1. Multivariate Data Analysis Chapter 2 – Examining Your Data

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

5. Outliers Detecting Outliers Outlier Description and Profiling
Univariate Detection
Bivariate Detection
Outlier Designation
Outlier Description and Profiling
Retention or Deletion of the Outlier

6. Outliers (Cont.) An Illustrative Example of Analyzing Outliers
Univariate and Bivariate Detection
Multivariate Detection
Retention or Deletion of the Outliers

7. Testing the Assumptions of Multivariate Analysis
Assessing Individual Variables Versus the Variate
Normality
Graphical Analysis of Normality
Statistical Tests of Normality
Remedies for Nonnormality

8. Testing the Assumptions of Multivariate Analysis (Cont.)
Homoscedasticity
Graphical Tests of Equal Variance Dispersion
Statistical Tests for Homoscedasticity
Remedies for Heteroscedasticity

9. Testing 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

10. Testing the Assumptions of Multivariate Analysis (Cont.)
An Illustration of Testing the Assumptions Underlying Multivariate Analysis
Normality
Homoscedasticity
Linearity
Summary