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 and SPSS. 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.

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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

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