Interpreting Hierarchical Linear and Hierarchical Generalized Linear Models With Slopes as Outcomes

Current descriptions of results from hierarchical linear models (HLM) and hierarchical generalized linear models (HGLM), usually based only on interpretations of individual model parameters, are incomplete in the presence of statistically significant and practically important "slopes as outcomes" terms in the models. For complete description of the resulting interactive models, detailed simple effect descriptions analogous to those long used in factorial ANOVA-based research are needed. The required procedure is described and illustrated for 2- and 3-level HLM and HGLM relationships.