Designing general linear models to test research hypotheses (Q2904941)

The book is focused on designing multiple linear regression models to test research hypotheses. Hypotheses are considered that deal with the differences among group means, relationships between covariates, analysis of covariance, interaction effects, nonlinear relationships, and repeated measures. Most of the chapters contain applied hypothesis sections aimed to illustrate how analyses are conducted with Microsoft Excel and SPSS for Windows and how the outputs should be interpreted to test the hypotheses. The authors persistently stress the importance of designing regression models that precisely reflect the null and research hypotheses. The text relies on using vectors instead of matrices. Few formulae are presented; instead the authors focus on conceptual understanding. The book can be quite useful for graduate students and researchers in applied fields.NEWLINENEWLINEContents: 1. Introduction to the General Linear Model (GLM). 2. Hypothesis testing. 3. Vectors and vector operations. 4. Research hypotheses employing dichotomous predictor variables. 5. Research hypotheses employing continuous predictor variables. 6. Multiple continuous predictor variables. 7. Interaction. 8. Statistical control of possible confounding variables. 9. Nonlinear relationships. 10. Detection of change. 11. Dichotomous criterion variable. 12. The strategy of research as viewed from the GLM approach.