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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