Using Multivariate Statistics

Tabachnick, Barbara G.; Fidell, Linda S.

ISBN 10: 0205459382 / ISBN 13: 9780205459384

Published by Pearson College Div, 2006

About this Item

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Bibliographic Details

• Title: Using Multivariate Statistics
• Publisher: Pearson College Div
• Publication Date: 2006
• Language: English
• ISBN 10: 0205459382
• ISBN 13: 9780205459384
• Binding: Hardcover
• Edition: 5th or later Edition
• Condition: Good

About this title

Synopsis

Using Multivariate Statistics provides practical guidelines for conducting numerous types of multivariate statistical analyses. It gives syntax and output for accomplishing many analyses through the most recent releases of SAS, SPSS, and SYSTAT, some not available in software manuals. The book maintains its practical approach, still focusing on the benefits and limitations of applications of a technique to a data set — when, why, and how to do it. Overall, it provides advanced students with a timely and comprehensive introduction to today's most commonly encountered statistical and multivariate techniques, while assuming only a limited knowledge of higher-level mathematics.

From the Back Cover

Using Multivariate Statistics provides advanced students with a timely and comprehensive introduction to today s most commonly encountered statistical and multivariate techniques, while assuming only a limited knowledge of higher level mathematics.

This long-awaited revision reflects extensive updates throughout, especially in the areas of Data Screening (Chapter 4), Multiple Regression (Chapter 5), and Logistic Regression (Chapter 12). A brand new chapter (Chapter 15) on Multilevel Linear Modeling explains techniques for dealing with hierarchical data sets. Also included are syntax and output for accomplishing many analyses through the most recent releases of SAS and SPSS.

As in past editions, each technique chapter:

discusses tests for assumptions of analysis (and procedures for dealing with their violation)
presents a small example, hand-worked for the most basic analysis
describes varieties of analysis
discusses important issues (such as effect size)
provides an example with a real data set from tests of assumptions to write-up of a results section
compares features of relevant programs