i believe that the weighted sd would be ... and i will just use a 2 sample case:

global sd as you call it: [var1(n1-1) + var2(n2-1)] / n1+n2 ... then take the sq root of it all

NOTE: the denominator MAY be n1-1 + n2-1 ...

just extend to 3 or more groups

At 12:57 PM 11/27/2002, David Robinson wrote:
I've searched for this, and failed to come up with an answer. It's not
a homework question, or even a purely accademic question - I'm trying
to combine psychoacoustic data from a number of sources, because I
need to calculate the percentage of the population who are able to
detect certain sounds.


Here's the problem:

I have several measurements of the SAME QUANTITY from different tests.
For each test, I have:

number of subjects, mean value, standard deviation (or standard error
of the mean.)

(All standard errors will be converted to standard deviations - I know
they don't quite mean the same thing (even when divided by root N),
but I believe SD is more appropriate in this context).

I want to combine all the data, to get a global mean, and a global
standard deviation. The results I want for these two values should be
the results I would get if I had ALL the original data from all the
tests, and simply calculated the mean and standard deviation over all
the data.

(I do not have all the original data - some of it does not exist
anymore)

I can calculate the global mean easily (n1*x_bar1 + n2*x_bar2 +
...)/n_total

I do not know how to calculate the global standard deviation. Please
can you help?

(I don't understand it, but from what I have read I believe the usual
technique of simply adding the squares of the SDs is not appropriate
because I have (hopefully) correlated data, and different sample
sizes)

Cheers,
David.
http://www.David.Robinson.org/


P.S. The SD is AS IMPORTANT as the mean for this work, because I will
integrate the resulting gaussian to give a psychometric function.
(Well, I have the integrated form giving a psychometric function, and
I can put the numbers in!) I have some real measured psychometric
functions to compare with, and these match the psychometric formula
predictions (from mean and SD values) very well.
.
.
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dennis roberts, educational psychology, penn state university
208 cedar, AC 8148632401, mailto:[EMAIL PROTECTED]
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