Data Analysis also describes how the model comparison approach and uniform framework can be applied to models that include product predictors (i.e., interactions and nonlinear effects) and to observations that are nonindependent. Indeed, the analysis of nonindependent observations is treated in some detail, including models of nonindependent data with continuously varying predictors as well as standard repeated measures analysis of variance. This approach also provides an integrated introduction to multilevel or hierarchical linear models and logistic regression. Finally, provides guidance for the treatment of outliers and other problematic aspects of data analysis. It is intended for advanced undergraduate and graduate level courses in data analysis and offers an integrated approach that is very accessible and easy to teach. Highlights of the third edition include: