Page 303 - DMGT404 RESEARCH_METHODOLOGY

Unit 13: Multivariate Analysis

7. Choose DENDROGRAM then on the box called ICICLE, Choose "All Clusters" and "Vertical". Notes
8. Click OK on the main dialogue box to get the output of the hierarchical cluster analysis.
Stage 2
This stage is used to know how many clusters are required. This stage is called K- MEANS
CLUSTERING.
1. Click CLASSIFY, followed by K- FANS CLUSTER desired.
2. Fill in the desired number of clusters that has been identified from stage 1.

3. Click OPTIONS on the main dialogue box. Select "Initial Cluster Centers". Then click
CONTINUE to return to the main dialogue box.
4. Click OK on the main dialogue box to get the output which has final clusters.

Self Assessment

Fill in the blanks:

12. ....................... Analysis is a technique used for classifying objects into groups.
13. The ....................... application of cluster analysis is in customer segmentation and estimation
of segment sizes.

13.6 Multidimensional Scaling (MDS)

In addition to fulfilling the goals of detecting underlying structure and data reduction that is
shares with other methods, multidimensional scaling (MDS) provides the researcher with a
spatial representation of data that can facilitate interpretation and reveal relationships. Therefore,
we can define MDS as “a set of multivariate statistical methods for estimating the parameters in
and assessing the fit of various spatial distance models for proximity data.”
The spatial display of data provided by MDS is why it is also sometimes referred to as perceptual
mapping. MDS has much more flexibility about the types of data that can be used to generate the
solution. Almost any measures of similarity and dissimilarity can be used, depending on what
your statistical computer software will accept.
Types of MDS

In general, there are two types of MDS:
1. Metric
2. Non-metric
Metric MDS makes the assumption that the input data is either ratio or interval data, while the
non-metric model requires simply that the data be in the form of ranks. Therefore, the non-
metric model has more fewer restrictions than the metric model, but also less rigor. One technique
to use if you are unsure whether your data is ordinal or can be considered interval is to try both
metric and non-metric models. If the results are very close, the metric model may be used.

An advantage of the non-metric models is that they permit the researcher to categorize and
examine preference data, such as the kind obtained in marketing studies or other areas where
comparisons are useful.
Another technique, correspondence analysis, can work with categorical data, i.e., data at the
nominal level of measurement, however that technique will not be described here.

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