Multivariate Data Analysis, Pearson New International Edition + Foolproof Guide to Statistics Using IBM SPSS, 7th Edition

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Multivariate Data Analysis, Pearson New International Edition + Foolproof Guide to Statistics Using IBM SPSS, 7th Edition

By Joseph F. Hair, William C. Black, Barry J. Babin, Rolph E. Anderson, Adelma Hills
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This pack contains 1 copy of Multivariate Data Analysis, Pearson New International Edition, and 1 copy of Foolproof Guide to Statistics Using IBM SPSS.

The name SPSS will be used throughout this ediiton which is intended to provide a foundation for understanding statistics and the use of SPSS. It has been developed primarily for students of psychology, but has application to other disciplines where quantitative research is used (provided the reader can stand having examples drawn from psychology).


  • How to boxes for SPSS procedures, updated for PASW 18
  • A List of How To Boxes following the Table of Contents
  • The new style for nonparametric tests shown in Appendix 2
  • Sample Results sections updated consistent with the new edition of the APA Manual1
  • A new section on overlapping confidence intervals and statistical significance (p. 141)
Each chapter contains a worked example, and students are advised to treat these as tutorial exercises, working through each one, exploring the other options available in each of the SPSS procedures, examining the output that is produced, and comparing it to the extracts provided in the chapter.

The shaded How to boxes show the steps needed to perform various operations and analyses, and pages illustrating output from analyses are identified by shaded bars down the outside margin.

Multivariate Data Analysis

For graduate and upper-level undergraduate marketing research courses.

For over 30 years, this text has provided students with the information they need to understand and apply multivariate data analysis. 

The authors provide an applications-oriented introduction to multivariate analysis for the non-statistician. By reducing heavy statistical research into fundamental concepts, the text explains to students how to understand and make use of the results of specific statistical techniques.

In this 7th Edition, the organisation of the chapters has been greatly simplified. New chapters have been added on structural equations modeling, and all sections have been updated to reflect advances in technology, capability, and mathematical techniques.

Multivariate Data Analysis
  • Chapter Reorganisation: Chapters now focus on a single topic and begin with providing basic information and application techniques. This is followed by more in-depth discussions later in the chapter.
  • “Rule of Thumb” Feature Expanded: This feature has been improved so students learn how to best use different techniques.
  • Use of Technical Terms and Statistical Notation Minimised: In order to make the text more accessible to management and non-mathematically focused students, the authors explain complex techniques in everyday language.
  • Additional Chapters: Structural Equations Modeling has been expanded and reorganised, now covering 4 chapters.

Pack items
Table of contents
Foolproof Guide to Statistics Using IBM SPSS
  • 1. Introduction
  • 2. Entering and Saving Data in SPSS
  • 3. The Problem of Variability: Descriptive Statistics for Central Tendency and Variance
  • 4. The Problem of Probability: Frequency Distributions, The Normal Distribution, and Standard Scores
  • 5. The Logic of Significance Testing: One- and Two-Sample z Tests
  • 6. Preparing for Analysis: Data Screening for Parametric Statistics 
  • 7. The One-Sample t Test
  • 8. The Dependent-Samples t Test, and Wilcoxon Signed-Rank T Test Nonparametric Alternative
  • 9. The Independent-Samples t Test, and the Mann-Whitney U Test Nonparametric Alternative
  • 10. One-Way Analysis of Variance (ANOVA), and the Kruskal-Wallis Nonparametric Alternative
  • 11. One-Way Repeated Measures ANOVA, and Friedman Alternative
  • 12. Factorial Between-Groups ANOVA
  • 13. ANOVA Alternatives: Planned Comparisons, and Trend Analysis
  • 14. Factorial and Mixed Repeated Measures ANOVA
  • 15. Analysis of Covariance (ANCOVA) 
  • 16. Multivariate Analysis of Variance (MANOVA)
  • 17. Discriminant Function Analysis 
  • 18. Bivariate Correlation
  • 19. Bivariate Regression
  • 20. Multiple Regression
  • 21. Reliability Analysis
  • 22. Exploratory Factor Analysis 
  • 23. Chi-Square
  • 24. Multiway Frequency Analysis (Loglinear Analysis) 
  • Appendix 1: The new style for nonparametric tests 
  • Appendix 2: New and old ways to create graphs 
  • References
  • Index
  • List of Main Statistical Symbols
Multivariate Data Analysis
  • I    Introduction
  • II     Preparing For a MV Analysis
  • 2    Examining Your Data
  • 3    Factor Analysis
  • III    Dependence Techniques
  • 4    Multiple Regression Analysis
  • 5    Multiple Discriminate Analysis and Logistic Regression
  • 6    Multivariate Analysis of Variance
  • IV    Interdependence Techniques
  • 7  Cluster Analysis
  • 8  Multidimensional Scaling and Correspondence Analysis
  • V    Moving Beyond the Basic Techniques
  • 9  Structural Equation Modeling: Overview
  • 10  Appendix – SEM
  • 10a  CFA: Confirmatory Factor Analysis
  • 11  Appendix – CFA
  • 11a  SEM: Testing A Structural Model
  • 12  Appendix – SEM
  • 12a  Conjoint Analysis
  • A    Basic Stats