Multivariate Data Analysis Chapter 2 – Examining Your Data

1. Multivariate Data AnalysisChapter 2 – Examining Your Data MIS 6093 Statistical Method Instructor: Dr. Ahmad Syamil

2. Chapter 2 • Introduction • Graphical Examination of the Data • The Nature of the Variable: Examining the Shape of the Distribution • Examining the Relationship Between Variables • Examining Group Differences • Multivariate Profiles • Summary

3. Chapter 2Missing Data • A Simple Example of a Missing Data Analysis • Understanding the Reasons Leading to Missing Data • Ignorable Missing Data • Other Types of Missing Data Processes • Examining the Patterns of Missing Data • Diagnosing the Randomness of the Missing Data Process

4. Chapter 2Missing Data Cont. • Approaches for Dealing with Missing Data • Use of Only Observations with Complete Data • Delete Case(s) and/or Variable(s)

5. Chapter 2Missing Data Cont. • Imputation Methods • Using All-Available Information as the Imputation Technique • The Replacement of Missing Data • Case substitution • Mean substitution • Cold deck imputation • Regression imputation • Multiple imputation • Model-based Procedures

6. Chapter 2 Missing Data Cont. • An Illustration of Missing Data Diagnosis • Examining the Patterns of Missing Data • Diagnosing Randomness of the Missing Data • Remedies for Missing Data • A Recap of the Missing Value Analysis • Summary

7. Chapter 2Outliers • Detecting Outliers • Univariate Detection • Bivariate Detection • Outlier Designation • Outlier Description and Profiling • Retention or Deletion of the Outlier

8. Chapter 2Outliers Cont. • An Illustrative Example of Analyzing Outliers • Univariate and Bivariate Detection • Multivariate Detection • Retention or Deletion of the Outliers

9. Chapter 2Testing the Assumptions of Multivariate Analysis • Assessing Individual Variables Versus the Variate • Normality • Graphical Analysis of Normality • Statistical Tests of Normality • Remedies for Nonnormality

10. Chapter 2Testing the Assumptions of Multivariate Analysis Cont. • Homoscedasticity • Graphical Tests of Equal Variance Dispersion • Statistical Tests for Homoscedasticity • Remedies for Heteroscedasticity

11. Chapter 2Testing the Assumptions of Multivariate Analysis Cont. • Absence of Correlated Errors • Identifying Correlated Errors • Remedies for Correlated Errors • Data Transformations • Transformations to Achieve Normality and Homoscedasticity • Transformations to Achieve Linearity • General Guidelines for Transformations

12. Chapter 2Testing the Assumptions of Multivariate Analysis Cont. • An Illustration of Testing the Assumptions Underlying Multivariate Analysis • Normality • Homoscedasticity • Linearity • Summary

13. Chapter 2 • Summary • Questions