Tests if the correlation between two variables (r12) differs from the correlation between the first and a third one (r13), given the intercorrelation of the compared constructs (r23). All correlations are automatically transformed with the Fisher z-transformation prior to computations. The output provides the compared correlations, test statistic as z-score, and p-values.
diffcor.dep(r12, r13, r23, n, cor.names = NULL,
alternative = c("one.sided", "two.sided"), digit = 3)Correlation between the criterion with which both competing variables are correlated and the first of the two competing variables.
Correlation between the criterion with which both competing variables are correlated and the second of the two competing variables.
Intercorrelation between the two competing variables.
Test statistic for correlation difference in units of z distribution
p value for one- or two-sided testing, depending on alternative = c("one.sided", "two.sided)
Correlation between the criterion with which both competing variables are correlated and the first of the two competing variables.
Correlation between the criterion with which both competing variables are correlated and the second of the two competing variables.
Intercorrelation between the two competing variables.
Sample size in which the observed effect was found
OPTIONAL, label for the correlation. DEFAULT is NULL
A character string specifying if you wish to test one-sided or two-sided differences
Number of digits in the output for all parameters, DEFAULT = 3
Christian Blötner c.bloetner@gmail.com
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum.
Eid, M., Gollwitzer, M., & Schmitt, M. (2015). Statistik und Forschungsmethoden (4.Auflage) [Statistics and research methods (4th ed.)]. Beltz.
Steiger, J. H. (1980). Tests for comparing elements of a correlation matrix. Psychological Bulletin, 87, 245-251.
diffcor.dep(r12 = .76, r13 = .70, r23 = .50, n = 271, digit = 4,
cor.names = NULL, alternative = "two.sided")
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