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Is PCA and Factor Analysis same? Both are used for Data dimension reduction but theoretically I am not able to find the difference between them?

I did FA in SPSS to reduce number of variables in my data set. I think the same think can be done using PCA as well. Never did PCA so wanted to know exact difference between two techniques.

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  • $\begingroup$ If you look at Wikipedia you will immediately see that the two are different. $\endgroup$ – user2974951 Sep 20 '18 at 10:43
  • $\begingroup$ Could you please explain with an example so that I get an clear idea $\endgroup$ – CodeMaster GoGo Sep 20 '18 at 10:47
  • $\begingroup$ The Wikipedia page actually has a dedicated section on this comparison with its references. Definitely worth exploring deeper... $\endgroup$ – mapto Sep 20 '18 at 11:38
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Principal components don't have to make sense, they are just artificial numbers, summarized data if you will, used e.g. for visualizations

With factor analysis you try to find a latent variable (an underlying mechanism) that explains the data. Factor analysis is often used in psychology, eg the personality trait extraversion (the factor) explains certain behaviours (eg certain behaviours reported in a questionnaire)

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