1. McGraw-Hill/Irwin McGraw-Hill/Irwin Copyright © 2009 by The McGraw-Hill Companies, Inc. All rights reserved.

2. Chapter 17 Overview of Multivariate Analysis Methods

3. 1. Define multivariate analysis. 2. Understand when and why you should use multivariate analysis in marketing research. 3. Distinguish between dependence and interdependence methods. 4. Apply factor analysis, cluster analysis, discriminant analysis and conjoint analysis to examine marketing research problems. Learning Objectives 17-3

4. Multivariate Analysis These techniques are important in marketing research because most business problems are multidimensional and can only be understood when multivariate techniques are used.... statistical techniques used when there are multiple measurements of each element/concept and the variables are analyzed simultaneously. 17-4

5. Classification of Multivariate Methods 17-5

6. Summary of Selected Multivariate Methods 17-6

7. Multivariate Techniques InterdependenceInterdependenceDependenceDependence 17-7

8. Dependence Methods Examples: multiple regression analysis, discriminant analysis, ANOVA and MANOVA... multivariate techniques appropriate when one or more of the variables can be identified as dependent variables and the remaining as independent variables. 17-8

9. Interdependence Methods Goal of these methods – to group variables together into variates. Examples: cluster analysis, factor analysis, and multidimensional scaling. Goal of these methods – to group variables together into variates. Examples: cluster analysis, factor analysis, and multidimensional scaling.... multivariate statistical techniques in which a set of interdependent relationships is examined – analysis involves either the independent or dependent variables separately. 17-9

10. Factor Analysis Purpose – to simplify the data. Dependent and independent variables are analyzed separately, not together. Purpose – to simplify the data. Dependent and independent variables are analyzed separately, not together.... used to summarize information contained in a large number of variables into a smaller number of subsets or factors. All variables being examined are analyzed together – to identify underlying factors. 17-10

11. A Factor Analysis Application to a Fast-Food Restaurant 17-11

12. Factor Analysis Steps Examine factor loadings & percentage of variance Interpret & name factors Decide on number of factors 17-12

13. Factor loadings are calculated between all factors and each of the original variables. Starting point for interpreting factor analysis. Factor Loadings = correlations between the variables and the new composite factor. Measure the importance of each variable relative to each composite factor. Like correlations – factor loadings vary from +1.0 to –1.0 17-13

14. Factor Loadings for the Two Factors 17-14

15. Percentage of Variation in Original Data Explained by Each Factor 17-15

16. Factor Analysis Applications in Marketing Research... Factor Analysis Applications in Marketing Research... Advertising – to better understand media habits of various customers Advertising – to better understand media habits of various customers Pricing – to identify the characteristics of price-sensitive and prestige-sensitive customers Pricing – to identify the characteristics of price-sensitive and prestige-sensitive customers Product – to identify brand attributes that influence consumer choice Product – to identify brand attributes that influence consumer choice Distribution – to better understand channel selection criteria among distribution channel members Distribution – to better understand channel selection criteria among distribution channel members Interdependence Techniques 17-16

17. SPSS Dialog Boxes for Factor Analysis 17-17

18. SPSS Output for Factor Analysis of Restaurant Perceptions 17-18

19. Factor Scores for Restaurant Perceptions 17-19

20. SPSS Dialog Boxes for Regression with Factor Scores 17-20

21. Multiple Regression with Factor Scores – Descriptive Statistics 17-21

22. Cluster Analysis... classifies or segments objects into groups that are similar within groups and as different as possible between segments.... classifies objects into relatively homogeneous groups based on the set of variables analyzed.... identifies natural groupings or segments among many variables, none of which are considered a dependent variable. 17-22

23. Multiple Regression with Factor Scores – Model Results 17-23

24. Cluster Analysis Distance between any pair of points is related to how similar the corresponding objects are when the clustering variables are compared. Degree of similarity between objects is determined based on a distance measure. StatisticalProcedureStatisticalProcedure 17-24

25. Applications in Marketing Research Applications in Marketing Research New product research – to examine product offerings relative to the competition. New product research – to examine product offerings relative to the competition. Test marketing – to group test cities into homogeneous clusters for test marketing purposes. Test marketing – to group test cities into homogeneous clusters for test marketing purposes. Buyer behavior – to identify similar groups of buyers who have similar choice criteria. Buyer behavior – to identify similar groups of buyers who have similar choice criteria. Market segmentation – to develop distinct market segments on the basis of geographic, demographic, psychographic, and behavioral variables. Market segmentation – to develop distinct market segments on the basis of geographic, demographic, psychographic, and behavioral variables. Interdependence Techniques 17-25

26. Cluster Analysis Based on Two Characteristics 17-26

27. SPSS Dialog Boxes for Cluster Analysis 17-27

28. Cluster Analysis Agglomeration Schedule Coefficients 17-28

29. New cluster variable New Cluster Variable to Identify Group Membership 17-29

30. Comparing Cluster Means Using ANOVA SPSS Dialog Boxes 17-30

31. Discriminant Analysis Dependent variable – nonmetric or categorical.... dependence technique used for predicting group membership on the basis of two or more independent variables. Independent variables – metric, but non- metric dummy variables are possible. 17-31

32. SPSS ANOVA Output – Results for Cluster of X23 – X24 17-32

33. 33 Discriminant Analysis CharacteristicsCharacteristics Discriminant function – a linear combination of independent variables that bests discriminates between the dependent variable groups.... develops a linear combination of independent variables and uses it to predict group membership.... predicts categorical dependent variable based on group differences using a linear combination of independent variables. 17-33

34. Discriminant score (Z-score) – basis for predicting to which group the particular individual belongs and is determined by a linear function. Each respondent is assigned a score by the calculated discriminant function. Discriminant score (Z-score) – basis for predicting to which group the particular individual belongs and is determined by a linear function. Each respondent is assigned a score by the calculated discriminant function. Z i =b 1 X 1i + b 2 X 2i + b n X ni Z i = ith individuals discriminant score Z i = ith individuals discriminant score b n = discriminant coefficient for the nth variable b n = discriminant coefficient for the nth variable X ni = individuals value on the nth independent variable X ni = individuals value on the nth independent variable Analysis of Dependence 17-34

35. Discriminant Analysis... multipliers of variables in the discriminant function when variables are in the original units of measurement.... estimates of the discriminatory power of a particular independent variable. DiscriminantFunctionCoefficientsDiscriminantFunctionCoefficients 17-35

36. Discriminant Analysis.. Classification (Prediction) Matrix – shows whether the estimated discriminant function is a good predictor.... shows the number of correctly and incorrectly classified cases.... the prediction is referred to as the hit ratio. 17-36

37. Discriminant Analysis Scatter Plot of Lifestyle and Income Data for Fast-Food Restaurant Patronage 17-37

38. Classification Matrix for BYB Patrons and Non-patrons 17-38

39. Applications for Marketing Research Applications for Marketing Research Product research – to distinguish between heavy, medium, and light users of a product in terms of their consumption habits and lifestyles. Product research – to distinguish between heavy, medium, and light users of a product in terms of their consumption habits and lifestyles. Image research – to discriminate between customers that exhibit favorable perceptions of a store or company and those who do not. Image research – to discriminate between customers that exhibit favorable perceptions of a store or company and those who do not. Advertising research – to determine how market segments differ in media consumption habits. Advertising research – to determine how market segments differ in media consumption habits. Direct marketing – to identify characteristics of consumers who respond to direct marketing solicitations and those who do not. Direct marketing – to identify characteristics of consumers who respond to direct marketing solicitations and those who do not. Analysis of Dependence 17-39

40. SPSS Dialog Boxes for Discriminant Analysis Comparing Two Restaurants 17-40

41. SPSS Discriminant Analysis of Favorite Mexican Restaurant 17-41

42. Discriminant Output for Favorite Mexican Restaurant (continued) 17-42

43. Group Means for Favorite Mexican Restaurants (continued) 17-43

44. Discriminant Analysis of Customer Loyalty Clusters and Nutrition Lifestyle Variables 17-44

45. Discriminant Analysis – Customer Loyalty Clusters 17-45

46. Nutrition Variable Means for Customer Loyalty Clusters 17-46

47. Sample Conjoint Survey Profiles 17-47

48. Conjoint Part-Worth Estimates for Restaurant Survey 17-48

49. Importance Calculations for Restaurant Data 17-49