Understanding Society User Support

Hi Olena,

I was wondering if you could possibly provide some guidance on weighting in my study in two parts. The first looks at access to training (e.g., trainany where trwho1 == 1, so employers), for different periods in the UKHLS.

To give some context: I am using data from waves f to n (2014-2022). When this is visualised using cross-sectional weights, to give a national annual representation, to compare with the LFS for example, the results are very different to the longitudinal weights (indinub_lw), which is to be expected. My time periods are coded as -1 (2014-2016), 0 (2017-2019) and 1 (2020-2022), as I am doing a Covid-study and testing longer-term trends. I opted for the pooled approach as annual estimates have more noise and may not be representative of a whole period. However, when the longitudinal weights are applied they drop a huge number of cases which are useful for the study as I am trying to test differences between different groups at that period in time as opposed to longitudinally over the whole period.

I used your guidance document: https://iserredex.essex.ac.uk/support/attachments/download/379/Worksheet%20ex6%20R.pdf , however, I don't think it covers my situation. Would it be better for me to consider using separate weights for each of the time-periods to mimic cross-sectional ones? The individual over time here is less important than the integrity of the sample groups (key workers and furlough groups) which are defined with SOC and SIC combinations.

So, in terms of my options - should I use longitudinal weights or cross-sectional weights from the last wave of each of those 3 periods, e.g., indinus? Or do I need to tailored weight based on the pool for each of the 3 periods?

Also - just a final clarification - what is the difference between indinus_lw and indinub_lw, as they have slightly different values for several waves which would be in the study? The description on the website is very similar - is it just that indinub_lw is from wave 3 and takes into account attrition?

Many thanks in advance,

Michael Francis