Cohen d-ning Effekt Hajmini Talqin Qilish

Interaktiv vizualizatsiya

Tomonidan yaratilgan Kristoffer Magnusson

Tarjima qildi Mirsoli Mirsultonov

Ulashish

Cohen d-ning effekt hajmi psixologiyada juda mashhur. Biroq, uning talqini oddiy emas va tadqiqotchilar ko’pincha effektni sharhlashda kichik (0,2), o’rta (0,5) va katta (0,8) kabi umumiy ko’rsatmalardan foydalanadilar. Bundan tashqari, ko’p hollarda standartlashtirilgan o’rtacha farq standartlashtirilmagan o’rtacha farqdan ko’ra ko’proq izohlanadimi, degan savol tug’iladi.

Cohen d-ni talqin qilishga yordam berish uchun ushbu vizualizatsiya Cohen d-ning turli xil tasvirlarini taqdim etadi: vizual qoplama, Cohen U3, ustunlik ehtimoli, bir-biriga mos kelish foizi va raqam davolash uchun zarur. Shuningdek, u standart og’ishni o’zgartirishga imkon beradi va standartlashtirilmagan farqni ko’rsatadi.

Cohen d-ning

Vizualizatsiya yuklanmoqda

Cohen U3-ning

% Qoplash

Ustunlik ehtimoli

Davolash uchun zarur bo'lgan raqam

Umumiy til izohi

Cohen d 0.80 ga teng bo'lsa, "treatment" guruhining 78.8% "control" guruhining o'rtacha qiymatidan yuqori bo'ladi (Cohen U3), 68.9% ikkita guruh bir-biriga mos keladi va davolash guruhidan tasodifiy tanlangan kishi nazorat guruhidan tasodifiy tanlangan kishiga qaraganda yuqori ballga ega bo'lish ehtimoli 71.4% (ustunlik ehtimoli). Bundan tashqari, davolash guruhida nazorat guruhiga nisbatan yana bitta ijobiy natijaga erishish uchun biz o'rtacha 3.5 kishini davolashimiz kerak. Bu shuni anglatadiki, agar har bir guruhda 100 kishi bo'lsa va biz nazorat guruhida 20 kishi ijobiy natijaga ega deb hisoblasak, davolash guruhidagi 20 + 28.3 kishi ijobiy natijalarga erishadi.1

1Qiymatlar oʻrtacha qiymatlar boʻlib, nazorat guruhining 20 tasi (CER) "qulay natijalarga" ega, ya'ni ularning natijalari ba'zi chegaralardan past deb taxmin qilinadi. Slayderning o'ng tomonidagi sozlamalar belgisini bosib, buni o'zgartiring. Qo'shimcha ma'lumot olish uchun formulalar bo'limiga o'ting.

Klinik psixologiya bo'yicha tadqiqotchi Kristoffer Magnusson tomonidan yozilgan. Siz uni Twitter-da kuzatib borishingiz mumkin va Git Gud Science ochiq ilmiy discord sahifasiga tashrib buyuring.

FAQ

Cohen d-ni o’zgartiring

Cohen d-ni o’zgartirish uchun slayderdan foydalaning yoki sozlamalar panelini oching va parametrlarni o’zgartiring. Kirishlarni klaviatura strelkalari yordamida ham boshqarish mumkin.

Sozlamalar

Slayderning o’ng tomonidagi sozlamalar belgisini bosish orqali quyidagi sozlamalarni o’zgartirishingiz mumkin.

  • Parametrlar
    • O’rta 1
    • O’rta 2
    • Oddiy og’ish
    • Nazorat guruhidagi hodisalar tezligi (NHT)
  • Belgilar
    • X o’qi
    • Taqsimot 1
    • Taqsimot 2
  • Slayder sozlamalari
    • Slayderder Max
    • Slayder qadami: Slayderning qadam o’lchamini boshqaradi

Sozlamalarni saqlash

Sozlamalar brauzeringizning localStorage da saqlanishi mumkin va shu tariqa tashriflar davomida saqlanib qoladi.

Panorama va oʻlchamni oʻzgartirish

Displeyni bosish va uni sudrab borish orqali X o’qini panorama qilishingiz mumkin. Vizualizatsiyani markazlashtirish va hajmini o’zgartirish uchun ikki marta bosing. Displayni bosish va sudrab borish orqali x o’qini panorama qilishingiz mumkin. Vizualizatsiyani markazlashtirish va o‘lchamini o‘zgartirish uchun ikki marta bosing.

Oflayn foydalanish

Ushbu sayt service worker yordamida keshlangan va hatto oflayn bo’lganingizda ham ishlaydi.

Cohen d

Cohen d-ning oddiygina standartlashtirilgan o’rtacha farq,

,

bu yerda - Cohen d-ning populyatsiya parametri. Qayerda , ya’ni bir hil populyatsiya dispersiyalari deb taxmin qilinadi. Va - tegishli aholining o’rtacha qiymati.

Cohen U3

Cohen (1977) U3ni bir-biriga mos kelmaslik o’lchovi sifatida ta’riflagan, bu erda biz “A populyatsiyasining B populyatsiya holatlarining yuqori yarmidan oshib ketgan foizini olamiz”. Cohen d-ni quyidagi formula yordamida Cohen U3 ga aylantirish mumkin

bu yerda standart normal taqsimotning kumulyativ taqsimot funksiyasi va Cohen d-ning populyatsiyasi.

O’xshashlik

Odatda bir-biriga o’xshash koeffitsient (OVL) deb ataladi. Cohen dni quyidagi formula yordamida OVL ga aylantirish mumkin (Reiser va Faraggi, 1999)

bu yerda standart normal taqsimotning kumulyativ taqsimot funksiyasi va Cohen d-ning populyatsiyasi.

Ustunlik ehtimoli

Bu ko’plab nomlarga ega effekt hajmi: umumiy til effekti o’lchami (UT), qabul qiluvchining ishlash xususiyatlari ostidagi maydon (QIM) yoki uning parametrik bo’lmagan versiyasi uchun faqat A (Ruscio & Mullen, 2012). Bu statistika bo’yicha hech qanday ma’lumotga ega bo’lmagan odamlar uchun yanada intuitiv bo’lishi uchun mo’ljallangan. Ta’sir hajmi davolash guruhidan tasodifiy tanlangan kishi nazorat guruhidan tasodifiy tanlangan kishiga qaraganda yuqori ball olish ehtimolini beradi. Cohen d ni quyidagi formula yordamida UT ga aylantirish mumkin (Ruscio, 2008)

bu yerda standart normal taqsimotning kumulyativ taqsimot funksiyasi va Cohen d-ning populyatsiyasi.

Davolash uchun zarur bo’lgan raqam

NNT - bu nazorat guruhiga nisbatan ko’proq ijobiy natijaga erishish uchun biz aralashuv bilan davolashimiz kerak bo’lgan bemorlar soni. Furukawa va Leucht (2011) Cohen d-ni NNT ga aylantirish uchun quyidagi formulani beradi

bu yerda standart normal taqsimotning kümülatif taqsimot funksiyasi va uning teskarisi, CER - nazorat guruhining hodisa tezligi va populyatsiyasi Cohen d-ning. N.B. Yuqoridagi vizualizatsiyada CER 20% ga sozlangan. Buni slayderning oʻng tomonidagi sozlamalar belgisini bosish orqali oʻzgartirishingiz mumkin. “Hodisa” yoki “javob” ta’rifi o’zboshimchalik bilan va remissiyada bo’lgan bemorlarning nisbati sifatida belgilanishi mumkin, masalan. standartlashtirilgan so’rovnoma bo’yicha ba’zi bir kesish. Cohen d-ning ni NNT ning nazorat guruhining hodisalar tezligiga o’zgarmas versiyasiga aylantirish mumkin. Qiziqqan o’quvchi Furukawa va Leucht (2011) ga qarashi kerak, bu erda nima uchun bu NNT talqinini murakkablashtirishi haqida ishonchli dalil berilgan.

Cohen d-dan NNTni hisoblash uchun R kodi

Ko’pchilik yuqoridagi formula uchun R kodi haqida so’raganligi sababli, bu erda

Iqtiboslar

  • Baguley, T. (2009). Standardized or simple effect size: what should be reported? British journal of psychology, 100(Pt 3), 603–17.
  • Cohen, J. (1977). Statistical power analysis for the behavioral sciencies. Routledge.
  • Furukawa, T. A., & Leucht, S. (2011). How to obtain NNT from Cohen’s d: comparison of two methods. PloS one, 6(4).
  • Reiser, B., & Faraggi, D. (1999). Confidence intervals for the overlapping coefficient: the normal equal variance case. Journal of the Royal Statistical Society, 48(3), 413-418.
  • Ruscio, J. (2008). A probability-based measure of effect size: robustness to base rates and other factors. Psychological methods, 13(1), 19–30.
  • 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(2), 201–223.

Sevimli uslublar qo’llanmasiga ko’ra ushbu sahifadan iqtibos keltiring. Quyidagi havolalar avtomatik tarzda yaratiladi va ma’lumotlarni o’z ichiga oladi.

APA 7

Magnusson, K. (2022). Interpreting Cohen's d effect size: An interactive visualization (Version 2.6.0) [Web App]. R Psychologist. https://rpsychologist.com/cohend/

BibTex

Iltimos, GitHub-da muammo ochish orqali xatolar yoki takliflar haqida xabar bering, agar savol bermoqchi bo‘lsangiz GitHub muhokamalaridan foydalaning.

Yo’q, yaxshi bo’ladi. Ilova brauzeringizda ishlaydi, shuning uchun server faqat fayllarga xizmat qiladi.

Bu ataylab qilingan, mening sabablarim haqida ushbu blog postda ko’proq o’qishingiz mumkin: Where Cohen went wrong – the proportion of overlap between two normal distributions

Ha, davom eting! Men ikkita bir-biriga o’xshash Gauss taqsimotini tuzishni o’ylab topmaganman. Bu vizualizatsiya ommaviy domenga bag’ishlangan bo’lib, bu “siz ishni ko’chirish, o’zgartirish, tarqatish va hatto tijorat maqsadlarida ham ruxsat so’ramasdan bajarishingiz mumkin” degan ma’noni anglatadi ([Creative common CC0-litsenziya](https://creativecommons.org/publicdomain/zero/1.0/)-siga qarang). Garchi atribut talab qilinmasa ham, u har doim qadrlanadi!

Ushbu sahifaning manba kodi MIT yordamida litsenziyalangan va sahifadagi matn CC-BY 4.0.

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Mendan 327 dona kofe sotib olgan 140 qo'llab-quvvatlovchiga katta rahmat!

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I've been looking for applets that show this for YEARS, for demonstrations for classes. Thank you so much! Students do not need to tolarate my whiteboard scrawl now. I'm sure they'd appreciate you, too.l

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I wish I could learn more from you about stats and math -- you use language in places that I do not understand. Cohen's D visualizations opened my understanding. Thank you

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Thank you for the website, made me smile AND smarter :O enjoy your coffee! :)

Deb ☕ (1) coffee sotib olgan

Struggling with statistics and your interactive diagram made me smile to see that someone cares enough about us strugglers to make a visual to help us out!😍 

Someone ☕ (1) coffee sotib olgan

@exerpsysing ☕ (1) coffee sotib olgan

Much thanks! Visualizations are key to my learning style! 

Someone ☕ (1) coffee sotib olgan

Homiylar

Siz mening ochiq manbali ishimga GitHub Sponsors yordamida homiylik qilishingiz mumkin va bu yerda ismingiz ko'rsatilishi mumkin.

Qo'llab-quvvatlovchilar ✨❤️

Pull Request so'rovlari ham qabul qilinadi yoki siz yangi funksiyalarni taklif qilish, foydali havolalar qo'shish yoki matn terish xatolarini tuzatishga yordam berish orqali o'z hissangizni qo'shishingiz mumkin. Faqat GitHub da issues bo'limini oching.

Webmentions

Mirsoli Mirzaahmad õğli
Thank you too sir!

(2021-yilgacha yuborilgan veb-saytlar, afsuski, bu yerda ko'rinmaydi.)

Ko'proq vizualizatsiya

Understanding p-values Through Simulations

An interactive simulation to help explain p-values

Maximum Likelihood

An interactive post covering various aspects of maximum likelihood estimation.

Cohen's d

An interactive app to visualize and understand standardized effect sizes.

Statistical Power and Significance Testing

An interactive version of the traditional Type I and II error illustration.

Confidence Intervals

An interactive simulation of confidence intervals

Bayesian Inference

An interactive illustration of prior, likelihood, and posterior.

Correlations

Interactive scatterplot that lets you visualize correlations of various magnitudes.

Equivalence and Non-Inferiority Testing

Explore how superiority, non-inferiority, and equivalence testing relates to a confidence interval

P-value distribution

Explore the expected distribution of p-values under varying alternative hypothesises.

t-distribution

Interactively compare the t- and normal distribution.