Description
Book SynopsisThe 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.
Trade ReviewThe 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 book can be quite useful for graduate students and researchers in applied fields. * Zentralblatt MATH *
Table of ContentsChapter 1: Introduction to the General Linear Model Chapter 2: Hypothesis Testing Chapter 3: Vectors and Vector Operations Chapter 4: Research Hypotheses Employing Dichotomous Predictor Variables Chapter 5: Research Hypotheses Employing Continuous Predictor Variables Chapter 6: Multiple Continuous Predictor Variables Chapter 7: Interaction Chapter 8: Statistical Control of Possible Confounding Variables Chapter 9: Nonlinear Relationships Chapter 10: Detection of Change Chapter 11: Dichotomous Criterion Variable Chapter 12: The Strategy of Research as Viewed from the GLM Approach Appendixes References Index Authors
