Designing General Linear Models to Test Research Hypotheses

Designing General Linear Models to Test Research Hypotheses

by Keith McNeil, Isadore Newman, John W. Fraas
     
 

The authors 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. Illustrations of the various analyses using Microsoft Excel and SPSS for

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Overview

The authors 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. Illustrations of the various analyses using Microsoft Excel and SPSS for Windows are presented.

Editorial Reviews

Zentralblatt MATH
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.

Product Details

ISBN-13:
9780761857686
Publisher:
University Press of America
Publication date:
12/14/2011
Pages:
494
Product dimensions:
5.80(w) x 9.00(h) x 1.50(d)
Age Range:
18 Years

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