A comprehensive model framework for between-individual differences in longitudinal data

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Across different fields of research the similarities and differences between various longitudinal models are not always eminently clear due to differences in data structure, application area, and terminology. Here we propose a comprehensive model framework that will allow simple comparisons between longitudinal models, to ease their empirical application and interpretation. At the within-individual level our model framework accounts for various attributes of longitudinal data, such as growth and decline, cyclical trends, and the dynamic interplay between variables over time. At the between-individual level our framework contains continuous and categorical latent variables to account for between-individual differences. This framework encompasses several well-known longitudinal models, including multilevel regression models, growth
curve models, growth mixture models, vector-autoregressive models, and multilevel vector-autoregressive models. The general model framework is specified and its key characteristics are illustrated using famous longitudinal models as concrete examples. Various longitudinal models are reviewed and it is shown that all these models can be united into our comprehensive model framework. Extensions to the model framework are discussed.
Recommendations for selecting and specifying longitudinal models are made for
empirical researchers who aim to account for between-individual differences.
Original languageEnglish
JournalPsychological Methods
Early online date2023
Publication statusE-pub ahead of print - 2023


  • intensive longitudinal data
  • interindividual differences
  • intraindividual differences
  • mixture models
  • multilevel mixture models
  • random effects

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