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JBP posted on Tuesday, August 23, 2011  10:35 pm



Dear Professors, I'm running a simple LGM (7 repeated measures of single outcome) with two observed predictors (one categorical and one continuous that could be dichotomized if necessary). I use the following syntax: USEVARIABLES ARE P1 P2 VD1VD7 inter; DEFINE: inter= P1* P2; ANALYSIS: TYPE=MISSING; estimator=MLR; MODEL: i s  VD1@0 VD2@1 VD3@2 VD4@3 VD5@4 VD6@5 VD7@6; i s ON P1 P2 inter; However, I first did my analyses with multilevel modeling and I’m trying to replicate it on Mplus to profit from additional features. When I do not enter the interactions I get exactly the same estimates from both approaches. However, when I enter the interaction, the estimates are not the same. So I wanted to see if the syntax I’m using is correct. Also, in multilevel I first entered the interaction of P1xP2 for the intercept and then for the slope (which corresponds to a threeway interaction with time). Is it ok to fit the two following models or it does not make sense: i s ON P1 P2 ; i ON inter; and a second: i s ON P1 P2 ; i s ON inter; I hope the questions are relevant, Thanks! 


Please send the relevant outputs and your license number to support@statmodel.com. 


Dear Professors I am also running a simple growth curve model for my dissertation with 8 time points (violent behaviors across 4 months, a time point exists for every two weeks). I have two observed predictor variables (both continuous)  trait anger and anger rumination. I have hypothesized that the interaction between my two predictor variables predicts an increase in violent behaviors over time so that individuals who tend to ruminate to anger and are also high on trait anger (but not low on trait anger) show an increase in aggressive behaviors over time. I was wondering if I should use the same syntax as above to create the interaction term. If it is significant, I was wondering how I should followup on this interaction and how this interaction would be interpreted. Any help is greatly appreciated  thanks so much! 


For observed variables, the interaction can be created in DEFINE. This interaction would be interpreted in the regular way. 


Great! Thanks so much for the quick reply. How would I followup on this significant interaction to determine if individuals who ruminate to anger and are high on trait anger show an increase in aggressive behaviors? Would I use the same methods as I would in a regular regression model  look at people high and low on trait anger (1SD above and below) and see if anger rumination is still a significant predictor of an increase in aggressive behaviors? 


Yes, you would do this the same way as in regression. 


I am examining if the effect of an intervention on change in an outcome over time (linear latent growth curve model with dichotomous indicators and WLSMV estimator) is moderated by a number of observed, categorical demographic covariates. My question is, when examining interaction effects on the latent slope term, is it also important to include the relation between the interaction with the intercept term? My slope and intercepts are significantly correlated (p < .01), and depending on when I include the relation of the interaction with the slope only or slope and intercept (both using an ON statement) I get different results. 


First, you need to settle if time point 1 is pre intervention, with intervention starting between time 1 and time 2, where the intercept factor is defined at time 1 by time score = 0 at time 1. If so, I think you should include the main effects of demographics on the intercept factor because otherwise you are saying that its mean is the same across these demographic groups, but you should not include the demographic interaction with treatment because treatment has not happened yet at time 1. 

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