I would like to know whether it's possible in MPlus to determine for a significant interaction for which regions in the range of the moderator variable, the effects of the focal predictor on the outcome variable are statistically significant, according to the Johnson-Neyman technique (Hayes & Matthes, 2009; Bauer & Curran, 2009). I found some examples of MPlus input to plot simple slopes, but in those cases you choose the values yourself (often 1 sd below and above the mean) whereas I would like to identify for which values of the moderator variable the effects of the predictor on the outcome are significant. Do you have an example of an MPlus input to do this? Many thanks in advance for your response! Jolien
great - i've been able to adapt that code without a problem. i have a quick follow up question. When generating the plots, is mplus assuming a value of zero for the included covariates? I plotted the interaction by hand (using the mean of each covariate), and the shape of the plots is identical to the plots generated by Mplus, but the values of Y are shifted. Thanks!
I have a regression model with a predictor (paredmarg), moderator (schpress), interaction (int), and two covariates (female, w1grade). I'm curious how (or if) the generated plots take into account the value of the covariates. Here is the syntax i'm using (adapted from 3.18):
MODEL: !main model; lonely2 on paredmarg (b1) schpress (b2) int (b3) female w1grade;
it looks like you are computing predicted values for the lonely2 outcome. Because you don't include values for the female and w1grade covariates in those expressions, you are computing the predicted outcome at zero for those two covariates.