Using Multivariate Statistics

Fidell, Linda S., Tabachnick, Barbara G.

ISBN 10: 0321056779 / ISBN 13: 9780321056771

Published by Allyn & Bacon, Incorporated, 2000

About this Item

Ships from the UK. Former library book; may include library markings. Used book that is in excellent condition. May show signs of wear or have minor defects.

Seller Inventory # 7522122-6

Bibliographic Details

• Title: Using Multivariate Statistics
• Publisher: Allyn & Bacon, Incorporated
• Publication Date: 2000
• Language: English
• ISBN 10: 0321056779
• ISBN 13: 9780321056771
• Binding: Hardcover
• Edition: 4th.
• Condition: Very Good

About this title

Synopsis

This text takes a practical approach to multivariate data analysis, with an introduction to the most commonly encountered statistical and multivariate techniques.

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