Fr. 230.00

Applied Manova and Discriminant Analysis

English · Hardback

Shipping usually within 1 to 3 weeks (not available at short notice)

Description

Read more

Informationen zum Autor 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. Klappentext A complete introduction to discriminant analysis--extensively revised, expanded, and updatedThis Second Edition of the classic book, Applied Discriminant Analysis, reflects and references current usage with its new title, Applied MANOVA and Discriminant Analysis. Thoroughly updated and revised, this book continues to be essential for any researcher or student needing to learn to speak, read, and write about discriminant analysis as well as develop a philosophy of empirical research and data analysis. Its thorough introduction to the application of discriminant analysis is unparalleled.Offering the most up-to-date computer applications, references, terms, and real-life research examples, the Second Edition also includes new discussions of MANOVA, descriptive discriminant analysis, and predictive discriminant analysis. Newer SAS macros are included, and graphical software with data sets and programs are provided on the book's related Web site.The book features:* Detailed discussions of multivariate analysis of variance and covariance* An increased number of chapter exercises along with selected answers* Analyses of data obtained via a repeated measures design* A new chapter on analyses related to predictive discriminant analysis* Basic SPSS(r) and SAS(r) computer syntax and output integrated throughout the bookApplied MANOVA and Discriminant Analysis enables the reader to become aware of various types of research questions using MANOVA and discriminant analysis; to learn the meaning of this field's concepts and terms; and to be able to design a study that uses discriminant analysis through topics such as one-factor MANOVA/DDA, assessing and describing MANOVA effects, and deleting and ordering variables. Zusammenfassung This is a revision of the classic book Applied Discriminant Analysis by Carl Huberty. Dr. Huberty has taken on a co-author, Steve Olejnik, who helped to update existing material and write new chapters. New terms, updated discussion of topics that have recently become more important, new computer applications, and new references have been included. Inhaltsverzeichnis List of Figures. List of Tables. Preface to Second Edition. Acknowledgments. Preface to First Edition. Notation. I INTRODUCTION. 1 Discriminant Analysis in Research. 1.1 A Little History. 1.2 Overview. 1.3 Descriptive Discriminant Analysis. 1.4 Predictive Discriminant Analysis. 1.5 Design in Discriminant Analysis. 2 Preliminaries. 2.1 Introduction. 2.2 Research Context. 2.3 Data, Analysis Units, Variables, and Constructs. 2.4 Summarizing Data. 2.5 Matrix Operations. 2.6 Distance. 2.7 Linear Composite. 2.8 Probability. 2.9 Statistical Testing. 2.10 Judgment in Data Analysis. 2.11 Summary. II ONE-FACTOR MANOVA / DDA. 3 Group Separation. 3.1 Introduction. 3.2 Two-Group Analyses. 3.3 Test for Covariance Matrix Equality. 3.4 Yao Test. 3.5 Multiple-Group Analyses-Single Factor. 3.6 Computer Application. 3.7 Summary. 4 Assessing MANOVA Effects. 4.1 Introduction. 4.2 Strength of A...

Customer reviews

No reviews have been written for this item yet. Write the first review and be helpful to other users when they decide on a purchase.

Write a review

Thumbs up or thumbs down? Write your own review.

For messages to CeDe.ch please use the contact form.

The input fields marked * are obligatory

By submitting this form you agree to our data privacy statement.