Interaction in two-level model with r...
Message/Author
 Shuwen Tang posted on Friday, November 02, 2012 - 10:08 am
I want to examine a two-level model with random slopes. Usually I just use the "S | y on X" statement in the within model. But this time I want to use the within-part of X as the predictor. Instead of group-mean-centering X, I would like to use the latent variable approach. I tried the following syntax:

%WITHIN%
FXw by x @1;
x@0;
S| Y ON FXw ;
%BETWEEN%
FXb by x@1;
x@0;
S Y on T;

I got the error message saying that "THE ESTIMATED WITHIN COVARIANCE MATRIX COULD NOT BE INVERTED."

Is there anything fundamentally wrong with this model? I appreciate your thoughts and input!
 Linda K. Muthen posted on Friday, November 02, 2012 - 1:52 pm
See the third part of Example 9.2 where the input for this is shown.
 Shuwen Tang posted on Friday, November 02, 2012 - 4:00 pm
Thanks, Linda. In the example 9.2, I can only get an overall interaction estimation. What we want to see is to separate the interaction term into between and within parts. Do you have any ideas to get these using the latent variable approach, instead of group-mean-centering X?
 Shuwen Tang posted on Tuesday, November 06, 2012 - 10:12 am
Or, how to deal with the error message saying that "THE ESTIMATED WITHIN COVARIANCE MATRIX COULD NOT BE INVERTED." Is there anything fundamentally wrong with the model I mentioned above? Thanks.
 Linda K. Muthen posted on Tuesday, November 06, 2012 - 11:35 am
Regarding using only the within part of the latent variable decomposition as a covariate in the random slope, this is not possible.

 Shuwen Tang posted on Tuesday, November 06, 2012 - 12:22 pm
why it is not possible? Example 9.10 shows a model in which a within latent variable is used as a covariate in the random slope. How is our model different from that example, except that we only have one indicator for the latent factor?
 Linda K. Muthen posted on Tuesday, November 06, 2012 - 1:42 pm
 Linda K. Muthen posted on Friday, November 16, 2012 - 12:31 pm
Try fixing x at 0.0001 on both levels.

x@0.0001;
 John C posted on Monday, December 19, 2016 - 12:04 pm
Hello,

I would like to do a cross-level interaction model, i.e., I want to check if a cluster-level variable predicts the “slope” of a within-level predictor.

However, my outcome is binary.

Can this be done? If so, is this the same specification as with a continuous outcome except for declaring the dependent variable as categorical?

John C.
 Bengt O. Muthen posted on Monday, December 19, 2016 - 6:03 pm
That's right.
 Kimberly Hall posted on Thursday, January 12, 2017 - 1:21 pm
Hello,

I'm currently attempting to estimate a two-level longitudinal model (data are clustered within person), and am modeling my syntax off of Example 9.16 in the Mplus 7.0 manual.

I'd like to predict the random slope using another within-subjects variable and tried the following syntax:

%WITHIN%
s | SC on trial;
s on Phase;

But received an error message stating that the latent variable declared on the between level cannot be used on the within level. Is there a way to specify both within- and between-subject variation for a random slope using this syntax? If not, is there a different example you suggest?

 Bengt O. Muthen posted on Thursday, January 12, 2017 - 1:42 pm
The random slope varies across the between-level units, so no variation within. You can use the between-level variation part of Phase to predict s on the Between level.
 Kimberly Hall posted on Friday, January 13, 2017 - 7:01 am
Thank you for your reply. Is it possible to predict within level variation in random slopes using example 9.14?

We are interested in examining differences in the rate of extinction over the course of two separate sessions (i.e., Phase). We thus included the trials for both sessions in one time variable and were hoping to structure our Level 1 analyses as follows:

B0 = intercept
B1 = linear effect of time
B3 = moderator effect of phase
B4 = linear time by phase interaction

What we are struggling with now is how create the B4 term. Any suggestions are greatly appreciated.
 Bengt O. Muthen posted on Friday, January 13, 2017 - 5:22 pm
If you don't have too many time points within each phase you can do your growth modeling in single-level, wide format and let phase be represented via piecewise growth modeling - see UG ex 6.11.