- This tool does multiple Fisher Transformations, r-to-Z or Z-to-r, where
- r is Pearson's Correlation Coefficient
- Z is a variance stabilizing transformation of the sampling distribution of r.
- To use the tool, enter one value per line, ending each line by pressing the
- Z is calculated by applying an inverse hyperbolic tangent function to r. Conversely, r is calculated by applying a hyperbolic tangent function to Z.
- Unlike similar tools which transform one value at a time, our tool applies Fisher Transformations to an entire set of values, saving users time and effort.
- WARNING: Contrary to popular opinions, Fisher Transformations should not be applied arbitrarily to (x, y) paired data, but only when both (x, y) random variables are approximately normally distributed; i.e., both variables must describe bell-shaped curves (Garcia, 2016; 2017). If the variables are bivariate normal, the sampling distribution of Z is approximately normally distributed with mean as described by the r-to-Z transformation (Anderson, 2007).
- Data miners, statisticians, or anyone that need to apply Fisher Transformations.
- Anderson, C. J. Correlation. Edpsy 580 Course. Department of Education Psychology, University of Illinois at Urbana-Champaign.
- Garcia, E. (2012). The Self-Weighting Model. Communications in Statistics - Theory and Methods, 41:8,1421-1427.
- Garcia, E. (2015a). The Self-Weighting Model Tutorial: Part 1.
- Garcia, E. (2015b). The Self-Weighting Model Tutorial: Part 2.
- Garcia, E. (2016). A Tutorial on Standard Errors.
- Garcia, E. (2017). On the Nonadditivity of Correlation Coefficients Part 2: Fisher Transformations.
- Zimmerman, D. W., Zumbo, B. D., and Williams, R. H. (2003). Bias in estimation and hypothesis testing of correlation. Psicologica 24:133-158.
Contact us for any suggestion or question regarding this tool.