Q-type factor analysis: Forms groups of respondents or cases based on their similarity on a set of characteristics. Note that the goal of this type of analysis is the same as that of cluster analysis, though the procedure is different.This application is not used much today since a variety of clustering techniques have been developed that are designed specifically for the purpose of grouping multiple subjects into independent groups.
Step 1 Compute a k by k intercorrelation matrix. Compute the factorability of the matrix.
Step 2 Extract an initial solution
Step 3 From the initial solution, determine the appropriate number of factors to be extracted in the final solution
Step 4 If necessary, rotate the factors to clarify the factor pattern in order to better interpret the nature of the factors
Step 5 Depending upon subsequent applications, compute a factor score for each subject on each factor.

R-type factor analysis: Analyzes relationships among variables to identify groups of variables forming latent dimensions (factors). This is what is represented by the traditional useage of the term "factor analysis".

from https://bbs.pinggu.org/thread-318272-1-1.html

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