Another perspective on autocorrelation in intensive longitudinal data: Polynomial (straight line, quadratic…) ‘vs’ autoregressive models
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 tru...
| Autores: | , , , , |
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| Tipo de recurso: | conjunto de datos |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Universitat Jaume I (UJI) |
| Repositorio: | Repositori Universitat Jaume I |
| Idioma: | inglés |
| OAI Identifier: | oai:repositori.uji.es:10234/204504 |
| Acceso en línea: | http://hdl.handle.net/10234/204504 |
| Access Level: | acceso abierto |
| Palabra clave: | longitudinal data intensive longitudinal designs pooled time series panel data autocorrelation in residuals |
| Sumario: | 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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