Support #675
closed
Weighting youth data and average parental characteristics
Added by K.Samantha Russell Jonsson about 8 years ago.
Updated about 8 years ago.
Description
Dear support team,
I have another query regarding the use of weights in the youth data.
I am looking at reported happiness among young people at Waves 1 and Waves 5. I generally want to examine happiness in this group but also want to test if there is a change over time.
So my first question is
(1) If I want to control for parental characteristics(for example: SF12-physical and mental illness, parents age) in my mode1s, and I am using average parental characteristics (i.e. from the mother and father). Should I use cross sectional weights for the youth or their parents at wave 1?
(2) And does the same principle apply at wave 5?
(3) Am I correct in assuming that if I want to examine change at wave 5 then I should use a longitudinal weight?
Thank you for your time.
Best,
Kenisha
- Category set to Weights
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- Assignee set to K.Samantha Russell Jonsson
- % Done changed from 0 to 10
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Dear Kenisha,
I have added Peter Lynn as a watcher for this post and he will be able to get back to you about your weighting query.
Best wishes, Victoria.
Hello,
1) If your units of analysis are the children, and you are treating the parental characteristics as attributes of the children, then use the youth weight;
2) Yes
3) Depends what you mean by change. If your dependent variable is the difference between, say, happiness at wave 5 and happiness at wave 1, then this can only be measured in the youth questionnaire for people aged 10 or 11 at wave 1. I think you would have to use the wave 1 cross-sectional youth weight and then make an adjustment for attrition amongst these 10-11 year-olds by wave 5.
Peter
Dear Peter,
Yes, it was my plan to assess if youth reporting of happiness changed between the wave 1 and wave 5.
Could you kindly explain how I could make the adjusment you suggested for attrition.
/kenisha
- % Done changed from 10 to 50
Kenisha,
Create a data set consisting of wave 1 respondents aged 10 or 11 and a 0/1 indicator of whether or not they also responded at wave 5. Model this indicator based on relevant respondent characteristics from wave 1 (youth qre, hhd qre, hhd grid) (e.g. a logistic regression). This will give you a predicted probability for each wave 1 respondent of wave 5 response. Call this P. You then need to adjust the wave 1 cross-sectional weight for all the cases that can be included in your analysis (i.e. completed youth qre at both w1 and w5) by multiplying it by 1/P.
HTH,
Peter
- Status changed from In Progress to Closed
- % Done changed from 50 to 100
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