Another perspective on autocorrelation in intensive longitudinal data: Polynomial (straight line, quadratic…) ‘vs’ autoregressive models
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Altres documents de l'autoria: Rosel, Jesús F.; Puchol, Sara; Flor Arasil, Patricia; Machancoses, Francisco H.; Canales, Juan J.
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Mostra el registre complet de l'elementcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/8034
comunitat-uji-handle3:10234/180681
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Títol
Another perspective on autocorrelation in intensive longitudinal data: Polynomial (straight line, quadratic…) ‘vs’ autoregressive modelsAutoria
Data de publicació
2023Tipus de document
DatasetVersió
info:eu-repo/semantics/submittedVersionParaules clau / Matèries
Resum
While the theory of longitudinal data analysis (LDA) has a solid foundation, there are instances where the assumptions of the analytical model remain unverified. Failure to examine autocorrelation in residuals (ACR) ... [+]
While the theory of longitudinal data analysis (LDA) has a solid foundation, there are instances where the assumptions of the analytical model remain unverified. Failure to examine autocorrelation in residuals (ACR) can elevate the risk of committing a Type I error, leading to the rejection of a true null hypothesis. This study compares two distinct analytical models within LDA: the polynomial (straight line, quadratic…) model and the autoregressive (AR) model. Three separate studies were conducted to investigate this comparison. [-]
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Datos de investigación y preprint
Drets d'accés
info:eu-repo/semantics/openAccess