Bootstrap Correlation Confidence Interval Estimation: The Positive Impact of a Symmetric Distribution

References

• Anderson, A. A. (2019). Assessing statistical results: Magnitude, precision, and model uncertainty. The American Statistician, 73, 118–121. https://doi.org/10.1080/00031305.2018.1537889
   Web of Science ®Google Scholar
• Beaulieu-Prevost, D. (2006). Confidence intervals: From tests of statistical significance to confidence intervals, range hypotheses, and substantial effects. Tutorials in Quantitative Methods for Psychology, 2(1), 11–19. https://doi.org/10.20982/tqmp.02.1.p011
   Google Scholar
• Benjamin, D. J., Berger, J. O., Johannesson, M., Nosek, B. A., Wagenmakers, E. J., Berk, R., … Johnson, V. E. (2018). Redefine statistical significance. Nature Human Behavior, 2, 6–10. https://doi.org/10.1038/s41562-017-0189-z
   PubMed Web of Science ®Google Scholar
• Berry, K. J., & Mielke, P. W. (2000). A Monte Carlo investigation of the fisher z transformation for normal and nonnormal distributions. Psychological Reports, 87(3), 1101–1114. https://doi.org/10.2466/pr0.2000.87.3f.1101
   PubMed Web of Science ®Google Scholar
• Bishara, A. J., & Hittner, J. B. (2017). Confidence intervals for correlations when data are not normal. Behavior Research Methods, 49, 294–309. https://doi.org/10.3758/s13428-016-0702-8
   PubMed Web of Science ®Google Scholar
• Blanca, M. J., Arnau, J., Lopez-Montiel, D., Bono, R., & Bendayan, R. (2013). Skewness and kurtosis in real data samples. Methodology, 9(2), 78–84. https://doi.org/10.1027/1614-2241/a000057
   Web of Science ®Google Scholar
• Bliss, C. I. (1967). Statistics in biology; statistical methods for research in the natural sciences. McGraw-Hill.
   Google Scholar
• Bradley, J. V. (1978). Robustness? British Journal of Mathematical and Statistical Psychology, 31(2), 144–152. https://doi.org/10.1111/j.2044-8317.1978.tb00581.x
   Web of Science ®Google Scholar
• Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Lawrence Erlbaum Associates.
   Google Scholar
• DelosReyes, J., & Padilla, M. A. (in press). Estimation of the correlation confidence intervals via the bootstrap: Non-normal distributions. Journal of Modern Applied Statistical Methods.
   Google Scholar
• Efron, B., & Tibshirani, R. (1993). An introduction to the bootstrap. Chapman & Hall.
   Google Scholar
• Fieller, E. C., Hartley, H. O., & Pearson, E. S. (1957). Tests for rank correlation coefficients: I. Biometrika, 44(4), 470–481. https://doi.org/10.2307/2332878
   Web of Science ®Google Scholar
• Fisher, R. A. (1915). Frequency distribution of the values of the correlation coefficient in the samples from an infinitely large population. BIometrika, 10(4), 507–521. https://doi.org/10.2307/2331838
   Google Scholar
• Hald, A. (2007). A history of parametric statistical inference from Bernoulli to Fisher, 1713–1935. Springer.
   Google Scholar
• Headrick, T. C. (2002). Fast fifth-order polynomial transforms for generating univariate and multivariate nonnormal distributions. Computational Statistics & Data Analysis, 40(4), 685–711. https://doi.org/10.1016/S0167-9473(02)00072-5
   Web of Science ®Google Scholar
• Lunneborg, C. E. (1985). Estimating the correlation coefficient: The bootstrap approach. Psychological Bulletin, 98(1), 209–215. https://doi.org/10.1037/0033-2909.98.1.209
   Web of Science ®Google Scholar
• Muchinsky, P. M. (1996). The correction for attenuation. Education and Psychological Measurement, 56(1), 63–75. https://doi.org/10.1177/0013164496056001004
   Web of Science ®Google Scholar
• Padilla, M. A., & Veprinsky, A. (2012). Correlation attenuation due to measurement error: A new approach using the bootstrap procedure. Educational and Psychological Measurement, 72(5), 827–846. https://doi.org/10.1177/0013164412443963
   Web of Science ®Google Scholar
• Padilla, M. A., & Veprinsky, A. (2014). Bootstrapped deattenuated correlation: Nonnormal distributions. Educational and Psychological Measurement, 74(5), 823–830. https://doi.org/10.1177/0013164414531780
   Web of Science ®Google Scholar
• Rasmussen, J. L. (1987). Estimating the correlation coefficient: Bootstrap and parametric approaches. Psychological Bulletin, 101(1), 136–139. https://doi.org/10.1037/0033-2909.101.1.136
   Web of Science ®Google Scholar
• Rousselet, G. A., Pernet, C. R., & Wilcox, R. R. (2019). A practical introduction to the bootstrap: A versatile method to make inferences by using data-driven simulations. https://doi.org/10.31234/osf.io/h8ft7
   Google Scholar
• Spearman, C. (1904). The proof and measurement of association between two things. The American Journal of Psychology, 15(1), 72–101. https://doi.org/10.2307/1412159
   Google Scholar
• Wilkinson, L. (1999). Statistical methods in psychology journals. American Psychologist, 54(8), 594–604. https://doi.org/10.1037/0003-066X.54.8.594
   Web of Science ®Google Scholar
• Zeller, R. A., & Levine, Z. H. (1974). The effects of violating the normality assumption underlying r. Sociological Methods & Research, 2(4), 511–519. https://doi.org/10.1177/004912417400200406
   Web of Science ®Google Scholar