Message/Author 

Josie Booth posted on Wednesday, July 07, 2010  9:57 am



Hi, I'm carrying out mulitgroup SEM and looking for a bit of advice please. I have metric and scalar invariance and have compared the latent means of the two groups. In addition to this I would like to assess whether the regression weights for the structural paths are the same or not. I get a significant delta chisquare when I compare the model with the paths constrained to be equal to one where they are free. I have then freed each path individually while constraining the others to be equal. I get a significant delta chisquare every time but when I look at the regression weights for each group they are overlapping (using 1.96xSE) thus suggesting that they don't actually differ. I'm aware that the significant change to the chisquare could be due to the totality of the changes made by freeing just one parameter but I'm unsure as to the conclusions which can be drawn. Firstly, is my logic correct and if so can I conclude that the paths are the same between groups? Secondly, is there a more appropriate way of assessing this? In addition when all paths are free, I get extremely large standard errors for one group (Est 10.57, SE 14.36 & Est 12.71, SE 15.28). Should I be concerned about the size of these SE's? When looking at the standardised solution these SE's are greater than one  is this acceptable? Thank you for any help which you can provide. Josie 


You can instead make a path equal across the groups while letting the other paths be different across groups. If SEs for one group are much larger without its sample size being much smaller, I would do a separate analysis of this group to see if the SEs are large then also. 

Josie Booth posted on Thursday, July 08, 2010  1:25 pm



Thank you very much for your help. I have done a separate analysis for each group and the standard errors are much more appropriate. I wonder if you could possibly advise as to why it makes such a difference to the standard errors estimating the two groups in the same or different analysis? Thanks Josie 


The equality across groups may be causing this. If you have further questions, please send the outputs in question and your license number to support@statmodel.com. 

Brianna H posted on Thursday, March 20, 2014  1:37 pm



Hello. I am transitioning from conducting multigroup SEM in AMOS to Mplus. In Mplus, I would like to know whether a model where the structural weights of the two groups are constrained to be equal fits no worse than the saturated model where they are estimated uniquely. (In AMOS, this is determined via nested model comparisons assuming the unconstrained model to be correct, e.g. structural weights, intercepts, and means.) In the Mplus output for a multigroup model, I see only the usual model fit information such as the Model Chisq, CFI, RMSEA, etc. Does Mplus provide nested model comparison statistics for a multigroup model? The output I requested is "MODINDICES (ALL) STAND SAMPSTAT TECH4." Thank you for your help. 


Mplus does automatic difference testing only for measurement invariance. For your model you would need to do the difference test after running the two analyses. 

Brianna H posted on Friday, March 21, 2014  2:38 am



Thank you since there are no latent variables in my multigroup SEM model, I'm assuming that the measurement invariance test is not applicable to my model? Can you clarify what is the difference test and what syntax would allow me to run it? Or is the difference test just a chisquare difference test for the two models, subtracting the chisquare values and df's from each other? Thanks again for your reply. 


You are correct. Invariance testing involves latent variables. For ML, it is as you state. Difference tests for MLR are described on the website. See the left column. With WLSMV and MLMV, use the DIFFTEST option. See Example 13.12 

Brianna H posted on Friday, March 21, 2014  2:45 pm



Thank you. 

Rachel posted on Saturday, March 21, 2015  8:40 pm



Hello, I am doing a multigroup SEM and am trying to figure out which pathways are contributing to model non invariance. after releasing each constraint one at a time, the model fit barely improved. The mod indices suggest adding a path which doesn't theoretically make sense since it suggests I regress my mediator on my independent variable. I am trying to write these results. Do I interpret this as all the paths varying across groups? Or is there something I am missing? Thanks. 


Don't know if this is a multipleindicator model in which case the question is how well the configural model fits, or if it is path analysis with all observed variables but some kind of overidentification in each group in which case the question is what the fit in each group separately is. 

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