longitudinal weights for small sub-samples
Some questions re longitudinal weights. Having read “Weighting Strategy for Understanding Society”, I gather that absence from a single wave leads to longitudinal weights of 0 for all later waves. In my case, this means that I am losing more than 10% of the small (sub)sample (n=997) I want to analyze. For context, my analysis starts with people who were non-citizens in Wave 1 and then revisits them in Wave 6 (by which point some have become UK citizens).
To avoid losing so many cases I’m wondering about replacing the zero values with some reasonable substitute. Three possibilities, perhaps: a) the mean of f_indinus_lw for the sub-sample (as calculated -- here, 0.7254; b), the cross-sectional individual weight at Wave 6, f_indinui_xw; or c), a cross-sectional individual weight at Wave 1. No doubt each is sub-optimal, but losing >10% of the sample is also sub-optimal. Any comments as to the relative merits of these three options? (Or are they all bad...)
If I could assume that attrition is not related to the response variable, then perhaps instead use the cross-sectional weight from Wave 1 for everyone? Response variables are life satisfaction (sclfsato), interest in politics (Vote6), and importance of British identity (britid). Obviously it’s up to me to make some sort of informed choice about this, but I’d be grateful for any comment.
One additional point, perhaps important for context. Because svy doesn’t work with xt- commands, I can’t use subpop –- so, I have assigned a weight of 0 to all those not in the subpopulation of interest (i.e., all but the 997). Is that the correct approach for using e.g. xtologit? The mean of the weights for the small subpopulation is then no longer 1; do the weights nonetheless ensure that the subsample is (reasonably) representative of the subpopulation?