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Multivariate Data Analysis Chapter 2 – Examining Your Data

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.

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Multivariate Data Analysis Chapter 2 – Examining Your Data

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  1. Multivariate Data AnalysisChapter 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 • 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

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