Timeline for How can I combine different principal components into one index?
Current License: CC BY-SA 4.0
6 events
| when toggle format | what | by | license | comment | |
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| Jan 10, 2020 at 18:43 | comment | added | Ed Rigdon | I did not make an assumption, though I rejected the idea that the aim is to build plausible proxies for unobserved variables. Whatever the aim, principal components are just slices of the information available. If you are doing the slicing, then you can put the slices on your sandwich any way you want. | |
| Jan 10, 2020 at 18:29 | comment | added | Nick Cox | BTW, I too jumped to the conclusion that the aim was all preparatory to regression. Reading the question again, now I can't see any reason for that conclusion. | |
| Jan 10, 2020 at 18:21 | comment | added | Nick Cox | Thanks for the reply. I don't recognise the reference. I can't summon up any enthusiasm for this idea, if only because the scope for interpreting what you've done in substantive terms seems slight. Wanting that isn't universal but it's central to how I want to do statistics. | |
| Jan 10, 2020 at 18:19 | comment | added | Ed Rigdon | Summing or averaging (dividing by a constant) should have the same effect in most (but not all) cases. If sample size were low, it could make sense to assign equal weights to the original predictors--that is, to just sum them all up (Dana and Dawes 2004). journals.sagepub.com/doi/pdf/10.3102/10769986029003317 | |
| Jan 10, 2020 at 16:45 | comment | added | Nick Cox | If three PCs are better than one, why not just average them all? | |
| Jan 10, 2020 at 16:35 | history | answered | Ed Rigdon | CC BY-SA 4.0 |