Testing Research Hypotheses with the General Linear Model

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Overview

Because the technique of multiple linear regression has been accepted by the research community since 1975, Keith McNeil, Isadore Newman, and Francis J. Kelly devote little space to defending the equivalence of correlational and ANOVA procedures with multiple linear regression. Instead, they show how the multiple linear regression technique frees the researcher from wondering if an analysis can be done and refocuses him or her back to the central concern: the research question itself.

The first three sections of chapter 1 provide a conceptual, research, and statistical orientation to the entire text. The remainder of chapter 1 furnishes the rationale for the utility of a conceptual model of behavior, along with one such model that can be used to identify predictor variables. The authors strongly suggest that readers familiar with the general linear model read these three sections before delving into the more advanced material. Readers who are relatively unfamiliar with the general linear model should read the first eight chapters before branching off into topics that are of immediate interest.

Examples are provided throughout the text, all using the same data in the same widely available statistical analysis package. Although the technique can be taught with matrix algebra, the authors use the simpler approach of vector algebra, an approach more in line with the way data are conceptualized and entered into the computer.

All of the correlational statistical techniques are shown to be subsets of the general linear model. Of more importance, however, researchers are encouraged to think beyond these limitations and to ask the research questions they are interested in. Thus, the common researcher is freed from the shackles of the "right" statistical procedure and its associated "right" computer analysis.

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Editorial Reviews

Booknews
Briefly describes 777 serial bibliographies relating to modern literature in most of the major languages. Chapters cover comprehensive bibliographies, those for English and foreign literatures, for topics from African American studies to women's studies, and for particular authors. The 1982 edition has been updated and expanded to include information on electronic serial bibliographies. Paper edition (unseen), $19.75. Annotation c. Book News, Inc., Portland, OR (booknews.com)
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Product Details

  • ISBN-13: 9780809320196
  • Publisher: Southern Illinois University Press
  • Publication date: 9/17/1996
  • Edition description: 1st Edition
  • Pages: 392
  • Product dimensions: 7.00 (w) x 10.00 (h) x 1.40 (d)

Meet the Author

Keith McNeil is an associate professor at New Mexico State University specializing in applied statistics.

Isadore Newman is a professor at the University of Akron specializing in statistics and measurement.

Francis J. Kelly is a professor of educational psychology and special education at Southern Illinois University at Carbondale.

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Table of Contents

Preface
1 Introduction to the General Linear Model 1
2 Hypothesis Testing 17
3 Vectors and Vector Operations 32
4 Research Hypotheses That Employ Dichotomous Predictor Variables 48
5 Research Hypotheses That Employ Continuous Predictor Variables 79
6 Multiple Continuous Predictors 98
7 Interaction 116
8 Statistical Control of Possible Confounding Variables 149
9 Nonlinear Relationships 174
10 Detection of Change 216
11 Miscellaneous Questions about Research That Regression Helps Answer 252
12 Application to Evaluation 268
13 The Strategy of Research as Viewed from the GLM Approach 287
App. A Data Set of Sixty Subjects 313
App. B SAS Discussion 315
App. C Activities with Circles and Squares 318
App. D SAS Statements for the Various Applied Research Hypotheses 324
App. E ANOVA Source Tables 338
App. F Equivalency of F-test Formulae 340
App. G Power Tables 342
App. H Policy-Capturing Activity 348
App. I Microcomputer Setups for Selected Applied Research Hypotheses 354
App. J Index for SAS 361
References 363
Index 370
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