Multivariate Data Analysis Chapter 2 – Examining Your Data

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

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

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

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

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

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

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

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

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