From: tgee@alfred.carleton.ca (Travis Gee)
Subject: Re: Coding in qualitative analysis
Organization: Carleton University
Date: Wed, 27 Jan 1993 14:22:22 GMT

     First, let me warn you that this is a somewhat long posting. The
discussion which the coding thread has engendered is a rich one,
covering several topics.  I have collated the ones which concern my
previous postings in an effort to deal with what I see as key issues
in one fell swoop. References are provided at the end.

     Monika; thank you for the example of objective hermeneutics (OH).
However, it still seems to me rather like psychoanalysis without
benefit of a psychoanalysand (at least, after the initial phase). I
would have a good deal of cognitive dissonance applying it "as is" to
data. The problem of generating as many stories as possible
that fit the data may easily result in more than one theory that
accounts for *all* of the data.  To whom does the research team refer
for the tie-breaking vote? 
     Re: analysis, one *should* focus on data that seem to invalidate
the theory under consideration. I agree wholeheartedly. However, the
data are limited to those obtained by our method, and we too easily
select a method which will suit the theory being examined.  Should we
not consider a "null method" which is as theory-free as possible, to
maximize the potential for conflicting data?  I suggest that
"negotiating an understanding" in Harre's sense leaves at least part
of the control out of the hands of the researcher, minimizing the
potential for bias.

Nora L. Ishibashi then joins in thus: 

>On the other hand, if we are doing a study that looks for
>patterns in what the interviewee has stated, that compares aggregate kinds
>of themes or patterns from several interviewees or that analyzes the
>interview for the process rather than factual content, the interviewee may
>not  have more to add.

     This brings up the question of level-of-analysis.  In qualitative
research, are we willing to ignore the individual differences to come
up with some kind of normative statement about "the population"?  It
is true that if we are abstracting away content in favour of process
or syntactic/morphological items, then we are doing what statisticians
do by using numbers. Indeed, the final report will probably contain a
table or two.  But much qualitative research does not ignore the richness
of the single case. What it does seem to ignore is the fact that it is
hard to capture all of the possible riches in one sitting. 
     I'm not saying that abstracting away is an evil. Indeed, it is
the essence of our ability to cope with the world.  Rather, I'm saying
that abstraction without proper validation is easily construed as 
fiction. The mode of validation is a key issue, and what I really want
to deal with here. How can we figure out what people are on about with
as few errors as possible (a discussion of Types of Errors is below).
In addition, do our data have any use once we've counted up the number
of (variable of choice)? Can we analyze *both* quantitatively across
people and qualitatively within people? If we do both, do we get the
same answers? If we don't get the same answers from both, what's wrong
with our theory (or at least, what are its limitations)? 

I previously politicianed my way out of a question thus: 

>     Secondly, let's turn that last question around, and ask "how
>could the interviewee benefit from the researcher's evaluation of the
>interviewee's perspective."

On this topic, Nora continues: 
> If we introduce the question of whether it would be
>helpful to the interviewee to hear the results of the study, that seems to
>me to be a different issue altogether from a consideration of methods of
>arriving closer to an understanding of answers to a research question.

To which flames Nicholas Sturm adds fuel: 

>>  That may be a morally useful thing to do, but it's not quite the same
>>as the original "research goal".
>Sharing some of the
>findings could be of great benefit in such cases.  A fair payback for
>participation, don't you think?
>>  If the interview is being repaid, perhaps he should just be made
>>co-author of the report.  Seems rather like what one is desiring by
>>requesting the 'validation.'
>> But pardom my butting in.  I should just remain a watcher here.

     Point taken!  The rhetorical turn of phrase is too often the
irresistible turn of phrase. Rather than deflecting the question of
what the second phase of my proposed validation method adds to the
origian research goal (and dragging up the whole messy business of
*why* we do research), I should have tackled it head on. Here is my
belated attempt to do so.
     The immediate research goal is to make a statement about what is
going on.  My main objection to the standard method is an
epistemological one: "Can we make such a statement without reference
to any constructions which the person to whom the statement refers may
have with respect to said statement?"  If that's only clear to the
lawyers out there, it might be more simply put as "Who's the real
expert?" In OH, there appears to be a contradiction in that every
member of the culture is *in principle* able to come up with a
coherent model based on the same data.  However, the method excludes
*in principle* the possibility of alternative constructions by not
including the views of members of the culture who are not objective
hermeneuticists.
     Brian Little (1972) discusses Us-Models and Them-Models.
Us-models "describe the behaviors of those persons who share the
perspective of the theorist proposing the model."  Them-models
"describe the behaviors of anyone who does not share the particular
perspective of the theorist proposing the model <such as> subjects,
patients, wives, the dog next door, and rival theorists."
Psychoanalysis (and, so far as I understand it, OH) are particularly
duplicitous in that their assumptions about humankind are such that
Us-models apply to the analysts, while Them-models apply to everyone
else. (L.W. Brandt carries this point through in detail in his book
_Psychologists_Caught.) The reasons given for this unparsimonious
subdivision of humanity are at best vague, and at worst, nonexistent.
The key point to be made is that a theory about humans which
reflexively accounts for both the theorist and the theorized-about is
conceputally preferable to a non-reflexive theory which does not
account for this.  OH, as described by Monika, assumes a continuity
between theorists and the population, then proceeds as if there were a
disjunction. Thus it cannot provide such a comprehensive theory.

On a related topic, Al Furtrell writes (quoting >me):

>     My use of "Type I" and "Type II" just referred to the *kind* of
>errors that were being made, with no implication abut  statistical
>measures. (Some were used in my study, but I used a pretty minimalist
>approach to stats, at least by today's number-crunching standards.)
>The key point is that qualitative methods can be prone to such errors,
>because we make assertions based on data. In the spirit of this group
>I am loath to require quantification of such errors and am rather more
>interested in the form of the errors we researchers can make.

>>I don't want to let Travis off the hook so easily.  Because a Type I or
>>alpha error is committed when one rejects a null hypothesis when one should
>>not have (in "regular" talk that means that we reject a true null
>>hypothesis) and a Type II or beta error is committed when one fails to
>>reject a false null hypothesis, statistics and quantification is inherent
>>to any discussion of these types of errors.  More important, the introduction
>>of the possibility of these errors suggests a mind set at odds with the
>>notion of a "qualitative" study.  One cannot make a Type I or Type II error
>>unless one has tested an hypothesis -- and many folks on this list have
>>no problem with hypotheses as such except that the types of research questions
>>that interest them do no lend themselves to hypothesizing or to quantification.

>>Basically, I am surprised that the idea of Type I and Type II errors would
>>be an issue in the study he describes.  That is why I found it interesting
>>in the first place.  I thought he had developed an innovative way of
>>merging the qual/quant dichotomy.  His response suggests otherwise.

     No it doesn't!  With all due respect, Al, your post is evocative
of the extent to which the hypothesis-testing model has permeated the
minds of psychosocial researchers. One *cannot* infer that a statistic
has been perpetrated by the simple fact that a Type I or Type II error
has been made when we look at the nature of these errors.
     To return to a point made above (by me), the immediate goal of
the research is to make error-free statements about the state of the
world. If we say "there is x" when there is not, we make an error of
the first kind.  If we say "there is not x" or fail to say "there is
x" when there *is* an x out there, we commit an error of the second
kind.  Because the hypothesis- testing model offers clear decision
rules for making such statements, Type I and Type II errors have come
to be associated with statistical inference.  However, this is a
chance of historical development, not necessity.  While in qualitative
research, we may not be able to quantify the number of such errors as
easily as we can in pure quantitative research, we can still make
them!  We don't even have to have a hypothesis to test, because it is
the final *statement* which is or is not correct, irrespective of how
it was derived.  

Finally, Dr. Stephen K. Tagg observes: 

>>I believe any research is an overlap of a series of samples (purposive sample
>>not statistical) of various "universes of content"
>>Including:
>>	Informants
>>	Things researchers might ask informants to do (including answer quest)
>>	Ways that researchers get informants to inform
>>	meanings for informants
>>	meanings for researchers
>>	meanings for the market for research publication

     Exactly! And when the researcher intrudes into that universe of
content, he or she brings along residue from a different universe.
However, small the difference may be, the only way to check for
contamination is to validate against another sample. Indeed, in
research we step lightly from one universe to the next, carrying along
conceptual moondust from place to place on our clumsy feet.  This in
fact was my argument against extensive quantitative analysis of a
series of interviews (in my M.A. thesis).  Because I got better as an
interviewer, and learned more about my population as I went along, the
last interview would be incommensurable with the first.  Therefore,
the set of interview questions could be continually expanded as the
study progressed (reducing those nasty Type II errors).

     This fact led me to my position of the non-privileged researcher.
*I* was the student, and the interviewees were experts teaching me
about what it's like and what it takes to become a top jazz musician
(the topic of my thesis for those who just tuned in).  My statements
had to withstand *their* scrutiny as well as those of my committee.

     Finally, (Nicholas!) if validating those samples means more
interviewing, so be it.  We allow for that from the start and budget
ourselves accordingly. And if we say that something is there and 5 of
our experts say it's not, we have some explaining to do! In a
subsequent posting, Dr. Tagg notes that :

>>I don't think it is possible for the sampling of each universe to be perfect
>>(ie there's reliability/validity issues): effectively there are cost/benefit
>>optimizations to make.

And we all make them. I'd have loved to have had *all* of my
interviewees go over the report. But logistics are always a hassle,
and at some point we have to draw a line. Sort of like selecting 
alpha=.05 over alpha=.01  .

>> Unfortunately a lot of research design consists of
>><<lets re-do famous-article's research except tweaking this attribute>>
>>and so the whole body of research has no chance of being representative of the
>>universes of interest because previous researcher's purposive sampling
>>compromises are perpetuated..

Unfortunately accurate! Well, back to slugging away at my stats
courses! Thanks for reading this far, now go get a coffee :-)


References:

Brandt, Lewis Wolfgang, (1982). Psychologists caught. University of
     Toronto Press; Toronto.

Little, B.R. (1972). Psychological man as scientist, humanist and
     specialist. Journal of Experimental Research in Personality, 6,
     95-118. 



((((((((((((((((((((((((((((((((((((((((((((((((((((((((((
Travis Gee ()  tgee@ccs.carleton.ca     ()
           ()  tgee@acadvm1.uottawa.ca  ()        ()()()()
           ()                           ()              ()
           ()                           ()()()()()()()()() 
Recent government figures indicate that 43% of all statistics
are utterly worthless.  




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