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

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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.

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cc-by-nc-nd (c) Universidad de Murcia, 2023

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Articles publicats en revistes (Psicologia Social i Psicologia Quantitativa)

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

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Robustez de los Modelos Lineales Mixtos Generalizados para Diseños Split-Plot con Datos Binarios