Newsgroups: bit.listserv.psycgrad
Date:         Tue, 15 Dec 1992 10:07:00 CST
              <PSYCGRAD@UOTTAWA.BITNET>
From:         TBN@UTXVM.BITNET
Subject:      stats & science

Monika writes:
Tor,
I think that generally we agree...

I was taught that having statistically significant results does not always
mean that these results are meaningful...but having meaningful results means
that they are also statistically significant.
By giving the example I wanted to show that doing stats at all times isn't
always necessary, because by doing so we may be ignoring the true phenomenon
at hand.  If you have the time (that is a funny statement for grad students
:-)) read Robert.C. Boles "Why you should avoid statistics." Biol Psychiatry,
1988, vol.23, pages 79-85.

The author describes 6 rules, and although I do not agree with all of them,
they definitely makes us think:

Rule 1:  Look for things that are obvious.
Rule 2:  Do not use control groups.
Rule 3:  Test ridiculous hypotheses.
Rule 4:  What you want to do in research is "measure things".
Rule 5:  Look at the data, rather than statistics.
Rule 6:  Talk to Buddha.

* My response to this is essentially what I have said before: you can do
whatever you want to with statistics, numbers, and graphs (see Huff's
classic on _How to lie with statistics_ to gain a real insight on how
unscrupulous types uses stats and numbers to obfuscate the "truth" rather
than illuminate it---and I would advocate not following proscrustean rule
s in terms of selecting what particular statistical procedure to use for
any given analysis (many of them are based on the general linear model,
anyway--see Woodward's excellent textbook for an overview of the general
linear modeling approach to data analysis). However, once you pick a
certain technique to use, I think it's important to follow the convention
s set down by your field for using the technique because your readership
generally *assumes* that you carried out the technique in a certain way
that is common to the field. I'm not saying that it's wrong to deviate
from "the usual procedures", but if you do so it's your responsibility
to inform your readership of this deviation so that they don't assume you
did A when in fact you did B. *


Monika further writes:
                                        In terms of "peer review" I have no
problem with that, because 99% of these individuals have gone though, what
I have gone through, and they fully understand what this is all about.
In fact, I got commended by a core researcher in the field while he was
reviewing my paper for publication, who said that he wanted to congratulate
me because he was never persistent enough to stick with the research and
abandoned it eventually.

*So, you are reversing your previous statement that you alone know your
own research best?*

Monika further writes:

Maybe doing a TOTALLY independent research is a fallacy, but there is a real
distinction between fixing the electrical equipment, running the subjects,
racking one's brains whether to do regression or ANOVA with these data,
going to a Harvard Graphics and SPSSX tutorial so to be able to present
statistically and graphically to the best of one's abilities, as opposed to
calling the technician, hiring someone to run the subjects, calling the
statistician and maybe even a typist once everything is "regurgitated" for
us, since dealing with all these bloody word perfect commands, which can
easily spot any errors, is so time consuming and "unnecessary".
(Sarcasm intended).  Do not get me wrong Tor, I realize that in some areas
of research getting some help is vital, but then again there is also plain
laziness and lack of profesionalism...


                                 _
    ) (    Monika Trzcinska    _- o   ) (   054470@ACADVM1.UOTTAWA.CA
   )   (----------------------- o o  )   (----------------------------
    ) (                        -_ o   ) (
                                 -
   "The most important attitude that can be formed is that of a desire
    to go on learning".
                                                  Dewey (1938)

* First, I would argue that it's just as professional to learn how to wor
k with and supervise a group of dedicated undergraduates as it is to do
what you've mentioned above. In fact, I wish that more faculty I know
would spend time working with the undergraduates they teach to from a
distance--not only would they learn more about what's of concern to
today's undergrads, they might also gain more experience in working with
colleagues in a supervisory role and apply this knowledge to their
interactions with their grad student colleagues, too.
   Also, it can be the epitome of professionalism to realize & acknowledg
that one is over one's head when it comes to stats, too, and thus seek
out the help of a professional statistician or consultant to either do
the data analyses for you or to give you help in figuring out how to
complete the analyses. One phenomenon that I've seen all too frequently
(and which I think is very unprofessional b/c it often results in a
lowered quality of research product)
around my department is the "I have to look good" syndrome (I think this
may be one of the 'phobias' that Todd regaled us with a few days ago).
A healthy number of people in my department are *afraid* to ask colleague
s and advisors for help with stats or research design or lit searches
because they are afraid that asking for help will make them "look bad" or
"weak" in the eyes of others. Now, I'm not saying at all that you are
one of these types--obviously you are not one, based on the description
of the effort you went into to get your research program going as well as
the fact that you went to SPSS (it's not spssx anymore, btw, though I
guess it was spssx then) tutorials and HPG tutorials.

As far as I'm concerned, the bottom line is *quality* in science. I know
that I'm more comfortable in a collaborative role rather than an independ
-ent role--I feel being in collaboration enhances the quality of the
science I do--and that's why I go and ask all kinds of people for help
and assistance with my work, even if my lack of knowledge is embarrassing
to me sometimes. I'd rather suffer the short term pain of embarrassment
and learn something that will enhance the long-term quality of my work
than keep to myself and turn out a shoddier product as a result of my
reticence. I should note that my undergrad colleagues spotted some
things that they argued should be changed in my design, and the design
became better as a result of their suggestions. It sounds like your
circumstances are different in the sense that your research requires a
lot bench expertise to master and thus might not be appropriate for
undergrad colleagues whereas mine, having paper and pencil measures
with human subjects is less technically sophisticated in that area.

In short, I think what you and I have been talking about is that good
old slippery word, *intent*. I think that this is the "real distinction"
you are talking about. What is the researcher's *intent* in hiring the
the statistician? Is it unprofessional laziness or is it a professional
realization that the statistician can do a better job than the researcher
can in analyzing the data, thereby turning out a higher quality end
product? Just like I don't think there's a black and white answer for
independence vs. collaboration, I don't  think there's such a black and
white answer when it comes to getting help with research vs. not getting
help with research--the question that needs to be addressed is: "Why is
this person getting help--or not choosing to get help?" That's a really
tough question to answer, as any criminal lawyer will tell you! I see
some people I've met in the past who are afraid to collaborate and who ar
e afraid to get help on a research project as being unprofessional
because they're worried more about how they'll look to their advisors
who will be writing job recommendations for them instead of the quality
of the research product they're turning out.

Another oblique issue that's raised by this discussion is the issue
of specialization. In my department, the grad students are viewed by
many faculty (not all, thank goodness) as 'technicians'--that is, the
y can come up with ideas occassionally which fit into a prof's framework
, but usually the prof feels obliged to 'shape' and 'mold' the ideas of
the student to fit the prof's own research agenda. the prof then designs
an experiment with the student and the student runs the expt and does
the analyses. the student gets to write the methods section, if he's/
she's lucky--but the profs do the 'thinking' and the 'serious writing'
of the intros and the discussions (the irony here is that because many
of the profs themselves are so pressured for time, tenure, etc., they
wind up being 'technicians' of a different, slightly-higher level type,
churning out studies willy-nilly as if they were sausages in a factory).
Previous students have commented that they come out of the program feelin
g well-trained to be good grad students, but not to be well trained as
junior faculty (we get no grant writing experience, for example, and
minimal teaching experience).

The point of all of this is that specialization seems to be the wave of
the future, at least vis a vis some depts. Whether this is a good trend
or a bad trend is debatable (I think everyone, faculty, grads, and
undergrads need to think more and produce less, but that's just a persona
l opinion), but it may be something we all have to face sooner or later.

cheers,

Tor
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