We are wanting to look at overall differences in minutes spent ruminating in-person, over texting, over social media, and over the phone. Data were collected using twice-daily diaries over two weeks, so data are clustered within participants. Our hypothesis was that individuals ruminate more in person than they do via the other communication domains. I was able to test contrasts between minutes ruminating in-person versus the other domains, but now we want to see if sex moderates these contrasts. I have dummy coded sex and added it to the model as a between-level variable, and I have regressed sex on each type of co-rumination. However, I'm not sure how to test sex as a moderator of these contrasts. I have attached my input. Can you please advise?
usevariables are Sex_dummy Mins_Inperson Mins_Texting Mins_Socialmedia Mins_Overphone;
My understanding is that this would then compare the moderating effects for each of the variables. However, I would like to test whether the difference in mean levels among variables differs by sex. What I want to do is create variables that represents the difference in intercepts for each variable compared with the mins_inperson variable, and regress each of those on sex to see if there is significant moderation, but I can't seem to find a way to do that. Here is what I have tried but I can't seem to find a way to get this to run. Can you please advise? Is there a better way to go about this?
%between% Mins_Inperson Mins_Texting Mins_Socialmedia Mins_Overphone; [Mins_Inperson](p1); [Mins_Texting](p2); [Mins_Socialmedia](p3); [Mins_Overphone](p4); textdiff on Sex_dummy; Socdiff on Sex_Dummy; Phonediff on Sex_Dummy;
This is not how to do it. For each variable, you now have 2 parameters, not 1, e.g.:
textdiff on sex_dummy (b1); [textdiff] (a1);
where b1 is the slope and a1 is the intercept in this regression. Following regular dummy variable regression, for sex_dummy = 0, the intercept that you want to compare with other variables is a1. For sex_dummy=1, the intercept is a1+b1.