disorders Personalidad Psicometría 61 Psicología |
| Sumario: | Background and Objectives: The aim of this study was to explore the factorial structure of the 14 Personality Disorder (PD's) scales of the MCMI-III for the overlapping and non-overlapping scales, independently. Previous exploratory studies using different factor extraction procedures inform that the structure of MCMI-III personality disorders has between 2 and 4 factors. Methods: The present study used a large sample of 674 non-clinical subjects divided at random in two groups: a) calibration, and b) validation. In the calibration group, principal component analysis with orthogonal rotation was carried out, obtaining 2, 3 and 4 factors for the overlapping and non-overlapping scales independently. In the validation group, the three models were compared using confirmatory factorial analysis techniques. Results and Conclusions: The exploratory and confirmatory results indicate that the 4-factor solution is the most plausible. Although the congruence coefficients between non-overlapping and overlapping scales in the 4-factor solution were higher, confirmatory factor analysis showed that models designed from overlapping scales did not fit well to data. |
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