Hi,
I am exploring the use of sample weights so that the results of a current study may be interpreted as more generalizable. The study uses a DCE to measure seafood preferences. I used CDC NHANES data to calculate appropriate sample weights using equivalent data. There are two questions I have which may be more general but hopefully someone can point me in the right direction.
1) The sample weights are very large which caused issues with convergence. I rescaled the weights (dividing by powers of 10) until the models converged. This doesn't feel right to me and I'm concerned the weights may not be doing there job correctly. The weights still have the same relative relationship to one another but is that enough to achieve the goal of a "representative" result?
2) Within this line of thought, can the standard errors be interpreted as usual when using weights in MXL estimation? From what I've read so far about use of sample weights, the standard errors from conventional estimation seem useless for direct interpretation. If this is the same for MXL is there a recommendation for obtaining correct standard errors within Apollo's framework?
Thanks for your input.
Mike
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Interpretation of MXL Standard Errors using weighting
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stephanehess
- Site Admin
- Posts: 1330
- Joined: 24 Apr 2020, 16:29
Re: Interpretation of MXL Standard Errors using weighting
Hi Mike
in general, I am not a fan of using weights in estimation. It implies that some choices are more important than others. It's much better to use interactions with socio-demographics that relate to the variables along which the data is not representative, and then to reweight your results.
Re standard errors, weighted estimation indeed affects your standard errors, and there is no correction implemented for that
Stephane
in general, I am not a fan of using weights in estimation. It implies that some choices are more important than others. It's much better to use interactions with socio-demographics that relate to the variables along which the data is not representative, and then to reweight your results.
Re standard errors, weighted estimation indeed affects your standard errors, and there is no correction implemented for that
Stephane