Slide 1Slide 2The Value of Multivariate TechniquesClassification of Multivariate MethodsSummary of Selected Multivariate MethodsSlide 6Factor AnalysisFactor Analysis: An Example to ConsiderFactor Analysis: Another Example to ConsiderCluster AnalysisCluster Analysis: An Example to ConsiderDiscriminant AnalysisDiscriminant Analysis: An Example to ConsiderConjoint AnalysisConjoint Analysis: An Example to ConsiderPerceptual MappingPerceptual Mapping: An Example to ConsiderSummary of Learning Objectives1995 7888 4320 000 000001 00023Copyright © 2003 by The McGraw-Hill Companies, Inc. All rights reserved.1995 7888 4320 000 000001 00023Copyright © 2003 by The McGraw-Hill Companies, Inc. All rights reserved. CCHHAAPPTTEERR1234 0001 897251 000001818Data Analysis: Data Analysis: Multivariate Techniques Multivariate Techniques for the Research Processfor the Research Process18-218-21995 7888 4320 000 000001 00023Copyright © 2003 by The McGraw-Hill Companies, Inc. All rights reserved. The Value of Multivariate TechniquesMultivariate techniques draw upon and affirm the importance of recent and continued innovation in computer technology – what was impossible or improbable yesterday is possible today!Because of the vast quantity of information available, research practitioners maintain multivariate techniques are required to get beyond and beneath “the surface” of an information problem, or market opportunity.Most of the problems that excite researchers and decision-makers are complex, involving more than a single pair of variables – multivariate techniques address this complexity in an efficient and powerful way.18-318-31995 7888 4320 000 000001 00023Copyright © 2003 by The McGraw-Hill Companies, Inc. All rights reserved. Classification of Multivariate Methods18-418-4Nominal• Discriminant Analysis• ConjointDependent VariableLevel of MeasurementOrdinal• Spearman’s Rank CorrelationDependenceMethodsNumber of Dependent VariablesIntervalor Ratio• Multiple Regression• ANOVA• MANOVA• ConjointInterdependenceMethods• Factor Analysis• Cluster Analysis• Perceptual MappingOneNone(Metric)(Nonmetric)1995 7888 4320 000 000001 00023Copyright © 2003 by The McGraw-Hill Companies, Inc. All rights reserved. Summary of Selected Multivariate Methods18-5a18-5aMultiple regression enables the marketing researcher to predict a single dependent metric variable from two or more metrically measured independent variables.Multiple discriminant analysis can predict a single dependent nonmetric variable from two or more metrically measured independent variables.Factor analysis is used to summarize the information contained in a large number of variables into a smaller number of subsets called factors.1995 7888 4320 000 000001 00023Copyright © 2003 by The McGraw-Hill Companies, Inc. All rights reserved. Summary of Selected Multivariate Methods18-5b18-5bCluster analysis is used to classify respondents or objects (e.g., products, stores) into groups that are homogeneous, or similar within the groups but different between groups.Conjoint analysis is used to estimate the value (utility) that respondents associate with different product and/or service features, so that the most preferred combinationof features can be determined.Perceptual mapping is used to visually display respondents’ perceptions of products, brands, companies, and so on. Several multivariate methods can be used to develop the data to construct perceptual maps.1995 7888 4320 000 000001 00023Copyright © 2003 by The McGraw-Hill Companies, Inc. All rights reserved. Factor AnalysisA research team uses factor analysis to distill the information contained in a large number of variables into a smaller number of sub-groups called “factors”.18-618-61995 7888 4320 000 000001 00023Copyright © 2003 by The McGraw-Hill Companies, Inc. All rights reserved. Factor Analysis: An Example to Consider18-718-7ConsumerWaitingTime Cleanliness PersonnelFoodTasteFoodTemperature FreshnessDino 2 2 1 6 5 5Sammi 1 1 1 4 5 4Frank 2 2 2 5 5 5Debbie 2 1 2 4 6 5Joey 1 3 1 6 5 5Average 1.6 1.8 1.4 5.0 5.2 4.81995 7888 4320 000 000001 00023Copyright © 2003 by The McGraw-Hill Companies, Inc. All rights reserved. Factor Analysis: Another Example to Consider18-818-8Waiting TimeFreshness of FoodCleanlinessFriendly PersonnelFood TasteFood TemperatureService QualityFood QualityVariables Factors1995 7888 4320 000 000001 00023Copyright © 2003 by The McGraw-Hill Companies, Inc. All rights reserved. Cluster AnalysisMarketing researchers draw upon the power of cluster analysis to classify objects or respondents into groups that have something in common. Cluster analysis, to put it another way, pinpoints what’s similar within groups but different between them.18-918-91995 7888 4320 000 000001 00023Copyright © 2003 by The McGraw-Hill Companies, Inc. All rights reserved. Cluster Analysis: An Example to Consider18-1018-10Cluster 1Cluster 3Cluster 4Cluster 2CDEABHighHighLowLowExtent ofEating Outat RestaurantsExtent of Patronizinga Fast-Food Restaurant1995 7888 4320 000 000001 00023Copyright © 2003 by The McGraw-Hill Companies, Inc. All rights reserved. Discriminant AnalysisA research team uses discriminant analysis to classify groups or objects by a set of independent variables. Discriminant analysis enables a marketing researcher to determine linear combinations of the independent variables of interest to the client.18-1118-111995 7888 4320 000 000001 00023Copyright © 2003 by The McGraw-Hill Companies, Inc. All rights reserved. Discriminant Analysis: An Example to Consider18-1218-12Back Yard BurgerCustomersOther Fast-FoodRestaurantsX2X1Lifestyle-Eating Nutritious MealsIncome ($)1995 7888 4320 000 000001 00023Copyright © 2003 by The McGraw-Hill Companies, Inc. All rights reserved. Conjoint AnalysisMarketing researchers draw upon the power of conjoint analysis to estimate the value (utility) respondents associate with different product and/or service features. Conjoint analysis, then, lets a marketing research team communicate the most preferred combination of features to a client.18-1318-131995 7888 4320 000 000001 00023Copyright © 2003 by The McGraw-Hill Companies, Inc. All rights reserved. Conjoint Analysis: An Example to Consider18-1418-14Attribute Restaurant Profile A Restaurant Profile BPrice level Inexpensive ($3-$6) Moderate ($7-$10)Atmosphere Family style UpscaleMenu type Sandwiches Salad, entrée, dessertService level