Designing General Linear Models to Test Research Hypotheses

Keith McNeil has taught statistics and research methods since 1967. He has been active in the Special Interest Group of Multiple Linear Regression in the American Educational Research Association since its inception. He has co-authored three other books on GLM, as well as a text on research methods and a text on survey research and writing dissertations with Isadore Newman. McNeil is an emeritus professor at New Mexico State University.Isadore Newman has taught statistics and research methods since 1970. He has been active in the Special Interest Group of Multiple Linear Regression in the American Educational Research Association since its inception, serving as the editor of its journal for twenty years. He has co-authored seventeen books and book chapters related to research methods, three of which were co-authored with Keith McNeil on GLM, survey research, and writing dissertations. He is a distinguished professor emeritus at Akron University and currently serves as visiting scholar at Florida International University.John W. Fraas, the Board of Trustees’ distinguished professor emeritus, taught statistics at Ashland University in the Schools of Business, Economics, and Education for nearly four decades. While at Ashland University, he was awarded numerous teaching awards including the first Teaching Excellence Award given by the Association of Collegiate Business Schools and Programs. He has published over thirty-five journal articles dealing with applied statistics in the fields of business management, economics, and education. In addition, he authored a text entitled Basic Concepts in Educational Research and its corresponding workbook (University Press of America).

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 book can be quite useful for graduate students and researchers in applied fields.

• Chapter 1: Introduction to the General Linear Model Chapter 2: Hypothesis TestingChapter 3: Vectors and Vector OperationsChapter 4: Research Hypotheses Employing Dichotomous Predictor VariablesChapter 5: Research Hypotheses Employing Continuous Predictor VariablesChapter 6: Multiple Continuous Predictor Variables Chapter 7: Interaction Chapter 8: Statistical Control of Possible Confounding VariablesChapter 9: Nonlinear RelationshipsChapter 10: Detection of Change Chapter 11: Dichotomous Criterion Variable Chapter 12: The Strategy of Research as Viewed from the GLM Approach AppendixesReferencesIndexAuthors