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Principal Components Analysis is a multivariate statistical technique for the purpose of examining the interdependence among variables. The main characteristic of this technique is the ability to reduce data, and it is currently used as an adjunct for the development of psychiatric research tools and the classification of psychiatric disorders. It has been applied to the study of the Factorial Structure of a Brazilian screening questionnaire, the Adult Psychiatric Morbidity Questionnaire (QMPA). The questionnaire is made up of 45 yes/no items for the identification of psychiatric symptoms and the use of psychiatric services and psychotropic drugs. The questionnaire was applied to 6.470 subjects over 15 years old in representative samples from three urban areas: Brasília, Porto Alegre and S. Paulo. Seven factors were found to explain 42.7% of the total variance: Anxiety/Somatization (eigenvalue = 3.81, 10.9%); Irritability/Depression (eigenvalue = 2.41, 6.9%); Cognitive Impairment (eigenvalue = 2.01, 5.8%); Alcoholism (eigenvalue = 1.90, 5.4%); Mood Elation (eigenvalue = 1.62, 4.6%); Hallucinatory/Delusional Disorders (eigenvalue = 1.60, 4.6%); and Drug/Therapies (eigenvalue = 1.60, 4.5%). A similar pattern of results was found when the analysis was carried out in the three places. It is suggested, on the banis of these findings, that some questions should be modified and some excluded in any future version of the questionnaire.

Estrutura fatorial do questionário de morbidade psiquiátrica de adultos aplicado em amostras populacionais de cidades brasileiras