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The Hitchhiker's guide to longitudinal models: A primer on model selection for repeated-measures methods
Ethan M McCormick1, Michelle L Byrne2, John C Flournoy3
1Methodology & 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, Radboud University Medical Center, Nijmegen, Netherlands.
Developmental Cognitive Neuroscience
|August 3, 2023
Summary
This primer introduces longitudinal models for developmental neuroimaging researchers. It offers a framework for selecting appropriate statistical models to study changes in biology, behavior, and cognition over time.
Area of Science:
- Developmental neuroscience
- Neuroimaging
- Developmental psychology
Background:
- Longitudinal data are increasingly prevalent in developmental neuroimaging research.
- There is a growing need for robust training in longitudinal methods for developmental neuroscientists.
- Existing longitudinal models from broader developmental sciences offer valuable frameworks.
Purpose of the Study:
- To provide a beginner-friendly primer on longitudinal models for developmental neuroscientists.
- To offer a heuristic framework for selecting appropriate longitudinal modeling techniques.
- To connect researchers with resources for methodological development and practical implementation.
Main Methods:
- The primer focuses on selecting between different longitudinal modeling frameworks, such as multilevel and latent curve models.
- It provides a framework for aligning model selection with theoretical developmental models.
- Practical resources, including a codebook companion, are offered for model fitting.
Main Results:
- A heuristic framework for longitudinal model selection is presented.
- A curated repository of references is provided, linking to methodological traditions and practical applications.
- A codebook companion is available to demonstrate model fitting.
Conclusions:
- This primer aims to enhance training for developmental neuroscientists in longitudinal data analysis.
- It provides a foundation for utilizing advanced modeling techniques in developmental neuroimaging.
- The resources aim to empower researchers to effectively study developmental trajectories.
Keywords:
CovariatesDistal outcomesLongitudinal modelsMixed-effects modelsNonlinear trajectoriesStructural equation modelsMore Related Videos
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