Sample size 300.I have totally 5 latent variables with multiple indicators (continuous): 3 independent and 2 dependent. Both the IVs and DVs are answered by the same respondent, however with regards to three different firms i.e.) All the five variables are scales requesting the respondent to answer about Firm A, firm B and firm C. The survey looks like:
1. Rate satisfaction of service with (1 to 5):
1. Rate you ability … (1 to 5)
1.. , 2… , 3….
For CFA, to account for the correlated residuals I used Bayesian SEM (BSEM) and have got robust fit indices and factor lodgings, Scales show very good fit. Can I check measurement invariance using BSEM webnote 17; however I don't have a grouping variable, so I can't use Type=mixture & knownclass? Please advice how to use BSEM Measurement invariance for single group.
Dear Prof. Muthen. Thank you very much. I am using the same setup as in the longitudinal example. however the model is not converging. These are the steps I am following: 1.) I am using correlated CFA model as per the BSEM 2012 article. The model worked very well. 2.) I am testing BSEM measurement invariance using approximate invariance. The model is not converging for iterations 50000 and 100000. Please advice.
For my analysis (point 2 in my above post) I am still getting posterior probability to be 0. I first tried with DIFF variance of 0.1, then .01, and then .001. Still PPP is 0. I having three indicators with * in the difference output though.
Hello. Aside from considering the benefit and meaning: I am wondering if it is possible to specify approximate measurement invariance across time and groups simultaneously in Mplus Bayesian CFA? I haven't found a way to expand the label assigning feature together with type=mixture, knownclass and the do/diff options to include both types of invariance. Is there any?
I have a question about prior distributions in testing approximate measurement invariance. In Muthen & Asparouhov article (2013), prior distributions for DIFFERENCES between parameters were used to test approximate measurement invariance.
Loading1-Loading2 ~ N(0,0.01)
Because variance of 0.01 may represent different magnitude of variability of differences depending on scales of factors’ indicators, instead of priors for differences between parameters, I think that using priors for PROPORTIONS of two parameters can be used. So we may assign priors such as
Loading1/Loading2 ~N(1, small variance)
I tried to use this in Mplus, but Mplus gives error message. “Unknown parameter label: Loading1/Loading2”
Q1. What do you think about using priors for proportions, not for differences in tests of approximate measurement invariance? Q2. I don’t know why Mplus recognizes "Loading1/Loading2" as a new label although it recognizes "Loading1-Loading2" well. I also created new parameter for proportion (L1= Loading1/Loading2) under the model constraint and then assign a prior to new parameter (L1), but Mplus still gives error message (Unknown parameter label). Is there any way to use priors for proportions in this case?
The scale of the variables does influence the prior variance choices as you say. The size of a loading is related to the SD of the variable. Also, different variables may have very different SDs. But if you are concerned about this, I think you could transform your variables to be on a more similar scale and then check the sensitivity to prior variances.
If you want to work with ratios, you have to declare those parameters as "NEW" parameters in Model Constraint before applying priors to them.
Dear Dr. Muthen, Thank you so much for your clarification! Yes. It would be one option to transform variables to be on a similar scale. I got it! To work with ratios, I followed your advice. I declared parameters as "New" parameters in model constraint and apply priors to those new parameters but it didn't work. Could you let me know if there is anything wrong in my code?(for simplicity, I didn't include priors for intercept terms)
MODEL: %OVERALL% F1 BY X1-X4; X1-X4*; F1*;[F1*]; %CG#1% F1 BY X1-X4(CG1X1-CG1X4); [X1-X4*](CG1IX1-CG1IX4); X1-X4*; F1@1;[F1@0]; %CG#2% F1 BY X1-X4(CG2X1-CG2X4); [X1-X4*](CG2IX1-CG2IX4); X1-X4*; F1*;[F1*];