Designing General Linear Models to Test Research Hypotheses

The focus of this text is placed on designing General Linear Models (regression models) to test research hypotheses. The authors illustrate and discuss General Linear Models specifically designed to statistically test research hypotheses that deal with the differences among group means, relationships between continuous variables, analysis of covariance, interaction effects, nonlinear relationships, and repeated measures. Many of the chapters contain sections entitled General Hypothesis and Applied Hypothesis. The General Hypothesis sections are designed to provide the readers with road maps regarding how to conduct the various analyses presented in the text. The Applied Hypothesis sections illustrate how the various analyses are conducted with Microsoft Excel and SPSS for Windows and how the outputs should be interpreted to test the hypotheses. Throughout the text, the authors stress the importance of designing regression models that precisely reflect the null and research hypotheses.