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Re: st: Experimental design - ANOVA/GLM? - please help


From   khigbee@stata.com
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Experimental design - ANOVA/GLM? - please help
Date   Thu, 26 Sep 2002 16:25:47 -0500

Ricardo Ovaldia <ovaldia@yahoo.com> asks:

> I was approached by an investigator with the following
> problem. He had two groups of experimental rats, 10
> diabetic and 10 non-diabetic. Each of these rats had
> one liter of 10 pups (average). On each of the pups a
> series of biochemicals were measured. He wants me to
> compare the mean value of these biochemicals from the
> pumps from diabetic moms to the pups from non-diabetic
> pups. He then suggested that I do a simple t-test
> comparing the means of the two pup groups. I pointed
> out that the observations are not independent because
> of several pups from the same liter and that the liter
> effect needs to be taken into account.
> 
> How can I set this up in Stata?

Assuming I understand your setup, you have a nested ANOVA model.
I ran the following commands to create some fake data that I think
has a similar form as yours.

        clear
        set obs 2
        gen group = _n
        expand 10
        sort group
        qui gen mom = _n in 1/10
        qui replace mom = mom[_n-10] in 11/20
        set seed 32981
        gen z = 10 + round(uniform()*4-2,1)
        expand z
        drop z
        bysort group mom : gen pup = _n
        gen y = uniform()*8 + group 
        compress

I then looked at a table of pup counts and y value means for each
group (meaning diabetic & nondiabetic moms) and each mom within
group.  The nested ANOVA results are then shown.

    . table mom group, c(mean y freq)

    ------------------------------
              |       group       
          mom |        1         2
    ----------+-------------------
            1 | 4.290885  5.353604
              |       11         8
              | 
            2 | 5.389515  6.748766
              |        9         9
              | 
            3 | 4.533185  6.990387
              |        9        12
              | 
            4 | 4.732775  5.633358
              |        8        10
              | 
            5 | 6.142506  4.981017
              |       10         8
              | 
            6 | 5.124625  5.217069
              |        9        10
              | 
            7 | 5.285787  5.418501
              |       11        11
              | 
            8 | 5.234306  6.799588
              |       11         9
              | 
            9 | 5.171794  6.379981
              |        8         9
              | 
           10 | 6.112025  6.664215
              |       10         8
    ------------------------------

    . anova y group / mom|group /

                 Number of obs =     190     R-squared     =  0.1047
                 Root MSE      = 2.40589     Adj R-squared =  0.0046

        Source |  Partial SS    df       MS           F     Prob > F
    -----------+----------------------------------------------------
         Model |  115.021745    19  6.05377604       1.05     0.4119
               |
         group |   31.203949     1   31.203949       6.80     0.0178
     mom|group |  82.6298147    18  4.59054526   
    -----------+----------------------------------------------------
     mom|group |  82.6298147    18  4.59054526       0.79     0.7065
               |
      Residual |  984.009577   170  5.78829163   
    -----------+----------------------------------------------------
         Total |  1099.03132   189  5.81498054   


I added a little group effect into my fake data (notice the
p-value of .0178).


Ken Higbee    khigbee@stata.com
StataCorp     1-800-STATAPC

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