From: Joris Meys <jorismeys_at_gmail.com>

Date: Wed, 23 Jun 2010 12:36:04 +0200

Date: Wed, 23 Jun 2010 12:36:04 +0200

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 ______________________________________________ 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.Received on Wed 23 Jun 2010 - 10:38:46 GMT

Archive maintained by Robert King, hosted by
the discipline of
statistics at the
University of Newcastle,
Australia.

Archive generated by hypermail 2.2.0, at Wed 23 Jun 2010 - 12:30:35 GMT.

*
Mailing list information is available at https://stat.ethz.ch/mailman/listinfo/r-help.
Please read the posting
guide before posting to the list.
*