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• W2024331834 endingPage "566" @default.
• W2024331834 startingPage "547" @default.
• W2024331834 abstract "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." @default.
• W2024331834 created "2016-06-24" @default.
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• W2024331834 creator A5063623447 @default.
• W2024331834 date "2010-04-01" @default.
• W2024331834 modified "2023-09-26" @default.
• W2024331834 title "General Linear Mixed Model for Analysing Longitudinal Data in Developmental Research" @default.
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• W2024331834 doi "https://doi.org/10.2466/pms.110.2.547-566" @default.
• W2024331834 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/20499565" @default.
• W2024331834 hasPublicationYear "2010" @default.
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