Achim showed you how, but you might want to consider why.

If you are trying to learn more about your data, then plots or other strategies 
may work better than the test.

If you are testing for normality in order to meet the assumptions of a test, 
then the test may not be accomplishing what you think.  The assumptions of 
normality are most important when sample sizes are low, but when sample sizes 
are low, most normality tests have low power to detect non-normality (I only 
know of one that has high power in this case, but there are other issues with 
that one), so a lack of significance does not mean that your routine is safe to 
use.  As sample sizes get larger, the normality tests become more powerful, but 
the need for normality goes away (CLT).   So testing normality to satisfy 
assumptions is usually meaningless for small sample sizes, and meaningless in a 
different way for large samples.  See fortune(234) and fortune(117).

-- 
Gregory (Greg) L. Snow Ph.D.
Statistical Data Center
Intermountain Healthcare
greg.s...@imail.org
801.408.8111

> -----Original Message-----
> From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-
> project.org] On Behalf Of Ravi Kulkarni
> Sent: Sunday, February 14, 2010 9:49 AM
> To: r-help@r-project.org
> Subject: [R] Shapiro-Wilk for levels of factor
> 
> Hello,
>   I have data for an ANOVA where the between-subjects factor has three
> levels. How do I run a test of normality (using shapiro.test) on each
> of the levels of the factor for the dependent variable separately
> without creating extra datasets?
> 
>   Thanks,
> 
>     Ravi
> 
> ______________________________________________
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> PLEASE do read the posting guide http://www.R-project.org/posting-
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> and provide commented, minimal, self-contained, reproducible code.

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