If your analysis sample is a nonresponse-defined subset of a sample for which analysis weights have not been provided, you can derive your own analysis weight.
When to derive your own weight
You may consider doing this if no analysis weight has been provided for the combination of waves and instruments that you wish to include in your analysis and if the solutions suggested in this guide are not satisfactory.
For example, suppose you wish to carry out longitudinal analysis of responses to questions that were included at Waves 1, 4 and 7. Your analysis base is therefore sample members who completed an individual interview at each of those three waves (let’s assume that your survey questions of interest were not all included in the proxy questionnaire and that you therefore cannot include proxy responses in your analysis).
One option would be to use the Wave 7 longitudinal weight for the wave 1 sample, i.e. g_indinus_lw. However, this weight is only defined for sample members who gave a full personal interview at all seven waves, thus 18,510 persons have this weight, whereas 20,390 responded at Waves 1, 4 and 7 (so, 1,880 of those who responded at Waves 1, 4 and 7 must have failed to respond at one of Waves 2, 3, 5 or 6). Using this weight for your analysis would therefore cause almost 10% of your potential analysis sample to be dropped from the analysis. This reduction in sample size will cause a modest reduction in the precision of your analysis (increase in standard errors). The effect will be rather small, and you may well be willing to accept this slight reduction in sample size, unless you are producing estimates for very small population subgroups. But if you want to be able to include all 20,390 respondents in your analysis, you will need to derive your own weight.
How to derive your own weight
First, identify the (smallest) hierarchically-superior sample for which weights have been provided. In this example case, it is the wave 1 responding sample. For this sample, the weight a_indinus_xw has been provided. This will serve as your “base weight”, to which you will make an adjustment tailored to your analysis sample.
Next, fit a conditional weighted model (e.g. logit) of response to your wave-combination of interest. In the example case, the base for the model would be all Wave 1 responding OSMs (i.e. OSMs with a non-zero value of a_indinus_xw) and the dependent variable would be a 0/1 indicator of whether they also responded at both Wave 4 and Wave 7 (and removing from the base any known to have died or emigrated before Wave 7). Predictor variables in the model can be anything relevant observed at wave 1. We also suggest you correct for mortality as described in the ‘Creating tailored weights for UKHLS’ course. The model will give you a predicted probability for every wave 1 respondent of responding also at waves 4 and 7. Call this Pi.
To make the adjustment to your base weight you multiply a_indinus_xw by 1/Pi for all the cases in your analysis sample.
If you are creating weights for the first time this online training course provides guidance on the process: Creating tailored weights for UKHLS.
