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From |
Steven Joel Hirsch Samuels <sjhsamuels@earthlink.net> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Variance estimation with clusters |

Date |
Thu, 8 Nov 2007 10:43:06 -0500 |

--

Maury:

I would would only add to Austin's good advice:

1. If you are doing regressions and hypothesis tests, do not use the fpc terms. Imagined you had studied 100% of establishments and workers in a population; with the fpc's, all standard errors would be zero.

2. Stata's panel data and multi-level model -xt- commands will not respond to -svyset-. For panel data analysis, the options accommodating the survey design vary by command.

3. You should probably use the survey weights from year 1; but the study documentation may have other advice. Obviously these weights will not sum to the population size in either year 1 or year 2. If the survey deliberately over-sampled a class of workers which is the subject of your analysis (e.g. you wish to compare a minority to a majority group, and the survey over-sampled the minority group), you should probably ignore the survey weights altogether.

-Steven

On Nov 8, 2007, at 10:16 AM, Austin Nichols wrote:

Maury Gittleman <Gittleman.Maury@bls.gov>:

Just clustering on establishment is probably sufficient.

You can also specify two levels of clustering with -svyset- e.g.

webuse stage5a

svyset su1 [pweight=pw], fpc(fpc1) || su2

where su1 is your establishment id, fpc1 the number of distinct

employees in both years, and su2 is a person id.

Usually the second level of clustering is largely irrelevant. But not always...

svyset su1 [pweight=pw], fpc(fpc1) strat(strat)

svy: reg yreg x?

est sto c1lev

svyset su1 [pw=pw], fpc(fpc1) str(str) || su2, fpc(fpc2)

svy: reg yreg x?

est sto c2lev

esttab *, mti

On 11/8/07, Gittleman, Maury - BLS <Gittleman.Maury@bls.gov> wrote:

Hello,

I'm have a question concerning stata's approach to estimating standard

errors in the presence of clustered survey data. The survey I'm using

collects information on individual wages, by first selecting

establishments at random, and then collecting information on multiple

workers within each establishment. So, it is clear that, when I'm

running regressions, I need to cluster on establishment.

My question arises when I use two years of data from the same survey.

For about 4/5 of the individuals, there will be data for two years, and

I would expect that the correlation between the errors for any given

individual will be higher than the correlation between the errors for

two different individuals at the same establishment. My thinking is

that I still want to define clusters by establishments, as the variance

estimation is said to be robust to any arbitrary intra-cluster

correlation.

Is this the right way to go or is there an alternative approach that

might be superior?

Thanks very much.

Maury

* * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

Steven Samuels sjhsamuels@earthlink.net 18 Cantine's Island Saugerties, NY 12477 Phone: 845-246-0774 EFax: 208-498-7441 * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: Variance estimation with clusters***From:*"Austin Nichols" <austinnichols@gmail.com>

**References**:**st: How to obtain fit indices from a logistic regression model in Stata?***From:*tiago.pereira@incor.usp.br

**Re: st: How to obtain fit indices from a logistic regression model in Stata?***From:*Maarten buis <maartenbuis@yahoo.co.uk>

**st: Variance estimation with clusters***From:*"Gittleman, Maury - BLS" <Gittleman.Maury@bls.gov>

**Re: st: Variance estimation with clusters***From:*"Austin Nichols" <austinnichols@gmail.com>

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