Understanding Society User Support

Hi.

I think the best way to do this would be to start with the year X weight, derived as in our note on pooling, and then make an adjustment to this, to account for attrition by year Y. This would involve fitting a model (e.g. logit) based on all year X cases in your sample, in which the dep var is a 0/1 indicator of whether they also responded at year Y (and removing from the base any known to have died or emigrated before Y). Predictor variables can be anything relevant observed at X. This will give you a predicted probability for every year X respondent of responding at year Y. Call this P. You then need to adjust the year X weight by multiplying it by 1/P for all the cases that can be included in your analysis (those who responded in both years).

In the above, "year" refers to sample year rather than calendar year of interview. e.g. 2010 would mean the wave 2 interview for the year 1 sample and the wave 1 interview for the year 2 sample (regardless of whether those interviews actually took place in 2010: some will be in the early weeks of 2011).

Hope that makes sense.

Peter

Dear Peter,

Thanks for your advice and apologies for not getting back to you sooner. What you suggest is very helpful and answers my query. To improve consistency between the wave- and year-longitudinal weights I was wondering whether the predictor variables that are used to construct the standard longitudinal weights are documented anywhere.

Thanks again,

Agnes

Sadly not. But I could copy and paste a list of variables from our syntax files if you let me know which longitudinal weights you are (most) interested in. Is it w_indinub_lw, w_indpxub_lw, w_psnenub_lw, w_indinus_lw, w_indpxus_lw, w_psnenus_lw, or....?

Peter

Hi,
Sorry again for the delayed response - I was finalising exactly what sort of analysis I will be undertaking. If you could let me know what predictors are used to create w_indpxub_lw and w_psnenub_lw, that should be everything.
Thanks