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!
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.
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?
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?
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.
I am currently trying to replicate the second part of Example 9.2, using Mplus 8.1; i.e., a cross-level interaction where a cluster-level covariate moderates an influence of a within-level covariate. At first, I got the message that the command "type= twolevel random", which I took from Example 9.2, has to be combined with Bayes-Estimator. Is this always the case? After setting the Estimator on Bayes, I got the message "Mplus diagrams are currently not available for multilevel analysis. No diagram output was produced."
Best wishes, Daniel Schaefer
My syntax is the following:
WITHIN = EquPop AchPop NeePop UniNJ ZIncome Age Gender; BETWEEN = MehrMind EquPopG; CLUSTER = cluster;
DEFINE: CENTER EquPop (GROUPMEAN);
ANALYSIS: TYPE = TWOLEVEL RANDOM; ESTIMATOR=BAYES;
%WITHIN% s | RespPop ON EquPop AchPop NeePop UniNJ ZIncome Age Gender;
%BETWEEN% RespPop ON MehrMind EquPopG ;
[s] (gam0); s ON MehrMind (gam1) EquPopG;
RespPop WITH s;
MODEL CONSTRAINT: PLOT(ylow yhigh); LOOP(level1,-3,3,0.01); ylow = (gam0+gam1*(-1))*level1; yhigh = (gam0+gam1*1)*level1; PLOT: TYPE = PLOT2;
We recommend the Bayes estimator with random slopes because it makes possible latent variable decomposition on both levels (latent variable centering). See the paper on our website:
Asparouhov, T. & Muthén, B. (2018). Latent variable centering of predictors and mediators in multilevel and time-series models. Structural Equation Modeling: A Multidisciplinary Journal, DOI: 10.1080/10705511.2018.1511375 (Download scripts).