Provides an introduction to today's statistics and multivariate techniques. Assuming only a limited knowledge of higher-level mathematics, it provides an account of the considerations involved in determining the most appropriate technique, screening data for compliance, preparing follow-up analyses and preparing the results for journal publication.

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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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