diffcor: Fisher's z-Tests Concerning Difference of Correlations

Computations of Fisher's z-tests concerning differences between correlations. diffcor.one() could be used to test for differences regarding an expected value, e.g., in construct validation. diffcor.two() may be useful in replication studies, to test if the original study and the replication study differed in terms of effects. diffcor.dep() can be applied to check if the correlation between one construct with another one (r12) is significantly different/higher/smaller than the correlation of one of the constructs with a third construct (r13), given the correlation of the constructs that are compared (r23). The outputs for all the three functions provide the test statistic in z-units as well as p-values. For diffcor.one() and diffcor.two(), the effect size Cohens q is additionally printed. It is a descriptive index to evaluate differences of independent correlations. Cohen (1988) suggested q = |.10|, |.30| and |.50| as small, moderate, and large differences.

Version: 0.4.0
Published: 2020-09-19
Author: Christian Blötner
Maintainer: Christian Blötner <c.bloetner at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
CRAN checks: diffcor results

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Reference manual: diffcor.pdf
Package source: diffcor_0.4.0.tar.gz
Windows binaries: r-devel: diffcor_0.4.0.zip, r-release: diffcor_0.4.0.zip, r-oldrel: diffcor_0.4.0.zip
macOS binaries: r-release: diffcor_0.4.0.tgz, r-oldrel: diffcor_0.4.0.tgz
Old sources: diffcor archive

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