You should not use a variable as both a grouping variable and an analysis variable. You should do this as a single-group analysis leaving the variable as continuous and using XWITH. You can use DEFINE to create an interaction only if both variables are observed.
yao lu posted on Tuesday, March 06, 2012 - 2:58 pm
Dr. Muthen, thank you for your earlier reply.
I ran the model according to your suggestion. I encountered two problems.
1. I have two moderators (e.g., sim and fam below), each influencing three direct effects. Syntax as such (all variables are latent):
fcom on ccom; fcom on Int1 Int4; ftrust on ctrust; ftrust on Int2 Int5; fatt on catt; fatt on Int3 Int6;
The program indicated that the model is too large and the memory is not enough (after release all the possible memory). I tried to run just one moderator with three interaction effects, the program indicates that there are 50625 integration points. What would be the best alternative given this circumstance?
2. I have tested the model fit before incorporating interaction terms. How I can evaluate the model fit for the model with interaction terms? I did not see any fit indices in the output?
You should run the model with one interaction at a time. It is unlikely they are all significant and each one requires one dimension of integration.
Chi-square and related fit statistics are not available with TYPE=RANDOM. See the following FAQ on the website:
The variance of a dependent variable as a function of latent variables that have an interaction is discussed in Mooijaart and Satorra
yao lu posted on Tuesday, March 06, 2012 - 6:52 pm
I got it. Thank you very much!
I am sorry to trouble you again, but I have a follow up question. For Type = Random, the model results are unstandardized. In order to report the corresponding standardized statistics, I need to manually calculate?
For my study, I have a hypothesis of relationships of Y on X, and another hypothesis of the interaction effect of X and Z influencing Y. For testing the first hypothesis, should its coefficient be based on the model without or with interaction? I am asking this is because I found out that coefficient are very different between the model with and without interactions.