I wanted to decompose this variable into two latent variable parts (i.e., Xwij and Xbj) in my two-level model, and examine the effects of the level 2 assessment of X on my level 1 dependent variable (e.g., Ludkte et al., 2008, Psych Methods).
Hence, following the user manual (pg 230-231), I did not list X either in the within or the between statements. This allowed me to model X both in the within and between levels of my two-level model. Consequently, I was able to examine how level-2 assessment of x (i.e., Xbj) has a main effect on my level-1 Dependent variable.
However, now, I want to, go further, and test the interaction effect of the level-2 latent variable part (i.e., Xbj) with an observed between-level variable (e.g., Z) on my level-1 dependent variable.
Can you kindly guide me on how to test this interaction.
I tried creating a product term between X (where X is not specified as either a within or a between variable) and Z (specified as a between variable) using XWITH. But, I got an error message stating that I need to use the DEFINE function. However, I am not sure that I want to use the DEFINE function because I want to specifically create an interaction term between the level-2 "latent" assessment of X (i.e., Xbj) and an observed variable.
I would like to decompose an observed interaction term that is predicting intercept in a growth model. In other software programs, I have done this ala Jaccard & Turkesi using variance, covariance, and unstandardized slopes (B) etc to plot the slopes of high and low levels of the components of the interaction term. Most of these values appear easily accessed in MPlus output (assuming that I can use the unstandardized Estimate term as B values). For some reason, I'm having trouble ascertaining the unstandardized B regression model constant. Can you please point me to where to find this output or to another way to decompose my interaction?
Thanks for your help - indeed it was clearly indicated when I loaded my Version 5 - along with many other helpful points if information (i.e., p values, additional error information, etc). Thanks again for this amazing software and for your generosity with your time and skill.