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Support #1362

Deriving own weights

Added by Karen Mak almost 4 years ago. Updated over 2 years ago.

Status:
Resolved
Priority:
Normal
Assignee:
-
Category:
-
Start date:
06/16/2020
% Done:

100%

Description

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
predict p

gen weightW25 = (1/p)*b_indscus_xw

Thank you.

#1

Updated by Alita Nandi almost 4 years ago

  • Status changed from New to In Progress
  • Assignee set to Olena Kaminska
  • % Done changed from 0 to 10
  • Private changed from Yes to No

Hello,

Thank you for your query. We have assigned this issue to our weighting expert who will get back to you.

Best wishes,
Alita

#2

Updated by Olena Kaminska almost 4 years ago

Karen,

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,
Olena

#3

Updated by Karen Mak almost 4 years ago

Dear Olena,

Thank you so much for your prompt response. This is really helpful!
May I ask, for point 3, does it mean fitting the model like this: logit responseW25 b_indscus_lw ?

Best wishes,
Karen

#4

Updated by Karen Mak almost 4 years ago

I am sorry - I meant a model like this: logit responseW5 ageW2 if b_indscus_lw>0 & b_indscus_lw!=. ?
Would it matter if I included more W2 predictors in the logit model? Are there any specific W2 predictors that need to be included?

With appreciation,
Karen

#5

Updated by Olena Kaminska almost 4 years ago

Karen,

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,
Olena

#6

Updated by Karen Mak almost 4 years ago

Thank you so much for your helo Olena! Hugely grateful.

#7

Updated by Karen Mak almost 4 years ago

Karen Mak wrote:

Thank you so much for your help Olena! Hugely grateful.

#8

Updated by Alita Nandi almost 4 years ago

  • Status changed from In Progress to Feedback
  • % Done changed from 10 to 90
#9

Updated by Understanding Society User Support Team over 2 years ago

  • Status changed from Feedback to Resolved
  • Assignee deleted (Olena Kaminska)
  • % Done changed from 90 to 100

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