Re: [R] "save scores" from sem

From: Steve Powell <steve_at_promente.net>
Date: Wed, 23 Jun 2010 14:03:40 +0200

Dear Joris,
thanks again for these very useful insights. Your point about PCA, sem, FA is clear to me now. And I understand what you say about it not being the point of sem to make corrections in a model and then try to "save the scores" for another analysis. But still I am wondering what would happen if you did? suppose I had:
a measured variable A and a latent variable B with indicators B1-B10, and A and B are supposed theoretically to correlate strongly. Then suppose I do a FCA and extract one component B'. If I did a similar CFA in an sem package and added a couple of correlated errors which improve the model, and if I could "save the scores" of the latent variable, wouldn't I expect it to correlate better with A than B' does?

Best Wishes
Steve

www.promente.org | skype stevepowell99 | +387 61 215 997

On Wed, Jun 23, 2010 at 12:36 PM, Joris Meys <jorismeys_at_gmail.com> wrote:
> I should have specified: lavaan is not familiar to me. I'm also not
> familiar enough with the sem package to tell you how to obtain the
> scores, but all information regarding the fit is in the object. With
> that, it shouldn't be too difficult to get the scores you want. This
> paper might give you some more information, in case you didn't know it
> yet :
>
> http://socserv.mcmaster.ca/jfox/Misc/sem/SEM-paper.pdf
>
> On a side note, sem with a single latent variable might be seen as a
> factor analysis with one component, but definitely not as a PCA. A PCA
> is constructed based on the total variance, rendering an orthogonal
> space with as many dimensions as there ara variables. Not so for a FA,
> as the matrix used to calculate the eigenvectors and eigenvalues is a
> reduced matrix, in essence only taking into account part of the
> variation in the data for calculation of the loadings. This makes PCA
> absolutely defined, but FA only up to a rotation.
>
> On a second side note, using the saved scores in some subsequent
> analysis is in my view completely against the idea behind sem.
> Structural equation modelling combines those observed variables
> exactly to be able to take the variation on the combined latent
> variable into account. If you use those latent variables as input in a
> second analysis, you lose the information regarding the variation.
>
> Cheers
> Joris
>
>
>
> On Wed, Jun 23, 2010 at 9:53 AM, Steve Powell <steve_at_promente.net> wrote:
>> Dear Joris,
>> thanks for your reply - it is the sem case which interests me. Suppose
>> for example I use sem to construct a CFA for a set of variables, with
>> a single latent variable, then this could be equivalent to a PCA with
>> a single component, couldn't it? From the PCA I could "save" the
>> scores as new variables; is there an equivalent with sem? This would
>> be particularly useful if e.g. in sem I let some of the errors covary
>> and then wanted to use the "saved scores" in some subsequent analysis.
>>
>> By the way, lavaan is at cran.r-project.org/web/packages/lavaan/index.html
>>
>> Best Wishes
>> Steve
>>
>> www.promente.org | skype stevepowell99 | +387 61 215 997
>>
>>
>>
>>
>> On Tue, Jun 22, 2010 at 7:08 PM, Joris Meys <jorismeys_at_gmail.com> wrote:
>>> PCA and factor analysis is implemented in the core R distribution, no
>>> extra packages needed. When using princomp, they're in the object.
>>>
>>>  pr.c <- princomp(USArrests,scale=T)
>>>  pr.c$scores # gives you the scores
>>>
>>> see ?princomp
>>>
>>> When using factanal, you can ask for regression scores or Bartlett
>>> scorse. See ?factanal.
>>> Mind you, you will get different -i.e. more correct- results than
>>> those obtained by SPSS.
>>>
>>> I don't understand what you mean with scores in the context of
>>> structural equation modelling. Lavaan is unknown to me.
>>>
>>> Cheers
>>> Joris
>>>
>>> On Tue, Jun 22, 2010 at 3:11 PM, Steve Powell <steve_at_promente.net> wrote:
>>>>  Dear expeRts,
>>>> sorry for such a newbie question -
>>>> in PCA/factor analysis e.g. in SPSS it is possible to save scores from the
>>>> factors. Is it analogously possible to "save" the implied scores from the
>>>> latent variables in a measurement model or structural model e.g. using the
>>>> sem or lavaan packages, to use in further analyses?
>>>> Best wishes
>>>> Steve Powell
>>>>
>>>> www.promente.org | skype stevepowell99 | +387 61 215 997
>>>>
>>>> ______________________________________________
>>>> R-help_at_r-project.org mailing list
>>>> https://stat.ethz.ch/mailman/listinfo/r-help
>>>> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
>>>> and provide commented, minimal, self-contained, reproducible code.
>>>>
>>>
>>>
>>>
>>> --
>>> Joris Meys
>>> Statistical consultant
>>>
>>> Ghent University
>>> Faculty of Bioscience Engineering
>>> Department of Applied mathematics, biometrics and process control
>>>
>>> tel : +32 9 264 59 87
>>> Joris.Meys_at_Ugent.be
>>> -------------------------------
>>> Disclaimer : http://helpdesk.ugent.be/e-maildisclaimer.php
>>>
>>
>
>
>
> --
> Joris Meys
> Statistical consultant
>
> Ghent University
> Faculty of Bioscience Engineering
> Department of Applied mathematics, biometrics and process control
>
> tel : +32 9 264 59 87
> Joris.Meys_at_Ugent.be
> -------------------------------
> Disclaimer : http://helpdesk.ugent.be/e-maildisclaimer.php
>



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