Robustez de los Modelos Lineales Mixtos Generalizados para Diseños Split-Plot con Datos Binarios
Resumen
Este artículo examina la robustez del modelo lineal mixto generalizado (GLMM, por sus siglas en inglés). El GLMM estima efectos fijos y efectos aleatorios y es especialmente útil cuando la variable dependiente es binaria. También es útil cuando la variable dependiente es de medidas repetidas, ya que puede modelar la correlación. El presente estudio utilizó la simulación de Monte Carlo a fin de analizar las tasas de error de Tipo I empíricas de los GLMM en diseños split-plot. Las variables manipuladas fueron el tamaño de muestra, el tamaño de grupo, el número de medidas repetidas y la correlación entre las medidas repetidas. También se consideraron condiciones extremas, tales como muestras pequeñas, grupos no balanceados y diferente correlación en cada grupo (emparejamiento entre tamaño de grupo y correlación entre medidas repetidas). Para grupos balanceados, los resultados mostraron que el efecto grupo era robusto en todas las condiciones, mientras que para grupos no balanceados el efecto tendía a ser conservador con emparejamiento positivo y liberal con emparejamiento negativo. Con respecto a los efectos tiempo e interacción, los resultados mostraron, tanto para grupos balanceados como para no balanceados, que: (a) la prueba fue robusta con baja correlación (.2), pero conservadora para valores medios de correlación (.4 y .6), y (b) la prueba tendía a ser conservadora para emparejamiento positivo y negativo, especialmente en este último.
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