Note that simple element correlations as a measure of similarity are flawed as they are not invariant to construct
reflection (Mackay, 1992; Bell, 2010). A correlation index invariant to construct reflection is Cohen's rc measure
(1969), which can be calculated using the argument rc=TRUE which is the default option.
Arguments
- x
repgridobject.- rc
Use Cohen's rc which is invariant to construct reflection (see description above). It is used as the default.
- method
A character string indicating which correlation coefficient is to be computed. One of
"pearson"(default),"kendall"or"spearman", can be abbreviated. The default is"pearson".- trim
The number of characters a construct is trimmed to (default is
20). IfNAno trimming occurs. Trimming simply saves space when displaying correlation of constructs with long names.- index
Whether to print the number of the construct.
References
Bell, R. C. (2010). A note on aligning constructs. Personal Construct Theory & Practice, (7), 42-48.
Cohen, J. (1969). rc: A profile similarity coefficient invariant over variable reflection. Psychological Bulletin, 71(4), 281-284.
Mackay, N. (1992). Identification, Reflection, and Correlation: Problems In The Bases Of Repertory Grid Measures. International Journal of Personal Construct Psychology, 5(1), 57-75.
Examples
elementCor(mackay1992) # Cohen's rc
#>
#> ############################
#> Correlation between elements
#> ############################
#>
#> Type of correlation: Cohens's rc (invariant to scale reflection)
#>
#> 1 2 3 4 5 6
#> (1) Self 1 0.59 0.28 0.29 -0.61 -0.62
#> (2) Ideal self 2 -0.04 -0.20 -0.38 -0.81
#> (3) Mother 3 0.63 0.37 -0.43
#> (4) Father 4 0.00 0.00
#> (5) Spouse 5 0.00
#> (6) Disliked person 6
elementCor(mackay1992, rc = FALSE) # PM correlation
#>
#> ############################
#> Correlation between elements
#> ############################
#>
#> Type of correlation: pearson
#> Note: Standard correlations are not invariant to scale reflection.
#>
#> 1 2 3 4 5 6
#> (1) Self 1 -0.37 -0.19 0.32 -0.75 0.96
#> (2) Ideal self 2 -0.71 -0.56 -0.26 -0.30
#> (3) Mother 3 0.63 0.57 -0.12
#> (4) Father 4 0.05 0.38
#> (5) Spouse 5 -0.74
#> (6) Disliked person 6
elementCor(mackay1992, rc = FALSE, method = "spearman") # Spearman correlation
#>
#> ############################
#> Correlation between elements
#> ############################
#>
#> Type of correlation: spearman
#> Note: Standard correlations are not invariant to scale reflection.
#>
#> 1 2 3 4 5 6
#> (1) Self 1 -0.25 0.01 0.64 -0.77 0.98
#> (2) Ideal self 2 -0.70 -0.57 -0.29 -0.19
#> (3) Mother 3 0.55 0.45 0.06
#> (4) Father 4 -0.14 0.65
#> (5) Spouse 5 -0.74
#> (6) Disliked person 6
# format output
elementCor(mackay1992, trim = 6)
#>
#> ############################
#> Correlation between elements
#> ############################
#>
#> Type of correlation: Cohens's rc (invariant to scale reflection)
#>
#> 1 2 3 4 5 6
#> (1) Self 1 0.59 0.28 0.29 -0.61 -0.62
#> (2) Ideal 2 -0.04 -0.20 -0.38 -0.81
#> (3) Mother 3 0.63 0.37 -0.43
#> (4) Father 4 0.00 0.00
#> (5) Spouse 5 0.00
#> (6) Dislik 6
elementCor(mackay1992, index = FALSE, trim = 6)
#>
#> ############################
#> Correlation between elements
#> ############################
#>
#> Type of correlation: Cohens's rc (invariant to scale reflection)
#>
#> 1 2 3 4 5 6
#> Self 1 0.59 0.28 0.29 -0.61 -0.62
#> Ideal 2 -0.04 -0.20 -0.38 -0.81
#> Mother 3 0.63 0.37 -0.43
#> Father 4 0.00 0.00
#> Spouse 5 0.00
#> Dislik 6
# save as object for further processing
r <- elementCor(mackay1992)
r
#>
#> ############################
#> Correlation between elements
#> ############################
#>
#> Type of correlation: Cohens's rc (invariant to scale reflection)
#>
#> 1 2 3 4 5 6
#> (1) Self 1 0.59 0.28 0.29 -0.61 -0.62
#> (2) Ideal self 2 -0.04 -0.20 -0.38 -0.81
#> (3) Mother 3 0.63 0.37 -0.43
#> (4) Father 4 0.00 0.00
#> (5) Spouse 5 0.00
#> (6) Disliked person 6
# change output of object
print(r, digits = 5)
#>
#> ############################
#> Correlation between elements
#> ############################
#>
#> Type of correlation: Cohens's rc (invariant to scale reflection)
#>
#> 1 2 3 4 5 6
#> (1) Self 1 0.58761 0.28090 0.29096 -0.61237 -0.61828
#> (2) Ideal self 2 -0.03745 -0.20365 -0.38100 -0.81461
#> (3) Mother 3 0.63049 0.36860 -0.43346
#> (4) Father 4 0.00000 0.00000
#> (5) Spouse 5 0.00000
#> (6) Disliked person 6
print(r, col.index = FALSE)
#>
#> ############################
#> Correlation between elements
#> ############################
#>
#> Type of correlation: Cohens's rc (invariant to scale reflection)
#>
#> (1) Self (2) Ideal self (3) Mother (4) Father (5) Spouse
#> (1) Self 0.59 0.28 0.29 -0.61
#> (2) Ideal self -0.04 -0.20 -0.38
#> (3) Mother 0.63 0.37
#> (4) Father 0.00
#> (5) Spouse
#> (6) Disliked person
#> (6) Disliked person
#> (1) Self -0.62
#> (2) Ideal self -0.81
#> (3) Mother -0.43
#> (4) Father 0.00
#> (5) Spouse 0.00
#> (6) Disliked person
print(r, upper = FALSE)
#>
#> ############################
#> Correlation between elements
#> ############################
#>
#> Type of correlation: Cohens's rc (invariant to scale reflection)
#>
#> 1 2 3 4 5 6
#> (1) Self 1 1.00 0.59 0.28 0.29 -0.61 -0.62
#> (2) Ideal self 2 0.59 1.00 -0.04 -0.20 -0.38 -0.81
#> (3) Mother 3 0.28 -0.04 1.00 0.63 0.37 -0.43
#> (4) Father 4 0.29 -0.20 0.63 1.00 0.00 0.00
#> (5) Spouse 5 -0.61 -0.38 0.37 0.00 1.00 0.00
#> (6) Disliked person 6 -0.62 -0.81 -0.43 0.00 0.00 1.00
# accessing elements of the correlation matrix
r[1, 3]
#> [1] 0.2809003