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Chapter 19

Chapter 19. Multivariate Analysis: An Overview. Learning Objectives. Understand . . . How to classify and select multivariate techniques. That multiple regression predicts a metric dependent variable from a set of metric independent variables.

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Chapter 19

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  1. Chapter 19 Multivariate Analysis: An Overview

  2. Learning Objectives Understand . . . • How to classify and select multivariate techniques. • That multiple regression predicts a metric dependent variable from a set of metric independent variables. • That discriminant analysis classifies people or objects into categorical groups using several metric predictors.

  3. Learning Objectives Understand . . . • How multivariate analysis of variance assesses the relationship between two or more metric dependent variables and independent classificatory variables. • How structural equation modeling explains causality among constructs that cannot be directly measured.

  4. Learning Objectives Understand . . . • How conjoint analysis assists researchers to discover the most importance attributes and the levels of desirable features. • How principal components analysis extracts uncorrelated factors from an initial set of variables and exploratory factor analysis reduces the number of variables to discover the underlying constructs.

  5. Learning Objectives Understand . . . • The use of cluster analysis techniques for grouping similar objects or people. • How perceptions of products or services are revealed numerically and geometrically by multidimensional scaling.

  6. PulsePoint: Research Revelation 49 The percent of line employees that have “trust and confidence” in their company’s senior management.

  7. Prying with Purpose “Research is formalized curiosity. It is poking and prying with a purpose.” Zora Neal Hurston Anthropologist and author

  8. Classifying Multivariate Techniques Dependency Interdependency

  9. Multivariate Techniques

  10. Multivariate Techniques

  11. Multivariate Techniques

  12. Right Questions. Trusted Insight. When using sophisticated techniques you want to rely on the knowledge of the researcher. Harris Interactive promises you can trust their experienced research professionals to draw the right conclusions from the collected data.

  13. Dependency Techniques Multiple Regression Discriminant Analysis MANOVA Structural Equation Modeling (SEM) Conjoint Analysis

  14. Uses of Multiple Regression Develop self-weighting estimating equation to predict values for a DV Control for confounding Variables Test and explain causal theories

  15. Generalized Regression Equation

  16. Multiple Regression Example

  17. Selection Methods Forward Backward Stepwise

  18. Evaluating and Dealing with Multicollinearity Choose one of the variables and delete the other Create a new variable that is a composite of the others

  19. Discriminant Analysis A. B.

  20. MANOVA

  21. MANOVA Output

  22. Bartlett’s Test

  23. MANOVA Homogeneity-of-Variance Tests

  24. Multivariate Tests of Significance

  25. Univariate Tests of Significance

  26. Structural Equation Modeling (SEM) Model Specification Estimation Evaluation of Fit Respecification of the Model Interpretation and Communication

  27. Structural Equation Modeling (SEM)

  28. Concept Cards for Conjoint Sunglasses Study

  29. Conjoint Analysis

  30. Conjoint Results for Participant 8 Sunglasses Study

  31. Conjoint Results for Sunglasses Study

  32. Interdependency Techniques Factor Analysis Cluster Analysis Multidimensional Scaling

  33. Factor Analysis

  34. Factor Matrices

  35. Orthogonal Factor Rotations

  36. Correlation Coefficients, Metro U MBA Study

  37. Factor Matrix, Metro U MBA Study

  38. Varimax Rotated Factor Matrix

  39. Cluster Analysis Select sample to cluster Define variables Compute similarities Select mutually exclusive clusters Compare and validate cluster

  40. Cluster Analysis

  41. Cluster Membership

  42. Dendogram

  43. Similarities Matrix of 16 Restaurants

  44. Positioning of Selected Restaurants

  45. Average linkage method Backward elimination Beta weights Centroid Cluster analysis Collinearity Communality Confirmatory factor analysis Conjoint analysis Dependency techniques Discriminant analysis Dummy variable Eigenvalue Factor analysis Key Terms

  46. Factors Forward selection Holdout sample Interdependency techniques Loadings Metric measures Multicollinearity Multidimensional scaling (MDS) Multiple regression Multivariate analysis Multivaria analysis of variance (MANOVA) Nonmetric measures Path analysis Key Terms (cont.)

  47. Path diagram Principal components analysis Rotation Specification error Standardized coefficients Stepwise selection Stress index Structural equation modeling Utility score Key Terms (cont.)

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