I would recommend that you do not use multiple imputation. If you have missing data on covariates you can convert these to dependent variables (usually by mentioning their variances) so that the missing data is modeled through the model. This would work well in almost all cases.
If you have to do MI and the above alternative is not an option you can use 3-level imputation where the weight variable or a log of it is added as a variable (so information from that variable can be used) during the imputation process, in addition to any other variables used for constructing the weights.
Unfortunately, I think I do need to do MI since complete case analysis would reduce my sample by 30%.
I am not sure I understand your 3-level imputation option but will look into it - or if you know of any resources I should consult, please direct me.
Alternatively, I might consider doing MI in Stata and then importing to MPlus for: 1) confirmatory factor analysis; 2)logistic regression; and 3) a fully longitudinal mediation analysis with a binary IV, continuous M, and a binary DV. Is that something you think might work?
Thank you. I will try to move forward with FIML then. But I do have a question about FIML.
I have a variable that has missingness at the item level and at the scale level - and I'm not sure how to handle this with FIML.
My knowledge score is comprised of 15 dichotomous items. I would like to use the average score as a predictor variable in my logistic regression. There is some missingness on these items - and also some people who didn't answer any of the 15 items - so, missingness at the scale level.
I was thinking I'd follow the guidance set by Mazza, Enders and Ruehlman (2015) to set the scale as missing if any item is missing and then also add auxiliary variables. Is this what you'd recommend?
They provide some code in their article and I just wanted to be sure I understood it... Are the variables after the first semicolon the auxiliary variables? What does the word "with" invoke?
MODEL: interf on txgrp female age severity depress; sever2 sever3 dep2-dep6 interf2-interf6 with txgrp female age interf severity depress sever2 sever3 dep2-dep6 interf2-interf6;