Title: Fisher's z-Tests Concerning Difference of Correlations
Description: 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.

Packaged: 2020-09-19 17:29:32 UTC; Christian Blötner

Description: 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, p-values, and alpha levels that were corrected in terms of multiple testing according to Bonferroni (if you did not set bonferroni = FALSE). To help interpret the output, the procedure prompts if a single p value is smaller than the corrected alpha. For diffcor.one() and diffcor.two(), the effect size Cohens q is additionally provided. 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.
Packaged: 2020-10-16 09:58:46 UTC; Christian Blötner