Testing Research Hypotheses with the General Linear Model

Testing Research Hypotheses with the General Linear Model

by Keith McNeil, Francis J. Kelly, Isadore Newman, Isadore Newman
     
 

ISBN-10: 0809320207

ISBN-13: 9780809320202

Pub. Date: 01/28/1996

Publisher: Southern Illinois University Press

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

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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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Product Details

ISBN-13:
9780809320202
Publisher:
Southern Illinois University Press
Publication date:
01/28/1996
Edition description:
1st Edition
Pages:
392
Product dimensions:
7.00(w) x 10.00(h) x 1.13(d)

Table of Contents

Preface
1Introduction to the General Linear Model1
2Hypothesis Testing17
3Vectors and Vector Operations32
4Research Hypotheses That Employ Dichotomous Predictor Variables48
5Research Hypotheses That Employ Continuous Predictor Variables79
6Multiple Continuous Predictors98
7Interaction116
8Statistical Control of Possible Confounding Variables149
9Nonlinear Relationships174
10Detection of Change216
11Miscellaneous Questions about Research That Regression Helps Answer252
12Application to Evaluation268
13The Strategy of Research as Viewed from the GLM Approach287
App. AData Set of Sixty Subjects313
App. BSAS Discussion315
App. CActivities with Circles and Squares318
App. DSAS Statements for the Various Applied Research Hypotheses324
App. EANOVA Source Tables338
App. FEquivalency of F-test Formulae340
App. GPower Tables342
App. HPolicy-Capturing Activity348
App. IMicrocomputer Setups for Selected Applied Research Hypotheses354
App. JIndex for SAS361
References363
Index370

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