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Longitudinal Models for Studying Multivariate Changes and Dynamics
Author(s) -
Emilio Ferrer,
Joseph E. Gonzales
Publication year - 2014
Publication title -
annals of nutrition and metabolism
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.926
H-Index - 81
eISSN - 1421-9697
pISSN - 0250-6807
DOI - 10.1159/000365581
Subject(s) - multivariate statistics , structural equation modeling , longitudinal data , dynamics (music) , computer science , multivariate analysis , system dynamics , latent variable , longitudinal study , econometrics , data mining , statistics , machine learning , artificial intelligence , mathematics , psychology , pedagogy
In this paper, we describe a longitudinal modeling approach for examining multivariate changes and dynamics. This technique is based on latent change scores and is executed using a structural equation modeling framework. We provide an overview of the model, describing desirable features for identifying dynamics among multiple processes. We then illustrate its application using empirical data consisting of longitudinal processes and conclude the paper with some potential steps for advancing the modeling possibilities.

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