The models specified on within and between should be guided by your research questions and hypotheses.
(1) Yes. (2) Yes. Example 9.2 shows a random slope model. (3) There are no equalities in your model. Structural parameters are not held equal across groups as the default.
I would not use MUML. I would use the default estimator.
patelrj posted on Thursday, August 11, 2011 - 3:14 am
Dear dr. Muthen,
thank you very much for your clear reply. I've added equality constraints to my model, and found all hypothetical links homogeneous between groups, except for one; but the scale used there is very unreliable, so I'm inclined to point to power problems before I theorize a lot about it.
Due to very unequal cluster sizes (unequal classroom composition) I used MUML to handle that type of data. Is it ok to use it in this instance?
1. Is this way correct even though my moderator is dichotomous (boys/girls)? The interpretation would be then, the more someone is a girl, the more predictor affects the outcome...?
2. For the moderation to take place, the interaction term has to be significant. But what about the model fit? Can I compare the fit of a model with freely estimated path from the interaction term to the outcome with a model where this path is constrained to zero?
1. If gender=1 for girl and 0 for boy then the intercept Y is interpreted as the intercept for group boy, while the intercept for group girl would be [Y] + Y on gender. Similarly, Y on X is the coefficient for boy and the coefficient for girl is Y on X + Y on interaction.
2. Yes. Such a test would be equivalent to testing equality of the regression coefficient Y on X between the two groups.
Hello everyone. Iím trying to estimate a multilevel (2 levels: individual and neighborhood) multigroup model (two cities) where I want to see the differences in the effect of a latent variable created at the individual level in an exogenous variable that are related to the between level.
I tried to code as below, but Mplus return an error message saying that Parameters involving between-level variables are not allowed to vary across classes. This makes sense. However, how can I specify the model to enable this comparison? Any tips?
CLUSTER IS GEOCODI; CLASSES = CLASS (2); CATEGORICAL ARE CA D CI CB TF AS SQ SC ILUM_B PAV_B CAL_B;
WITHIN = GI I S TR FCM CAR_DISP;
BETWEEN = ILUM_B PAV_B CAL_B pm;
knownclass = CLASS (CDD);
type = twolevel mixture random;
model: %within% %overall%
WALK BY CA (LCA) D (LD) CI (LCI) CB (LCB) TF (LTF) AS (LAS) SQ (LSQ) SC (LSC); WALK SEG SAT ON GI I S TR FCM CAR_DISP;
WALKB BY CA D CI CB TF AS SQ SC; INFRA BY ILUM_B PAV_B CAL_B;