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Effect Size as a Measure of Difference Between Two Populations

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Encyclopedia of Social Network Analysis and Mining

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Synonyms

Comparison criteria

Glossary

Cliff’s δ :

Probability that a unit picked at random from one group will have a higher response than a unit picked at random from another group, for groups typically identified as treatment and control. Also known as the probability of superiority or the precedence probability.

Cohen’s d :

Standardized difference in two independent sample means, standardized using average variance, used as a measure effect size.

Cohen’s h :

Arcsine-transformed difference in two proportions that are typically used as a measure of distance between two groups.

Cramér’s V :

Standardized χ2-statistic.

Effect size:

Difference in outcome between two (or more) groups.

Glass’ Δ:

Standardized difference in two independent sample means typically used as a measure effect size when the groups are classified as treatment and control.

Hodges’ g :

Standardized difference in two independent sample means, standardized using a pooled (i.e., weighted) variance, used as a measure...

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References

  • Bartolucci AA, Tendera M, Howard G (2011) Meta-analysis of multiple primary prevention trials of cardiovascular events using aspirin. Am J Cardiol 107(12):1976–1801

    Article  Google Scholar 

  • Bravais A (1846) Analyse mathamatique sur les probabilities des erreurs de situation d’un point. Mem par Divers Savans 9:255–332

    Google Scholar 

  • Cancer.org sites (2016) http://www.cancer.org/cancer/ovariancancer/detailedguide/ovarian-cancer-survival-rates. Accessed 15 Aug 2016

  • Chase JM, Knight TM (2013) Scale-dependent effect sizes of ecological drivers on biodiversity: why standardized sampling is not enough. Ecol Lett 16:17–26

    Article  Google Scholar 

  • Chen H, Cohen P, Chen S (2010) How big is a big odds ratio: interpreting the magnitude of odds ratios in epidemiological studies. Commun Stat-Simul Comput 39(4):860–864. doi:10.1080/03610911003650383

    Article  MathSciNet  MATH  Google Scholar 

  • Cliff N (1996). Ordinal methods for behavioral data analysis. Routledge, Hove, UK

    Google Scholar 

  • CNN (2016) http://www.cnn.com/election/primaries/polls/in/Rep

  • Cohen J (1962) The statistical power of abnormal-social psychological research: a review. J Abnorm Soc Psychol 65:145–153

    Article  Google Scholar 

  • Cohen J (1969) Statistical power analysis for the behavioral sciences. Academic, New York

    MATH  Google Scholar 

  • Cohen J (1977) Statistical power analysis for the behavioral sciences. Academic, New York

    MATH  Google Scholar 

  • Cohen J (1988) Statistical power analysis for the behavioral sciences, Revised edn. Academic, New York

  • Cowles M (1989) Statistics in psychology: an historical perspective. Lawrence Erlbaum, Hillsdale

    MATH  Google Scholar 

  • Cramér H (1946) The two-dimensional case, Chapter 21. In: Mathematical methods of statistics. Princeton University Press, Princeton, p 282. ISBN 0-691-08004-6

    Google Scholar 

  • Dey R (2011) Inference for the K-sample problem based on precedence probabilities. Doctoral dissertation, Kansas State University: Manhattan

    Google Scholar 

  • Ellis P (2010) The essential guide to effect sizes: statistical power, meta-analysis, and the interpretation of research results. Cambridge University Press, Cambridge. ISBN 9780521194235

    Google Scholar 

  • Fritz CO, Morris PE (2012) Effect size estimates: current use, calculations, and interpretation. J Exp Psychol Gen 141(1):2–18

    Article  Google Scholar 

  • Galton F (1888) Co-relations and their measurement. Proc R Soc Lond 45:135–145

    Article  Google Scholar 

  • Glass G (1976) Primary, secondary, and meta-analysis of research. Educ Res 5(10):3–8

    Article  Google Scholar 

  • Hays WL (1963) Statistics for psychologists. Holt, Rinehart & Winston, New York

    Google Scholar 

  • Hedges LV (1981) Distribution theory for Glass’s estimator of effect size and related estimators. J Educ Stat 6(2):106–128

    Article  Google Scholar 

  • Huberty CJ (2002) A history of effect size indices. Educ Psychol Meas 62(2):227–240

    Article  MathSciNet  Google Scholar 

  • Ivarsson A, Andersen MB, Johnson U, Linwall M (2013) To adjust or not adjust: nonparametric effect sizes, confidence intervals and real-world meaning. Psychol Sport Exerc 14:97–102

    Article  Google Scholar 

  • Pearson K (1900) On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. Phil Mag Ser 5 50(302):157–175. doi:10.1080/14786440009463897

    Article  MATH  Google Scholar 

  • Pepe MS (2003) The statistical evaluation of medical tests for classification and prediction. Oxford University Press, New York. ISBN 0-19-856582-8

    Google Scholar 

  • Rice ME, Harris GT (2005) Comparing effect sizes in follow-up studies: ROC area, Cohen’s d, and r. Law Hum Behav 29(5):615–620

    Article  Google Scholar 

  • Rosenthal R, Rosnow RL, Rubin DB (1999) Contrasts and effect sizes in behavioral research: a correlational approach. Cambridge University Press, New York

    Book  Google Scholar 

  • Ruscio J, Mullen T (2012) Confidence intervals for the probability of superiority effect size measure and the area under a receiver operating characteristic curve. Multivar Behav Res 47(2):201–223

    Article  Google Scholar 

  • Stigler SM (1986) The history of statistics. Belknap, Cambridge, MA

    MATH  Google Scholar 

  • Sullivan GM, Feinn R (2012) Using effect size – or why the p value is not enough. J Grad Med Educ 4(3):279–282. doi:10.4300/JGME-D-12-00156.1

    Article  Google Scholar 

  • Teasdale N, Bard C, La Rue J, Fleury M (1993) On the cognitive penetrability of posture control. Exp Aging Res 19:1–13

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Mathematics & Statistics, University of South Alabama, Mobile, AL, 36688, USA

    Rajarshi Dey & Madhuri S. Mulekar

Authors
  1. Rajarshi Dey
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  2. Madhuri S. Mulekar
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Correspondence to Rajarshi Dey .

Editor information

Editors and Affiliations

  1. Computer Science, University of Calgary, Calgary, Alberta, Canada

    Reda Alhajj

  2. University of Calgary Computer Science, Calgary, Canada

    Jon Rokne

Section Editor information

  1. American University of Sharjah, Sharjah, United Arab Emirates

    Suheil Khoury

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Dey, R., Mulekar, M.S. (2017). Effect Size as a Measure of Difference Between Two Populations. In: Alhajj, R., Rokne, J. (eds) Encyclopedia of Social Network Analysis and Mining. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7163-9_110195-1

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  • DOI: https://doi.org/10.1007/978-1-4614-7163-9_110195-1

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  • Print ISBN: 978-1-4614-7163-9

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