Re: Factor analysis

[Matthias Faeth]

Hi

I support ftr's and Justo's view that saving factor scores would be very welcome. I routinely  use factor scores for further regression analysis to determine the "importance" of factors for a dependent variable. Of course a FA without saving option is not worthless but only halfway useful.

Regards

Matthias

2016-01-06 23:49 GMT+01:00 Alan Mead <address@hidden>:

FTR,

The regression procedure does contain an option to save the scores, but it's not that hard to just create a compute statement:

compute yhat = -2.4567 + X1 * 0.2348 + X2 * (-2.4500).

Or whatever.

I don't deny that factor scores can be useful and my understanding is that they are more complex to compute but I have not ever used factor scores because I almost always want to know what a real score is, not a factor score.  That is, if I have a measure and find that items 1, 2, 3, all measure factor 1, I DO NOT want the F1 score (usually), I want the score one those three items, which is also trivial to create:

* assume items 1 and 3 are revrse-coded. All variables are on a 5-point likert.
compute V1R = 6 - V1.
compute V3R = 6 - V3.
compute F1 = mean(V1R, V2, V3R)*3.

So, your statement that EFA is worthless unless you can save the factor scores is incorrect.

-Alan

On 1/6/2016 4:35 PM, news wrote:

Saving factor scores would be a very welcomed innovation. What is the sense of factor analysis if you can't reuse the scores ?
The same is true of regression scores.

- ftr

On 04/01/2016 21:19, Alan Mead wrote:

I don't think PSPP has an option to save factor scores (yet):

https://www.gnu.org/software/pspp/manual/html_node/FACTOR.html

-Alan

On 1/3/2016 9:07 AM, Justo de Jorge Moreno wrote:

Dear list

I am new to PSPP. As I can save the factors estimated with the factor analysis?

Many thanks in advance

Justo

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-- 

Alan D. Mead, Ph.D.
President, Talent Algorithms Inc.

science + technology = better workers

+815.588.3846 (Office)
+267.334.4143 (Mobile)

http://www.alanmead.org

Announcing the Journal of Computerized Adaptive Testing (JCAT), a
peer-reviewed electronic journal designed to advance the science and
practice of computerized adaptive testing: http://www.iacat.org/jcat

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