Robustness of generalized linear mixed models for split-plot designs with binary data

This paper examined the robustness of the generalized linear mixed model (GLMM). The GLMM estimates fixed and random effects, and it is especially useful when the dependent variable is binary. It is also useful when the dependent variable involves repeated measures, since it can model correlation. T...

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Detalles Bibliográficos
Autores: Bono Cabré, Roser, Alarcón Postigo, Rafael, Arnau Gras, Jaume, García-Castro, F. Javier, Blanca Mena, M. José
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2023
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:2445/205661
Acceso en línea:https://hdl.handle.net/2445/205661
Access Level:acceso abierto
Palabra clave:Models lineals (Estadística)
Mètodes de simulació
Linear models (Statistics)
Simulation methods
Descripción
Sumario:This paper examined the robustness of the generalized linear mixed model (GLMM). The GLMM estimates fixed and random effects, and it is especially useful when the dependent variable is binary. It is also useful when the dependent variable involves repeated measures, since it can model correlation. The present study used Monte Carlo simulation to analyze the empirical Type I error rates of GLMMs in split-plot designs. The variables manipulated were sample size, group size, number of repeated measures, and correlation between repeated measures. Extreme conditions were also considered, including small samples, unbalanced groups, and different correlation in each group (pairing between group size and correlation between repeated measures). For balanced groups, the results showed that the group effect was robust under all conditions, while for unbalanced groups the effect tended to be conservative with positive pairing and liberal with negative pairing. Regarding time and interaction effects, the results showed, for both balanced and unbalanced groups, that: (a) The test was robust with low correlation (.2), but conservative for medium values of correlation (.4 and .6), and (b) the test tended to be conservative for positive and negative pairing, especially the latter.