Description
As a measure for element similarity correlations between elements are
frequently used. Note that product moment correlations as a measure of
similarity are flawed as they are not invariant to construct reflection
(Bell, 2010; Mackay, 1992). 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.
R-Code
As a default the construct reflection invariant correlation Cohen’s rc is calculated.
Note how the values change when the simple product-moment correlation
is used instaed of Cohen’s rc. Make sure you know what you are doing
when setting rc=FALSE
.
elementCor(mackay1992, rc = F)
#
# ############################
# 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
Although nor recommended different measures, not invariant to
construct relfection, can be prompoted, when setting
rc=FALSE
.
- Pearson or Product-moment correlation (PMC)
- Kendall’s tau rank correlation
- Spearman’s rank correlation
To request these types of correlations use the method
argument plus rc=FALSE
.
elementCor(mackay1992, rc = FALSE, meth = "kendall") # Kendalls tau correlation
elementCor(mackay1992, rc = FALSE, meth = "spearman") # Spearman rank correlation
Several arguments to format the output are available.
elementCor(mackay1992, index = F, 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
If the correlations are calculated for further processing, the correlations can be saved.
r <- elementCor(mackay1992)
The object is matrix, so you can eassily acces the results. E.g. all
correlations with the element Self
.
r[1, ]
# (1) Self (2) Ideal self (3) Mother (4) Father (5) Spouse (6) Disliked person
# 1.0000000 0.5876060 0.2809003 0.2909572 -0.6123724 -0.6182840