Correct multilevel multigroup model? PreviousNext
Mplus Discussion > Multilevel Data/Complex Sample >
 patelrj posted on Wednesday, August 10, 2011 - 4:03 am
Dear readers,

I am slightly confused how to define a multilevel model, especially what to put in the between model. So far, I've copied the within model and added a between level predictor.

I'm analysing a model in which nigh all variables are measured at the within level, and only one variable is measured at the between. All within variables have high ICC's (> .10).

I'm wondering if my syntax is correct when I want to model:
(1) random intercepts for all within variables
(2) no random slopes
(3) equal models for all groups

VARIABLE: NAMES ARE Clus Group w X1 X2 X3 Y M1 M2 M3;
GROUPING IS Group (0=g1 1=g2 2=g3);


Y ON M1 M2 M3;
M1 ON X2;
M2 ON X2 X3;
M3 ON X3;
X2 ON X1;
X3 ON X1;
X2 with X3;
M1 WITH M2 M3;

Y ON M1 M2 M3;
M1 ON X2 w;
M2 ON X2 X3 w;
M3 ON X3;
X2 ON X1 w;
X3 ON X1 w;
X2 with X3;
M1 WITH M2 M3;

I get no error messages, and nice fit measures, but I'm not sure if I defined my model properly
 Linda K. Muthen posted on Wednesday, August 10, 2011 - 10:46 am
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?
 Linda K. Muthen posted on Friday, August 12, 2011 - 8:46 am
MUML has no advantage with unequal cluster sizes. In fact, it has a disadvantage. When cluster sizes are equal, MUML is ML. When they are not, it is not ML.
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