Objective

 

reduce data complexity by discovering the underlying dimensions. Use Principal Component Analysis(PCA) and Exploratory Factor Analysis (EFA) to help minimize data dimensions, meanwhile to keep those dimensions interpretable in terms of the original variables. 

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Dataset Summary

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The dataset asks consumers on a scale from 1 to 10—where 1 is least and 10 is most—how [Adjective] is [An Aspect of Brand A]?

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Adjective: ​

• perform

• leader

• latest

• fun

• serious

• bargain

• value

• trendy

• repurchase

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Content

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1. Clean dataset.

2. Inspect bivariate relationship.

3. Aggregate mean ratings by brand.

4. Visualize brand mean.

5. Use Principal component analysis (PCA) to select primary dimensions.

6. Use Exploratory Factor Analysis (EFA) to select primary dimensions.

7. Build perceptual map. 

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Tools

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R + RStudio

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Library

 

library(corrplot)

library(RColorBrewer)

library(gplots)

library(nFactors)

library(GPArotation)

library(cluster)

library(MASS)

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