diffcor.dep {diffcor}

Fisher's z-Tests of dependent correlations

Description

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.

Usage

diffcor.dep(r12, r13, r23, n, cor.names = NULL,
alternative = c("one.sided", "two.sided"), digit = 3)

Arguments

r12

Correlation between the criterion with which both competing variables are correlated and the first of the two competing variables.

r13

Correlation between the criterion with which both competing variables are correlated and the second of the two competing variables.

r23

Intercorrelation between the two competing variables.

n

Sample size in which the observed effect was found

cor.names

OPTIONAL, label for the correlation. DEFAULT is NULL

alternative

A character string specifying if you wish to test one-sided or two-sided differences

digit

Number of digits in the output for all parameters, DEFAULT = 3

Value

r12

Correlation between the criterion with which both competing variables are correlated and the first of the two competing variables.

r13

Correlation between the criterion with which both competing variables are correlated and the second of the two competing variables.

r23

Intercorrelation between the two competing variables.

z

Test statistic for correlation difference in units of z distribution

p

p value for one- or two-sided testing, depending on alternative = c("one.sided", "two.sided)

Author(s)

Christian Blötner [e-mail]

References

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.

Examples

diffcor.dep(r12 = .76, r13 = .70, r23 = .50, n = 271, digit = 4,
cor.names = NULL, alternative = "two.sided")