Hi all,
I am running a bivariate moderation model where the phenotype of interest if dichotomous and the moderator is continuous (centered and scaled, but highly skewed). The estimates that I am getting are way off and don't match to the analysis without the moderation, this is in particular true for the correlation estimates. That is, when I compare estimates at the zero level of moderator with the estimates when there is no moderation in the model, they are very different. For example, rE=-0.27 at M=0 and rE=0.06 if moderation is not modelled; rP=-0.21 at M=0 and rP=0.17 if no moderation (also 0.17 observed from the data).
I have three moderators that we are interested in and such discrepancies are observed for two of them.
What could be a reason for that? I'm a bit lost and don't know how to proceed further, whether I can trust the results of the moderation model or not.
A bit of background: I am running ADE model based on the previous results, but CI for D are very wide and D could be dropped out of the model without significant deterioration of the fit. We decided to keep it in the model for now due to the reviews we got for our previous results (since CI's are large).
For one of the moderators, these estimates are off if D is present, but become consistent with the previous analysis if D is dropped. For the other two moderators, dropping D did not change rE and rP values.
Variance estimates for the main phenotype are consistent and in agreement with the previous analysis if D is dropped out of the model.
I did try both Purcell's bivariate moderation model (based on the Cholesky decomposition) and correlated factor solution with moderation of the paths, and they both consistently give weird estimates for the two of three moderators.
Also, there seem to be no significant moderation of any of the paths.
If anyone has any insight and idea what is going on there and why estimates at M=0 don't match with the estimates when there is no moderation, I would be very grateful!!!
Thank you in advance!
Julia