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The Hitchhiker's guide to longitudinal models: A primer on model selection for repeated-measures methods.
McCormick, Ethan M; Byrne, Michelle L; Flournoy, John C; Mills, Kathryn L; Pfeifer, Jennifer H.
Afiliación
  • McCormick EM; Methodology & Statistics Department, Institute of Psychology, Leiden University, Leiden, Netherlands; Department of Psychology and Neuroscience, University of North Carolina, Chapel Hill, United States; Cognitive Neuroscience Department, Donders Institute for Brain, Cognition and Behavior, Radbo
  • Byrne ML; Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, Australia; Department of Psychology, University of Oregon, Eugene, United States.
  • Flournoy JC; Department of Psychology, Harvard University, Cambridge, United States.
  • Mills KL; Department of Psychology, University of Oregon, Eugene, United States.
  • Pfeifer JH; Department of Psychology, University of Oregon, Eugene, United States.
Dev Cogn Neurosci ; 63: 101281, 2023 Oct.
Article en En | MEDLINE | ID: mdl-37536082
Longitudinal data are becoming increasingly available in developmental neuroimaging. To maximize the promise of this wealth of information on how biology, behavior, and cognition change over time, there is a need to incorporate broad and rigorous training in longitudinal methods into the repertoire of developmental neuroscientists. Fortunately, these models have an incredibly rich tradition in the broader developmental sciences that we can draw from. Here, we provide a primer on longitudinal models, written in a beginner-friendly (and slightly irreverent) manner, with a particular focus on selecting among different modeling frameworks (e.g., multilevel versus latent curve models) to build the theoretical model of development a researcher wishes to test. Our aims are three-fold: (1) lay out a heuristic framework for longitudinal model selection, (2) build a repository of references that ground each model in its tradition of methodological development and practical implementation with a focus on connecting researchers to resources outside traditional neuroimaging journals, and (3) provide practical resources in the form of a codebook companion demonstrating how to fit these models. These resources together aim to enhance training for the next generation of developmental neuroscientists by providing a solid foundation for future forays into advanced modeling applications.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Dev Cogn Neurosci Año: 2023 Tipo del documento: Article Pais de publicación: Países Bajos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Dev Cogn Neurosci Año: 2023 Tipo del documento: Article Pais de publicación: Países Bajos