1. Business Research Methods
Multivariate Analysis

3. Multivariate Statistical Analysis
Statistical methods that allow the simultaneous investigation of more than two variables

4. A Classification of Selected Multivariate Methods
All multivariate
methods
Are some of the
variables dependent
on others? No
Yes
Dependence
methods
Interdependence
methods

5. 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

6. Dependence
Methods
How many
variables are
dependent
Several
Dependent Variable
One dependent
Variable

7. Analysis(IV-Metric/Non Metric) 2 Conjoint Analysis(IV-Non Metric)
Dependence
Methods
How many
variables are
dependent
One dependent
variable
Metric
Nonmetric
1 Multiple Regression
Analysis(IV-Metric/Non Metric)
2 Conjoint Analysis(IV-Non Metric)
Multiple
discriminant
Analysis( IV-Metric)

8. Variance (Independent Variables non metric) Canonical Correlation
Dependence
Methods
How many
variables are
dependent
Several
dependent
variables
Metric
Non metric /
Metric
Multivariate
analysis of
Variance (Independent
Variables non metric)
Canonical Correlation
Analysis( Independent
Variables- Metric /Non metric)

9. 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

10. Factor Analysis groups variables
Cluster Analysis groups cases or respondents
MDS & Correspondence Analysis groups Objects

11. Interdependence
methods
Are inputs metric?
Metric
Nonmetric

12. Metric Factor analysis Cluster analysis Metric multidimensional
Interdependence
methods
Are inputs metric?
Metric
Factor
analysis
Cluster
analysis
Metric
multidimensional
scaling

13. Scaling or Correspondence Analysis
Interdependence
methods
Are inputs metric?
Nonmetric
Nonmetric
multidimensional
Scaling or Correspondence Analysis

14. 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

15. 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

16. 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

17. 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

18. 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

19. 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

20. 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.

21. 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

22. 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.

23. 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

24. 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

25. 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.

26. Copyright © 2000 Harcourt, Inc. All rights reserved.
Factor Analysis
Height
Size
Weight
Occupation
Social Status
Education
Source of
Income
Copyright © 2000 Harcourt, Inc. All rights reserved.

27. 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

28. 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

29. 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)

30. 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

31. 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

32. Multidimensional 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
Aqua-Fresh vs. Crest
Crest vs. Aim .
Colgate vs. Aqua-Fresh
The number of pairs to be evaluated is n (n -1)/2, where n is the number of stimuli.

33. Rating Of Toothpaste Brands

34. A Spatial Map of Toothpaste Brands
0.5
-1.5
Dentagard
-1.0
-2.0
0.0
2.0
Close Up
-0.5
1.0
1.5
Pepsodent
Ultrabrite
Macleans
Aim
Crest
Colgate
Aqua- Fresh
Gleem

35. 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

36. 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