Cohen d-ning Effekt Hajmini Talqin Qilish

Interaktiv vizualizatsiya

Tomonidan yaratilgan Kristoffer Magnusson

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.

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.

Ranglar

Dist 1

Qoplash

Dist 2

Ranglarni o'zgartirish uchun bosing

Qorong'i rejim:

Parametrlar

Sozlamalarni saqlash

Sozlamalarni brauzerda saqlang

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

Ushbu vizualizatsiyadan qanday foydalanaman?

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.

Formulalar qanday?

Cohen d

Cohen d-ning oddiygina standartlashtirilgan o’rtacha farq,

$δ = \frac{μ _{2} - μ _{1}}{σ}$ ,

bu yerda $δ$ - Cohen d-ning populyatsiya parametri. Qayerda $σ_{1} = σ_{2} = σ$ , ya’ni bir hil populyatsiya dispersiyalari deb taxmin qilinadi. Va $μ_{i}$ - 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

$U_{3} = Φ (δ)$

bu yerda $Φ$ standart normal taqsimotning kumulyativ taqsimot funksiyasi va Cohen d-ning $δ$ 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)

$OVL = 2 Φ (- ∣ δ ∣ / 2)$

bu yerda $Φ$ standart normal taqsimotning kumulyativ taqsimot funksiyasi va Cohen d-ning $δ$ 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)

$CL = Φ (\frac{δ}{2})$

bu yerda $Φ$ standart normal taqsimotning kumulyativ taqsimot funksiyasi va Cohen d-ning $δ$ 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

$NNT = \frac{1}{Φ ( δ + Ψ ( C E R ) ) - C E R}$

bu yerda $Φ$ standart normal taqsimotning kümülatif taqsimot funksiyasi va $Ψ$ uning teskarisi, CER - nazorat guruhining hodisa tezligi va $δ$ 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.

Ushbu sahifadan qanday qilib iqtibos keltiraman?

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. (2023). A Causal Inference Perspective on Therapist Effects. PsyArXiv. https://DOI

BibTex

Men bug/xato/matbaa xatosini moliyalashtiraman yoki taklif qilmoqchiman!

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

Men ko'p sonli talabalardan ushbu saytga tashrif buyurishlarini so'rayman. Bu sizning serveringizni buzadimi?

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Bir-biriga o'xshashlik statistikasi Cohen-ning hisob-kitoblaridan farq 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

Ushbu vizualizatsiyani kitobim/maqolam/va hokazolarimga qo'shishim mumkinmi?

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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Chris SG ☕ (1) coffee sotib olgan

Very nice.

Gray Church ☕ (1) coffee sotib olgan

Thank you for the visualizations. They are fun and informative.

Qamar ☕ (1) coffee sotib olgan

Tanya McGhee ☕ (1) coffee sotib olgan

@schultemi ☕ (1) coffee sotib olgan

Neilo ☕ (1) coffee sotib olgan

Really helpful visualisations, thanks!

Someone ☕ (1) coffee sotib olgan

This is amazing stuff. Very slick.

Someone ☕ (1) coffee sotib olgan

Sarko ☕ (1) coffee sotib olgan

Thanks so much for creating this! Really helpful for being able to explain effect size to a clinician I'm doing an analysis for.

@DominikaSlus ☕ (1) coffee sotib olgan

Thank you! This page is super useful. I'll spread the word.

Someone ☕ (1) coffee sotib olgan

Melinda Rice ☕ (1) coffee sotib olgan

Thank you so much for creating these tools! As we face the challenge of teaching statistical concepts online, this is an invaluable resource.

@tmoldwin ☕ (1) coffee sotib olgan

Fantastic resource. I think you would be well served to have one page indexing all your visualizations, that would make it more accessible for sharing as a common resource.

Someone ☕ (1) coffee sotib olgan

Fantastic Visualizations! Amazing way to to demonstrate how n/power/beta/alpha/effect size are all interrelated - especially for visual learners! Thank you for creating this?

@jackferd ☕ (1) coffee sotib olgan

Incredible visualizations and the best power analysis software on R.

Cameron Proctor ☕ (1) coffee sotib olgan

Great website!

Someone ☕ (1) coffee sotib olgan

Hanah Chapman ☕ (1) coffee sotib olgan

Thank you for this work!!

Someone ☕ (1) coffee sotib olgan

Jayme ☕ (1) coffee sotib olgan

Nice explanation and visual guide of Cohen's d

Bart Comly Boyce ☕ (1) coffee sotib olgan

thank you

Dr. Mitchell Earleywine ☕ (1) coffee sotib olgan

This site is superb!

Florent ☕ (1) coffee sotib olgan

Zampeta ☕ (1) coffee sotib olgan

thank you for sharing your work.

Mila ☕ (1) coffee sotib olgan

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 ✨❤️

Bazyli Brzóska

Joseph Bulbulia

Darren L Dahly

bradfde Sizning Ismingiz

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.