This talk proposes and investigates an alternate approach to pseudo-likelihood model selection in the generalized linear mixed modeling framework. The problem with the natural approach to the computation of pseudo-likelihood model selection criteria is that the pseudo-data vary for each candidate model, leading to criteria based on fundamentally different goodness-of-fit statistics, rendering them incomparable. An alternative technique will be introduced that circumvents this problem. This new approach can be implemented using a SAS macro that obtains and applies the pseudo-data from the full model to fitting candidate models based on all possible subsets of predictor variables. A justification of the propriety of the resulting pseudo-likelihood selection criteria will be provided through an extensive study designed as a factorial experiment. The new method is then illustrated in a modeling application pertaining to bullying in public schools. The data set for the application is taken from three waves of the Iowa Youth Survey.