RESUMO
Self-concept is widely conceptualized as multidimensional (Shavelson et al., 1976). The Five-Factor Self-Concept Questionnaire (AF5, García and Musitu, 2009) assesses five specific dimensions (i.e., academic, social, emotional, family, and physical). It is a psychometrically sound questionnaire, developed, and normed in Spain, which is widely used with Spanish-speaking samples. The validation of the AF5 in Brazil would expand its potential, and would facilitate cross-cultural research. To validate the Brazilian version of the AF5, the present study apply confirmatory factor analysis and multi-sample invariance analysis across sex (women vs. men), age (11-18 years old), and language (Brazilian [Portuguese] vs. Spanish). The sample consisted of 4,534 students (54.6%, women, 53.7%, Spanish) ranging in age from 11 to 18 years old (M = 14.61, SD = 2.09). The findings of the present study confirmed that the five-dimensional AF5 factorial structure provided the better fit to the data compared to alternative one-dimensional and orthogonal five-dimensional structures. The 30 items loaded appropriately on the five dimensions. Multi-group analysis for invariance between sex, age, and language groups showed equal loading in the five factors, equal covariation between the five dimensions, and equal error variances of items. Additionally, in order to obtain an external validity index, the five AF5 factors were related to both acceptance/involvement and strictness/imposition parenting dimensions. These results provide an adequate basis for meaningful comparative studies on a highly relevant construct, multidimensional self-concept, between male and female adolescents of different ages, and Brazilian (Portuguese) and Spanish-speaking samples. These results validate the instrument and confirm its suitability in cross-cultural research.
RESUMO
Many areas of psychological, social, and health research are characterised by hierarchically structured data. Growth curves are usually represented by means of a two-level hierarchical structure in which observations are the first-level units nested within subjects, the second-level units. With data such as these, the best option for analysis is the general linear mixed model, which can be used even with longitudinal data series in which intervals are not constant or for which over the passage of time there is loss of data. In this paper an overview is given of the general linear mixed model approach to the analysis of longitudinal data in developmental research. The advantages of this model in comparison with the traditional approaches for analysing longitudinal data are shown, emphasising the usefulness of modelling the covariance structure properly to achieve a precise estimation of the parameters of the model.