Chapter 19
Measuring
Treatment
Merle Canfield
During the past 50 years there
has been a steady increase in the number of schools or styles of psychotherapy.
In 1984 Karasu, T., et. al. reported 418 systems. At the same time there has been a moderate
increase in the elements of psychotherapy.
In recent years there has been a trend to merge these systems with
common factors. It is the purpose of
this section to show how research designs empirically support the process of
sorting out the characteristics of these system. At the same time the designs should support
the further elaboration and search for the elements of psychotherapy. Frank (1971) proposed that there were common
factors in addition to specific factors in psychotherapy that might be related
to outcome (see also Parloff, 1986).
In an attempt to determine
whether the treatment has been implemented three approaches have been used: (1)
developing therapy manuals, (2) labeling or coding psychotherapy as it unfolds,
and (3) rating the process by the use of scales. In 1979 Russell and Stiles reviewed the
coding systems and attempted to devise a taxonomy and resulting coding system
that would include all elements of the existing coding systems. They generated a logical or rational
taxonomy. Although their task was
different they did attempt do develop a taxonomy of the psychotherapeutic
elements. Many of the taxonomies that
have been developed have been developed for specific style of school of
therapy. We are proposing methods to
perform empirical taxonomies, or a combination of judgments and empirical
These methods can be used in to
ways: (1) develop a taxonomy of the styles of therapy, or (2) develop a
taxonomy of the elements of therapy.
Probably both of these would be useful.
If both were developed they would complement each other so that
identifying a particular style or school of therapy would be a matter of
selecting a set of elements of therapy.
The techniques are similar for the two areas. Finally, modes other than psychotherapy are
presented.
For example, of the 400
different schools what is the overlap and how would one determine the overlap
between the schools? It would be useful
to identify the overlap or common factors.
What are the interactions that
would place a therapeutic interaction within a specific school and separate the
interaction from other schools (unique factors)? What therapeutic interactions overlap with
other schools (common factors). There
are two major tasks to be accomplished if one is to make such
discriminations. The first task is to be
able to identify and measure (either by counting or by assessing some degree)
of the client/therapist interactions. If
that can be accomplished the second task is to indicate the taxonomy of
performances that each of the styles need.
That is, a profile of the style in terms of the performances is needed.
The most fruitful method of identifying these performances has been to code the
utterances of the psychotherapy process.
There are four statistical
methods that might be used for this process: (1) cluster analysis, (2)
discriminant function analysis, (3) multidimensional scaling, and (4) factor
analysis. Four basic therapy processes
discussed are: (1) psychotherapy, (2) group therapy, (3) ancillary therapies,
and (3) milieu therapy. The literature
indicates that the descriptive or taxonomy process has be accomplished most the
psychotherapy, next with group therapy, next the ancillary therapies and
finally milieu is the least identified.
Trochim (19--) and
_______________ used a combination of cluster analysis and multidimensional
scaling to develop maps of attitudes of toward organizations. It is proposed here that the same method
could be used to build a taxonomy of the elements of psychotherapy.
In this example participants were asked to identify processes or
characteristics of psychotherapy that they thought were curative. The following is that list (along with an abbreviated
name):
Develop
insight INSIGHT
desensitize DESENS
reflect REFLECT
introspection INTROSP
develop trust DEVTRUST
reframe REFRAME
acceptance ACCEPT
interpret INTERP
being consistent CONSIT
being nurturing BEINGNUR
address anxiety ADDRESA
correct faulty cognition CORRECT
try new behaviors TRYBEHAV
challenge CHALLENG
set limits SETLIMIT
help cope HLPCOPE
define expectations DEFEXP
demythetize DEMYTH
counter transference CONTRAN
be a good mom BGDMOM
identify conflicts IDCONFL
These statements were put on
slips of paper and the participants were asked to place them into stacks. They were instructed that there must be fewer
stacks than slips of paper and there must be more than one stack. Once these stacks were created the
information was transferred to a coding sheet in the following manner (the
coding sheet is on the following page).
Assume that ACCEPT, DEVTRUST, BEINGNUR, and BGDMOM were placed in the same
stack. Marks would be place on the
coding sheet at the intersection of all of these pairs. Note that there is a mark where DEVTRUST
intersects with DEVTRUST, ACCEPT, BEINGNUR, AND BGDMOM. Again there is a mark where ACCEPT intersects
with DEVTRUST, ACCEPT, BEINGNUR, and BGDMOM.
The same procedure is performed for BEINGNUR and BGDMOM. The coding sheet has the marks filled in for
this one stack (DEVTRUST, ACCEPT, BEINGNUR and BGDMOM). The same sheet would be used to complete the
remaining stacks.
Twenty-four participants
completed the task of sorting the items and completing the tally sheets. The cells of a summary sheet were then
completed by counting the number of participants who had a check (or one (1))
in each in the corresponding cell. That
data is presented in Frame CURET.DBF the labels across the top are not part of
the file. The tallies are the number
students who raised their hand when the cells were identified. The tallies are actually an estimate of the
number of hands raised when they were more than about 5. The cells now give an indication of the
similarity of the items or labels for the cell.
For example, the cell in Figure __ identified by REFRAME and REFLECT is
12 indicating that 12 of the respondents put those two items in the same
stack. That indicates a moderate to high
similarity of the items. The cell
labeled CHALLENG and DEVTRUST has a 0 indicating that none of the respondents
put those two items in the same stack and therefore judge them to be
dissimilar. Consequently, a high score
indicates similarity and a low score indicates dissimilarity. The upper right triangle and lower left of
the triangle are identical. The
estimates were in fact not identical (because of errors in estimation) but the
computer program requires and the lower left was used to duplicate the upper
right.
Figure 1. A coding sheet for recording .....
NAME |
INSIGHT |
DESENS |
REFLECT |
INTROSP |
DEVTRUST |
REFRAM |
ACCEPT |
INTERP |
CONSIT |
BEINGNUR |
ADDRESA |
CORRECT |
TRYBEHAV |
CHALLENG |
SETL IMI T |
HLPCOPE |
DEFEXP |
DEMYTH |
CONTRAN |
BGDMOM |
IDCONFL |
INSIGHT |
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DESENS |
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REFLECT |
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INTROSP |
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DEVTRUST |
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1 |
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1 |
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1 |
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1 |
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REFRAM |
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ACCEPT |
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1 |
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1 |
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1 |
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1 |
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INTERP |
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CONSIT |
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BEINGNUR |
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1 |
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1 |
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1 |
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1 |
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ADDRESA |
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CORRECT |
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TRYBEHAV |
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CHALLENG |
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SETLIMIT |
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HLPCOPE |
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DEFEXP |
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DEMYTH |
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CONTRAN |
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BGDMOM |
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1 |
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1 |
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1 |
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1 |
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IDCONFL |
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Table
2. Representation of data base file
CURET.DBF (in dBase IV format).
NAME |
INSIGHT |
DESENS |
REFLECT |
INTROSP |
DEVTRUST |
REFRAM |
ACCEPT |
INTERP |
CONSIT |
BEINGNUR |
ADDRESA |
CORRECT |
TRYBEHAV |
CHALLENG |
SETL IMI T |
HLPCOPE |
DEFEXP |
DEMYTH |
CONTRAN |
BGDMOM |
IDCONFL |
INSIGHT |
24 |
6 |
6 |
10 |
2 |
6 |
1 |
8 |
0 |
1 |
5 |
1 |
4 |
2 |
3 |
7 |
2 |
6 |
5 |
1 |
7 |
DESENS |
6 |
24 |
4 |
4 |
0 |
5 |
0 |
2 |
1 |
0 |
12 |
5 |
6 |
6 |
4 |
6 |
3 |
6 |
4 |
1 |
5 |
REFLECT |
6 |
4 |
24 |
6 |
3 |
12 |
5 |
8 |
4 |
4 |
4 |
3 |
2 |
4 |
2 |
3 |
3 |
8 |
3 |
2 |
6 |
INTROSP |
10 |
4 |
6 |
24 |
3 |
5 |
3 |
12 |
1 |
2 |
4 |
2 |
4 |
3 |
2 |
2 |
0 |
7 |
5 |
2 |
4 |
DEVTRUST |
2 |
0 |
3 |
3 |
24 |
2 |
18 |
1 |
12 |
14 |
1 |
2 |
1 |
0 |
4 |
3 |
3 |
3 |
1 |
12 |
2 |
REFRAM |
6 |
5 |
12 |
5 |
2 |
24 |
3 |
4 |
5 |
2 |
5 |
5 |
5 |
7 |
2 |
5 |
3 |
9 |
2 |
2 |
4 |
ACCEPT |
1 |
0 |
5 |
3 |
18 |
3 |
24 |
1 |
12 |
15 |
0 |
3 |
1 |
1 |
4 |
1 |
3 |
1 |
0 |
13 |
0 |
INTERP |
8 |
2 |
8 |
12 |
1 |
4 |
1 |
24 |
1 |
0 |
2 |
5 |
2 |
4 |
3 |
2 |
2 |
7 |
7 |
1 |
3 |
CONSIT |
0 |
1 |
4 |
1 |
12 |
5 |
12 |
1 |
24 |
9 |
1 |
4 |
1 |
0 |
7 |
2 |
6 |
0 |
0 |
9 |
0 |
BEINGNUR |
1 |
0 |
4 |
2 |
14 |
2 |
15 |
0 |
9 |
24 |
0 |
3 |
1 |
1 |
3 |
1 |
3 |
1 |
0 |
15 |
0 |
ADDRESA |
5 |
12 |
4 |
4 |
1 |
5 |
0 |
2 |
1 |
0 |
24 |
6 |
7 |
5 |
5 |
5 |
3 |
6 |
5 |
0 |
7 |
CORRECT |
1 |
5 |
3 |
2 |
2 |
5 |
3 |
5 |
4 |
3 |
6 |
24 |
4 |
6 |
4 |
7 |
4 |
6 |
6 |
4 |
2 |
TRYBEHAV |
4 |
6 |
2 |
4 |
1 |
5 |
1 |
2 |
1 |
1 |
7 |
4 |
24 |
9 |
6 |
7 |
3 |
4 |
2 |
1 |
4 |
CHALLENG |
2 |
6 |
4 |
3 |
0 |
7 |
1 |
4 |
0 |
1 |
5 |
6 |
9 |
24 |
3 |
8 |
4 |
8 |
7 |
0 |
4 |
SETLIMIT |
3 |
4 |
2 |
2 |
4 |
2 |
4 |
3 |
7 |
3 |
5 |
4 |
6 |
3 |
24 |
2 |
8 |
5 |
1 |
3 |
4 |
HLPCOPE |
7 |
6 |
3 |
2 |
3 |
5 |
1 |
2 |
2 |
1 |
5 |
7 |
7 |
8 |
2 |
24 |
3 |
4 |
2 |
2 |
7 |
DEFEXP |
2 |
3 |
3 |
0 |
3 |
3 |
3 |
2 |
6 |
3 |
3 |
4 |
3 |
4 |
8 |
3 |
24 |
8 |
1 |
3 |
6 |
DEMYTH |
6 |
6 |
8 |
7 |
3 |
9 |
1 |
7 |
0 |
1 |
6 |
6 |
4 |
8 |
5 |
4 |
8 |
24 |
4 |
1 |
9 |
CONTRAN |
5 |
4 |
3 |
5 |
1 |
2 |
0 |
7 |
0 |
0 |
5 |
6 |
2 |
7 |
1 |
2 |
1 |
4 |
24 |
0 |
6 |
BGDMOM |
1 |
1 |
2 |
2 |
12 |
2 |
13 |
1 |
9 |
15 |
0 |
4 |
1 |
0 |
3 |
2 |
3 |
1 |
0 |
24 |
1 |
IDCONFL |
7 |
5 |
6 |
4 |
2 |
4 |
0 |
3 |
0 |
0 |
7 |
2 |
4 |
4 |
4 |
7 |
6 |
9 |
6 |
1 |
24 |
The
first method used to develop a taxonomy is cluster analysis. It should be remembered that this process is
a descriptive process and not hypothesis testing. The purpose is to describe the relative
position of one element to another. The
result of cluster analysis is a distance indicator of one element to
another. Frame CURCLS1.SPS is a
jobstream for SPSS, Frame CURET.DBF contains the data in the dBase IV file that
the jobstream will use.
File Name = curcls1.sps |
get
file = '\proeval\curet.sav'/keep= NAME INSIGHT DESENS
REFLECT INTROSP DEVTRUST
REFRAM ACCEPT INTERP
CONSIT BEINGNUR ADDRESA
CORRECT TRYBEHAV CHALLENG SETLIMIT
HLPCOPE DEFEXP DEMYTH
CONTRAN BGDMOM IDCONFL . cluster
insight to idconfl /id=name /print=distance /print=schedule cluster(9) /plot=dendrogram hicicle. |
┌───────────────────────────────────────────────────────────────────────────┐
│ CURCLS1.LIS │
├───────────────────────────────────────────────────────────────────────────┤
│Dendrogram using Average Linkage (Between Groups) │
│
│
│ Rescaled Distance
Cluster Combine │
│
│
│ C A S E 0
5 10 15 20 25
│
│ Label
Seq +‑‑‑‑‑‑‑‑‑+‑‑‑‑‑‑‑‑‑+‑‑‑‑‑‑‑‑‑+‑‑‑‑‑‑‑‑‑+‑‑‑‑‑‑‑‑‑+ │
│
│
│ DEVTRUST
5 ‑+‑‑‑+
│
│ ACCEPT
7 ‑+ +‑‑‑‑‑+ │
│ BEINGNUR
10 ‑‑‑+‑+ +‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑+ │
│ BGDMOM
20 ‑‑‑+ | | │
│ CONSIT
9 ‑‑‑‑‑‑‑‑‑‑‑+ | │
│ SETLIMIT
15 ‑‑‑‑‑‑‑‑‑‑‑‑‑+‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑+ | │
│ DEFEXP
17 ‑‑‑‑‑‑‑‑‑‑‑‑‑+ | | │
│ DESENS
2 ‑‑‑‑‑+‑‑‑‑‑‑‑‑‑‑‑‑‑+ | | │
│ ADDRESA 11 ‑‑‑‑‑+ +‑+ | | │
│ TRYBEHAV
13 ‑‑‑‑‑‑‑‑‑‑‑‑‑+‑+ | |
+‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑+ │
│ CHALLENG
14 ‑‑‑‑‑‑‑‑‑‑‑‑‑+
+‑‑‑+ +‑‑‑+ | │
│ HLPCOPE
16 ‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑+ |
| | │
│ CORRECT
12 ‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑+ |
| │
│ REFLECT
3 ‑‑‑‑‑‑‑+‑‑‑‑‑‑‑‑‑‑‑+ +‑‑‑+ │
│ REFRAME
6 ‑‑‑‑‑‑‑+ +‑‑‑+ | │
│ DEMYTH
18 ‑‑‑‑‑‑‑‑‑‑‑‑‑+‑‑‑‑‑+ | | │
│ IDCONFL
21 ‑‑‑‑‑‑‑‑‑‑‑‑‑+ +‑+ │
│ INTROSP
4 ‑‑‑‑‑‑‑+‑‑‑‑‑+ | │
│ INTERP
8 ‑‑‑‑‑‑‑+ +‑‑‑‑‑‑‑+
| │
│ INSIGHT
1 ‑‑‑‑‑‑‑‑‑‑‑‑‑+ +‑+ │
│ CONTRAN
19 ‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑+ │
└───────────────────────────────────────────────────────────────────────────┘
The frame CURCLS1.LIS contains
part of the output from the CURCLS1.SPS computer run. The horizontal axis of the dendrogram
represents distance between the
variables listed on the vertical axis.
Moving to the right indicates greater distance. A plus (+) indicates that two variables have
joined to form a cluster. In the diagram
DEVTRUST and ACCEPT were the first to join (when moving from left to right) are
the most similar. The next pair to join
are BEINGNUR and BGDMOM indicating they are next pair in close proximity. The next pair to join are DEFEXP and
DESENS. The next joining is not a pair
of variables but the joining of two clusters; the cluster formed by DEVTRUST
and ACCEPT is joined with BEINGNUR and BGDMOM.
The final joining (the further to the right) represents the joining of
two clusters that are the most distant.
One of the clusters is made up of DEVTRUST, ACCEPT, BEINGUUR, and BGDMOM
and the cluster to join it is made up of all of the other variables. The method proposed for determining the
number of clusters is to find the greatest horizontal distance where no
variables or clusters join and draw a vertical line. All clusters that have formed up to that line
are considered to be clusters. In the
Figure that would be line A. That is,
there are no joinings between about 15 and 25; there is no other distance that
great when no variables or clusters join.
Using that criteria there are two clusters in this solution since there
are two clusters to the left of line A.
This solution is not very satisfying theoretically. Many of the elements in cluster two seem
different it does not help our taxonomy to combine them all in a single
cluster. Like factor analysis there is a
second method for determining the number of clusters and that is
interpretability. Further, we are not
testing hypotheses but building a taxonomy.
The next greatest distance when no joinings occur is at line B. That vertical line intersects 9 horizontal
lines indicating that 9 clusters have be formed up to that point. The 9 clusters are presented along with the
cluster names.
1. Intrapshychic
INSIGHT
INTROSP
INTERP
2. Anxiety
DESENS
ADDRESA
3. Give Feedback
REFLECT
REFRAME
4. Warmth
DEVTRUST
ACCEPT
CONSIT
BEINGNUR
BGDMOM
5. Correct
CORRECT
6. Directive
TRYBEHAV
CHALLENG
HLPCOPE
7. Set Limits
SETLIMIT
DEFEXP
8. ??
DEMYTH
IDCONFL
9. Countertransference
CONTRAN
This
solution appears to give a better taxonomy than does the first solution. Cluster 1 INSIGHT, INTROSP, and INTERP would
appear to similar type of therapist interventions; REFLECT and REFRAME are
similar and so forth. There are two
clusters that contain single items and they do not seem to belong to any of the
clusters that exists.
Although
there is some indication in the dendrogram of the distance between clusters
it does not give a graphic picture. For
example, in the 9 cluster solution the distance between cluster REFLECT and
REFRAME and the cluster SETLIMIT and DEFEXP is not readily apparent. Is that distance about the same or much
greater than the distance between DESENS and ADDRESA and the cluster SETLIMIT
and DEFEXP?
The
method of multidimensional scaling offers a more graphic picture of the
distance between variables. The
following jobstream uses the same set of data as that used in the cluster
analysis. The task requests a three
dimension solution.
File
Name = curcls3.sps |
get
file = '\proeval\curet.sav'/keep= NAME INSIGHT DESENS
REFLECT INTROSP DEVTRUST
REFRAM ACCEPT INTERP
CONSIT BEINGNUR ADDRESA
CORRECT TRYBEHAV CHALLENG SETLIMIT
HLPCOPE DEFEXP DEMYTH
CONTRAN BGDMOM IDCONFL . als
var = insight to idconfl /level=ordinal(similar) /criteria=dimensions(3) /plot=all. |
The weights for each item on the
three dimensions are presented in Frame CURALS3.LST.
┌─────────────────────────────────────────────────────────────────────────┐
│ CURALS3.LST │
├─────────────────────────────────────────────────────────────────────────┤
│ Dimension
1 Dimension 2 Dimension 3 │
├─────────────────────────────────────────────────────────────────────────┤
│BEINGNUR ‑2.2475 INTROSP ‑1.5347 CORRECT
‑1.2737 │
│ACCEPT ‑2.1976 INTERP ‑1.4272 CONTRAN ‑1.1266 │
│BGDMOM ‑2.1645 REFLECT ‑1.1540 CHALLENG ‑0.9879 │
│CONSIT ‑2.1478 CONTRAN ‑1.0630 REFRAME ‑0.8759 │
│DEVTRUST ‑1.8399 INSIGHT ‑0.9176 HLPCOPE ‑0.6125 │
│DEFEXP ‑0.4503 ACCEPT ‑0.4117 TRYBEHAV ‑0.4709 │
│SETLIMIT ‑0.4328 DEVTRUST ‑0.3511 BGDMOM ‑0.2777 │
│CORRECT ‑0.1885 BEINGNUR ‑0.3222 INTROSP ‑0.2690 │
│REFLECT ‑0.0692 DEMYTH ‑0.2027 DESENS ‑0.1460 │
│REFRAME
0.0569 REFRAME ‑0.1758 BEINGNUR ‑0.0709 │
│INTROSP
0.3780 BGDMOM ‑0.0736 ADDRESA ‑0.0234 │
│HLPCOPE
0.5617 IDCONFL 0.0142 INTERP ‑0.0207 │
│TRYBEHAV
1.0587 CONSIT 0.4554 ACCEPT 0.0023
│
│DEMYTH
1.0670 CORRECT 0.5163 CONSIT 0.0217
│
│INTERP
1.0758 CHALLENG 0.6593 REFLECT 0.3745
│
│IDCONFL
1.1449 ADDRESA 0.7462 DEVTRUST 0.6676
│
│INSIGHT
1.1548 DESENS 0.8300 DEMYTH 0.7658
│
│CHALLENG
1.1846 SETLIMIT 0.9680 INSIGHT 0.8362
│
│CONTRAN
1.2479 DEFEXP 1.0765 SETLIMIT 1.1014
│
│ADDRESA
1.3813 HLPCOPE 1.1151 IDCONFL 1.1337
│
│DESENS
1.4265 TRYBEHAV 1.252?
DEFEXP 1.2520
│
└─────────────────────────────────────────────────────────────────────────┘
It should
be noted that this is not direct output from the SPSS run CURALS3.SPS, each
dimension has been arranged from the most negative weight to the most positive
weight. Dimension 1 has at one pole
BEINGNUR, ACCEPT, BGDMOM, CONSIT, and DEVTRUST while the other pole is DESENS,
ADDRESA, CONTRAN, CHALLENG, and INSIGHT.
This dimension seems to be warmth (possibly emotional) to relearning (possibly
cognitive). Dimension 2 has at one pole
INTROSP, INTERP, REFLECT, CONTRAN, and INSIGHT; at the other pole is TRYBEHAV,
HLPCOPE, DEFEXP, and SETLIMIT. The
continuum seems to go from intrapsychic understanding to a directive or
didactic approach. The third dimension
has CORRECT, CONTRAN, CHALLENG, and REFRAME at one pole and DEFEXP, IDCONFL,
and SETLIMIT at the other pole.
Dimensions 1 and 2 have been plotted in the next figure while dimensions
1 and 3 have been plotted in the subsequent figure.
|
The
figure gives a graphic picture of the distance between cluster 1 (from the
previous calculation; DEVTRUST, ACCEPT, BEINGNUR, and BGDMOM) and cluster 2
(SETLIMIT and DEFEXP). It also shows the
distance between cluster 1 and cluster 3 (DESENS and ADDRESA; the variable
CHALLENG is added to this cluster). Further, the distance between cluster 2 and
cluster 3 is presented in this graphic.
It is important to remember that the task as presented here is not to
test theory but develop taxonomies (in a sense to develop theory). The task is to help the researcher visualize
(understand) the complexities of the relationships among the variables.
|
Multidimensional
scaling provides information beyond cluster analysis as presented here. The two dimensions represented in the
circumplex provides to additional bits of information: (1) distance between the
clusters (and individual variables) and (2) where along each of dimensions each
variable and cluster lies. Although the
dendrogram in cluster analysis does provide information of the distance between
cluster 1 (INSIGHT, INTROSP, and INTERP) and cluster 3 (REFLECT and REFRAME) it
is a much clearer in the circumplex model of multidimensional scaling. Further, one can readily note the relation to
other clusters.
Multidimensional
scaling is not limited to two dimensions, like factor analysis there can be as
many dimensions as their are variables and in the same manner that there can be
as many factors as there are variables.
Unlike factor analysis the methods of determining the number of
dimensions is not as advanced as is the method for determining the number of
factors. As multidimensional scaling is
presented here that is not a problem
One could
think of these 21 elements being used to describe a school or style of
psychotherapy. In a simplified form
psychoanalysis might be thought of as made up of interpretation, transference
and countertransference, and working through.
This set
of statistics can be used on a range of taxonomic or descriptive problems. The creation of the input matrix determines
the issue studied. The method presented
here combined the data from a panel as described by Trochim. This process assists the clinician in sorting
out their judgments. However, a single
clinician could fill in the above chart by making judgments of the similarity
of the pairs (zero might represent similar--or no difference while 8 might represent
a great difference). In the cell
identified by ACCEPT (acceptance) and BEINGNUR (being nurturing) the judgment
might be 1 (quite similar). The cell
identified by DEVTRUST and CHALLENG might be judged 6 (quite dissimilar). The same set of statistics could then be
computed on the matrix of this single clinician. This would result in a map of the
clinician. Such maps could be used be
used in comparing theories. Students could
be compared to a panel of experts. These
methods could be used to empirically support the judgements of clinicians.
Personality
Theory Rating Scale
Name:
_________________________________________
Date: ________________
Use the scale below to rate the
personality theory of ____________________________.
╔═══════════════════════════════════════════════════════════════════════════╗
║
None A Little Somewhat Quite a Bit A Lot ║
╟───────────────────────────────────────────────────────────────────────────╢
║ 0 1
2 3 4
5 6 7
8 ║
╚═══════════════════════════════════════════════════════════════════════════╝
╔══════════════════════╗
║LEAVE
THE QUESTION ║
║BLANK IF
YOU DON'T ║
║KNOW OR
IF IT DOESN'T ║
║APPLY. ║
╚══════════════════════╝
ACCORDING
TO THIS THEORY:
_____
...motivation is based on drive reduction.
_____
...the person is an intentional (goal-oriented) being.
_____
...people are hedonistic.
_____
...cognition accounts for the actions of people.
_____
...values account for the actions of people.
_____
...people are actively involved in the development of their personality.
_____
...people's early experiences influence their personality.
_____
...the person imposes perception on the world.
_____
...the environment or learning accounts for the person's actions.
_____
...people are basically good.
_____
...heredity effects the person's actions.
_____
This theory stresses the individual's conscious view of the world.
_____
This theory stresses the individual's unconscious view of the world.
_____
This theory stresses the individual's social consciousness.
_____
This theory accounts for the individual's perception of reality.
_____
This theory has influenced psychology (clinical, research, literature).
_____
This theory focus on "the here and now", the past, or the
future.
(0 = past, 4 = here and now, 8 =
future)
_____
This theory is empirically based.
_____
This theory is parsimonious.
_____
This theory assumes that the individual has free choice.
_____
This theory employs a method of therapeutic intervention.
_____
This theory emphasizes psychopathology.
_____ I agree with this theory.
The names for the respective
items are as follows:
TDATE
THER
THID
CLUS
DRIVE
GOAL
HEDON
COG
VALUE
ACTIVE
EARLY
IMPOSE
LEARN
GOOD
HERED
CONSCI
UNCONS
SOCIAL
PERCEP
INFLU
TIME
DATA
PARSI
FREE
THERA
PATH