Maria Rueda posted on Thursday, December 01, 2011 - 4:00 pm
Hi, I'm using CFA to estimate jointly a set of latent variables. I save the factor scores and I have the following questions. 1. I have read that factor scores are predicted using regression method. Where I can find references that explain the estimation approach and assumptions. 2. Are the scores unbiased and do you have some references that help me support that? 3. I'm using them as explanatory variables but I also would like to use them as dependent variables. I read in another post that I can not and that I would need other method for estimating the scores. Can I choose another method in mplus or does it means that I need to use another program? Thanks for all your help
Maria Rueda posted on Thursday, December 01, 2011 - 5:11 pm
I would like to add something. I'm estimating three latent factors jointly. Even in that case, can I use them as dependent variables?. Thanks again
Maria Rueda posted on Sunday, December 11, 2011 - 4:39 am
Dear Bengt, Thanks for the reference. One more question. When predicting factor scores, how mplus handle missing items? I'm doing a CFA (ML). It seems that for the persons that have missing in some items, it still predicts a factor score. Is it using the covariance matrix between the latent factors? Thanks
Factor scores are estimated using all available information on the observed variables for each person together with the parameter estimates for everyone.
Beth Bynum posted on Wednesday, March 14, 2012 - 5:36 pm
I have running CFA analyses to determine the factor structure of a set of scales. My model includes 30 observed variables, 13 first-order factors (8 Trait factors and 5 Method factors), and a second-order factor that the 8 trait factors load on to. I have saved the factor scores using the SAVE=FSCORES option. I would like to use the trait factor scores as independent variables in regression analyses. I understand that using factor scores can be problematic if the determinacy is low. I've specified the FSDETERMINACY option. The output provides determinacy estimates and factor score information for 114 different patterns.
1) What is the difference in the output for the different patterns? Is there one pattern I should look at over another?
2) Does the use of factor scores in the context I have described seem reasonable?
I have a question about comparability of the latent factor scores in two related models. Specifically, if I were to run the factor model for Data A and then use unstandardized loadings in estimating the same model for Data B (by fixing the loadings to be the same between two models) would the factor scores be computed in an essentially comparable way, since fixing loadings seem to be forcing the variance-covariance matrices to be the same? It is our understanding is that unstandardized factor loadings are used for the factor scores’ computation, but we are not sure whether other parameters, such as item intercepts are also used in calculating the scores in Mplus?
You should fix all of the parameters to use the model for prediction. You can use the SVALUES option of the OUTPUT command to get input with ending values as starting values and change the * symbol to @. Note that when you do this you make the assumption that both samples come from the same population.
See the FAQ on our web site called "Factor scores".
John Woo posted on Saturday, August 08, 2015 - 6:32 pm
Thank you. I have a couple more beginners-level questions..
(1) when Mplus does any kinds of latent variable regressions (e.g., UG Ex 6.12, Ex 6.13, etc), is it essentially using the Revised factor score regression method (as specified in Skrondal & Laake)? I had thought that Mplus estimates all measurement and structural parameters simultaneously, as opposed to using sequential regression approach? I might be confused here with the term "simultaneous"..
(2) I've read that factor scores cannot be used as DV because of biased estimations. Does this apply only to a crude way of factor score regression? Does Mplus make correction for this (for example, UG Ex 6.12) -- by, for example, using the Bartlett FSR? Bottom line: in UG 6.12, can we interpret the regression of the latent growth factors on x as producing unbiased estimates?