Request for consultation
Your form is submitting...
Overview
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.
- 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.
- Extended discussions of emerging topics, including causal treatments/inference (i.e. causal analysis of non-experimental data as well as propensity score models) along with multi-level and panel data models (extending regression into new research areas and providing a framework for cross-sectional/time-series analysis).
- Each chapter has been updated to reflect technical improvements, (for example adding material on multiple imputation for missing data treatments, and the merging of basic principles from the fields of data mining and its applications).
- The chapters on SEM have been updated to include: greater emphasis on psychometrics and scale development, discussions on the use of reflective versus formative scaling, an alternative approach for handing interactions (orthogonal moderators), higher order models, multi-group analyses, Bayesian SEM, and revised information on software availability (e.g. Lavaan and SmartPLS). The multi-group discussion also includes an alternative to partial metric invariance when cross-group variance problems are small.
- Online resources for researchers including continued coverage from past editions of all of the analyses from the latest versions of both SAS and SPSS (commands and outputs).
- 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.
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.