Multivariate Data Analysis 1st edition by Joseph Hair, Barry Babin, Rolph Anderson 0357755228 9780357755228

Multivariate Data Analysis 1st edition by Joseph F. Hair, Barry J. Babin, Rolph E. Anderson – Ebook PDF Instant Download/DeliveryISBN:  0357755228, 9780357755228 

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ISBN-10 :  0357755228 

ISBN-13 :  9780357755228

Author:  Joseph F. Hair, Barry J. Babin, Rolph E. Anderson 

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.

Multivariate Data Analysis 1st Table of contents:

Chapter 1: Overview of Multivariate Methods
What Is Multivariate Analysis?
Three Converging Trends
Multivariate Analysis in Statistical Terms
Some Basic Concepts of Multivariate Analysis
Managing the Multivariate Model
A Classification of Multivariate Techniques
Types of Multivariate Techniques
Guidelines for Multivariate Analyses and Interpretation
A Structured Approach to Multivariate Model Building
Databases
Organization of the Remaining Chapters
Summary
Questions
Suggested Readings and Online Resources
References
Section I: Preparing for Multivariate Analysis
Chapter 2: Examining Your Data
Introduction
The Challenge of Big Data Research Efforts
Preliminary Examination of the Data
Missing Data
Outliers
Testing the Assumptions of Multivariate Analysis
Data Transformations
An Illustration of Testing the Assumptions Underlying Multivariate Analysis
Incorporating Nonmetric Data with Dummy Variables
Summary
Questions
Suggested Readings and Online Resources
References
Section II: Interdependence Techniques
Chapter 3: Exploratory Factor Analysis
What Is Exploratory Factor Analysis?
A Hypothetical Example of Exploratory Factor Analysis
Factor Analysis Decision Process
Stage 1: Objectives of Factor Analysis
Stage 2: Designing an Exploratory Factor Analysis
Stage 3: Assumptions in Exploratory Factor Analysis
Stage 4: Deriving Factors and Assessing Overall Fit
Stage 5: Interpreting the Factors
Stage 6: Validation of Exploratory Factor Analysis
Stage 7: Data Reduction-Additional Uses of Exploratory Factor Analysis Results
An Illustrative Example
Summary
Questions
Suggested Readings and Online Resources
References
Chapter 4: Cluster Analysis
What Is Cluster Analysis?
How Does Cluster Analysis Work?
Cluster Analysis Decision Process
Implication of Big Data Analytics
An Illustrative Example
Summary
Questions
Suggested Readings and Online Resources
References
Section III: Dependence Techniques – Metric Outcomes
Chapter 5: Multiple Regression Analysis
What Is Multiple Regression Analysis?
Multiple Regression in the Era of Big Data
An Example of Simple and Multiple
A Decision Process for Multiple Regression Analysis
Stage 1: Objectives of Multiple Regression
Stage 2: Research Design of a Multiple Regression Analysis
Stage 3: Assumptions in Multiple Regression Analysis
Stage 4: Estimating the Regression Model and Assessing Overall Model Fit
Stage 5: Interpreting the Regression Variate
Stage 6: Validation of the Results
Extending Multiple Regression
Illustration of a Regression Analysis
Evaluating Alternative Regression Models
Summary
Questions
Suggested Readings and Online Resources
References
Chapter 6: MANOVA: Extending ANOVA
Re-Emergence of Experimentation
Experimental Approaches versus Other Multivariate Methods
MANOVA: Extending Univariate Methods for Assessing Group Differences
A Hypothetical Illustration of MANOVA
A Decision Process for MANOVA
Stage 1: Objectives of MANOVA
Stage 2: Issues in the Research Design of MANOVA
Stage 3: Assumptions of ANOVA and MANOVA
Stage 4: Estimation of the MANOVA Model and Assessing Overall Fit
Stage 5: Interpretation of the MANOVA Results
Stage 6: Validation of the Results
Advanced Issues: Causal Inference in Nonrandomized Situations
Summary
Illustration of a MANOVA Analysis
Example 1: Difference between Two Independent Groups
Example 2: Difference between K Independent Groups
Example 3: A Factorial Design for MANOVA with Two Independent Variables
Example 4: Moderation and Mediation
A Managerial Overview of the Results
Summary
Questions
Suggested Readings and Online Resources
References
Section IV: Dependence Techniques – Non-Metric Outcomes
Chapter 7: Multiple Discriminant Analysis
What Is Discriminant Analysis?
Similarities to Other Multivariate Techniques
Hypothetical Example of Discriminant Analysis
The Decision Process for Discriminant Analysis
Stage 1: Objectives of Discriminant Analysis
Stage 2: Research Design for Discriminant Analysis
Stage 3: Assumptions of Discriminant Analysis
Stage 4: Estimation of the Discriminant Model and Assessing Overall Fit
Stage 5: Interpretation of the Results
Stage 6: Validation of the Results
A Two-Group Illustrative Example
A Three-Group Illustrative Example
Summary
Questions
Suggested Readings and Online Resources
References
Chapter 8: Logistic Regression: Regression with a Binary Dependent Variable
What Is Logistic Regression?
The Decision Process for Logistic Regression
Stage 1: Objectives of Logistic Regression
Stage 2: Research Design for Logistic Regression
Stage 3: Assumptions of Logistic Regression
Stage 4: Estimation of the Logistic Regression Model and Assessing Overall Fit
Stage 5: Interpretation of the Results
Stage 6: Validation of the Results
An Illustrative Example of Logistic Regression
Summary
Questions
Suggested Readings and Online Resources
References
Section V: Moving beyond the Basics
Chapter 9: Structural Equation Modeling: An Introduction
What Is Structural Equation Modeling?
SEM and Other Multivariate Techniques
The Role of Theory in Structural Equation Modeling
A Simple Example of SEM
Six Stages in Structural Equation Modeling
Stage 1: Defining Individual Constructs
Stage 2: Developing and Specifying the Measurement Model
Stage 3: Designing a Study to Produce Empirical Results
Stage 4: Assessing Measurement Model Validity
Stage 5: Specifying the Structural Model
Stage 6: Assessing the Structural Model Validity
Summary
Questions
Suggested Readings and Online Resources
Appendix 9A: Estimating Relationships Using Path Analysis
Appendix 9B: SEM Abbreviations
Appendix 9C: Detail on Selected GOF Indices
References
Chapter 10: SEM: Confirmatory Factor Analysis
What Is Confirmatory Factor Analysis?
SEM Stages for Testing Measurement Theory Validation with CFA
Stage 1: Defining Individual Constructs
Stage 2: Developing the Overall Measurement Model
Stage 3: Designing a Study to Produce Empirical Results
Stage 4: Assessing Measurement Model Validity
CFA Illustration
Summary
Questions
Suggested Readings and Online Resources
References
Chapter 11: Testing Structural Equation Models
What Is a Structural Model?
A Simple Example of a Structural Model
An Overview of Theory Testing with SEM
Stages in Testing Structural Theory
Stage 5: Specifying the Structural Model
Stage 6: Assessing the Structural Model Validity
SEM Illustration
Summary
Questions
Suggested Readings and Online Resources
Appendix 11A
References
Chapter 12: Advanced SEM Topics
Reflective versus Formative Scales
Higher-Order Factor Models
Multiple Groups Analysis
Measurement Type Bias
Relationship Types: Mediation and Moderation
Developments in Advanced SEM Approaches
Summary
Questions
Suggested Readings and Online Resources
References
Chapter 13: Partial Least Squares Structural Equation Modeling (PLS-SEM)
What Is PLS-SEM?
Estimation of Path Models with PLS-SEM
PLS-SEM Decision Process
Stage 1: Defining Research Objectives and Selecting Constructs
Stage 2: Designing a Study to Produce Empirical Results
Stage 3: Specifying the Measurement and Structural Models
Stage 4: Assessing Measurement Model Validity
Stage 5: Assessing the Structural Model
Stage 6: Advanced Analyses Using PLS-SEM
PLS-SEM Illustration
Stage 4: Assessing Measurement Model Reliability and Validity
Stage 5: Assessing the Structural Model

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