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From |
Stas Kolenikov <skolenik@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: gllamm or SVY |

Date |
Fri, 23 Apr 2010 08:31:24 -0500 |

Survey design based inference is said to be doubly consistent. First, the parameter estimates are consistent for the finite population parameters (i.e., as you increase the sample size, the parameters of the model converge to the parameters of the so-called census regressions. The latter are regressions hypothetically fitted to the whole population. They may suffer from whole lots of problems like heteroskedasticity, nonlinearity, whatever have you, but at least these are well defined parameters: you take the population, you apply a certain computational rule to it, and voila). Second, if your model is correct, the parameter estimates converge to these model parameters (although here you need to think about complicated asymptotics with both population size and sample size going to infinity in some concordant manner). See http://www.citeulike.org/user/ctacmo/article/1036932. If your model is incorrect, God only knows what you are estimating in the model-only world; in survey statistics world, you are still estimating parameters of the finite population, although it might be harder to interpret them. When applied to the multilevel data, this means that you'd have to make a leap of trust. In fact, many leaps: (1) that every regression at every level is correctly specified; (2) that you modelled all the random effects you needed; (3) that your random effects are homoskedastic and have a good old normal distribution; (4) that you've chosen a procedure with appropriate numeric properties (-gllamm- is totally fine with sufficient number of integration points, but it gets polynomially slow as you increase the number of these). There are also conceptual/interpretational differences. It is believed that the multilevel models kinda think about random components as drawn from a nicely parameterized distribution, so you'd have to think that your countries are drawn at random from a hypothetical hyperpopulation of all possible countries, and yet their random effects have a nice normal distribution (you can relax that with -gllamm- that allows non-parametric distribution, but identifying these non-parametric distributions requires large number of units at the corresponding level). You can probably think about hospitals that way, but I would certainly second Steve's suggestion to model countries as fixed effects. For further discussion, see an article by -gllamm-'s authors: http://www.citeulike.org/user/ctacmo/article/850244 On Fri, Apr 23, 2010 at 4:57 AM, Irene Moral <imoral@eapsardenya.cat> wrote: > Dear all, > > I'm analysing a sample drawn from a stratified cluster designed study. > > In my study, I considered countries as strata, health centers in each > country as clusters and then patients are selected within each cluster. > > I'm using the svyset and all the svy commands to define the sample design > and to run analysis, but some people asked me about using multilevel > analysis instead of svy. > > I have understood that other options are to use the gllamm command or to > panel data as longitudinal and use xt commands, żit is true? > > can anybody explain in which cases is more useful to use one than the other? > or what type of errors am I exposed to when using svy? > -- Stas Kolenikov, also found at http://stas.kolenikov.name Small print: I use this email account for mailing lists only. * * 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: gllamm or SVY***From:*"Irene Moral" <imoral@eapsardenya.cat>

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