Notice: On April 23, 2014, Statalist moved from an email list to a forum, based at statalist.org.
[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]
Re: st: Using sampling/probability weights for mixed design ANOVA in STATA
From
Steven Samuels <[email protected]>
To
[email protected]
Subject
Re: st: Using sampling/probability weights for mixed design ANOVA in STATA
Date
Wed, 15 Jun 2011 21:06:52 -0400
Meg-
To answer your question, Example 17 in the Stata Manual entry for -anova- shows a repeated measures design with two within-subject factors.
But on further consideration, I don't think you will be able to emulate the -anova- calculations with -regress-, as you would have to calculated weighted mean squares, which are not available in the regression results. Moreover, -anova- relies on assumptions, such as the absence of certain interactions, which might not be reasonable.
Since you have 110 subjects, you can still use -regress- directly and analyze hemisphere and time as within-subject effects. I should have realized this sooner, and I apologize.. Create a hemisphere variable coded 1 & 2. (Strings for won't work for -regress-.)
Here is the code:
****************************
/* Main Effects */
regress brainvol i.time i.sex i.hemisphere ///
[pweight = yourweight], vce(cluster subject)
/* Interactions */
regress brainvol i.time##i.sex i.time##i.hemisphere i.sex##i.hemisphere ///
[pweight = yourweight], vce(cluster subject) // interactions
***************************
With the vce(cluster subject) option, -regress- will correctly identify time and hemisphere as within-subject factors
To compare to -anova- run the above without the [pweight=] option.
This approach ignores variability imposed by the sampling design and so from a sampling standpoint the standard errors will be incorrect. You can get an idea of how incorrect, by substituting "postcode" for "subject" in the vce() option.
Now the warning: If you do not correct your probability weights for non-participation, you risk serious bias, except under conditions which cannot be tested. You would be better off using no weights at all. If you do not make such a correction, then whether you use probability weights or not, you will have to state that the children are not representative.