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19-1
Part Four
                        ANALYSIS AND
                    PRESENTATION OF DATA



 19-2
McGraw-Hill/Irwin             © 2003 The McGraw-Hill Companies, Inc.,All Rights Reserved.
Chapter Nineteen
       MULTIVARIATE ANALYSIS:
             An Overview



19-3
Selecting a Multivariate Technique

       • Dependency
         – dependent (criterion) variables and
           independent (predictor) variables are
           present
       • Interdependency
         – variables are interrelated without
           designating some dependent and others
           independent


19-4
Dependency Techniques

       •   Multiple regression
       •   Discriminant analysis
       •   Multivariate analysis of variance
       •   (MANOVA)
       •   Linear structural relationships (LISREL)
       •   Conjoint analysis
           – Simalto+Plus


19-5
Uses for Multiple Regression

       • Predict values for a criterion variable by
         developing a self-weighting estimating
         equation
       • Control for confounding variables to
         better evaluate the contribution of other
         variables
       • Test and explain causal theories
         – Path analysis

19-6
Uses for Discriminant Analysis

       • Classify persons or objects into various
         groups

       • Analyze known groups to determine the
         relative influence of specific factors




19-7
Use for MANOVA

       • Assess relationship between two or
         more dependent variables and
         classificatory variables or factors
         samples
       • E.g. . . . measure differences between
         – employees
         – customers
         – manufactured items
         – production parts
19-8
Uses of LISREL

       • Explains causality among constructs not
         directly measured
       • Two parts
         – Measurement model
         – Structural Equation model




19-9
Two Models of LISREL

        • Measurement
          – used to relate the observed, recorded, or
            measured variables to the latent variables
            (constructs)
        • Structural equation
          – shows the causal relationships among the
            latent variables



19-10
Use for Conjoint Analysis

        • Market research

        • Product development




19-11
Interdependency Techniques

        • Factor analysis

        • Cluster analysis

        • Multidimensional Scaling (MDS)




19-12
Interdependency Techniques

        Factor Analysis
        • Computational techniques that reduce
          variables to a manageable number
          – construction of new set of variables based
            on relationships in the correlation matrix
             • Principal components analysis
             • Communalities
             • Rotation
        • Measurement statistics
19-13
Interdependency Techniques

        • Factor analysis

        • Cluster analysis




19-14
Steps in Cluster Analysis

        • Select sample to be clustered
        • Define measurement variables
        • Compute similarities among the entities
          through correlation, Euclidean
          distances, and other techniques
        • Select mutually exclusive clusters
        • Compare and validate the clusters


19-15
Interdependency Techniques

        • Factor analysis

        • Cluster analysis

        • Multidimensional Scaling (MDS)




19-16
Multidimensional Scaling

        • Creates a special description of a
          participant’s perception about a
          product, service, or other object of
          interest




19-17

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Business Research Methods Chap019

  • 2. Part Four ANALYSIS AND PRESENTATION OF DATA 19-2 McGraw-Hill/Irwin © 2003 The McGraw-Hill Companies, Inc.,All Rights Reserved.
  • 3. Chapter Nineteen MULTIVARIATE ANALYSIS: An Overview 19-3
  • 4. Selecting a Multivariate Technique • Dependency – dependent (criterion) variables and independent (predictor) variables are present • Interdependency – variables are interrelated without designating some dependent and others independent 19-4
  • 5. Dependency Techniques • Multiple regression • Discriminant analysis • Multivariate analysis of variance • (MANOVA) • Linear structural relationships (LISREL) • Conjoint analysis – Simalto+Plus 19-5
  • 6. Uses for Multiple Regression • Predict values for a criterion variable by developing a self-weighting estimating equation • Control for confounding variables to better evaluate the contribution of other variables • Test and explain causal theories – Path analysis 19-6
  • 7. Uses for Discriminant Analysis • Classify persons or objects into various groups • Analyze known groups to determine the relative influence of specific factors 19-7
  • 8. Use for MANOVA • Assess relationship between two or more dependent variables and classificatory variables or factors samples • E.g. . . . measure differences between – employees – customers – manufactured items – production parts 19-8
  • 9. Uses of LISREL • Explains causality among constructs not directly measured • Two parts – Measurement model – Structural Equation model 19-9
  • 10. Two Models of LISREL • Measurement – used to relate the observed, recorded, or measured variables to the latent variables (constructs) • Structural equation – shows the causal relationships among the latent variables 19-10
  • 11. Use for Conjoint Analysis • Market research • Product development 19-11
  • 12. Interdependency Techniques • Factor analysis • Cluster analysis • Multidimensional Scaling (MDS) 19-12
  • 13. Interdependency Techniques Factor Analysis • Computational techniques that reduce variables to a manageable number – construction of new set of variables based on relationships in the correlation matrix • Principal components analysis • Communalities • Rotation • Measurement statistics 19-13
  • 14. Interdependency Techniques • Factor analysis • Cluster analysis 19-14
  • 15. Steps in Cluster Analysis • Select sample to be clustered • Define measurement variables • Compute similarities among the entities through correlation, Euclidean distances, and other techniques • Select mutually exclusive clusters • Compare and validate the clusters 19-15
  • 16. Interdependency Techniques • Factor analysis • Cluster analysis • Multidimensional Scaling (MDS) 19-16
  • 17. Multidimensional Scaling • Creates a special description of a participant’s perception about a product, service, or other object of interest 19-17