Page 293 - DMGT404 RESEARCH_METHODOLOGY

Unit 13: Multivariate Analysis

SPSS Commands for Discriminate Analysis Notes

Input data has to be typed in an SPSS file.
1. Click on STATISTICS at the SPSS menu bar.
2. Click on CLASSIFY followed by DISCRIMINANT.
3. Dialogue box will appear. Select the GROUPING VARIABLE. This can be done by clicking
on the right arrow to transfer them from the variable list on the left to the grouping
variable box on the right.

4. Define the range of values by clicking on DEFINE RANGE. Enter Minimum and Maximum
value then click CONTINUE.
5. Select all the independent variable for discriminant analysis from the variable list by
clicking on the arrow that transfers them to box on the right.
6. Click on STATISTICS on the lower part of main dialogue box. This will open up a smaller
dialogue box.
7. Click on CLASSIFY on the lower part of the main dialogue box select SUMMARY TABLE
under the heading DISPLAY in a small dialogue box that appears.

8. Click OK to get the discriminant analysis output.
Self Assessment

Fill in the blanks:
4. In discriminant analysis, ....................... groups are compared.
5. If the discriminant analysis involves two groups, there are ....................... centroids.

13.3 Conjoint Analysis

Conjoint analysis is concerned with the measurement of the joint effect of two or more attributes
that are important from the customers’ point of view. In a situation where the company would
like to know the most desirable attributes or their combination for a new product or service, the
use of conjoint analysis is most appropriate.

Example: An airline would like to know, which is the most desirable combination of
attributes to a frequent traveller: (a) Punctuality (b) Air fare (c) Quality of food served on the
flight and (d) Hospitality and empathy shown.
Conjoint Analysis is a multivariate technique that captures the exact levels of utility that an
individual customer places on various attributes of the product offering. Conjoint Analysis
enables a direct comparison,

Example: A comparison between the utility of a price level of 400 versus 500, a delivery
period of 1 week versus 2 weeks, or an after-sales response of 24 hours versus 48 hours.

Once we know the utility levels for each attribute (and at individual levels as well), we can
combine these to find the best combination of attributes that gives the customer the highest
utility, the second best combination that gives the second highest utility, and so on. This
information is then used to design a product or service offering.

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