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Factor analysis is known as the dimension reductions technique. It works by reducing the size of the variables by integrating them into sub-variables. Factor means the relationship between the underlying variable with its meaning. Hence, in order to lessen the data and to make it easy for analysing.
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In this article, we will be seeing an example of how to factor analysing with PCA works and how it can lessen the burden while analysing the gathered data:
You conduct a survey with the customers to figure out why they wouldn’t prefer your brand over the others. Once the data starts flooding in, you will find it overwhelming to tell which responses go together and under what factors. The reasons for them to not prefer your brand over other brands can be:
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Factor analysis will help you determine the trends in each answer and tell which ones of them can be grouped together. Each of the variables can have 3 factors loadings.
Each principal component has a high value for each variable and its corresponding subset variable. It shows the weight for the subset of a variable. The first component shows heavy weights for cost, the second component weighs for variables related to IT and the third component weighs high related to organizational issues. If we proceed to reframe the working for the factor analysis of our example, it would look something like this:
If we present the above information in clusters, we can see trends. Customers rate high in either cost of the product or the org barriers.