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In a real life situation, several variables are operating. Some variables may be highly correlated among themselves.

In a real life situation, several variables are operating. Some variables may be highly correlated among themselves. For example, if manager of a restaurant has to analyse six attributes of a new product. He undertakes a sample survey and finds out the responses of potential consumers. He

Attribute
Correlation Matrix

We try to group the attributes by their correlations. The high correlation values are observed for the following attributes:

Attributes 1, 4 with a very high correlation coefficient of 0.95.

Attributes 2, 4 with a high correlation coefficient of 0.85.

Attributes 3, 4 with a high correlation coefficient of 0.85.

As a result, it is seen that not all the attributes are independent. The attributes 1 and 4 have mutual influence on each other while the attributes 2, 5 and 6 have mutual influence among themselves. As far as attribute 3 is concerned, it has little correlation with the attributes 1, 2 and 6. Even with the other attributes 4 and 5, its correlation is not high. However, we can say that attribute 3 is somewhat closer to the variables 4 and 5 rather than the attributes 1, 2 and 6. Thus, from the given list of 6 attributes, it is possible to find out 2 or 3 common factors as follows:

We try to group the attributes by their correlations. The high correlation values are observed for the following attributes:

Attributes 1, 4 with a very high correlation coefficient of 0.95.

Attributes 2, 4 with a high correlation coefficient of 0.85.

Attributes 3, 4 with a high correlation coefficient of 0.85.

As a result, it is seen that not all the attributes are independent. The attributes 1 and 4 have mutual influence on each other while the attributes 2, 5 and 6 have mutual influence among themselves. As far as attribute 3 is concerned, it has little correlation with the attributes 1, 2 and 6. Even with the other attributes 4 and 5, its correlation is not high. However, we can say that attribute 3 is somewhat closer to the variables 4 and 5 rather than the attributes 1, 2 and 6. Thus, from the given list of 6 attributes, it is possible to find out 2 or 3 common factors as follows:

I.

1. The common features of the attributes 1,3,4 will give a factor

2. The common features of the attributes 2, 5, 6 will give a factor

Or

II.

1. The common features of the attributes 1,4 will give a factor

2. The common features of the attributes 2,5,6 will give a factor

3. the attribute 3 can be considered to be an independent factor

1. The common features of the attributes 1,3,4 will give a factor

2. The common features of the attributes 2, 5, 6 will give a factor

Or

II.

1. The common features of the attributes 1,4 will give a factor

2. The common features of the attributes 2,5,6 will give a factor

3. the attribute 3 can be considered to be an independent factor

In the above example, we would like to filter down the attributes 1, 4 into a single attribute. Also we would like to do the same for the attributes 2, 5, 6. If a set of attributes (variables) A1, A2, …, Ak filter down to an attribute A

Tags : Research Methodology - Factor Analysis And Conjoint Analysis

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