Generalized Linear Mixed Models (GLMMs) are one of the most widely used analytical frameworks in ecology and evolution. GLMMs with group-level predictors can improve ecological inferences for group-level coefficients (i.e., intercepts and slopes). However, little is known about appropriate methods for fitting GLMMs with group-level predictors. We evaluated three different fitting approaches: fitting multiple times using Maximum Likelihood Estimation (MLESep), fitting once using Maximum Likelihood Estimation (MLEOne), and fitting once using Bayesian inference (BEOne). Through power analyses on simulated datasets, we compared their performance in normal and binomial GLMMs. We found that while all methods are effective for normal GLMMs with sufficiently large effect sizes, MLESep exhibits greater sensitivity to effect size variations compared to BEOne and MLEOne. However, for binomial GLMMs, MLESep shows limited accuracy in estimating group-level coefficients, suggesting its unsuitability for such models. Our findings provide helpful insights for ecologists and evolutionary biologists in choosing the most suitable GLMM fitting method for their data analysis.