Generalized linear mixed models in hearing science

Abstract

Linear mixed-effects models (LMEs), by virtue of allowing for the quantification of both random effects (e.g., associated with sampling of subjects) and the effects of fixed predictor variables, provide a powerful statistical modeling framework that can accommodate a large number of common experimental designs used in hearing science. Examples of designs for which LMEs are suitable include repeated-measures designs, longitudinal studies, and multilevel designs. An extensive literature of robust methods exists for fitting such models for normally distributed responses. On the other hand, often, data in hearing science experiments are binary (e.g., detected or missed), proportions, or counts (number of correct responses) and are better modeled using non-normal distributions (e.g., binomial). Generalized linear mixed models (GLMMs) provide a framework to retain the advantages of LMEs in modeling random effects by modeling the non-normal response variables using LMEs through (nonlinear) link functions. In thi...