效应值

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統計學中,效應值(Effect size)是量化現象強度的數值。[1]效應值實際的統計量包括了二個變數間的相關程度、迴歸模型中的迴歸係數、不同處理間平均值的差異……等等。無論哪種效應值,其絕對值越大表示效應越強,也就是現象越明顯。效應值與特效检验的概念是互補的。在估算統計檢定力、需要的樣本數英语Sample size與進行元分析時,效應值經常扮演重要角色。

在研究結果中報導效應值被視為洽當的或必須的。[2][3]相對於統計學上的顯著性,效應值有利於了解研究結果的強度。[4]特別是在社會科學醫學研究上,效應值更顯得重要。絕對與相對效應值可以傳遞不同的訊息,又可互相補充訊息。有個心理學的研究學會鼓勵學者報導效應值:

報告主要結果時必須一併報導效應值……如果測量值的單位在實際面上是有意義的(例如每人每日抽煙的香煙根數),則我們建議採用非標準化的效應值(例如迴歸係數或平均值差異)而非標準化的效應值(例如相關係數)。 —  L. Wilkinson and APA Task Force on Statistical Inference (1999, p. 599)

參考文獻[编辑]

  1. ^ Kelley, Ken; Preacher, Kristopher J. On Effect Size. Psychological Methods. 2012, 17 (2): 137–152. doi:10.1037/a0028086. 
  2. ^ Wilkinson, Leland; APA Task Force on Statistical Inference. Statistical methods in psychology journals: Guidelines and explanations. American Psychologist. 1999, 54 (8): 594–604. doi:10.1037/0003-066X.54.8.594. 
  3. ^ Nakagawa, Shinichi; Cuthill, Innes C. Effect size, confidence interval and statistical significance: a practical guide for biologists. Biological Reviews Cambridge Philosophical Society. 2007, 82 (4): 591–605. PMID 17944619. doi:10.1111/j.1469-185X.2007.00027.x. 
  4. ^ Ellis, Paul D. The Essential Guide to Effect Sizes: An Introduction to Statistical Power, Meta-Analysis and the Interpretation of Research Results. United Kingdom: Cambridge University Press. 2010. 

延伸閱讀[编辑]

  • Aaron, B., Kromrey, J. D., & Ferron, J. M. (1998, November). Equating r-based and d-based effect-size indices: Problems with a commonly recommended formula. Paper presented at the annual meeting of the Florida Educational Research Association, Orlando, FL. (ERIC Document Reproduction Service No. ED433353)
  • Bonett, D. G. Confidence intervals for standardized linear contrasts of means. Psychological Methods. 2008, 13: 99–109. doi:10.1037/1082-989x.13.2.99. 
  • Bonett, D. G. Estimating standardized linear contrasts of means with desired precision. Psychological Methods. 2009, 14: 1–5. doi:10.1037/a0014270. 
  • Brooks, M.E.; Dalal, D.K.; Nolan, K.P. Are common language effect sizes easier to understand than traditional effect sizes?. Journal of Applied Psychology. 2013. doi:10.1037/a0034745. 
  • Cumming, G.; Finch, S. A primer on the understanding, use, and calculation of confidence intervals that are based on central and noncentral distributions. Educational and Psychological Measurement. 2001, 61: 530–572. 
  • Imdadullah, M. (2014). Effect Size for dependent Sample t test. itfeature.com document on Effect Size for dependent Sample t test
  • Kelley, K. Confidence intervals for standardized effect sizes: Theory, application, and implementation. Journal of Statistical Software. 2007, 20 (8): 1–24. 
  • Lipsey, M. W., & Wilson, D. B. (2001). Practical meta-analysis. Sage: Thousand Oaks, CA.
  • Sawilowsky, Shlomo S. (2003). A Different Future For Social And Behavioral Science Research, Journal of Modern Applied Statistical Methods, Vol 2(1), 128-132.

外部連結[编辑]

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