Deriving own weights
Hope you are well.
My research focuses on the relationship between arts engagement (Wave 2) and wellbeing (Wave 5) using OLS regression. I understand that if I am using more than one wave, a longitudinal weight is more appropriate. But using that would lead to a significant drop in my sample size, therefore I would like to derive my own weight based on the the guidelines stated in "Understanding Society: Weighting and Sample Representation FAQ 2019". I have prepared the weighting codes and I would be extremely grateful if you could let me whether the coding is correct:
gen responseW5=1 if e_hidp!=. & b_hidp!=.
replace responseW5=0 if e_hidp==. & b_hidp!=.
logit responseW5 eventfqW2_v2 marstatW2 child16W2 ageW2
gen weightW25 = (1/p)*b_indscus_xw
#1 Updated by Alita Nandi about 2 months ago
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- Assignee set to Olena Kaminska
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Thank you for your query. We have assigned this issue to our weighting expert who will get back to you.
#2 Updated by Olena Kaminska about 2 months ago
Thank you for your question. A few comments:
1) as a base weight you should use a longitudinal weight b_indscus_lw, not cross-sectional weight;
2) please exclude those who died and left the country in a meantime - they should not be considered as nonrespondents;
3) condition your logit model on non-zero b_indscus_lw;
Hope this helps,
#5 Updated by Olena Kaminska about 2 months ago
Yes, I would recommend more predictors. Choose predictors to be related to both nonresponse and your own model of interest. But I would err on higher number of predictors if you are uncertain. Note, predictors need to be from wave 2 and should not have any missing values for non-zero b_indscus_lw.
Hope this helps,