| Abstract: | Averaging correlations leads to underestimation because the sampling distribution of the correlation coefficient is skewed. It is also known that if correlations are transformed by Fisher's z prior to averaging, the resulting average overestimates the population value of z. The behavior of these procedures for averaging correlations was investigated via Monte Carlo simulation, both in terms of bias (under- and overestimation) and precision (standard errors). It was found that average z backtransformed to r is less biased positively than average r is biased negatively. The standard error of average r was smaller than that of average z when the population correlation was small; however, the reverse was true when the population correlation exceeded .5. Regardless of sample size, back transformed average z was always less biased; therefore, the use of the z transformation is recommended when averaging correlation coefficients, particularly when sample size is small. (PsycINFO Database Record (c) 2010 APA, all rights reserved) |
