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Query on the possibility of using the 2023 calendar year (from UKHLS wave 14 data) in some trend analysis

Added by James Laurence 29 days ago. Updated 15 days ago.

Status:
Feedback
Priority:
Normal
Category:
Weights
Start date:
01/24/2025
% Done:

50%


Description

Hi there,

I hope this message finds you well. I was just hoping to ask a question regarding the use of the UKHLS to study calendar year trends in outcomes. The new wave 14 release of the UKHLS runs up to December 2023. Essentially, I'd like to track GHQ-12 by calendar year: so, for example, looking at trends in GHQ-12 in 2017, 2018, 2019, 2020, 2021, 2022, 2023. I understand the sample of individuals surveyed in 2023 only constitute a subsample of the respondents in the UKHLS, given it is only those surveyed in 'year 2' (months 13-24) that were surveyed in 2023 in the wave 14 data.

1. Firstly, I was therefore just wondering if such an analysis is even possible/recommended? It would obviously require restricting all the waves of the UKHLS being used to individuals surveyed in 'year 2' of each wave (which, from the notes, looks like it is primarily the GPS-GB, GPS2-GB and the EMB samples). It may be that such a restriction is not recommended or biases the sample too much, so do just let me know if so.

2. If such an approach is feasible, are there any other sample restrictions that need to be applied for such analysis?

3. I was also wondering if you had a sense of whether it is possible to model such trends in a multilevel mixed framework? To allow us to account for potential confounding variables, clustering, and weight the data accordingly? Or, is it only recommended to look at each calendar year separately?

4. Lastly, and this is perhaps the most complex issue I imagine, is it possible to weight this 'year 2' sample using the weights provided in the UKHLS data? Will any of them allow us to weight the 'year 2' sample to be nationally representative? Might we need to tweak the current weights available to do so?

Thanks so much in advance for your help. I understand this approach may not even by feasible in the first instance but I thought I would enquire in case, so any insights would be hugely appreciated.

All the best,

James

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