511 classes

test2r.t2

R Documentation

Test the difference between two dependent correlations with the the T2 method

Description

Differences in Pearson correlations are tested with the Williams T2 modification of Hoetellings method. The test is appropriate when the correlations are dependent. More specifically r(yx1) is tested versus r(yx2)in one sample of cases. The function requires the three Pearson product moment correlations between three variables called y, x1 and x2 in the notation here.

Usage

test2r.t2(ry.x1, ry.x2, rx1.x2, n, twotailed = TRUE)

Arguments

`ry.x1`	y is the variable common to the two correlations. It is labeled y since the most common usage of this test is when a dependent variable (y) is correlated with two different independent variables (x1, and x2). This argument `ry.x1` is the first of the two correlations of y with the IV's.
`ry.x2`	This argument `ry.x2` is the second of the two correlations of y with the IV's.
`rx1.x2`	The function and test require the Pearson correlation between the two X's as well.
`n`	Sample Size
`twotailed`	The test can be two-tailed (`twotailed=TRUE`) or one-tailed (`twotailed=FALSE`). The default is two-tailed.

Value

`t`	The test statistic value, a 't'.
`df`	degrees of freedom for the 't' (n-3).
`pvalue`	the one- or two-tailed probability of the 't'.

Related Functions

test2r.t2 is a member of a set of functions that provide tests of differences between independent and dependent correlations. See:

test2r.t2, the present function
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 Test two r(xy) from Independent Groups

Author(s)

Bruce Dudek bruce.dudek@albany.edu

References

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

Examples


test2r.t2(.6,.4,.3,75)
test2r.t2(.6,.4,.3,75,twotailed=TRUE)
test2r.t2(.6,.4,.3,75,twotailed=FALSE)
test2r.t2(.45,.03,.65,100)
test2r.t2(.45,.03,.15,35,twotailed=FALSE)

bcdudek/bcdstats documentation built on Jan. 3, 2024, 10:09 p.m.