Understanding Society User Support

Hi

I recently realised that Understanding Society weighting team has now included a longitudinal weight (explained on page 38 on the Guide 6.0 document) in the Covid datasets. So, each individual is assigned with a different longitudinal weight value in each Wave. I want to run some analysis that includes Waves 9 to up to November Covid Wave. So, I guess we would need to find the longitudinal weight value for the September Covid wave and assign the same value backwards until Wave 9, given that an individual has participated to all pre and post Waves.

However, because we are not getting the power needed to do some stratification by sex or specific age groups I was trying to find a way adding more individuals and observations, including individuals that stopped participating before the November Covid Wave. So I came up with a method I am not really sure is legit, using again a similar backward assignment using the weight value that corresponds to the last Wave they participated in. So if someone participated in Waves 9 to 13, then that individual will still be included in the analysis sample (even though they did not participate in Waves 14 & 15). In this case, the weight assigned to all previous Waves will be the Wave 13 relevant value. So in a nutshell, I would be including all individuals that have participated in pre and post waves, given that they have participated to all pre (i.e. waves 9 & 10-11) and at least one of the post Covid Waves.

As far as I can understand from the guide document, Covid Wave 3 weight, for example, is conditional on Covid-wave 1 and Covid-wave 2 and Wave 9 responses. But if I am right an individual is weighted based on previous weights, so at Covid-Wave 2 the weight is conditional on Covid-Wave 1 weight and Wave 9 responses, so if that individual stopped participating in Covid Wave 2 (May) s/he would be accounted for at Covid Wave 3 longitudinal weights, but not at Covid Wave 4 weights as they are conditional on Covid waves, 1,2 and 3 plus Wave 9 responses. So the probability an individual drops out at Wave 2 is included in Covid Wave 3 weights but not in Covid Wave 4 weights. So, it seems to me that the weighting model compares those with full responses, say at Wave 5 as compared to those who have just missed Wave 5 but had full responses up until Wave 4. So those who stopped at Wave 3 are included only in Wave 4 but not in Wave 5. That is why, I think, those who have missed one wave and then return back in the next one have zero weights. Based on my strategy explained above I am including those individuals but treat them as not returning two Waves after and assign them with their weight value at the last Wave they have participated in a consecutive way-before they drop out and come back again two waves after.

I hope this makes some sense. I am very looking forward hearing your thoughts on this.

Best,
Harry