The penalized quasi-likelihood (PQL) approach is the most common
estimation procedure for the generalized linear mixed effects model
(GLMM). However, it has been noticed that the PQL tends to
underestimate the variance components as well as the regression
coefficients in the previous literature. In this paper, we numerically
show that the bias in variance components is systematically related to
the bias in the regression coefficient estimates, and also show that
the bias in the variance components estimates of the PQL increase as
the random effects becomes more heterogeneous.