Timeline for What do the first $k$ factors from factor analysis maximize?
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| Apr 13, 2017 at 12:44 | history | edited | CommunityBot |
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| Dec 4, 2014 at 10:22 | comment | added | amoeba | @raegtin and NRH (+1 btw): Just to clarify. Above two comments are correct if by "covariance" we understand the "off-diagonal part of the covariance matrix". | |
| Jul 10, 2011 at 5:36 | vote | accept | raegtin | ||
| Jun 29, 2011 at 5:53 | history | edited | NRH | CC BY-SA 3.0 |
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| Jun 28, 2011 at 23:53 | comment | added | NRH | @raegtin, yes, I view the model as a model of the covariance matrix, and when you estimate the model, it is fair to say that you are maximizing the amount of explained covariance. | |
| Jun 28, 2011 at 12:11 | comment | added | raegtin | Thanks for the update, this is a great explanation of FA! So when you say "the objective with the model is to best explain the covariance", do you mean the k factors really do maximize the amount of explained covariance? | |
| Jun 28, 2011 at 5:40 | comment | added | NRH | @raegtin, I have edited the answer to explain my point of view, that this is a model of the covariance matrix. Any choice of factors obtained by rotations are, as I see it, equally good or bad at explaining the covariances in the data as they produce the same covariance matrix. | |
| Jun 28, 2011 at 5:34 | history | edited | NRH | CC BY-SA 3.0 |
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| Jun 27, 2011 at 5:18 | history | edited | NRH | CC BY-SA 3.0 |
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| Jun 26, 2011 at 9:07 | comment | added | raegtin | Yep, I understand that there's not a unique choice of k factors (since we can rotate them and get the same model). But does any choice of k factors selected by factor analysis do some kind of "maximal explanation of correlation"? | |
| Jun 26, 2011 at 6:44 | history | answered | NRH | CC BY-SA 3.0 |