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From |
Maarten buis <maartenbuis@yahoo.co.uk> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: indicators of nesting versus xtmelogit |

Date |
Wed, 22 Oct 2008 09:47:10 +0100 (BST) |

Using dummies to estimate a fixed effects model is not recomended in a non-linear model (especially if you have few observations in each category, and 17 does not seem large enough to me). This point is discussed here: http://www.stata.com/statalist/archive/2003-09/msg00103.html . So that precludes models 2 and 3. Model 1 is not without problems either: it assumes that the city and time specific intercepts are uncorrelated with the observed variables, which bothers quite a few people. If I had to choose one model I would probably go for model 1, as being the lesser of three evils. However, you will probably want to look at what is called a crossed-effects model, which can be estimated with -xtmelogit-. There is a discussion on how to do that in the XT manual on pages 239-242. Hope this helps, Maarten --- Noah Friedkin <friedkin@soc.ucsb.edu> wrote: > The data I am analyzing are longitudinal, consisting of individuals > located in 4 cities across 17 time periods. I.e., each individual > contributes 17 observations, and each observation occurs in one of > 4X17=68 settings. I am not concerned with the effects of these > settings, but instead with a set of individual-level predictors x1, > x2, x3 of the binary responses y of individuals in their city-time > contexts. There are three credible approaches to the analysis of > these > observations: > > (1) xtmelogit y x1 x2 x3 || city: || time: > (2) logit y x1 x2 x3 c2 c3 c4 t2 t3 ? t17, where c2-c4 and t2-t17 > are > indicator variables with city 1 and time 1 as the omitted settings > (3) logit y x1 x2 x3 c1t2 c1t3 ? c1t17 c2t1 c2t2 ? c2t17 ? c4t1 c4t2 > > ?c4t17 where c_t_ are indicator variables with c1t1 as the omitted > setting > > Model 2 is a popular approach. Model 3 appears closer to model 1. > However, my sense is that models 2-3 are excessively conservative and > > potentially misleading in allowing (what may be viewed as) > meaningless > ?nuisance? associations to affect the estimates for x1, x2, and x3. > Model 1 seems preferable. > > Question 1. What are the pros and cons of these three models? > > I am assuming that model 1 is equivalent to allowing different > intercepts for each sity-time group of observations. I'm also > assuming > that the estimated random effects for the 68 city-time groups are not > > constrained, so that (for example) they might be found to be all > positive values that monotonically increase with time. > > Question2. Are both of my assumptions about model 1 correct, or is my > > understanding of model 1 flawed? > > Comments on the above two questions would be much appreciated. > > > > > > > > > > > * > * 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/ > ----------------------------------------- Maarten L. Buis Department of Social Research Methodology Vrije Universiteit Amsterdam Boelelaan 1081 1081 HV Amsterdam The Netherlands visiting address: Buitenveldertselaan 3 (Metropolitan), room N515 +31 20 5986715 http://home.fsw.vu.nl/m.buis/ ----------------------------------------- * * 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: indicators of nesting versus xtmelogit***From:*Noah Friedkin <friedkin@soc.ucsb.edu>

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