Support #2291
open
Sampling and Analytic Weights
50%
Description
Hi all,
I hope you all are well. I will be using the harmonised BHPS data from 1991 to 2015 for a long panel study. I am aware of the section on weight selection in the user manual and the very helpful visual guide on YouTube. I plan on using matching with regression adjustment as part of my analysis and wonder how to combine probability weights due to design based stratified sampling and analytical weights that would arise from any matching algorithm I propose to use.
Best wishes
David
Updated by Understanding Society User Support Team 2 months ago
- Category set to Weights
- Status changed from New to In Progress
- Assignee changed from Understanding Society User Support Team to Olena Kaminska
- % Done changed from 0 to 10
- Private changed from Yes to No
Many thanks for your enquiry. The Understanding Society team is looking into it and we will get back to you as soon as we can. We aim to respond to simple queries within 48 hours and more complex issues within 7 working days.
Best wishes,
Understanding Society User Support Team
Updated by Understanding Society User Support Team 2 months ago
- Status changed from In Progress to Feedback
- Assignee changed from Olena Kaminska to Understanding Society User Support Team
- % Done changed from 10 to 50
Hello David,
I've included below the reply from one of the team experts.
(1) Matching based on Euclidian/Mahalanobis: you can probably get away with not bothering to incorporate any survey information here because you’re effectively looking for a match based on covariates X and ideally the distance will be close to zero. Mahalanobis requires calculating the variance-covariance matrix of the matching variables but one suspects the difference in matches obtained using the unweighted and weighted estimates of this matrix won’t be huge.
(2) Matching based on propensity scores: the key results that (i) residual e is independent D | p(X) and (ii) D independent X | p(X) are both based on the population propensity score p(X), so you need to use the survey weights when estimating p(X).
(3) Final-stage regression estimation is for making population inference so, with the matches obtained above substituted in, requires using the survey weights. Moreover, the clustering and stratification must also be set to ensure the standard errors are suitably adjusted. (If inverse probability weighting is used then both the 1/p(X) and 1 / (1 – p(X)) weights for treated and untreated should be such that the overall weight is w/p(X) and w/(1-p(X))).
I hope this information is helpful.
Best wishes,
Roberto Cavazos
Understanding Society User Support Team