Multivariate Statistical Techniques

Question 3. Give two differences between Correspondence analysis (CA) and other multivariate techniques.

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Make use of the following notes in order to have an idea how to answer question 3 given above.

Differences from Other Multivariate Techniques

Among the compositional techniques, factor analysis is the most similar because it defines compo-site dimensions (factors) of the variables (e.g., attributes) and then plots objects (e.g., products) on their scores on each dimension. In discriminant analysis, products can be distinguished by their pro-files across a set of variables and plotted in a dimensional space as well. Correspondence analysis extends beyond either of these two compositional techniques:

Correspondence analysis can be used with nominal data (e.g., frequency counts of preference for objects across a set of attributes) rather than metric ratings of each object on each object. This capability enables Correspondence analysis to be used in many situations in which the more traditional multivariate techniques are in-appropriate.

Correspondence analysis creates perceptual maps in a single step, where variables and objects are simultaneously plotted in the perceptual map based directly on the association of variables and objects. The relationships between objects and variables are the explicit objective of Correspondence analysis.

We first examine a simple example of Correspondence analysis to gain some perspective on its basic principles.