Business Research Methods Chap019
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Business Research Methods Chap019

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  • 1. 19-1
  • 2. Part Four ANALYSIS AND PRESENTATION OF DATA 19-2McGraw-Hill/Irwin © 2003 The McGraw-Hill Companies, Inc.,All Rights Reserved.
  • 3. Chapter Nineteen MULTIVARIATE ANALYSIS: An Overview19-3
  • 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 independent19-4
  • 5. Dependency Techniques • Multiple regression • Discriminant analysis • Multivariate analysis of variance • (MANOVA) • Linear structural relationships (LISREL) • Conjoint analysis – Simalto+Plus19-5
  • 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 analysis19-6
  • 7. Uses for Discriminant Analysis • Classify persons or objects into various groups • Analyze known groups to determine the relative influence of specific factors19-7
  • 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 parts19-8
  • 9. Uses of LISREL • Explains causality among constructs not directly measured • Two parts – Measurement model – Structural Equation model19-9
  • 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 variables19-10
  • 11. Use for Conjoint Analysis • Market research • Product development19-11
  • 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 statistics19-13
  • 14. Interdependency Techniques • Factor analysis • Cluster analysis19-14
  • 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 clusters19-15
  • 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 interest19-17