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From |
Maarten Buis <maartenlbuis@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Difference between factor score and linear prediction of latent variables after sem |

Date |
Tue, 15 Oct 2013 08:52:28 +0200 |

The factor scores gives you the estimate of the latent variables based on the measurement model, while the linear prediction gives you the predicted values based on the structural model. Hope this helps, Maarten On Mon, Oct 14, 2013 at 8:05 PM, Guillermo Montes <gmontes@childrensinstitute.net> wrote: > I estimated a sem model that regresses many observable predictors on two latent variables (SOC1 SOC2), each measured by 4 observable indicators. Then, I used the post estimation commands to calculate both the factor scores and the linear predictions of the latent variables using the commands below. > > Predict Lat*, xblatent(SOC1 SOC2) > Predict Fac*, latent(SOC1 SOC2) > > http://www.stata.com/manuals13/sempredictaftersem.pdf The manual says factor scores are predicted values of latent variables. > > What is the difference between a factor score and a linear prediction of a latent variable? The numbers are only correlated .7 or so in my case. > > Thanks > > Guillermo Montes Ph.D. > > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/faqs/resources/statalist-faq/ > * http://www.ats.ucla.edu/stat/stata/ -- --------------------------------- Maarten L. Buis WZB Reichpietschufer 50 10785 Berlin Germany http://www.maartenbuis.nl --------------------------------- * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: Difference between factor score and linear prediction of latent variables after sem***From:*Guillermo Montes <gmontes@childrensinstitute.net>

**References**:**st: Difference between factor score and linear prediction of latent variables after sem***From:*Guillermo Montes <gmontes@childrensinstitute.net>

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