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## Multivariate Data Analysis Chapter 2

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### Imputation Methods. Using All-Available Information as the Imputation Technique ... deck imputation. Regression imputation. Multiple imputation. Model-based ... – PowerPoint PPT presentation

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Title: Multivariate Data Analysis Chapter 2

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
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