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st: RE: Comparing Chi2/L2 in different samples using bootstrap


From   Dmitriy Poznyak <Dmitriy.Poznyak@soc.kuleuven.be>
To   "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
Subject   st: RE: Comparing Chi2/L2 in different samples using bootstrap
Date   Mon, 6 Dec 2010 15:43:17 +0100

Steve, Stas, thanks a lot for your time and input

I hear your points. Indeed, pooling the dataset sounds like a good option, but here's where the complicaton is: the model has six categorical variables and six continious ones. Interacting year with each predictor (and each category in case of the categorical vars.) will make the model quite complicated. Besides, I am not sure if I will be able to use the mlogview command in this case, which makes interpretation so much easier.
As for the clustering, the cluster size is actually quite big in each survey year - I think at least 40 observations. There's also at least 50 clusters in each year in total. As pointed, I do svyset the data before. However, I am willing to sacrifice clustering option in the model
I haven't tried bsweights yet, but I will read on this later today.

After all, and in a nutshell if things are kept as they are, can I stretch my conclusions this far to compare the coefficients? 

Thanks again for your help,
Dmitriy

________________________________________
From: Dmitriy Poznyak
Sent: Monday, December 06, 2010 2:44 PM
To: statalist@hsphsun2.harvard.edu
Subject: Comparing Chi2/L2 in different samples using bootstrap

Hello all,

I am estimating three identical multinomial models with bootstrap for the different years of survey data, for instance. 1991, 1999 and 2007. Aside from comparing predicted probabilities, which I assume shouldn't pose any problem, I need to compare Chi2/L2 coefficients for the different variables in the model. The rationale for doing this, is that the fit of the individual predictors (e.g. social-demographic stuff) declines through time. Here's where the question arises. Clearly, samples in different years have different size, and perhaps different design effects, and so on.

In order to possibly address these issues I ran the bootstrapped models with the same number of iterations in each case:
bootstrap, reps(2000) force: mlogit vote5  x y z ... ,base(1) cl(zip), [pweight=weight1], rrr
Next, I test the effect of the predictors:  test x;  test z, etc. Again, the models' specification is identical for all years; what differs is the sample size and design.

Considering the bootstrap method being used, will it be possible to compare Chi2/L2 and perhaps pseudo R2 coefficients for different samples in this case, and, if not, what would be my strategy. Note that pooling datasets is not feasible due to several reasons, like weighting, etc.

Thanks for your suggestions,
Dmitriy

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