Re:Principal component analysis: pictures, code and proofs (...
by abakker on Monday March 11, 2019 @10:57AM
(#19359416)
I think of survey responses as being low-magnitude, high signal sometimes.
Take a list of 100 questions all answered between 1-5 (disagree -agree). Sometimes, PCA can help you group these statements into like concepts. Which means instead of having to look at variation across all 100 statements you only need to look at a handful.
However, in doing so, you might miss out on an interesting pattern in a subset of the data where a given subset of respondents are consistently answering a single choice differently. Maybe in aggregate that signal doesn’t show up, and so you would wash it out in PCA. Obviously, in this case, each of the 100 questions is a dimension.
Take a list of 100 questions all answered between 1-5 (disagree -agree). Sometimes, PCA can help you group these statements into like concepts. Which means instead of having to look at variation across all 100 statements you only need to look at a handful.
However, in doing so, you might miss out on an interesting pattern in a subset of the data where a given subset of respondents are consistently answering a single choice differently. Maybe in aggregate that signal doesn’t show up, and so you would wash it out in PCA. Obviously, in this case, each of the 100 questions is a dimension.