Cohen d-ning Effekt Hajmini Talqin Qilish
Interaktiv vizualizatsiya
Tomonidan yaratilgan Kristoffer Magnusson
Tarjima qildi Mirsoli Mirsultonov
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.
Klinik psixologiya bo'yicha tadqiqotchiKristoffer 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,
,
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.
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. (2022). Interpreting Cohen's d effect size: An interactive visualization (Version 2.6.0) [Web App]. R Psychologist. https://rpsychologist.com/cohend/
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?
Yo’q, yaxshi bo’ladi. Ilova brauzeringizda ishlaydi, shuning uchun server faqat fayllarga xizmat qiladi.
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: Cohen qayerda xatoga yo’l qo’ygan - ikkita oddiy taqsimot o’rtasidagi o’xshashlik nisbati
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-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.
Hissa qo'shish/ehson qilish
Bepul va ochiq dasturiy ta'minotga hissa qo'shishning ko'plab usullari mavjud. Agar siz mening ishimni yoqtirsangiz va uni qo'llab-quvvatlamoqchi bo'lsangiz, quyidagilarni qilishingiz mumkin:
Menga 303 dona kofe sotib qilgan 134 qo'llab-quvvatlovchiga katta rahmat!
Jason Rinaldo ☕☕☕☕☕☕☕☕☕☕ (10) coffee-lar sotib olgan
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
Michael Villanueva ☕☕☕☕☕ (5) coffee-lar sotib olgan
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
Someone ☕☕☕☕☕ (5) coffee-lar sotib olgan
Thank you, Kristoffer
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Great webpage, I use it to illustrate several issues when I have a lecture in research methods. Thanks, it is really helpful for the students:)
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Hard to overstate the importance of this work Kristoffer. Grateful for all you are doing.
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Very helpful to helping teach teachers about the effects of the Good Behavior Game
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Regina ☕☕☕ (3) coffee-lar sotib olgan
Love your visualizations!
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Thanks. I really love the simplicity of your sliders. Thanks!!
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Thanks for making this Interpreting Correlations: Interactive Visualizations site - it's definitely a great help for this psych student! 😃
Zakaria Giunashvili, from Georgia ☕☕☕ (3) coffee-lar sotib olgan
brilliant simulations that can be effectively used in training
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Amazing illustrations, there is not enough coffee in the world for enthusiasts like you! Thanks!
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mikhail.saltychev@gmail.com ☕☕☕ (3) coffee-lar sotib olgan
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Thanks - this will help me to teach tomorrow!
SCCT/Psychology ☕☕☕ (3) coffee-lar sotib olgan
Keep the visualizations coming!
@elena_bolt ☕☕☕ (3) coffee-lar sotib olgan
Thank you so much for your work, Kristoffer. I use your visualizations to explain concepts to my tutoring students and they are a huge help.
A random user ☕☕☕ (3) coffee-lar sotib olgan
Thank you for making such useful and pretty tools. It not only helped me understand more about power, effect size, etc, but also made my quanti-method class more engaging and interesting. Thank you and wish you a great 2021!
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Used your vizualization in class today. Thanks!
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My students love these visualizations and so do I! Thanks for helping me make stats more intuitive.
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For a high school teacher of psychology, I would be lost without your visualizations. The ability to interact and manipulate allows students to get it in a very sticky manner. Thank you!!!
Chi ☕☕☕ (3) coffee-lar sotib olgan
You Cohen's d post really helped me explaining the interpretation to people who don't know stats! Thank you!
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Enjoy.
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Teaching stats to civil engineer undergrads (first time teaching for me, first time for most of them too) and grasping for some good explanations of hypothesis testing, power, and CI's. Love these interactive graphics!
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Thank you for using your stats and programming gifts in such a useful, generous manner. -Jess
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Thank you for sharing your visualization skills with the rest of us! I use them frequently when teaching intro stats.
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Eva ☕ (1) coffee sotib olgan
I've been learning about power analysis and effect sizes (trying to decide on effect sizes for my planned study to calculate sample size) and your Cohen's d interactive tool is incredibly useful for understanding the implications of different effect sizes!
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Thanks a lot!
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Thanks mate
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Your visualizations really help me understand the math.
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Really wonderful visuals, and such a fantastic and effective teaching tool. So many thanks!
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Thank you for this work!!
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Nice explanation and visual guide of Cohen's d
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thank you
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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!😍
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Homiylar
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Ko'proq vizualizatsiya
Statistical Power and Significance Testing
An interactive version of the traditional Type I and II error illustration.
Equivalence and Non-Inferiority Testing
Explore how superiority, non-inferiority, and equivalence testing relates to a confidence interval