Another perspective on autocorrelation in intensive longitudinal data: Polynomial (straight line, quadratic…) ‘vs’ autoregressive models
Impacto
Scholar |
Otros documentos de la autoría: Rosel, Jesús F.; Puchol, Sara; Flor Arasil, Patricia; Machancoses, Francisco H.; Canales, Juan J.
Metadatos
Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/8034
comunitat-uji-handle3:10234/180681
comunitat-uji-handle4:
INVESTIGACIONMetadatos
Título
Another perspective on autocorrelation in intensive longitudinal data: Polynomial (straight line, quadratic…) ‘vs’ autoregressive modelsAutoría
Fecha de publicación
2023Tipo de documento
DatasetVersión
info:eu-repo/semantics/submittedVersionPalabras clave / Materias
Resumen
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. [-]
Descripción
Datos de investigación y preprint
Derechos de acceso
info:eu-repo/semantics/openAccess