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.
Assuntos
Gráficos de Crescimento , Modelos Lineares , Estudos Longitudinais , Projetos de Pesquisa/estatística & dados numéricos , Análise de Variância , Argentina , Alimentação com Mamadeira , Aleitamento Materno , Criança , Pré-Escolar , Estudos de Coortes , Feminino , Humanos , Lactente , MasculinoRESUMO
El estadistico C de Young (1941) constituye una alternativa de análisis apropiada para evaluar cuantitativamente la presencia de cambios debidos a intervenciones, en series temporales cortas (propias de diseños conductuales). En este estudio se analizan datos de diseños AB simulados, con distintos patrones de cambio. Los resultados obtenidos permiten concluir que el estadistico C detecta tendencias y cambios de tendencia entre fases, con lo que es posible inferir el efecto de tratamiento.