Conflict measures bases on correlations

Description

The first approach to mathematically derive a conflict measure based on grid data was presented by (1979). They based their operationalization on an approach by Lauterbach (1975) who applied Heider’s (1946) balance theory for a quantitative assessment of psychological conflict. The measure uses inter-construct correlation as a basis. It assess via the sign of the correlations of a triad of constructs if the constructs triad is balanced or imbalanced. A triad is imbalanced if one or three of the correlations are negative, i. e. when they leading to contrary implications (Slade & Sheehan, 1979). The function indexConflict1 in OpenRepGrid calculates the proportion of conflictive triads in the grid as devised by Slade and Sheehan.

Slade and Sheehan’s approach has several drawbacks, as it does not take into account the magnitude of the inter-construct correlations. As a consequence significant and insignificant correlations are not differentiated. Also, correlations of small magnitude, i.e. near zero, which may have a positive or negative signe due to chance alone distort the measure (Bassler, Krauthauser, & Hoffmann, 1992; Winter, 1982). An improved version of the index that overcomes these shortcomings was proposed by (1992) and incorporated into the program CT (correlation test). The index enhances the identification of imbalanced triads by using a criterion that incorporates the magnitudes of correlations. From a psychological viewpoint, it remains unclear though what is measured. Connections to the concept of cognitive differentiation have been presumed (Krauthauser, Bassler, & Potratz, 1994). The function indexConflict2 in OpenRepGrid calculates the proportion of conflictive triads in the grid as devised by (1992).

Calculation

Slade & Sheehan (1979) approach

Multiply all correlations from a triad (formed by the constructs A, B and C) \(r_{AB} r_{AC} r_{BC}\). If the sign of the result is negative, the triad is imbalanced.

The table below shows when a triad made up of the constructs A, B, and C is balanced and imbalanced.

\(r_{AB}\) \(r_{AC}\) \(r_{BC}\) Triad characteristic
+ + + balanced
+ + - imbalanced
+ - + imbalanced
+ - - balanced
- + + imbalanced
- + - balanced
- - + balanced
- - - imbalanced
Bassler et al. (1992) approach

Order correlations of the triad by absolute magnitude, so that \(r_{max} > r_{mdn} > r_{min}\).

Apply Fisher’s Z-transformation and devision by 3 to yield values between 1 and -1 Check whether the triad is balanced by assessing if the following relation holds:

  • If \(Z_{max} Z_{mdn} > 0\), the triad is balanced if \(Z_{max} Z_{mdn} - Z_{min} <= crit\)
  • If \(Z_{max} Z_{mdn} < 0\), the triad is balanced if \(Z_{min} - Z_{max} Z_{mdn} <= crit\)

R-Code

To use the Slade and Sheehan approach use

indexConflict1(boeker)

################################
Conflicts based on correlations
################################

As devised by Slade & Sheehan (1979)

Total number of triads: 364
Number of imbalanced triads: 106

Proportion of balanced triads: 70.9 %
Proportion of imbalanced triads: 29.1 %

For the Bassler et al. approach use

indexConflict2(boeker)

###############################
Conflicts based on correlations
###############################

As devised by Bassler et al. (1992)

Total number of triads: 364
Number of imbalanced triads: 240

Proportion of balanced triads: 34.1 %
Proportion of imbalanced triads: 65.9 %
indexConflict2(boeker, crit =.05)   # change critical value

###############################
Conflicts based on correlations
###############################

As devised by Bassler et al. (1992)

Total number of triads: 364
Number of imbalanced triads: 219

Proportion of balanced triads: 39.8 %
Proportion of imbalanced triads: 60.2 %

Conflict measures based on distances

Description

Measure of conflict or inconsistency as proposed by Bell (2004). The identification of conflict is based on distances rather than correlations as in other measures of conflict indexConflict1 and indexConflict2. It assesses if the distances between all components of a triad, made up of one element and two constructs, satisfies the “triangle inequality” (cf. Bell, 2004). If not, a triad is regarded as conflictive. An advantage of the measure is that it can be interpreted not only as a global measure for a grid but also on an element, construct, and element by construct level making it valuable for detailed feedback. Also, differences in conflict can be submitted to statistical testing procedures.

R-Code

indexConflict3(leach2001a)

##########################################################
CONFLICT OR INCONSISTENCIES BASED ON TRIANGLE INEQUALITIES
##########################################################

Potential conflicts in grid:  819
Actual conflicts in grid:  340
Overall percentage of conflict in grid:  41.51 %

ELEMENTS
########

Percent of conflict attributable to element:

                      percentage
1 Child self             1088.24
2 Self now                794.12
3 Women in general        176.47
4 Men in general          823.53
5 Father                 1647.06
6 Partner                1029.41
7 Ideal self             1470.59
8 Mother                 1058.82
9 Abuser in childhood    1911.76

Chi-square test of equal count of conflicts for elements.

    Chi-squared test for given probabilities

data:  x$e.count
X-squared = 65, df = 8, p-value = 4.826e-11


CONSTRUCTS
##########

Percent of conflict attributable to construct:

                           percentage
1 assertive - not assert         8.09
2 confident - unconfiden         7.65
3 does not f - feels guil        9.12
4 abusive - not abusiv           7.35
5 frightenin - not fright        6.47
6 untrustwor - trustworth        6.76
7 powerful - powerless           6.18
8 big headed - not big he        6.18
9 independen - dependent         6.03
10 confusing - not confus        6.91
11 guilty - not guilty           6.18
12 cold - shows feel             6.03
13 masculine - feminine          7.50
14 interested - not intere       9.56

Chi-square test of equal count of conflicts for constructs.

    Chi-squared test for given probabilities

data:  x$c.count
X-squared = 16.1706, df = 13, p-value = 0.24

Literature

Bassler, M., Krauthauser, H., & Hoffmann, S. O. (1992). A new approach to the identification of cognitive conflicts in the repertory grid: An illustrative case study. Journal of Constructivist Psychology, 5(1), 95–111.

Bell, R. C. (2004). A new approach to measuring inconsistency or conflict in grids. Personal Construct Theory & Practice, 1, 53–59.

Heider, F. (1946). Attitudes and cognitive organization. Journal of Psychology, 21, 107–112.

Krauthauser, H., Bassler, M., & Potratz, B. (1994). A new approach to the identification of cognitive conflicts in the repertory grid: A nomothetic study. Journal of Constructivist Psychology, 7(4), 283–299.

Lauterbach, W. (1975). Assessing psychological conflict. The British Journal of Social and Clinical Psychology, 14(1), 43–47. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/1091319

Slade, P. D., & Sheehan, M. J. (1979). The measurement of “conflict” in repertory grids. British Journal of Psychology, 70(4), 519–524.

Winter, D. A. (1982). Construct relationships, psychological disorder and therapeutic change. The British Journal of Medical Psychology, 55 (Pt 3), 257–269. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/7126491

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