## Bibliography

Here follows a list of academic references partly or wholly sympathetic to the guiding principle of the Society for the Suppression of the Correlation Coefficient. I have taken the liberty of including references that include standardization in other contexts (e.g., articles critical of standardized effect size in general).

Abelson, R. P. (1995). *Statistics as principled argument*. Hillsdale, NJ: Erlbaum.

Achen, C. H. (1977). Measuring representation: Perils of the correlation coefficient. *American Journal of Political Science*, *21*, 805-815.

Baguley, T. (2004). Understanding statistical power in the context of applied research, *Applied Ergonomics*, *35*, 73-80.

Baguley, T. (2009). Standardized or simple effect size: What should be reported? *British Journal of Psychology. 100*, 603-617.

Baguley, T. (2010). When correlations go bad … *The Psychologist*, 23, 122-3.

Baguley, T. (2012). Serious stats: A guide to advanced statistics for the behavioral sciences. Basingstoke: Palgrave.

Brand, A. (2011). Income inequality and the prevalence of mental illness: A note of caution. *Journal of Epidemiology and Community Health. *Retrieved from: http://jech.bmj.com/content/60/7/646/reply#jech_el_2839 on 23/9/12.

Bond, C.F., Jr., Wiitala, W.L., & Richard, F.D. (2003). Meta-analysis of raw mean effect size in analysis of variance. Psychological Methods, 5, 425-433.

Brillinger, D. R. (2001). John Tukey and the correlation coefficient, *Computing Science and Statistics, 33*. 204-218.

Cohen, P., Cohen, J., Aiken, L. S., & West, S. G. (1999). The problem of units and the circumstance for POMP. *Multivariate Behavioral Research, 34*, 315–346.

Dunlap, W. P, Cortina, J. M., Vaslow, J. B., & Burke, M. J. (1996). Meta-analysis of experiments with matched groups or repeated measures designs. *Psychological Methods, 1*, 170–177

Fern, Edward F. and Kent B. Monroe (1996), Effect Size Estimates: Issues and Problems in Interpretation, Journal of Consumer Research , 23, 89-105.

Fichman, M. (1999). Variance Explained: Why Size Doesn’t (Always) Matter. *Research in Organizational Behavior, 21*, 295-331.

Fisher, R. A. (1925). *Statistical methods for research workers*. London: Oliver & Boyd.

Freedman, D. A. (2005). Linear statistical models for causation: A critical review. In B. Everitt and D. Howell (Eds.)* The Wiley Encyclopedia of Statistics in Behavioral Science*. New York: Wiley.

Frick, R. W. (1999). Defending the statistical status quo. *Theory and Psychology, 9*, 183-189.

Glass, G. V., McGaw, B., & Smith, M. L. (1981). *Meta-analysis in *CA: Sage.

Kim, J.-Q, & Feree, G. D. (1981). Standardization in causal analysis. *Sociological Methods & Research, 10*, 187-210.

Lenth, R. V., 2001. Some practical guidelines for effective sample size determination. *The American Statistician*, 55:187-193.

Morris, S. B., & DeShon, R. P. (2002). Combining effect size estimates in meta-analysis with repeated measures and independent-groups designs. *Psychological Methods, 7*, 105-125.

O’Grady, K. E., 1982. Measures of explained variance: cautions and limitations. *Psychological Bulletin*, 92:766-777.

Olejnik, S., & Algina, J. (2003). Generalized eta and omega squared statistics: Measures of effect size for some common research designs. Psychological Methods, 8, 434-447.

Robinson, D. H., Fouladi, R. T., Williams, N. J., & Bera, S. J. (2002). Some effects of providing effect size and “what if” information. *Journal of Experimental Education, 70*, 365-382.

Robinson, D. H., Whittaker, T., Williams, N., & Beretvas, S. N. (2003). It’s not effect sizes so much as comments about their magnitude that mislead readers. *Journal of Experimental Education, 72*, 51-64.

Tukey, J. W. (1954). Causation, regression and path analysis. In O. Kempthorne, T. A. Bancroft, J. W. Gowen & J. L. Lush (Eds.), *Statistics and mathematics in biology *(pp. 35-66). Ames, IA: Iowa State College Press.

Tukey, J.W. (1969). Analyzing data: Sanctification or detective work? *American Psychologist, 24*, 83-91.

Wilkinson, L., & APA Task Force on Statistical Inference (1999). Statistical methods in psychology journals: Guidelines and explanations. *American Psychologist, 54*, 594-604.