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A strategy for selecting among alternative models for continuous longitudinal data
Author(s) -
Knafl George J.,
Beeber Linda,
Schwartz Todd A.
Publication year - 2012
Publication title -
research in nursing and health
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.836
H-Index - 85
eISSN - 1098-240X
pISSN - 0160-6891
DOI - 10.1002/nur.21508
Subject(s) - covariance , longitudinal data , mixed model , selection (genetic algorithm) , variety (cybernetics) , econometrics , data set , set (abstract data type) , computer science , model selection , linear model , generalized linear mixed model , fixed effects model , statistics , mathematics , machine learning , panel data , data mining , programming language
Abstract Linear mixed models (LMMs) can be used to analyze continuous longitudinal response variables of research studies. Specific aims are then addressed through tests of fixed effects comparing means. However, generated fixed effects results can vary according to the choice of the covariance structure, and so strategies for selecting a model should be utilized first to identify an appropriate covariance structure. We describe alternative LMMs for analyzing continuous longitudinal data and discuss a strategy for using model selection criteria to choose among those models. This is accomplished through the analysis of an exemplar data set considering a wide variety of alternative models for the means, variances, and correlations. © 2012 Wiley Periodicals, Inc. Res Nurs Health 35:647–658, 2012