orddom-package: Ordinal Dominance Statistics
Description
This package provides ordinal, nonparametric statistics and effect sizes as an alternative to independent or paired group mean comparisons, with special reference to Cliff's delta statistics (or success rate difference, SRD), but also providing McGraw and Wong's common language effect size for the discrete case (i.e. Grissom and Kim's Probability of Superiority), Vargha and Delaney's A (or the Area Under a Receiver Operating Characteristic Curve AUC), and Cook & Sackett's number needed to treat (NNT) effect size (cf. Kraemer & Kupfer, 2006). For the nonparametric effect sizes, various bootstrap CI estimates may also be obtained. Nonparametric effect sizes are also expressed as Cohen's d based on percentages of group non-overlap (cf. Huberty & Lowman, 2000).
Details
|
Package: |
| anRpackage |
|
Type: |
| Package |
|
Version: |
| 3.1 |
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Date: |
| 2013-02-07 |
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License: |
| GPL-2 |
References
Cliff, N. (1993). Dominance statistics: Ordinal analyses to answer ordinal questions. Psychological Bulletin, 114, 494-509.
Cliff, N. (1996a). Ordinal Methods for Behavioral Data Analysis. Mahwah, NJ: Lawrence Erlbaum.
Cliff, N. (1996b). Answering ordinal questions with ordinal data using ordinal statistics. Multivariate Behavioral Research, 31, 331-350.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd edition). New York: Academic Press.
Cook, R.J. & Sackett, D. L. (1995). The number needed to treat: A clinically useful measure of treatment effect. British Medical Journal, 310, 452-454.
Delaney, H.D. & Vargha, A. (2002). Comparing Several Robust Tests of Stochastic Equality With Ordinally Scaled Variables and Small to Moderate Sized Samples. Psychological Methods, 7, 485-503.
Feng, D., & Cliff, N. (2004). Monte Carlo Evaluation of Ordinal d with Improved Confidence Interval. Journal of Modern Applied Statistical Methods, 3(2), 322-332.
Feng, D. (2007). Robustness and Power of Ordinal d for Paired Data. In Shlomo S. Sawilowsky (Ed.), Real Data Analysis (pp. 163-183). Greenwich, CT : Information Age Publishing.
Grissom, R.J. (1994). Probability of the superior outcome of one treatment over another. Journal of Applied Psychology, 79, 314-316.
Grissom, R.J. & Kim, J.J. (2005). Effect sizes for research. A broad practical approach. Mahwah, NJ, USA: Erlbaum.
Huberty, C. J. & Lowman, L. L. (2000). Group overlap as a basis for effect size. Educational and Psychological Measurement, 60, 543-563.
Kraemer, H.C. & Kupfer, D.J. (2006). Size of Treatment Effects and Their Importance to Clinical Research and Practice. Biological Psychiatry, 59, 990-996.
McGraw, K.O. & Wong, S.P. (1992). A common language effect size statistic. Psychological Bulletin, 111, 361-365.
Long, J. D., Feng, D., & Cliff, N. (2003). Ordinal analysis of behavioral data. In J. Schinka & W. F. Velicer (eds.), Research Methods in Psychology. Volume 2 of Handbook of Psychology (I. B. Weiner, Editor-in-Chief). New York: John Wiley & Sons.
Romano, J., Kromrey, J. D., Coraggio, J., & Skowronek, J. (2006). Appropriate statistics for ordinal level data: Should we really be using t-test and Cohen's d for evaluating group differences on the NSSE and other surveys?. Paper presented at the annual meeting of the Florida Association of Institutional Research, Feb. 1-3, 2006, Cocoa Beach, Florida. Last retrieved January 2, 2012, from www.florida-air.org/romano06.pdf
Ruscio, J. & Mullen, T. (2012). Confidence Intervals for the Probability of Superiority Effect Size Measure and the Area Under a Receiver Operating Characteristic Curve. Multivariate Behavioral Research, 47, 221-223.
Vargha, A., & Delaney, H. D. (1998). The Kruskal-Wallis test and stochastic homogeneity. Journal of Educational and Behavioral Statistics, 23, 170-192.
Vargha, A., & Delaney, H. D. (2000). A critique and improvement of the CL common language effect size statistic of McGraw and Wong. Journal of Educational and Behavioral Statistics, 25, 101-132.