You could also try bootstrapping your coefficients from a random effects
model, which would eliminate the small sample bias in your variance
estimates.
On 1/7/05 2:33 AM, "statalist-digest" <[email protected]>
wrote:
> At 22:06 05/01/2005, Krishna wrote:
>> Dear All:
>>
>> I had previously posted this and would like to thank
>> Roger Newson for his reply. Here is my problem in more
>> detail.
>>
>> I am trying to fit a regression model to data which
>> was generated from cluster sampling. There are 12
>> clusters (clinics) and the average cluster size is
>> around 30. The outcome of interest is a continuous
>> variable (patient satisfaction) and I am interested in
>> the factors which affect it. Since the data is
>> clustered and it is potentially correlated, I need to
>> adjusted for this in my regression model. What would
>> be the appropriate regression technique/model to do
>> this ? I understand that models such as multiple
>> regression with sandwich estimator (regress with
>> cluster option), and GEE are not appropriate as they
>> require a large number of clusters to provide
>> consistent estimates. Would a Random Effects (xtreg,
>> re) or Fixed Effects (xtreg, fe) model be appropriate
>> ? Are there any others you would recomend ? Many
>> thanks for all your help..
>>
>> Best regards
>> Krishna
>> Dept. of International Health
>> Johns Hopkins University
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