Re: st: RE: Cluster analysis on survey data

[Steven Samuels <sjhsamuels@earthlink.net>]

It appears to me that a cluster analysis will not serve Jessica's  
purpose: "to describe who are the kinds of people that report medical  
debt and medical bankrupcy".  Implied in this is "compared to people  
who do not report these events". (If Jessica does not think so, I  
hope that she will show an example of what a cluster-analysis might  
find.)

Better I think would be a discriminant analysis to describe the  
differences between the two groups (perhaps three, if Jessica  
considers medical debt without bankrupcty and medical bankruptcy to  
be different).  This could be be done with -logit- and ---probit- (- 
mlogit- and -mprobit- for three groups), all survey-enabled.  (Stata  
has other kinds of discriminant analysis-see help for -discrim- and - 
candisc-, but these take no survey features except pweights.)  Such  
analyses could include interactions and  might show, for example,  
that the odds of being older and male are greater for the debt/ 
bankrupcy group than for the comparison group.

The most flexible way of describing group differences, to my mind, is  
Classification and Regression Trees (CART); The only implementation  
in STATA that I know of is the user-contributed -cart- command, but  
it applies only to Cox regression and does not  take weights.

-Steve
On Aug 28, 2008, at 11:55 AM, Nick Cox wrote:


> What is BRFSS?
>
> On the main question, it is evident that -cluster- does not support  
> any
> kind of weights, so that is one short answer.
>
> I am unclear on how in principle any kind of weights could inform
> cluster analysis. Although there are different recipes, cluster  
> analysis
> as implemented in Stata is in essence a more or less elaborate way of
> quantifying information on similarity or differences between
> observations in a multivariate space.
>
> Suppose for example that I am in a survey, you are too, and several
> other people are as well. Cluster analysis offers methods for plotting
> me, you and the others in a space. How are those differences  
> affected by
> the sampling design behind who is and who isn't in the dataset,
> particularly as no parameter estimation or hypothesis testing is
> involved?
>
> Nick
> n.j.cox@durham.ac.uk
>
> Jessica M. Tullar, PhD
>
> I am using a BRFSS dataset and therefore it has a complex sampling
> design. I would like to describe who are the kinds of people that  
> report
> medical debt and medical bankruptcy and therefore thought cluster
> analysis might be appropriate.
>
> I've looked through all the manuals and even searched survey analysis
> and on cluster analyses and can't find the answer. Is there a way to
> perform a cluster analysis and account for the survey weights?
>
> A second possibility would involve creating a new representative  
> dataset
> on which to perform the standard cluster analysis. How best would one
> create a new dataset using the survey weighting which would  
> approximate
> the population?
>
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