Understanding Society User Support

Ivelina,

It's likely that you won't be able to include all the variables used in our weighting model as there are too many of them (see below). But more importantly, if you want to correct for missingness using multiple imputation you would need to start at wave 1 (where non-contacted households are not interviewed but still need correction). Between 30% and 60% of total nonresponse comes from wave 1. Predictors from interviews won't give you information to correct for this missingness.
Another problem is a need to adjust for unequal selection probabilities. The difference in these probabilities is relatively large in UKHLS, and without account of them you have too many minorities, too much NI, and too many households selected recently, too few recent immigrants etc. You can't correct this with imputation, because this is not missingness.

My suggestion is to look whether you can start from cov-var matrices rather than data for your analysis. These could be obtained from UKHLS weighted and should be possible to feed into Mplus, though not sure how it works now. Long time ago I used LEM and could feed in matrices directly.

On variables used. These are the sets of variables used in h_indscui_lw:
- Census, other geographically linked variables, and sampling frame variables from 1991, separately from 2001, and from 2011 (note, there are 3 Censuses in UK, so separate variables for each);
- Variables from indall, hhresp and inresp from all waves of BHPS and all waves of UKHLS up until and including wave g. Our correction is wave on wave, so variables come from each wave.
- There is also post-stratification to external geographical variables at some points in time, and mortality correction (the latter has its own modelling, though covariates are similar to the above).

I worked on UKHLS weights, and not on BHPS weights. For UKHLS if you open the data and look at any question asked to everyone, it's in the model (unless excluded for collinearity). Only significant variables are included in each nonresponse model.

Hope this helps,
Olena