Multivariate Data Analysis Chapter 10 - Multidimensional Scaling

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Multivariate Data AnalysisChapter 10 -
Multidimensional Scaling
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Chapter 10

• What Is Multidimensional Scaling?
• A Simplified Look at How Multidimensional Scaling
  Works
• Comparing MDS to Other Interdependence Techniques
  
• Individual As the Unit of Analysis
• Lack of a Variate

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A Decision Framework for Perceptual Mapping

• Stage 1 Objectives of Multidimensional
• Scaling
• Key Decisions in Setting Objectives
• Identification of All Relevant Objects to Be
  Evaluated
• Similarities Versus Preference Data
• Aggregate Versus Disaggregate Analysis

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A Decision Framework for Perceptual Mapping
(Cont.)

• Stage 2 Research Design of Multidimensional
  Scaling
• Selection of Either a Decompositional
  (Attribute-free) or Compositional
  (Attribute-based) Approach
• Decompositional or Attribute-free Approach
• Compositional or Attribute-based Approach
• Selecting Between Compositional and
  Decompositional Techniques
• Objects Their Number and Selection
• Nonmetric Versus Metric Methods
• Collection of Similarity or Preference Data
• Similarities Data
• Comparison of Paired Objects
• Confusion Data
• Derived Measures
• Collecting Preference Data
• Direct Ranking
• Paired Comparisons
• Preference Data Versus Similarity Data

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A Decision Framework for Perceptual Mapping
(Cont.)

• Stage 3 Assumptions of Multidimensional Scaling
  Analysis
• Stage 4 Deriving the MDS Solution and Assessing
  Overall Fit
• Determining an Object's Position in the
  Perceptual Map
• Selecting the Dimensionality of the Perceptual
  Map
• Incorporating Preferences into Multidimensional
  Scaling
• Ideal Points
• Positioning the Ideal Point
• Internal Analysis
• External Analysis
• Vector Versus Point Representations
• Summary

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Decision Framework for Perceptual Mapping (Cont.)

• Stage 5 Interpreting the MDS Results
• Identifying the Dimensions
• Subjective Procedures
• Objective Procedures
• Selecting Between Subjective and Objective
  Procedures
• Stage 6 Validating the MDS Results

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Correspondence Analysis

• A Simple Example of Correspondence Analysis
• Calculating A Measure of Association
• Creating the Perceptual Map
• Stage 1 Objectives of Correspondence Analysis
• Stage 2 Research Design of Correspondence
• Analysis
• Stage 3 Assumptions in Correspondence
• Analysis

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Correspondence Analysis (Cont.)

• Stage 4 Deriving the Correspondence
• Analysis Results and Assessing
• Overall Fit
• Stage 5 Interpretation of Results
• Stage 6 Validation of the Results
• Overview of Correspondence Analysis

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Illustration of Multidimensional Scaling and
Correspondence Analysis

• Stage 1 Objectives of Perceptual Mapping
• Stage 2 Research Design of the Perceptual
• Mapping Study
• Similarity Data
• Attribute Ratings
• Preference Evaluations
• Stage 3 Assumptions in Perceptual Mapping

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Illustration of Multidimensional Scaling and
Correspondence Analysis (Cont.)

• Multidimensional Scaling Stages 4 and 5
• Stage 4 Deriving the Multidimensional Scaling
  Results and Assessing Overall Fit
• Incorporating Preferences in the Perceptual Map
• Stage 5 Interpretation of the Results
• Overview of the Decompositional Results

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Illustration of Multidimensional Scaling and
Correspondence Analysis (Cont.)

• Correspondence Analysis Stages 4 and 5
• Stage 4 Deriving the Correspondence Analysis
• Stage 5 Interpreting the Correspondence
• Analysis Results
• An Overview of Correspondence Analysis
• Stage 6 Validation of the Results
• A Managerial Overview of the Multidimensional
  Scaling Results