Analyzing Small Samples of Repeated Measures Data with the Mixed-Model AdjustedFTest

Jaime Arnau, Roser Bono, Guillermo Vallejo
2009 Communications in statistics. Simulation and computation  
This research examines the Type I error rates obtained when using the mixed model with the Kenward-Roger correction and compares them with the Between-Within and Satterthwaite approaches in split-plot designs. A simulated study was conducted to generate repeated measures data with small samples under normal distribution. The data were obtained via three covariance matrices (unstructured, heterogeneous first-order auto-regressive and random coefficients), the one with the best fit being selected
more » ... fit being selected according to the Akaike criterion. The results of the simulation study showed the Kenward-Roger test to be more robust, particularly when the population covariance matrices were unstructured or heterogeneous first-order auto-regressive. Abstract This research examines the Type I error rates obtained when using the mixed model with the Kenward-Roger correction and compares them with the Between-Within and Satterthwaite approaches in split-plot designs. A simulated study was conducted to generate repeated measures data with small samples under normal distribution conditions. The data were obtained via three covariance matrices (unstructured, heterogeneous first-order auto-regressive and random coefficients), the one with the best fit being selected according to the Akaike criterion. The results of the simulation study showed the Kenward-Roger test to be more robust, particularly when the population covariance matrices were unstructured or heterogeneous first-order autoregressive. The main contribution of this study lies in showing that the Kenward-Roger method corrects the liberal Type I error rates obtained with the Between-Within and Satterthwaite approaches, especially with positive pairings between group sizes and covariance matrices.
doi:10.1080/03610910902785746 fatcat:64osag5glvgclato2kbt6kvwaq