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A Practical Approach to using Multivariate Analyses
Using Multivariate Statistics , 6th edition provides advanced undergraduate as well as graduate 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 text’s practical approach focuses on the benefits and limitations of applications of a technique to a data set – when, why, and how to do it.
Learning Goals
Upon completing this book, readers should be able to:
Learn to conduct
…mehr

Produktbeschreibung
A Practical Approach to using Multivariate Analyses

Using Multivariate Statistics, 6th edition provides advanced undergraduate as well as graduate 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 text’s practical approach focuses on the benefits and limitations of applications of a technique to a data set – when, why, and how to do it.

Learning Goals

Upon completing this book, readers should be able to:

Learn to conduct numerous types of multivariate statistical analyses

Find the best technique to use

Understand Limitations to applications

Learn how to use SPSS and SAS syntax and output

Features + Benefits

Provides hands on guidelines for conducting numerous types of multivariate statistical analyses

Maintains a 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

Presents a comprehensive introduction to today's most commonly encountered statistical and multivariate techniques, while assuming only a limited knowledge of higher-level mathematics.

Datasets available at www.pearsonhighered.com/tabachnick

MySearchLab with eText can be packaged with this text.
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eText – Just like the printed text, you can highlight and add notes to the eText or download it to your iPad.

Assessment – Chapter quizzes and flashcards offer immediate feedback and report directly to your gradebook.

Writing and Research – A wide range of writing, grammar and research tools and access to a variety of academic journals, census data, Associated Press newsfeeds, and discipline-specific readings help you hone your writing and research skills.

In this Section:

1. Brief Table of Contents

2. Full Table of Contents

1. BRIEF TABLE OF CONTENTS



Chapter 1 Introduction

Chapter 2 A Guide to Statistical Techniques: Using the Book

Chapter 3 Review of Univariate and Bivariate Statistics

Chapter 4 Cleaning Up Your Act: Screening Data Prior to Analysis

Chapter 5 Multiple Regression

Chapter 6 Analysis of Covariance

Chapter 7 Multivariate Analysis of Variance and Covariance

Chapter 8 Profile Analysis: The Multivariate Approach to Repeated Measures

Chapter 9 Discriminant Analysis

Chapter 10 Logistic Regression

Chapter 11 Survival/Failure Analysis

Chapter 12 Canonical Correlation

Chapter 13 Principal Components and Factor Analysis

Chapter 14 Structural Equation Modeling

Chapter 15 Multilevel Linear Modeling

Chapter 16 Multiway Frequency Analysis



2. FULL TABLE OF CONTENTS

Chapter 1: Introduction

Multivariate Statistics: Why?

Some Useful Definitions

Linear Combinations of Variables

Number and Nature of Variables to Include

Statistical Power

Data Appropriate for Multivariate Statistics

Organization of the Book

Chapter 2: A Guide to Statistical Techniques: Using the Book

Research Questions and Associated Techniques

Some Further Comparisons

A Decision Tree

Technique Chapters

Preliminary Check of the Data

Chapter 3: Review of Univariate and Bivariate Statistics

Hypothesis Testing

Analysis of Variance

Parameter Estimation

Effect Size

Bivariate Statistics: Correlation and Regression.

Chi-Square Analysis

Chapter 4: Cleaning Up Your Act: Screening Data Prior to Analysis

Important Issues in Data Screening

Complete Examples of Data Screening

Chapter 5: Multiple Regression

General Purpose and Description

Kinds of Research Questions

Limitations to Regression Analyses

Fundamental Equations for Multiple Regression

Major Types of Multiple Regression

Some Important Issues.

Complete Examples of Regression Analysis

Comparison of Programs

Chapter 6: Analysis of Covariance

General Purpose and Description

Kinds of Research Questions

Limitations to Analysis of Covariance

Fundamental Equations for Analysis of Covariance

Some Important Issues

Complete Example of Analysis of Covariance

Comparison of Programs

Chapter 7: Multivariate Analysis of Variance and Covariance

General Purpose and Description

Kinds of Research Questions

Limitations to Multivariate Analysis of Variance and Covariance

Fundamental Equations for Multivariate Analysis of Variance and Covariance

Some Important Issues

Complete Examples of Multivariate Analysis of Variance and Covariance

Comparison of Programs

Chapter 8: Profile Analysis: The Multivariate Approach to Repeated Measures

General Purpose and Description

Kinds of Research Questions

Limitations to Profile Analysis

Fundamental Equations for Profile Analysis

Some Important Issues

Complete Examples of Profile Analysis

Comparison of Programs

Chapter 9: Discriminant Analysis

General Purpose and Description

Kinds of Research Questions

Limitations to Discriminant Analysis

Fundamental Equations for Discriminant Analysis

Types of Discriminant Analysis

Some Important Issues

Comparison of Programs

Chapter 10: Logistic Regression

General Purpose and Description

Kinds of Research Questions

Limitations to Logistic Regression Analysis

Fundamental Equations for Logistic Regression

Types of Logistic Regression

Some Important Issues

Complete Examples of Logistic Regression

Comparison of Programs

Chapter 11: Survival/Failure Analysis

General Purpose and Description

Kinds of Research Questions

Limitations to Survival Analysis

Fundamental Equations for Survival Analysis

Types of Survival Analysis

Some Important Issues

Complete Example of Survival Analysis

Comparison of Programs

Chapter 12: Canonical Correlation

General Purpose and Description

Kinds of Research Questions

Limitations

Fundamental Equations for Canonical Correlation

Some Important Issues

Complete Example of Canonical Correlation

Comparison of Programs

Chapter 13: Principal Components and Factor Analysis

General Purpose and Description

Kinds of Research Questions

Limitations

Fundamental Equations for Factor Analysis

Major Types of Factor Analysis

Some Important Issues

Complete Example of FA

Comparison of Programs

Chapter 14: Structural Equation Modeling

General Purpose and Description

Kinds of Research Questions

Limitations to Structural Equation Modeling

Fundamental Equations for Structural Equations Modeling

Some Important Issues

Complete Examples of Structural Equation Modeling Analysis.

Comparison of Programs

Chapter 15: Multilevel Linear Modeling

General Purpose and Description

Kinds of Research Questions

Limitations to Multilevel Linear Modeling

Fundamental Equations

Types of MLM

Some Important Issues

Complete Example of MLM

Comparison of Programs

Chapter 16: Multiway Frequency Analysis

General Purpose and Description

Kinds of Research Questions

Limitations to Multiway Frequency Analysis

Fundamental Equations for Multiway Frequency Analysis

Some Important Issues

Complete Example of Multiway Frequency Analysis

Comparison of Programs
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.
Autorenporträt
Barbara Tabachnick is Professor Emerita of Psychology at California State University, Northridge, and co-author with Linda Fidell of Using Multivariate Statistics and Experimental Designs Using ANOVA. She has published over 70 articles and technical reports and participated in over 50 professional presentations, many invited. She currently presents workshops in computer applications in univariate and multivariate data analysis and consults in a variety of research areas, including professional ethics in and beyond academia, effects of such factors as age and substances on driving and performance, educational computer games, effects of noise on annoyance and sleep, and fetal alcohol syndrome. She is the recipient of the 2012 Western Psychological Association Lifetime Achievement Award.