# Correlations between z-transformed correlation coefficients

For a little meta-analysis project that I’m working on with Jingru, we are dealing with a database of correlation coefficients, where some of the included studies report correlations for more than one instrument or sub-scale for one of the relevant variables. This leads to every meta-analytic methodologist’s favorite tongue-twister of distribution theory: inter-correlated correlation coefficients. Fortunately, Grandpa Ingram worked out the distribution theory for this stuff long ago (Olkin and Siotani, 1976; reviewed in Olkin and Finn, 1990).

Suppose that we have three variables, \(a\), \(b\), and \(c\), where \(b\) and \(c\) are different measures of the same construct. From a sample of size \(N\), we have correlation estimates \(r_{ab}\) and \(r_{ac}\), both of which are relevant for understanding the underlying construct relation. These correlations are estimates of underlying population correlations \(\rho_{ab}\) and \(\rho_{ac}\) respectively, and the population correlation between \(b\) and \(c\) is \(\rho_{bc}\). Typically, we would analyze the correlations after applying Fisher’s variance stabilizing and normalizing transformation. Denote the Fisher transformation as \(Z(r)= \frac{1}{2}\ln\left(\frac{1 + r}{1 - r}\right)\)), with derivative given by \(Z'(r) = \frac{1}{1 - r^2}\). Let \(z_{ab} = Z(r_{ab})\), with the other transformed correlations defined similarly. The question is then: how strongly correlated are the sampling errors of the resulting effect size estimates—that is, what is \(\text{cor}(z_{ab}, z_{ac})\)?

Olkin and Finn (1990) give the following expression for the covariance between two sample correlation coefficients that share a common variable: \[ \text{Cov}(r_{ab}, r_{ac}) = \frac{1}{N - 1}\left[\left(\rho_{bc} - \frac{1}{2}\rho_{ab}\rho_{ac}\right)\left(1 - \rho_{ab}^2 - \rho_{ac}^2\right) + \frac{1}{2} \rho_{ab} \rho_{ac} \rho_{bc}^2\right]. \] If correlations are converted to the Fisher-z scale, then we can use a delta method approximation to obtain an expression for the covariance between two sample z estimates that share a common variable. Using \(N-3\) in place of \(N-1\), the covariance between the z-transformed correlations is approximately \[ \begin{aligned} \text{Cov}(z_{ab}, z_{ac}) &= Z'(\rho_{ab}) \times Z'(\rho_{ac})\times \text{Cov}(r_{ab}, r_{ac}) \\ &=\frac{1}{N - 3} \frac{\left(\rho_{bc} - \frac{1}{2}\rho_{ab}\rho_{ac}\right)\left(1 - \rho_{ab}^2 - \rho_{ac}^2\right) + \frac{1}{2} \rho_{ab} \rho_{ac} \rho_{bc}^2}{\left(1 - \rho_{ab}^2\right) \left(1 - \rho_{ac}^2\right)}. \end{aligned} \] The corresponding correlation is thus \[ \text{cor}(z_{ab}, z_{ac}) = \frac{\left(\rho_{bc} - \frac{1}{2}\rho_{ab}\rho_{ac}\right)\left(1 - \rho_{ab}^2 - \rho_{ac}^2\right) + \frac{1}{2} \rho_{ab} \rho_{ac} \rho_{bc}^2}{\left(1 - \rho_{ab}^2\right) \left(1 - \rho_{ac}^2\right)}. \]

In the context of a meta-analysis, we might expect that the focal correlations will usually be very similar, if not exactly equal. For simplicity, let’s assume that they’re actually identical, \(\rho_{ab} = \rho_{ac}\). The sampling correlation then simplifies further to

\[
\begin{aligned}
\text{cor}(z_{ab}, z_{ac}) &= \frac{\left(\rho_{bc} - \frac{1}{2}\rho_{ab}^2\right)\left(1 - 2\rho_{ab}^2\right) + \frac{1}{2} \rho_{ab}^2 \rho_{bc}^2}{\left(1 - \rho_{ab}^2\right)^2} \\
&=1 - \frac{(1 - \rho_{bc})\left[2 - \rho_{ab}^2(3 - \rho_{bc})\right]}{2\left(1 - \rho_{ab}^2\right)^2}.
\end{aligned}
\].

To apply this formula, we need to specify values of \(\rho_{ab}\) and \(\rho_{bc}\). The following table gives the resulting correlation between z-transformed sample correlations for a few values of \(\rho_{ab} = \rho_{ac}\) and \(\rho_{bc}\).

\(\rho_{bc}\) | \(\rho_{ab} = 0.2\) | \(\rho_{ab} = 0.25\) | \(\rho_{ab} = 0.33\) | \(\rho_{ab} = 0.4\) | \(\rho_{ab} = 0.5\) |
---|---|---|---|---|---|

0.4 | 0.383 | 0.373 | 0.351 | 0.327 | 0.280 |

0.5 | 0.485 | 0.476 | 0.456 | 0.433 | 0.389 |

0.6 | 0.587 | 0.579 | 0.562 | 0.542 | 0.502 |

0.7 | 0.689 | 0.683 | 0.670 | 0.653 | 0.620 |

0.8 | 0.793 | 0.788 | 0.778 | 0.766 | 0.742 |

0.9 | 0.896 | 0.894 | 0.888 | 0.882 | 0.869 |

When the focal correlations are fairly small, then the sampling correlation is the same order of magnitude as \(\rho_{bc}\). It’s only when the focal correlations are stronger that the sampling correlation is noticeably attenuated from \(\rho_{bc}\), and the degree of attenuation is weaker when \(\rho_{bc}\) is larger. Thus, for strongly related instruments or sub-scales, \(\text{cor}(z_{ab}, z_{ac})\) won’t be much different from \(\rho_{bc}\).