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Using Multivariate Statistics, 7th edition

Published by Pearson (July 2, 2018) © 2019

  • Barbara G. Tabachnick California State University - Northridge
  • Linda S. Fidell California State University - Northridge

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For advanced undergraduate and graduate statistics courses in Social and Behavioral Sciences.

An in-depth introduction to the most commonly used statistical and multivariate techniques

Using Multivariate Statistics presents complex statistical procedures in a way that is accessible to beginning students. The authors' practical approach focuses on the benefits and limitations of applying a technique to a data set: when, why, and how to do it. The authors guide students on how to conduct numerous types of multivariate statistical analyses, how to find the best technique to use, and how to use SPSS and SAS syntax and output.

The 7th Edition includes updated references in every chapter.

Hallmark features of this title

  • The text provides hands-on guidelines for conducting numerous types of multivariate statistical analyses.
  • Using a practical approach, the authors focus on the benefits and limitations of applications of a technique to a data set.

New and updated features of this title

  • NEW: All output is up to date, showing tables from IBM SPSS version 24 and SAS version 9.4.
  • UPDATED: References in all chapters have been updated. For references prior to 2000, only classic citations are included.
  • NEW: The text includes references and online facilities for sample size and power analysis.
  • NEW: Work on relative importance has been incorporated in multiple regression, canonical correlation, and logistic regression analysis, complete with demonstrations.
  • UPDATED: Procedures for multiple imputation of missing data are included and illustrated. This allows users to keep the data set intact, despite missing data points on several variables.
  • NEW: The automated time-series example takes advantage of an IBM SPSS expert modeler that replaces previous tea leaf reading aspects of the analysis.
  1. Introduction
  2. A Guide to Statistical Techniques: Using the Book
  3. Review of Univariate and Bivariate Statistics
  4. Cleaning Up Your Act: Screening Data Prior to Analysis
  5. Multiple Regression
  6. Analysis of Covariance
  7. Multivariate Analysis of Variance and Covariance
  8. Profile Analysis: The Multivariate Approach to Repeated Measures
  9. Discriminant Analysis
  10. Logistic Regression
  11. Survival/Failure Analysis
  12. Canonical Correlation
  13. Principal Components and Factor Analysis
  14. Structural Equation Modeling by Jodie B. Ullman
  15. Multilevel Linear Modeling
  16. Multiway Frequency Analysis
  17. Time-Series Analysis
  18. An Overview of the General Linear Model

About our authors

Barbara G. Tabachnick is Professor Emerita of Psychology at California State University, Northridge. She has published over 80 articles and technical reports and participated in over 60 professional presentations, many invited. She currently presents workshops in computer applications in univariate and multivariate data analysis and has consulted in a variety of research areas, including professional ethics in and beyond academia, effects of such factors as age and substances on driving and other 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 and a 2015 Western Psychological Association Presidential Citation.

Linda S. Fidell is Professor Emerita of Psychology at California State University, Northridge. She has published several articles and given numerous professional presentations. She taught research design and statistics at CSUN for 32 years and retired in 2001. In 2015 she received a Western Psychological Association Presidential Citation. She now lives in Morro Bay, where she is delighted to contribute to the community.

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