Description Usage Arguments Details Value Author(s) References Examples
Implicative dilemmas are closely related to the notion of conflict. An implicative dilemma arises when a desired change on one construct is associated with an undesired implication on another construct. E. g. a timid subject may want to become more socially skilled but associates being socially skilled with different negative characteristics (selfish, insensitive etc.). Hence, he may anticipate that becoming less timid will also make him more selfish (cf. Winter, 1982). As a consequence the subject will resist to the change if the negative presumed implications will threaten the patients identity and the predictive power of his construct system. From this stance the resistance to change is a logical consequence coherent with the subjects construct system (Feixas, Saul, & Sanchez, 2000). The investigation of the role of cognitive dilemma in different disorders in the context of PCP is a current field of research (e.g. Feixas & Saul, 2004, Dorough et al. 2007).
1 2 3 4 | indexDilemma(x, self, ideal, diff.mode=1, diff.congruent=NA,
diff.discrepant=NA, diff.poles=1, r.min=0.35,
exclude=FALSE, digits=2, show=F, output=1, index=T,
trim=20)
|
x |
|
self |
Numeric. Index of self element. |
ideal |
Numeric. Index of ideal self element. |
diff.mode |
Numeric. Mode to classify construct pairs into congruent and
discrepant. |
diff.congruent |
Is used if |
diff.discrepant |
Is used if |
diff.poles |
Not yet implemented. |
r.min |
Minimal correlation to determine implications between constructs. |
exclude |
Whether to exclude the elements self and ideal self
during the calculation of the inter-construct correlations.
(default is |
output |
The type of output printed to the console. |
show |
Whether to additionally plot the distribution
of correlations to help the user assess what level
is adequate for |
index |
Whether to print index numbers in front of each construct
(default is |
trim |
The number of characters a construct (element) is trimmed to (default is
|
digits |
Numeric. Number of digits to round to (default is
|
The detection of implicative dilemmas happens in two steps. First the constructs are classified as being 'congruent' or 'discrepant'. Second the correlation between a congruent and discrepant construct pair is assessed if it is big enough to indicate an implication.
Classifying the construct
To detect implicit dilemmas the construct pairs are first
identified as 'congruent' or 'discrepant'. The assessment
is based on the rating differences between the elements
'self' and 'ideal self'.
A construct is 'congruent' if the construction of the 'self' and the
preferred state (i.e. ideal self) are the same or similar.
A construct is discrepant if the construction of the 'self' and
the 'ideal' is dissimilar.
Suppose the element 'self' is rated 2 and 'ideal self' 5 on
a scale from 1 to 6. The ratings differences are 5-2 = 3. If this
difference is smaller than e.g. 1 the construct is 'congruent', if it
is bigger than 3 it is 'discrepant'.
The values used to classify the constructs 'congruent' or 'discrepant' can be determined in several ways (cf. Bell, 2009):
They are set 'a priori'.
They are implicitly derived by taking into account the rating differences to the other constructs. Not yet implemented.
The value mode is determined via the argument diff.mode
.
If no 'a priori' criteria to determine if the construct
is congruent or discrepant is supplied as an argument, the values are chosen
acording to the range of the rating scale used. For the following scales
the defaults are chosen as:
Scale | 'A priori' criteria |
1 2 | --> con: <=0 disc: >=1 |
1 2 3 | --> con: <=0 disc: >=2 |
1 2 3 4 | --> con: <=0 disc: >=2 |
1 2 3 4 5 | --> con: <=1 disc: >=3 |
1 2 3 4 5 6 | --> con: <=1 disc: >=3 |
1 2 3 4 5 6 7 | --> con: <=1 disc: >=4 |
1 2 3 4 5 6 7 8 | --> con: <=1 disc: >=5 |
1 2 3 4 5 6 7 8 9 | --> con: <=2 disc: >=5 |
1 2 3 4 5 6 7 8 9 10 | --> con: <=2 disc: >=6 |
Defining the correlations
As the implications between constructs cannot be derived from a
rating grid directly, the correlation between two constructs
is used as an indicator for implication. A large correlation means
that one construct pole implies the other. A small correlation
indicates a lack of implication. The minimum criterion for a correlation
to indicate implication is set to .35 (cf. Feixas & Saul, 2004).
The user may also chose another value. To get a an impression
of the distribution of correlations in the grid, a visualization can
be prompted via the argument show
.
When calculating the correlation used to assess if an implication
is given or not, the elements under consideration (i. e. self and ideal self)
can be included (default) or excluded. The options will cause different
correlations (see argument exclude
).
Example of an implicative dilemma
A depressive person considers herself as timid and
wished to change to the opposite pole she defines as extraverted.
This construct is called discrepant as the construction of the 'self'
and the desired state (e.g. described by the 'ideal self') on
this construct differ. The person also considers herself as
sensitive (preferred pole) for which the opposite pole is selfish.
This construct is congruent, as the person construes herself as
she would like to be. If the person now changed on the discrepant
construct from the undesired to the desired pole, i.e. from timid
to extraverted, the question can be asked what consequences such a
change has. If the person construes being timid and being sensitive
as related and that someone who is extraverted will not be timid, a
change on the first construct will imply a change on the congruent
construct as well. Hence, the positive shift from timid to extraverted
is presumed to have a undesired effect in moving from sensitive towards
selflish. This relation is called an implicative dilemma. As the
implications of change on a construct cannot be derived from a rating
grid directly, the correlation between two constructs is used as an
indicator for implication.
Called for console output. Invisbly returns a list containing the result dataframes and all results from the calculations.
Mark Heckmann
Bell, R. C. (2009). Gridstat version 5 - A Program for Analyzing the Data of A Repertory Grid (manual). University of Melbourne, Australia: Department of Psychology.
Dorough, S., Grice, J. W., & Parker, J. (2007). Implicative dilemmas and psychological well-being. Personal Construct Theory & Practice, (4), 83-101.
Feixas, G., & Saul, L. A. (2004). The Multi-Center Dilemma Project: an investigation on the role of cognitive conflicts in health. The Spanish Journal of Psychology, 7(1), 69-78.
Feixas, G., Saul, L. A., & Sanchez, V. (2000). Detection and analysis of implicative dilemmas: implications for the therapeutic process. In J. W. Scheer (Ed.), The Person in Society: Challenges to a Constructivist Theory. Giessen: Psychosozial-Verlag.
Winter, D. A. (1982). Construct relationships, psychological disorder and therapeutic change. British Journal of Medical Psychology, 55 (Pt 3), 257-269.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ## Not run:
indexDilemma(boeker, self=1, ideal=2)
indexDilemma(boeker, self=1, ideal=2, out=2)
# additionally show correlation distribution
indexDilemma(boeker, self=1, ideal=2, show=T)
# adjust minimal correlation
indexDilemma(boeker, 1, 2, r.min=.25)
# adjust congruence and discrepance ranges
indexDilemma(boeker, 1, 2, diff.con=0, diff.disc=4)
## End(Not run)
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