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eBook for Multivariate Data Analysis

Author(s): Joseph F Hair | Barry J. Babin | Rolph E. Anderson | William C. Black

ISBN: 9789353505448

8th Edition

India Release: 2023

₹855

Binding: eBook

Pages: 832

Trim Size: 279 x 216 mm

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For over 30 years, this text has provided students with the information they need to understand and apply multivariate data analysis. The eighth edition of Multivariate Data Analysis provides an updated perspective on the analysis of all types of data as well as introducing some new perspectives and techniques that are foundational in today’s world of analytics. Multivariate Data Analysis serves as the perfect companion for graduate and postgraduate students undertaking statistical analysis for business degrees, providing an application-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.

Unique “Rule of Thumb” feature helps students learn how to best use different techniques.
Assumes that students will come from a business, rather than mathematics, background. The authors use non-complex language to make complex techniques accessible.
Provides an application-oriented introduction to multivariate analysis for the non-statistician.
Aimed at students taking postgraduate and high-level graduate degrees across all the business areas.
New chapter on partial least squares structural equation modeling (PLS-SEM), an emerging technique which can be applied by researchers in both the academic and business domains.
Each chapter highlights the implications of Big Data, underlining the role of multivariate data analysis in this new era of analytics.

Chapter 1 Overview of Multivariate Methods

Section 1: Preparing for Multivariate Analysis

Chapter 2: Examining Your Data

Section 2: Interdependence Techniques

Chapter 3: Exploratory Factor Analysis

Chapter 4: Cluster Analysis

Section 3: Dependence Techniques

Chapter 5: Multiple Regression

Chapter 6: MANOVA: Extending ANOVA

Chapter 7: Discriminant Analysis

Chapter 8: Logistic Regression: Regression with a Binary Dependent Variable

Section 4: Moving Beyond the Basic Techniques

Chapter 9: Structural Equation Modeling: An Introduction

Chapter 10: Confirmatory Factor Analysis

Chapter 11: Testing Structural Equation Models

Chapter 12: Advanced Topics in SEM

Chapter 13: Partial Least Squares Modeling (PLS-SEM)

In addition to the chapters in the print book, e-copies of all other chapters in the previous editions are available to download on the companion website, including canonical correlation, conjoint analysis, multidimensional scaling, and correspondence analysis.

Joseph F Hair, University of South Alabama

Barry J. Babin, Louisiana Tech University

Rolph E. Anderson, Drexel University

William C. Black