jamovi

What was the correspondence between lavaan and SEMLj?

HC3 is appropriated when you have issues with etheroschedasticity. Normality violation is better addressed with bootstrap methods. In general, what to do with assumptions violation really depends on which assumption is violated and how much the data depart from the assumption

that's usually a sign that the model did not converge or there are issues with convergence. Can you share a omv file to see the model?

I do not have access to Mplus (it's not open source). Did you try to check the results with R lavaan?

Hi
why can you not replicate the UI of SEMLj? Do you want to have something different? If you want to use the same approach of SEMLj, you can simply use the code (javascript and yaml) you find in the module. Start from there and change it as you wish

it's coming out in a few days

Done, robust standard error for poisson and other generalized models are now implemented. Check version 3.6.4 on monday

great

[this is equivalent to gamlj results in R file<-"local/data/mer_jasp.csv" data<-read.csv(file) data$Framing<-factor(data$Framing) contrasts(data$Framing)<--contr.sum(2)/2 data$BothDisliked<<-factor(data$BothDisliked) contrasts(data$BothDisliked)<--contr.sum(2)/2 data$Participant<-factor(da...

you should also be sure that the random component is the same in jasè and jamovi. If I am not mistaken, in gamlj you did not include the random intercept.