Spot on, Michael.
Michael Granaas wrote:
> 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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--
Alan McLean ([EMAIL PROTECTED])
Department of Econometrics and Business Statistics
Monash University, Caulfield Campus, Melbourne
Tel: +61 03 9903 2102 Fax: +61 03 9903 2007
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