Re: The (Clinical) Point
Sat, 9 Mar 1996 11:15:43 -0500
Content-ID: <>
Content-type: text/plain

attach file
Content-ID: <>
Content-type: text/plain;
Content-Transfer-Encoding: quoted-printable

Thank you for your interest and patience. =

Do ask questions and make criticisms.
I am not afraid of change. Perhaps I will get
lucky and you will show me that I am wrong
in all this. Then I can get corresponding
regressions off my shoulders. Big ideas can =

get old, especially when shouldering them elicits
ridicule from others. I do believe a lot of people =

could be helped by corresponding regressions,
if it is valid, and so I feel responsible for getting
its case out in the public eye. What happens
after that may not be on my shoulders or
First, the list of experiences you select is
your decision.They may be concrete or
types of experiences; restricted to particular
times or contexts, or more general. The
math of CR is indifferent to this.Your
selection would, however, detemine what
generalizations you may make. If you are
interested in an individual's constructs =

measure the individual's. It might be handy =

to have a more general (nomothetic)
perspecitve- derived from a sample of similar
clients- in order to guide you in your =

selection of experiences, but it is not =

essential. Your client may be unique.
Your choice of a scale is largely up to you.
It should be at least a four or five point scale,
however. I would use a five of seven point
scale, based on Nunnally's recommendation =

in his book Psychometric Theory. It depends
on what the client can use meaningfully.
Concerning prediction. It is not as simple as
using regression to predict events. With
regression analysis we predict but we do
not explain the causes. As far as the =

mathematics of traditional regression goes,
we can predict a dependent variable from an
independent variable just as easily as we =

can the IV from the DV. What is worse, we =

will not know if either is an IV or a DV. =

Many variables may correlate with our =

variable of interest (Cutting) but be causally =

irrelevant. Thus prediction alone will not help
us develop a diagnostic and treatment
strategy. Knowing the actuarial signs of =

cutting does not tell us why. Some signs
are just correlated and incidental. =

With CR we should be able to say that
across a number of events (including =

arguing with boy friend, seeing parents fight,
etc.) the client's feeling empty inside is a
component of later cutting. We might also
conclude that although her getting angry
does correlate with both feeling empty and
cutting, anger does not make up one of the
components of cutting. Thus the anger is a
red herring, in a therapeutic sense. The
emptiness would be the thing to get at. =

The fuzziness that you mention is a function
of grid design- not simply mathematics. You
must choose the elements (experiences) as
a group, understanding that your generalizations
are limited to the clarity and extent of those
elements you sample. =

We may do the grids on either one person
or on many persons. Many would be
logistically easier in tems of data collection.
A grid based on the responses of only one
person would require some work from the one
person, but it would not be so bad as not to
be worth the time. People spend more time
sitting in their doctor's office, waiting to see the
physician, than would be needed to rate 50
elements on a few important constructs.
Again, we are not simply predicting events. =

Actuarial statistics predicts events by finding
what correlates with the event.The actuarial =

statistician is not interested in why these
things predict the event, just whether or not =

they do. We are interested in what causes
an event.That is much more interesting clinically,
because it gives us an idea of how to stop the
event. This may require a follow up grid. Having
determined a causal relationship between
feeling empty and cutting, we would want to do
another grid to get at the causes of feeling
empty. These causes may in turn, need to be
further analyzed until we can get at some
causes in the chain that the client can take
actions to prevent. That way we do not just =

come across sounding wise but useless with =

statements like "Your cutting is caused by =

your feelings of emptiness." The patient may =

in fact feel better understood by such a =

statement, but this being understood may not
get at the even deeper roots of the chain, and
the thrill beneath the doctors understanding
eye passes. She cuts again. =

This is directly pertinent to the thrust of =

all the postings I have made on this net. =

Believe it or not, pain is not just a social
construct, to be transformed by the blessing
of the clinician. Getting the attention of =

someone playing doctor may be temporarily
comforting, but unless the doctor goes beyond
the surface relief derived from the client's
getting attention, and goes on to the root of
the problem, there will only be a kind of abuse
of authority. The roots of the pain remain,
whether we diagnose and dismiss it or not..
When we treat clients as fellow scientists,
instead of as just invalids who should be
thankful that we condescend to them even
briefly,we place the experiences of the client =

in a different light. The client is no longer seen =

as invalid because of his pain, but is seen
instead as a fellow scientist, seeking
consultation concerning the best ways to
discover the causes of the pain and the
actions needed to stop it or cope with it.
If corresponding regressions can help put
an end to the abuses perpetrated by those
who would judge others because of their
pain, then enduring such abuses as a
scientist will have been worth it. So, I
thank you, Lois, for taking me seriously
as a fellow scientist. =

Bill =