Hello, I have complex data so I need to use MLR to obtain robust standard errors. I have also used the model constraints command to obtain standard error estimates associated with indirect effects. Can I assume that these SE estimates are also robust to the nonindependence caused by the complex sample? Thank you for your help.
I am modelling a contrast effect (Big Fish Little Pond Effect) using group mean centering and model constraint:
model: %within% Y on indACH (b_within);
%between% Y on classACH (b_betwn);
model constraint: new (BFLPE); BFLPE = b_betwn - b_within;
Additionally, I am interested in whether there is a cross-level-interaction between average group achievement (classACH) and another level 1 variable. However, once I include the random slope "Beta1j | " in order to generate the cross level interaction, the parameter labels for the model constraint part are not recognized.
My question is: Is there a way to combine cross-level interaction and model constraint?
Also, using stdyx there are no standardized results for the model constraint "BFLPE". Is there any way to compute standardized coefficients for the New/Additional parameter?