logging in or signing up Multivariate Analysis piyoosh Download Post to : URL : Related Presentations : Let's Connect Share Add to Flag Embed Email Send to Blogs and Networks Add to Channel Copy embed code: Embed: Flash iPad Dynamic Copy Does not support media & animations Automatically changes to Flash or non-Flash embed WordPress Embed Customize Embed URL: Copy Thumbnail: Copy The presentation is successfully added In Your Favorites. Views: 1154 Category: Education License: All Rights Reserved Like it (0) Dislike it (0) Added: August 09, 2012 This Presentation is Public Favorites: 1 Presentation Description No description available. Comments Posting comment... Premium member Presentation Transcript Business Research Methods: Business Research Methods Multivariate Analysis PIYOOSH BAJORIAMultivariate Statistical Analysis: Multivariate Statistical Analysis Statistical methods that allow the simultaneous investigation of more than two variables PIYOOSH BAJORIAA Classification of Selected Multivariate Methods: All multivariate methods Are some of the variables dependent on others? Yes No Dependence methods Interdependence methods A Classification of Selected Multivariate Methods PIYOOSH BAJORIADependence Methods: Dependence Methods A category of multivariate statistical techniques Dependence methods explain or predict a dependent variable(s) on the basis of two or more independent variables PIYOOSH BAJORIAPowerPoint Presentation: Dependence Methods How many variables are dependent One dependent Variable Several Dependent Variable PIYOOSH BAJORIAPowerPoint Presentation: Dependence Methods How many variables are dependent One dependent variable Nonmetric Metric Multiple discriminant Analysis( IV-Metric) 1 Multiple Regression Analysis(IV-Metric/Non Metric) 2 Conjoint Analysis(IV-Non Metric) PIYOOSH BAJORIAPowerPoint Presentation: Dependence Methods How many variables are dependent Non metric / Metric Metric Canonical Correlation Analysis( Independent Variables- Metric /Non metric) Multivariate analysis of Variance (Independent Variables non metric) Several dependent variables PIYOOSH BAJORIAInterdependence Methods : Interdependence Methods A category of multivariate statistical techniques Do not discriminate between dependent & independent variable. All variables have equal status. It attempts to find out how all variables are interrelated Interdependence methods give meaning to a set of variables or seek to group things together PIYOOSH BAJORIAPowerPoint Presentation: Factor Analysis groups variables Cluster Analysis groups cases or respondents MDS & Correspondence Analysis groups Objects PIYOOSH BAJORIAPowerPoint Presentation: Interdependence methods Are inputs metric? Metric Nonmetric PIYOOSH BAJORIAPowerPoint Presentation: Metric Metric multidimensional scaling Cluster analysis Factor analysis Interdependence methods Are inputs metric? PIYOOSH BAJORIAPowerPoint Presentation: Nonmetric Nonmetric multidimensional Scaling or Correspondence Analysis Interdependence methods Are inputs metric? PIYOOSH BAJORIAMultiple Regression: Multiple Regression Multiple Regression is appropriate when research problem involves a single metric dependent variable presumed to be related to two or more metric (sometimes non metric) independent variables The objective is to predict changes in dependent variable in response to changes in independent variables PIYOOSH BAJORIAMultiple Regression: Multiple Regression Monthly expenditure on dining out (DV) can be predicted from information regarding Family Income, Family Size & Age of the head of the HH ( IVS) Company’s sales can be predicted from information on its expenditure on ads, number of sales people and number of retail outlets carrying its product PIYOOSH BAJORIADiscriminant analysis: Discriminant analysis Discriminant analysis helps in discriminating between two or more sets of objects or people based on the knowledge of some of their characteristics It is a technique for analyzing data when the criterion or dependent variable is categorical (hence non metric) and the predictor or independent variables are metric in nature The primary objective is to understand group differences and to predict the likelihood that an entity ( individual or object) will belong to a particular group based on several metric independent variables PIYOOSH BAJORIADiscriminant Analysis: Discriminant Analysis Discriminate between Bones or skeletons of males or females Dividing people into potential buyers or non buyers Classifying individuals as good or bad credit risk Classifying companies as good or bad investment risks Classifying consumers as brand loyal or brand switchers PIYOOSH BAJORIAConjoint Analysis: Conjoint Analysis It is an emerging dependence technique for assessing consumer utility levels for specific product attributes and their levels Consumers are required to evaluate only a few product profiles which are combinations of product levels It can answer questions such as what utility consumers see in price levels, in after sales service levels, product features etc It can be used in evaluation of new as well as existing products or services PIYOOSH BAJORIAConjoint Analysis: Conjoint Analysis Assume a product has three attributes Price(H,M,L) , quality( Super Deluxe,Deluxe,Normal) & colour ( red, yellow, blue) each at three possible levels . Instead of having to evaluate 27 possible combinations a subset of 9 or more can be evaluated for their attractiveness to the consumers Researcher knows not only how important each attribute is but also the importance of each level.( Attractiveness of red vs yellow vs blue) Results can also be used to simulate various product designs & their acceptances PIYOOSH BAJORIAMultivariate Analysis Of Variance(MANOVA): Multivariate Analysis Of Variance(MANOVA ) Multivariate Analysis of Variance is appropriate when research problem involves multiple metric dependent variables presumed to be dependent to one or more non metric independent variables (usually referred to as treatments). With MANOVA a significance test of mean difference between groups can be made simultaneously for two or more dependent variables. PIYOOSH BAJORIAMultivariate Analysis Of Variance(MANOVA): Multivariate Analysis Of Variance(MANOVA) By manipulating sales compensation system in an experimental situation and holding compensation system constant in a controlled situation the researcher may be able to identify effect of the new compensation system (IV) on sales volume( DV) as well as on job satisfaction (DV) Impact of type of Ad (IV : humorous vs non humorous ) on perception of customers about company & its products on several dimensions(DV) such as modern vs traditional, high quality vs low quality can be studied with MANOVA PIYOOSH BAJORIACanonical Correlation: Canonical Correlation It is a logical extension of multiple regression analysis involving several dependent and several independent variables. It determines linear association between two sets of variables each consisting of several variables. PIYOOSH BAJORIACanonical Correlation: Canonical Correlation Canonical correlation analysis can be used to find out how several personality traits (IV) influence shopping behaviours such as list preparation, use of store coupons, number of stores visited and number of trips per week (DV) This indicates personality profile that tends to be associated with various shopping patterns PIYOOSH BAJORIACanonical Correlation: Canonical Correlation A company conducts a study to find out correlation between service quality of the company & those of the world class companies. The study uses questions (50) from published service quality research and includes benchmarking information on the perception of the service quality of world class companies as well as for the company being studied. The technique would provide information on overall correlation of perception as well as correlation between each of 50 questions PIYOOSH BAJORIAFactor Analysis : Factor Analysis A type of analysis used to discern the underlying dimensions or regularity in phenomena. Its general purpose is to summarize the information contained in a large number of variables into a smaller number of factors. PIYOOSH BAJORIAFactor Analysis: Height Weight Occupation Education Source of Income Size Social Status Factor Analysis Copyright © 2000 Harcourt, Inc. All rights reserved. PIYOOSH BAJORIAFactor Analysis: Factor Analysis Factors determining buying behaviour of small cars Factors determining choice of an airlines Factors leading to cigarette smoking Underlying dimensions for willingness to donate regenerative & non regenerative body parts Factors determining choice of a bank PIYOOSH BAJORIACluster Analysis : Cluster Analysis A body of techniques with the purpose of classifying individuals or objects into a small number of mutually exclusive groups, ensuring that there will be as much likeness within groups and as much difference among groups as possible PIYOOSH BAJORIAUses of Cluster Analysis: Uses of Cluster Analysis It is a multivariate interdependence procedure ideally suited to segmentation application in marketing Cluster by definition is a group of similar objects Segmentation involves identifying groups of target customers who are similar in buying habits , demographic characteristics or psychographics There could be clusters of brands similar to each other & different from other clusters( Soaps on the basis of various characteristics ) Cluster analysis is also used in selection of test market cities that are sufficiently similar so that no extraneous variation can cause difference between the experimental & control markets.( population, retail sales, number of retail outlets, proportion of various income groups etc) PIYOOSH BAJORIAMultidimensional Scaling: Multidimensional Scaling A statistical technique that measures objects in multidimensional space on the basis of respondents’ judgments of the similarity of objects If objects A & B are judged by respondents as the most similar compared with all other possible pairs of objects MDS (perceptual mapping ) places them closest to each other in terms of distance in multidimensional map The unfolding of attributes of each object aids in understanding why objects are judged to be similar or dissimilar PIYOOSH BAJORIAMultidimensional Scaling: Multidimensional Scaling An owner of a Burger King wants to know whether the strongest competitor is McDonald’s or Wendy’s A sample o customers is asked to rate pair of restaurants from most similar to least similar The results of MDS shows that Burger King is most similar to Wendy’s. So the strongest competition is from Wendy’s PIYOOSH BAJORIAMultidimensional Scaling Input Data: Perception Data: Direct Approaches. In direct approaches to gathering perception data, the respondents are asked to judge how similar or dissimilar the various brands or stimuli are, using their own criteria. These data are referred to as similarity judgments. Very Very Dissimilar Similar Crest vs. Colgate 1 2 3 4 5 6 7 Aqua-Fresh vs. Crest 1 2 3 4 5 6 7 Crest vs. Aim 1 2 3 4 5 6 7 . . . Colgate vs. Aqua-Fresh 1 2 3 4 5 6 7 The number of pairs to be evaluated is n ( n -1)/2, where n is the number of stimuli. Multidimensional Scaling Input Data PIYOOSH BAJORIARating Of Toothpaste Brands: Rating Of Toothpaste Brands PIYOOSH BAJORIAA Spatial Map of Toothpaste Brands: A Spatial Map of Toothpaste Brands 0.5 -1.5 Dentagard -1.0 -2.0 0.0 2.0 0.0 Close Up -0.5 1.0 1.5 0.5 2.0 -1.5 -1.0 -2.0 -0.5 1.5 1.0 Pepsodent Ultrabrite Macleans Aim Crest Colgate Aqua- Fresh Gleem PIYOOSH BAJORIACorrespondence Analysis: Correspondence Analysis Correspondence analysis differs from other interdependence techniques in its ability to accommodate non metric data It employs contingency table which is the cross tabulation of two categorical variables PIYOOSH BAJORIACorrespondence Analysis: Correspondence Analysis Respondents brand preferences can be cross tabulated on demographic variables ( gender, income categories, occupation)by indicating how many people preferring each brand fall into each category of demographic variables Through CA correspondence of brands & characteristics of those preferring each brand are shown in 2or3 dimensional map of both brands & respondent characteristics. Brands perceived as similar are located closed to one another.Likewise characteristics of respondents preferring each brand are also determined by proximity of the demographic variable categories to brand’s position PIYOOSH BAJORIA You do not have the permission to view this presentation. In order to view it, please contact the author of the presentation.