Prior to testing the study hypotheses, we will examine data for possible covariates. Potential confounding variables will be controlled for by entering these variables as covariates in the regression models. These explanatory variables will improve the detection of treatment effects by entering these variables as covariates in the GLMMs. These variables will be entered as a covariate if the univariate two-sided chi-square test comparing the PWP and control groups is significant at the alpha level of 0.20. In preliminary analysis, we will examine potential covariates using single-level however will we also examine potential covariates using hierarchical linear models. We will use a liberal alpha of 0.20 for the decision of whether to adjust for a covariate so that important confounding effects are not overlooked.