On Thu, 13 Apr 2000, dennis roberts wrote:
> At 10:23 AM 4/13/00 -0500, Michael Granaas wrote:
>
> >In addition to defining the variables some areas do a better job of
> >defining and therefore testing their models. The ag example is one where
> >not only the variables are relatively clear so are the models. That is
> >there is one highly plausible reason for rejecting a null that fertilizer
> >does not effect crop production: Fertilizer increases crop production.
> >You have rejected a model of no effect in favor of a model positing an
> >effect.
>
> i did not know that ag research ... in this case, production figures ...
> was so easily accomplished ...
I didn't say that there wasn't a lot of work involved. What I said was
that there is a clear link between the experimental manipulation, the
outcome variable, the hypothesis test results, and the question asked.
This particular example was based on the type of question that Fisher
might have been dealing with circa 1925. If my interpretation of
history is correct and Fisher's ag research was focused on treatment/no
treatment effects I think it helps us understand both the strength of his
method in that setting and identifies a potential weakness in our own use.
The methods of Fisher are useful when there is a strong link between the
substantive and statistical hypothesis. The convergent validity example I
used, I thought, showed a weak link between the substantive question and
the statistical hypothesis (rho =? 0). (I admit my ignorance to current
practice but I am pretty sure a correlation merely different from 0 is not
evidence that two measures are measuring the same thing.) The weakness
of that link leaves the researcher with out any useful information from
their statistical decision. (i.e., knowing that the correlation is other
than zero does not establish convergent validity.) Testing a null
hypothesis of, for example, rho <? .7 (there may be a more appropriate
value, I just picked this one because I like 7's and it illustrates my
point) would provide a much better match between the statistical decision
and the substantive question. (Rejecting rho <? .7 would indicate a
degree of correlation consistent with convergent validity, failing to do
so would leave the question open.)
There are areas in psychology that have also done a good job of making the
links between their substantive and statistical hypotheses and seem to
have made a good deal of progress in knowledge generation. There are
others that have not. (I have heard roughly the same argument made for
physics, chemistry, and biology and I expect that this is generally
true for a number of other research disciplines.)
With or without a link between substantive and statistical hypothesis I
acknowledge, nay, I proclaim, that research in any discipline is hard
work for all the reasons you suggest and more. Any disrepect for the
field of agricultural research was unintended and I appologize to anyone
that I may have offended.
I also freely admit that ag research today is going to be very different
from the ag research of 1925.
Michael
*******************************************************************
Michael M. Granaas
Associate Professor [EMAIL PROTECTED]
Department of Psychology
University of South Dakota Phone: (605) 677-5295
Vermillion, SD 57069 FAX: (605) 677-6604
*******************************************************************
All views expressed are those of the author and do not necessarily
reflect those of the University of South Dakota, or the South
Dakota Board of Regents.
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