Wow. I'm impressed with this group's thoughtful responses both privately
and on the server. Yes, Hayes calls this the Behrens-Fisher problem too.
I was always taught to use equal n's and then the homogeneity of variance
assumptions were not as big of an issue (the ttest post alluded to this
too). Since I'm working with a clinical sample, I'm stuck. Just to give
more info.
n1=6; n2=8
I started computing multiple ttests just to see how things changed when the
n's were kept constant. Of course I knew in advance which was the one I
wanted to use. The SD's are quite different for some of the comparisons
since one group is impaired and one is generally normal.
satterthwaite weighted df=
a =SEM1^2
b =SEM2^2
c =a^2/(n+1)
d =b^2/(n+1)
[(a+b)^2/(c+d)]-2
(I hope I got this coding correct-see Hayes p328). I checked the spss
algorithms web site you gave and all the formulas for ttests and
t-statistics used only 1 term for n (I did find Satterthwaite listed in
appendix 2 so I might try redoing this with spss) so I used Minitab (someone
else suggested this package) after trying the calculations by hand (excel).
Here are the SEM's for means 1 and 2. It looks like the df decreases as the
difference (diff) between the SEM's goes up. I also added the SEM's (sum)
just to see if there was a relationship to overall variability. It looks
like it's working well for me.
Thanks everyone!
Allyson
Here are my calculations with minitab
SEM1= 51
SEM2 =73
diff =-22
sum= 124
df =11
SEM1 =39
SEM2 =114
diff =-75
sum =153
df =8
SEM1 = 42
SEM2 = 23
diff = 19
sum = 65
df = 8
SEM1 = 17
SEM2 = 20
diff = -3
sum = 37
df = 11
SEM1 = 21
SEM2 = 180
diff = -159
sum = 201
df = 7
SEM1 = 52
SEM2 = 36
diff = 16
sum = 88
df = 9
Rich Ulrich wrote in message <[EMAIL PROTECTED]>...
>On Wed, 28 Feb 2001 08:26:30 -0500, Christopher Tong
><[EMAIL PROTECTED]> wrote:
>
>> On Tue, 27 Feb 2001, Allyson Rosen wrote:
>>
>> > I need to compare two means with unequal n's. Hayes (1994) suggests
using a
>> > formula by Satterthwaite, 1946. I'm about to write up the paper and I
can't
>> > find the full reference ANYWHERE in the book or in any databases or in
my
>> > books. Is this an obscure test and should I be using another?
>>
>> Perhaps it refers to:
>>
>> F. E. Sattherwaite, 1946: An approximate distribution of estimates of
>> variance components. Biometrics Bulletin, 2, 110-114.
>>
>> According to Casella & Berger (1990, pp. 287-9), "this approximation
>> is quite good, and is still widely used today." However, it still may
>> not be valid for your specific analysis: I suggest reading the
>> discussion in Casella & Berger ("Statistical Inference", Duxbury Press,
>> 1990). There are more commonly used methods for comparing means with
>> unequal n available, and you should make sure that they can't be used
>> in your problem before resorting to Sattherwaite.
>
>I don't have access to Casella & Berger, but I am curious about what
>they recommend or suggest. Compare means with Student's t-test or
>logistic regression; or Satterthwaite t if you can't avoid it if both
>means and variances are different enough, and you wouldn't rather do
>some transformation (for example, to ranks: then test Ranks). And
>there's randomization and bootstrap. Anything else?
>
>Yesterday (so it should still be on your server), there was a post
>with comments about the t-tests.
>======== from the header
>From: [EMAIL PROTECTED] (Jay Warner)
>Newsgroups: sci.stat.edu
>Subject: Re: two sample t
>========
>
>There are *additional* methods for comparing, but the one that is
>*more common* is probably the Student's t, which ignores the
>inequality.
>
>Any intro-stat-book with the t-test is likely to have one or another
>version of the Satterthwaite t. The SPSS website includes algorithms
>for what that stat-package uses, under t-test, for "unequal
>variances." I find it almost impossible to find the algorithms by
>navigating the site, so here is an address --
>http://www.spss.com/tech/stat/Algorithms.htm
>
>--
>Rich Ulrich, [EMAIL PROTECTED]
>http://www.pitt.edu/~wpilib/index.html
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