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# Re: st: providing raw weights for multivariate meta-analysis

 From Nick Darson To statalist@hsphsun2.harvard.edu Subject Re: st: providing raw weights for multivariate meta-analysis Date Mon, 11 Jul 2011 03:43:33 +1000

```Thanks for your help, Stas.

The book I referred to is: Joop J. Hox 2010: Multilevel analysis:
techniques and applications, 2nd edition, Routledge

To my understanding, "aweights" normalizes the weights (such that the
sum of the weights =N, the number of observations).

However, it is important for the multivariate meta-analysis approach
that the weights are not normalized (so called precision weights or
raw weights).

This is one of the two mentioned requirements by Hox (2010). The
second requirement for multivariate meta-analyses is the constraint of
the lowest-level variance to 1 - what is given in Stata - please
correct me if I am wrong on this).
Therefore, I was wondering whether the weight issue is the reason that
Stata cannot be used for multivariate meta-analyses.

Cheers,
Nick

On Mon, Jul 11, 2011 at 3:12 AM, Stas Kolenikov <skolenik@gmail.com> wrote:
> I personally think that -help weights- explains it all. Variance
> weights are aweights: they state that the measurement error in a given
> observation is such and such. The pweights are probability weights,
> and when you invoke these, the calculation of not only the point
> estimates, but also the standard errors is different (according to
> probability sampling theory rather than the likelihood theory). There
> are also frequency weights, which is essentially the result of
> -collapse (count) ... , by(*)-. The iweights are "all other weights,
> whichever these may mean for you" -- there is no strict definition,
> and the program that allows them usually has its own idea what to do
> with them.
>
> Knowing very little about meta-analysis beyond the fancy name, I have
> a feeling that you should be looking towards -gllamm- rather than
> -xtmixed- to find the flexibility with the weights that you need. You
> might still want to run (the much faster) -xtmixed- first to get good
> starting values that you'd feed to -gllamm-. Joop Hox (a proper
> reference is in place, according to the rules of Statalist; I have a
> vague idea that this is a famous multilevel author) is a somewhat
> opinionated guy, to my impression (although I am much more opinionated
> than he is :)).
>
> On Sun, Jul 10, 2011 at 7:02 AM, Nick Darson <nick.darson@googlemail.com> wrote:
>> Dear Statalisters,
>>
>> I attempt to carry out a multivariate meta-analysis using "xtmixed". I
>> have three outcome measures (the first level).
>>
>> When using a multilevel approach for the multivariate meta-analysis, I
>> read that it is important to provide raw-weights (non-normalized) of
>> the inverse sampling variances for the first level (containing the
>> outcome measures).
>>
>> I know that HLM6 has a special function for this ("V-known") for two
>> and three level models.
>>
>> Now, I wanted to know whether "pweights" is an equivalent solution for
>> xtmixed in Stata?
>>
>> I must admit that I am a bit confused by the various descriptions of
>> the weight functions I found online (aweights, iweights, etc - though
>> this are not allowed for xtmixed) .
>>
>> Moreover, surprisingly,  I read in Hox (2010,p. 230) that public
>> domain software for multilevel analysis does not support the required
>> options for a multivariate model so far (i.e. providing raw weights
>> and being able to constrain the lowest-level variance to 1 - however,
>> both should be feasible).
>>
>> Thanks a lot for your support.
>> Nick Darson
>> *
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>>
>
>
>
> --
> Stas Kolenikov, also found at http://stas.kolenikov.name
> Small print: I use this email account for mailing lists only.
>
> *
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>

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```