test2r.ind | R Documentation |
Differences in Pearson correlations of two variables are tested when they are measured in independent samples, for example r(xy) in group 1 vs r(xy) in group 2. The method employed is one that utilizes Fisher's Z transformation of the Pearson correlation coefficients. The test statistic is a standard normal deviate. The method can be found in standard textbook sources (e.g., Hays, 1993, Howell, 2013.) If the user wishes to perform tests of two r's from independent groups with standard methods that do no utilize Fisher's Z transform, they are encouraged to use linear regression models that employ an interaction term.
test2r.ind(r1, r2, n1, n2, twotailed = TRUE)
r1 |
The Pearson correlation coefficient (rxy) in group 1. |
r2 |
The Pearson correlation coefficient (rxy) in group 2. |
n1 |
Sample size in group 1 |
n2 |
Sample size in group 1 |
twotailed |
The test can be either two- or one-tailed by specifying
|
test2r.ind
is a member of a set of
functions that provide tests of differences between independent and
dependent correlations. The functions were inspired by the paired.r
function in the psych package and some of the code is modeled on code
from that function. See:
test2r.t2
Test two dependent correlations
with the the T2 method: r(yx1) vs r(yx2)
test2r.mengz1
, Test the difference between
two dependent correlations with the the Meng z1 method: r(yx1) vs r(yx2)in one
sample of cases.
test2r.steigerz1
, Test the difference between
two dependent correlations with the the Steiger z1 method: r(yx1) vs r(yx2) in one
sample of cases.
test2r.steigerz2
, Test the difference between
two dependent correlations with the the Steiger z2 method: r(jk) vs r(hm) in one
sample of cases.
test2r.ind
, the present function
Bruce Dudek bruce.dudek@albany.edu
Cheung, M. W. L., & Chan, W. (2004). Testing dependent
correlation coefficients via structural equation modeling.
Organizational Research Methods, 7(2), 206-223.
Dunn, O. J., &
Clark, V. (1971). Comparison of tests of the equality of dependent
correlation coefficients. Journal of the American Statistical
Association, 66(336), 904-908.
Hays, W. L. (1994). Statistics
(5th ed.). Fort Worth: Harcourt College Publishers.
Hendrickson, G. F.,
Stanley, J. C., & Hills, J. R. (1970). Olkin's new formula for significance
of r13 vs. r23 compared with Hotelling's method. American Educational
Research Journal, 7(2), 189-195.
Hittner, J. B., May, K., & Silver, N.
C. (2003). A Monte Carlo evaluation of tests for comparing dependent
correlations. The Journal of general psychology, 130(2), 149-168.
Howell, D. C. (2013). Statistical methods for psychology (8th ed.).
Belmont, CA: Wadsworth Cengage Learning.
Meng, X. L., Rosenthal, R., &
Rubin, D. B. (1992). Comparing correlated correlation coefficients.
Psychological Bulletin, 111(1), 172-175.
Neill, J. J., & Dunn, O.
J. (1975). Equality of dependent correlation coefficients.
Biometrics, 31(2), 531-543.
Olkin, I., & Finn, J. D. (1990).
Testing correlated correlations. Psychological Bulletin, 108(2),
330-333.
Silver, N. C., Hittner, J. B., & May, K. (2004). Testing
dependent correlations with nonoverlapping variables: A Monte Carlo
simulation. The Journal of experimental education, 73(1), 53-69.
Steiger, J. H. (1980). Tests for comparing elements of a correlation matrix.
Psychological Bulletin, 87(2), 245-251.
Wilcox, R. R. (2012).
Introduction to robust estimation and hypothesis testing
Analysts are also encouraged to explore robust methods for evaluation of correlation comparison hypotheses. For example, see work of R. Wilcox (texts above and also http://dornsife.usc.edu/labs/rwilcox/software/
test2r.ind(.30,.35,n1=50,n2=60)
test2r.ind(.10,.45,n1=60,n2=80)
test2r.ind(.41,.59,n1=100,n2=105)
test2r.ind(.41,.59,n1=100,n2=105,twotailed=FALSE)
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