Applied MANOVA and Discriminant Analysis

Name: Applied MANOVA and Discriminant Analysis
Price: 139.64 USD
Availability: InStock
ISBN: 9780471468158

ISBN-10: 0471468150

ISBN-13: 9780471468158

Edition: 2nd 2006 (Revised)

Authors: Carl J. Huberty, Stephen Olejnik

List price: $181.95

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Description:

In its new Second Edition, this classic introduction to discriminant analysis is now completely revised, with new and updated topics, computer applications, and references, as well as more real-life research examples. The new edition retains the effective structure of the previous edition, with three parts on prediction, description, and issues and problems and a final section of appendices and answers to exercises.

Book details

List price: $181.95
Edition: 2nd
Copyright year: 2006
Publisher: John Wiley & Sons, Incorporated
Publication date: 5/5/2006
Binding: Hardcover
Pages: 528
Size: 6.26" wide x 9.55" long x 1.19" tall
Weight: 1.892
Language: English

CARL J. HUBERTY, PhD, is Professor Emeritus in the Department of Educational Psychology and Instructional Technology at The University of Georgia. He received his PhD in statistical methods from The University of Iowa and has written chapters in many books throughout his career.STEPHEN OLEJNIK, PhD, is Professor in the Department of Educational Psychology and Instructional Technology at The University of Georgia. He received his PhD in educational psychology, applied statistics, and research design from Michigan State University.



List of Figures


List of Tables


Preface to Second Edition


Acknowledgments


Preface to First Edition


Notation



Introduction



Discriminant Analysis in Research



A Little History



Overview



Descriptive Discriminant Analysis



Predictive Discriminant Analysis



Design in Discriminant Analysis



Preliminaries



Introduction



Research Context



Data, Analysis Units, Variables, and Constructs



Summarizing Data



Matrix Operations



Distance



Linear Composite



Probability



Statistical Testing



Judgment in Data Analysis



Summary



One-Factor Manova Dda






Introduction



Two-Group Analyses



Test for Covariance Matrix Equality



Yao Test



Multiple-Group Analyses_Single Factor



Computer Application



Summary



Assessing MANOVA Effects



Introduction



Strength of Association



Computer Application I



Group Contrasts



Computer Application II



Covariance Matrix Heterogeneity



Sample Size



Summary



Describing MANOVA Effects



Introduction



Omnibus Effects



Computer Application I



Standardized LDFWeights



LDF Space Dimension



Computer Application II



Computer Application III



Contrast Effects



Computer Application IV



Summary



Deleting and Ordering Variables



Introduction



Variable Deletion



Variable Ordering



Contrast Analyses



Computer Application II



Comments



Reporting DDA Results



Introduction



Example of Reporting DDA Results



Computer Package Information



Reporting Terms



MANOVA/DDA Applications



Concerns



Overview



Factorial Manova, Mancova, And Repeated Measures



Factorial MANOVA



Introduction



Research Context



Univariate Analysis



Multivariate Analysis



Computer Application I



Computer Application II



Nonorthogonal Design



Outcome Variable Ordering and Deletion



Summary



Analysis of Covariance



Introduction



Research Context



Univariate ANCOVA



Multivariate ANCOVA (MANCOVA)



Computer Application I



Comparing Adjusted Means_Omnibus Test



Computer Application II



Contrast Analysis



Computer Application III



Summary



Repeated-Measures Analysis



Introduction



Research Context



Univariate Analyses



Multivariate Analysis



Computer Application I



Univariate and Multivariate Analyses



Testing for Sphericity



Computer Application II



Contrast Analysis



Computer Application III



Summary



Mixed-Model Analysis



Introduction



Research Context



Univariate Analysis



Multivariate Analysis



Computer Application I



Contrast Analysis



Computer Application II



Summary



Group Membership Prediction



Classification Basics



Introduction



Notion of Distance



Distance and Classification



Classification Rules in General



Comments



Multivariate Normal Rules



Introduction



Normal Density Functions



Classification Rules Based on Normality



Classification Functions



Summary of Classification Statistics



Choice of Rule Form



Comments



Classification Results



Introduction



Research Context



Computer Application



Individual Unit Results



Group Results



Comments



Hit Rate Estimation



Introduction



True Hit Rates



Hit Rate Estimators



Computer Application



Choice of Hit Rate Estimator



Outliers and In-Doubt Units



Sample Size



Comments



Effectiveness of Classification Rules



Introduction



Proportional Chance Criterion



Maximum-Chance Criterion



Improvement over Chance



Comparison of Rules



Computer Application I



Effect of Unequal Priors



PDA Validity Reliability



Applying a Classification Rule to New Units



Comments



Deleting and Ordering Predictors



Introduction



Predictor Deletion



Computer Application



Predictor Ordering



Reanalysis



Comments



Side Note



Two-Group C