Date: Wed, 19 Jan 2011 17:33:48 -0500
Reply-To: R B <email@example.com>
Sender: "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From: R B <firstname.lastname@example.org>
Subject: Re: Bonferroni correction and number of comparisons
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See a couple of comments interspersed below.
On Wed, Jan 19, 2011 at 4:47 PM, Bruce Weaver <email@example.com> wrote:
> Ryan, multiplying the observed (uncorrected) p-values by the number of
> contrasts can give you results that make no sense. E.g., if the uncorrected
> p-value = .25 and the number of contrasts is 6, you'll get a corrected
> p-value = 1.5. But p-values are conditional probabilities, and must fall
> within the range 0-1.
This is a valid point, but I've yet to encounter a situation in my
work where I would've made a different conclusion by correcting alpha
> Here's an example using oneway ANOVA. Notice that the Bonferroni-corrected
> p-values (or Sig. values as SPSS labels them) are not simply 3 x the
> uncorrected (LSD) p-values.
I disagree. In your example, Bonferroni corrected p values associated
with 1v2 and 1v3 are exactly the same uncorrected p-values times 3. Is
there something I'm missing here?
My guess is that SPSS automatically replaced the Bonferroni corrected
p-value associated with 2v3 with 1.0, since as you pointed out the
conditional probability cannot be above 1.0.
> MATRIX DATA VARIABLES=Group ROWTYPE_ Score /FACTORS=Group.
> BEGIN DATA
> 1 N 96
> 2 N 96
> 3 N 96
> 1 MEAN 22.98
> 2 MEAN 25.78
> 3 MEAN 26.56
> 1 STDDEV 8.79
> 2 STDDEV 9.08
> 3 STDDEV 8.50
> END DATA.
> ONEWAY Score BY group /
> matrix = in(*) /
> POSTHOC=LSD BONFERRONI ALPHA(0.05)
> Jan, given that figuring out how to compute corrected p-values may be quite
> time-consuming, I would see if I could persuade the person who asked that
> the usual approach (i.e., comparing p to a corrected alpha level) is
> R B wrote:
>> You asked about applying a Bonferroni correction in post hoc tests-->
>> "Is the Bonferroni adjustment for 6 comparisons and the corrected
>> p-value (.05/6=.008333)"
>> Answer. No. You have presumably adjusted the alpha level, not the
>> p-values. Multiply the each p-value by the number of post hoc tests
>> performed (6 in the example you provided). Then compare the adjusted
>> p-values to the alpha level you set (e.g., .05).
>> On Wed, Jan 19, 2011 at 1:56 PM, J McClure <firstname.lastname@example.org> wrote:
>>> I am doing a post hoc analysis comparing column proportions and
>>> adjusting (Bonferroni) for multiple comparisons (CTables, test
>>> statistics option).
>>> I've been asked to provide the corrected p-value.
>>> I'd like to know if I am correct in reporting the corrected value as
>>> The row variable has two levels (yes and no) and the column variable has
>>> 4 levels, so I am making 6 comparisons for 'no' and 6 for 'yes'.
>>> The output shows the comparison for the 'no' and for the yes level of
>>> the row variable. Is the Bonferroni adjustment for 6 comparisons and the
>>> corrected p-value (.05/6=.008333)
>>> Also, I have 19 row variables for which I am running column proportion
>>> comparisons. I think that the comparisons for each of the 19 variables
>>> are considered independent from each other so no further adjustment is
>>> made by SPSS. Is this the way reviewers look at the question?
>>> Thanks for any help,
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> Bruce Weaver
> "When all else fails, RTFM."
> NOTE: My Hotmail account is not monitored regularly.
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