Cross-classified mediation model PreviousNext
Mplus Discussion > Multilevel Data/Complex Sample >
 Bettina R posted on Monday, March 07, 2016 - 5:51 am
Dear Profs. Muthén,

I want to fit two multilevel mediation models with type=crossclassified.

In the first model, the mediation is on level 1 with a categorical mediator variable. As cross-classified models use Bayes estimation, I’m not sure whether I have to define the mediator variable as categorical like in a linear regression model. Or can I just use it as a continuous variable with Bayes?

In the second model, the mediation is on level 2. Can I calculate the indirect effect with ind = a*b when using cross-classified with Bayes estimation?

Thank you very much in advance!

Best, Bettina
 Tihomir Asparouhov posted on Tuesday, March 08, 2016 - 9:59 am
You can use both continuous and categorical mediator and categorical should be preferred since it reflects your data more accurately. Also take a look at the mediator=observed/latent option, see page 643 in the user's guide.

To calculate the indirect effect you need this command

model constraints:
new(ind); ind=a*b;
 Jukka Marjanen posted on Tuesday, April 25, 2017 - 11:49 pm
In my data, pupils have responded to the same questionnaire multiple times during different school lessons. In other words, the responses are cross-classified by lessons and pupils.

I'm trying to conduct a multilevel mediation analysis with multiple mediators. The causal variable (X) is an intervention, which is assumed to increase students' use of imagination (Y). Variable X is dichotomous, and variable Y is measured on a 4-point Likert scale.

The M-variables are also dichotomous: the pupils were asked whether they were building models, conducting research, reading textbooks etc. when prompted to respond to the questionnaire.

When it comes to pupils, there is a 1 -> 1 -> 1 type of mediation, and all paths could/should be random. When it comes to lessons, the mediation type is 2 -> 1 -> 1 and only the paths between the M and Y variables could/should be random.

I tried to estimate the model using TYPE=CROSSCLASSIFIED RANDOM, but the variables being categorical is a problem. I get the following message:


How should I proceed with the analysis?

Thank you very much in advance!
 Bengt O. Muthen posted on Wednesday, April 26, 2017 - 2:59 pm
Please send your output to Support along with your license number.
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