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From |
Robert Lineira <robert.lineira@uab.es> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: RE: clustered standard errors |

Date |
Fri, 30 Apr 2010 17:08:39 +0200 |

Thanks a lot Steve for answering so quick. I will try to implement your proposal! Best Al 29/04/2010 20:07, En/na Steve Samuels ha escrit:

Robert, Are any of the "local forces" or "national forces" identical for all voters in a region? For those forces that do not vary within a region, your sample size is effectively n = 17. It appears that regions are not "clusters", but are strata, which by definition are units that together constitute the entire population of interest. One approach to this analysis, and the one I recommend in absence of other information, is to treat regional differences as fixed effects , and to use Stata's -svyset- command to specify the design. The strata would be the original strata in the 17 surveys, suitably coded so that there are no duplicate numbers. In the analysis, you would have a fixed indicator of region, but also regional variables that might explain the regional differences. Although the samples in each region can be considered random, they are not "simple random samples". Pooling the samples without adjustment for the original sample designs will give biased estimates (if the analysis is not weighted) and improper standard errors. -Steve On Thu, Apr 29, 2010 at 11:46 AM, Robert Lineira<robert.lineira@uab.es> wrote:Dear, The population are the 17 Spanish regions and the samples are post-election surveys in each region. The purpose of the analysis is to look for variances on the strength of local and national forces on voting and turnout. Although the multi-stage sampling procedure takes advantage of some strata and clusters to select the individuals, the samples may be considered as random samples of voters in each region. The pool of samples consists in the aggregation of this random samples. I hope this helps in having a better idea of the research. Thanks in advance! Robert. Al 29/04/2010 14:06, En/na Steve Samuels ha escrit:I wonder what the purpose of the analysis is, what the sampled populations are, and what the sample designs are. Survey samples can be complex creations with their own strata and clusters. Until Robert provides more detail, I'm not sure that 1 sample = 1 cluster. Steve Steve On Thu, Apr 29, 2010 at 6:03 AM, Schaffer, Mark E<M.E.Schaffer@hw.ac.uk> wrote:Robert,-----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Robert Lineira Sent: 29 April 2010 10:08 To: statalist@hsphsun2.harvard.edu Subject: st: clustered standard errors Dear all, I found on the net a presentation by Austin Nichols and Mark Schaffer on the net on clustered standard errors. After reading it, some questions emerged to me on how to use them. I want to run an analysis using a pool of 17 survey samples. Supposedly, standard errors will be correlated within the clusters, but the presentation advises that to use clustered standard error might be a very bad solution. They suggest to perform some test before using the corrected errors running 'cltest' and 'xtcltest' stata commands. Unfortunately, I just found 'cltest' command, I am not sure is the same they use given that is previous to the Kédzi (2007) paper they quote.No, that's a different test. The test code Austin and I referred to in our presentation is still languishing in alpha testing. But I'm not sure it or other tests can help you. The problem is that this test, like White's general heteroskedasticity test and related tests, works via a vector-of-contrasts. The contrast is between the elements of the robust and non-robust VCVs. Under the null, the robust VCV is consistent. If the non-robust VCV is also consistent, its elements will be similar to those of the robust VCV, and the vector of contrasts will be small. If the non-robust VCV is inconsistent, the contrast will be large. You can see the problem now. To do this or a related test in your application, you need a robust VCV that is consistent. Your cluster-robust VCV is indeed consistent, but with only 17 clusters, you are not very far along the way to infinity, and it's likely to be a poor estimator of the VCV. Contrasting it with the non-robust VCV is not going to give you a reliable test - the contrast could be big because the cluster-robust VCV is poor, for example. Hope this helps. Cheers, MarkMy question is if anyone knows a test which I could use before applying clustered standard errors and (if not) which solution do you find better in a case such as this. Regards Robert. * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/-- Heriot-Watt University is a Scottish charity registered under charity number SC000278. * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/-- Robert Liñeira Dpt. Ciència Política - UAB 08193 Bellaterra - Barcelona Tlf: +34 93 581 46 33 Despatx B1-185 * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

-- Robert Liñeira Dpt. Ciència Política - UAB 08193 Bellaterra - Barcelona Tlf: +34 93 581 46 33 Despatx B1-185 * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: clustered standard errors***From:*Robert Lineira <robert.lineira@uab.es>

**st: RE: clustered standard errors***From:*"Schaffer, Mark E" <M.E.Schaffer@hw.ac.uk>

**Re: st: RE: clustered standard errors***From:*Steve Samuels <sjsamuels@gmail.com>

**Re: st: RE: clustered standard errors***From:*Robert Lineira <robert.lineira@uab.es>

**Re: st: RE: clustered standard errors***From:*Steve Samuels <sjsamuels@gmail.com>

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