Understanding Society User Support

Hi Alita,

Brilliant, thank you. However, (if I can ask a follow-up?) I see how the longitudinal weight adjusts for non-response by effectively removing non-responders from the analysis and that it does this even in cases where they have responded to the waves that are within my range of interest (i.e. waves 4, 5 and 6), but they missed an earlier wave.

I want to keep the people who responded to waves 4, 5 and 6, even if they missed previous waves. To achieve this, someone suggested to me that I could divide wave 6's cross-sectional weight by wave 4's cross-sectional weight to create a new weight, which would still provide the necessary weighting, without losing the sample. Would that be a possibility do you think?

Thank you,
Colin

Colin,

Alita's previous suggestion on the weight is correct. I see your point that you would want to include more people in your analysis as you have information from them.
A few points to consider:
1. First use our weight with your analysis and look at your results. It is only worth doing tailored weight if your results are marginally significant (e.g. p=0.06) and your journals are not happy to publish with such results;
2. You can indeed create your tailored weight. The suggestion your mentioned is not complete - what you will get is only the correction for nonresponse between waves 4 and 6. So no correction for unequal selection probabilities or any nonresponse in wave 1 (most of it comes from wave 1 by the way), and any nonresponse until wave 4 will not be taken into account. In other words your results will not be representative of a population.
3. If you are interested in creating your own tailored weight I can share with you a syntax example. Please just email us a request.

Hope this helps,
Olena