Robert J. MacG. Dawson wrote:
Oh, it never is (strictly), outside of a few industrial
applications. Nobody ever took a random equal-probability sample from
all turnips, all cancer patients, all batches of stainless steel, all
white mice, or all squirrels. However, there are good
Thom Baguley wrote:
however, I think the
defence of convenience samples can be stronger than this. Unless we
have reason to believe that a sample is biased in such a way as to
generate our pattern of results a convenience sample is just as
At 12:39 PM 8/16/01 +0100, Thom Baguley wrote:
For example, if a new drug is administered to a
treatment group made up of serious cases and compared to a control
group of mild cases obtaining more cures for the treatment group
might be considered better evidence than a random sample.
Thom
Dennis Roberts wrote:
sorry ... i can't agree with this ...
it could be that in the serious cases ... there is a unidentifiable gene
factor that INTERACTS with the treatment ... that is not available in the
mild cases group (that's why you have serious and mild cases) ... so, it
is not
On 14 Aug 2001, Nolan Madson wrote:
I have a data set of answers to questions on employee performance.
The answers available are:
Exceeded Expectations
Met Expectations
Did Not Meet Expectations
The answers can be assigned weights [that is, scores -- DFB]
of 3,2,1 (Exceeded, Met,
Donald Burrill wrote:
I agree on all of this. I'd add that at issue is whether people find
the mean format useful, whether it is misleading. I'd use -1, 0 and
+1, rather than 1-3. In this case the mean gives you at-a-glance
summary of the extent to which the people who exceeded expectations
:[EMAIL PROTECTED]]
Sent: Wednesday, August 15, 2001 3:34 AM
To: Nolan Madson
Cc: [EMAIL PROTECTED]
Subject: Re: Presenting results of categorical data?
On 14 Aug 2001, Nolan Madson wrote:
I have a data set of answers to questions on employee performance.
The answers available
Silvert, Henry wrote:
I would like to add that with this kind of data [three-level ordinal]
we use the median instead of the average.
Might I suggest that *neither* is appropriate for most purposes? In
many ways, three-level ordinal data is like dichotomous data - though
there are a
I do not see how (probabilistic) inference is appropriate here at all.
I assume that _all_ employees are rated. There is no sampling, random
or otherwise.
Jon Cryer
At 11:14 AM 8/15/01 -0300, you wrote:
Silvert, Henry wrote:
I would like to add that with this kind of data [three-level
Jon Cryer wrote:
I do not see how (probabilistic) inference is appropriate here at all.
Oh, it never is (strictly), outside of a few industrial
applications. Nobody ever took a random equal-probability sample from
all turnips, all cancer patients, all batches of stainless steel, all
Nolan Madson writes:
I have a data set of answers to questions on employee performance.
The answers available are:
Exceeded Expectations
Met Expectations
Did Not Meet Expectations
The answers can be assigned weights of 3,2,1 (Exceeded, Met, Did Not
Meet).
One of my colleagues says that it is
I have a data set of answers to questions on employee performance.
The answers available are:
Exceeded Expectations
Met Expectations
Did Not Meet Expectations
The answers can be assigned weights of 3,2,1 (Exceeded, Met, Did Not
Meet).
Our client wants to see the results averaged, so, for
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