Applied MANOVA and Discriminant Analysis

ISBN-10: 0471468150

ISBN-13: 9780471468158

Edition: 2nd 2006 (Revised)

List price: $162.00
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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.
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Book details

List price: $162.00
Edition: 2nd
Copyright year: 2006
Publisher: John Wiley & Sons, Incorporated
Publication date: 5/5/2006
Binding: Hardcover
Pages: 488
Size: 6.25" wide x 9.50" long x 1.00" 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
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