The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
A Critique and Improvement of the CL Common Language Effect Size Statistics of McGraw and Wong
Abstract
McGraw and Wong (1992) described an appealing index of effect size, called CL, which measures the difference between two populations in terms of the probability that a score sampled at random from the first population will be greater than a score sampled at random from the second. McGraw and Wong introduced this "common language effect size statistic" for normal distributions and then proposed an approximate estimation for any continuous distribution. In addition, they generalized CL to the n-group case, the correlated samples case, and the discrete values case. In the current paper a different generalization of CL, called the A measure of stochastic superiority, is proposed, which may be directly applied for any discrete or continuous variable that is at least ordinally scaled. Exact methods for point and interval estimation as well as the significance tests of the A = .5 hypothesis are provided. New generalizations ofCL are provided for the multi-group and correlated samples cases.
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Cites
Statistical Power Analysis for the Behavioral Sciences
Cohen, J. (2013) · doi:10.4324/9780203771587 · Cited by 25,373
A common language effect size statistic
McGraw, K. O., & Wong, S. P. (1992) · Psychological Bulletin · doi:10.1037/0033-2909.111.2.361 · Cited by 934 · cited 6×
“Discussion The CL effect size indicator proposed by McGraw and Wong (1992) expresses the difference between two populations with regard to a variable in terms of a probability.”
BackgroundMethodInfluential
Cited by
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Fritz, C. O., Morris, P. E., & Richler, J. J. (2012) · Journal of Experimental Psychology: General · doi:10.1037/a0024338 · Cited by 5,199
An improved effect size for single-case research: nonoverlap of all pairs.
Parker, R. I., & Vannest, K. (2009) · Behavior Therapy · doi:10.1016/j.beth.2008.10.006 · Cited by 1,126
Modern robust statistical methods: An easy way to maximize the accuracy and power of your research.
Erceg-Hurn, D. M., & Mirosevich, V. M. (2008) · American Psychologist · doi:10.1037/0003-066x.63.7.591 · Cited by 959
Fraser, G., & Arcuri, A. (2013) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2012.14 · Cited by 586
A probability-based measure of effect size: Robustness to base rates and other factors.
Ruscio, J. (2008) · Psychological Methods · doi:10.1037/1082-989x.13.1.19 · Cited by 398
Metadata
- Authors
- Vargha, A., & Delaney, H. D.
- Venue
- Journal of Educational and Behavioral Statistics
- Published
- 2000-06
- Volume
- 25
- Issue
- 2
- Pages
- 101-132
- ISSN
- 1076-9986 (ISSN-L)
- 1935-1054
- OpenAlex
- W1965335252
- Cited by
- 938
- References
- 2
- FWCI
- 3.12
- Type
- journal article
- Language
- en
- Open access
- closed
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