I have a question for the MI experts about imputations and experiments:
We often run experiments in which we hypothesize that responses will vary across conditions. We expose subjects to a condition -- CONDa, CONDb, or CONDc, say -- and then measure responses to a dependent variables across all conditions, say DV1, DV2, etc. We also collect data on various independent variables, say IV1, IV2, etc.
But because we anticipate the relationship between the IVs and DVs to vary across the conditions it seems like we ought to do one of two things when imputing missing data:
(1) interact every IV with the conditions so that we have, in effect DV1a, DV1_CONDb, DV1_CONDc, DV2_CONDa, etc. But quite often, the result of that equation will just result in a zero when the dummy for that condition is zero rather than one. This seems wasteful of information to me. Which leads me to my alternative...
(2) Rather than computing DV_CONDa as equalling zero when CONDa is also zero, I'm tempted to treat every alternative DV (DV1_CONDa, DV1_CONDb, DV1_CONDc ...) as missing for each case and impute it. Missingness will be high, of course (only 1/conditions of DVs will be present), but at least I won't be throwing away lots of valuable data. I hesitate to do so because I can't find anyone else who has done this and that makes me think I am probably misguided.
ps. I realize that this is a question about imputation generally, but I thought I'd post it here since I use Amelia for my imputation needs -- let me know if I should not post something like this here & I'll look elsewhere