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 compared correlations, test statistic in z-units, and p-values. For diffcor.one() and diffcor.two(), the output further provides confidence intervals of the empirical correlations and the effect size Cohens q. According to Cohen (1988), q = |.10|, |.30| and |.50| are considered small, moderate, and large differences, respectively.
Packaged: 2021-06-02 08:00:33 UTC; Christian Blötner

Description: Computations of Fisher's z-tests concerning differences between correlations. diffcor.one() can be used to test whether an expected value differs from an observed value, for example, in construct validation. diffcor.two() can be used to test if the correlation between two constructs differed between two studies. diffcor.dep() can be applied to check if the correlation between two constructs (r12) is significantly different from the correlation of the first construct with a third one (r13), given the intercorrelation of the compared constructs (r23). All outputs provide the compared correlations, test statistic in z-units, and p-values. For diffcor.one() and diffcor.two(), the output further provides confidence intervals of the empirical correlations and the effect size Cohens q. According to Cohen (1988), q = |.10|, |.30| and |.50| are considered small, moderate, and large differences, respectively.

Description: Computations of Fisher's z-tests concerning differences between correlations. diffcor.one() tests whether an expected value differs from an observed value, for example, in construct validation. diffcor.two() tests whether the correlation between two constructs differed across two studies/samples. diffcor.dep() checks whether the correlation between two constructs (r12) is significantly different from the correlation between the first construct with a third one (r13), given the intercorrelation of the compared constructs (r23). All outputs provide the compared correlations, test statistic in z-units, and p-values. For diffcor.one() and diffcor.two(), the output further provides confidence intervals of the empirical correlations and the effect size Cohens q. According to Cohen (1988), q = |.10|, |.30| and |.50| are considered small, moderate, and large differences, respectively.