Tolk Cohens d effektstørrelse

En interaktiv visualisering

Utviklet av Kristoffer Magnusson

Oversatt av Harald Groven

Del

Cohens d et mål på effektstørrelse som er utrolig populært i psykologifaget. Tolkinga av denne er derimot ikke helt enkel, og forskere benytter ofte generelle retningslinjer, som å inndele i lav (0.2), middels (0.5), og høy (0.8) verdi når de tolker effekten. I mange tilfeller er det likevel tvilsomt om den standardiserate gjennomsnittsverdiforskjellen (dvs. Cohens d) er mer tolkbar er den ustandardiserte forskjellen.

For å forenkle tolkningen presenteres Cohens d på flere forskjellige måter i denne visualiseringen: Visuelt overlapp, Cohen’s U3, probability of superiority, prosentuelt overlapp, og number needed to treat. Du kan også endre standardavviet samt se den ustandardiserte forskjellen.

Cohens d

Laster inn visualiseringen

Cohens U3

% Overlapp

Probability of Superiority

Number Needed to Treat

Forklart med klarspråk

Med en Cohens d0.80, kommer 78.8% av "treatment"-gruppa å være over gjennomsnittsverdien for "control"-gruppa (Cohens U3), 68.9% av de to gruppene til å være overlappende, og det er en 71.4% sjanse å tilfeldig utvalgt person fra "treatment"-gruppa kommer ha en høyere poengscore enn en tilfeldig utvalgt person fra "control"-gruppa (probability of superiority). For å få et mer positivt utfall i "treatment"-gruppa sammenlignet med "control"-gruppa behøver vi behandle 3.5 personer i gjennomsnitt. Dette innebærer at om det er 100 personer i hver gruppe, og om vi antar at 20 personer har et positivt utfall i "control"-gruppa, så kommer 20 + 28.3 personer i "treatment"-gruppa ha positive utfall.1

1Verdiene er gjennomsnitt, og det antas at 20 (CER) i "control"-gruppa har et "positivt utfall", dvs. deres utfall havner under en terskelverdi. Du kan endra på denne verdien i kontrollpanelet. Mer informasjon om utregningene finnes i formel-delen i FAQen.

Written by Kristoffer Magnusson, a researcher in clinical psychology. You should follow him on Bluesky or on Twitter.

FAQ

Endre Cohens d

Bruk skyvebryteren (slider’en) for å endre Cohens d, eller åpne kontrollpanelet og endre parameterne. Du kan også endre ved hjelp av piltastene på tastaturet.

Innstillinger

Du kan endre følgende innstillinger ved å klikke på innstillingsikonene til høyre for skyvebryteren.

  • Parametrar
    • Gjennomsnitt 1
    • Gjennomsnitt 2
    • SD
    • Control group event rate (CER)
  • Etiketter
    • X-akse
    • Fordeling 1
    • Fordeling 2
  • Skjutreglagets inställningar
    • Maksverdi
    • Stegstørrelse: Styrer størrelse på stegene i skyvebryteren

Lagre innstillinger

Innstillingene kan lagres i nettleserens localStorage, og kommer til å vedvare ved fremtidige besøk.

Panorer og endre skalering

Du kan panorere langs x-aksen ved å klikke og dra på visualiseringen. Dobbeltklikk på visualiseringen for å sentrere og skalere.

Offline-användning

Denne nettsiden bruker en service worker og vil fungere selv når den er uten nett-tilgang.

Cohens d

Cohens d er enkelt sagt den standardiserte forskjellen i gjennomsnitt,

,

der er populasjosparameteret for Cohens d. Der det antas at , dvs., homogene populationsvarianser, og er respektive populasjonens gjennomsnittsverdi.

Cohens U3

Cohen (1977) definerte U3 som et mål på ikke-overlapp, der vi “tar andelen av A-populasjonen som blir overgått av den øvre halvdelen av Β-populasjonen”. Cohens d kan konverteres til Cohens U3 ved hjelp av følgende formel

,

der er standardnormalfordelingens kumulative fordelningsfunksjon, og populationsverdien for Cohens d.

Overlapp

Kalles generelt for overlapping coefficient (OVL). Cohens d kan konverteres til OVL ved hjelp av følgende formel (Reiser and Faraggi, 1999),

der standardnormalfordelingens kumulative fordelningsfunksjon, og populationsverdien for Cohens d.

Probability of superiority

Dette er en effektstørrelse med mange navn: common language effect size (CL), Area under the receiver operating characteristics (AUROC) eller bare A for sin ikke-parametriske versjon (Ruscio & Mullen, 2012). Det er tenkt som en mer intuitiv effektstørrelse for personer uten statistikkutdanning. Effektstørrelsen gir sannsynligheten for at en tilfeldig utvalgt person fra tiltaksgruppa har større score enn en tilfeldig utvalgt person fra kontrollgruppa. Cohens d kan konverteres til CL ved hjelp av følgende formel (Ruscio, 2008),

der standardnormalfordelingens kumulative fordelningsfunksjon, og populasjonsverdien for Cohens d.

Number Needed to Treat

NNT er antallet pasienter som må behandles for å få 1 flere positive utfall i tiltaksgruppa sammenlignet med kontrollgruppen. Furukawa og Leucht (2011) gav følgende formel for å regne om Cohens d til NNT,

hvor standardnormalfordelingens kumulative fordelningsfunksjon og dens inverse, CER er kontrollsgruppa hendelsesfrekvens og populationsverdien for Cohens d. OBS. CER er 20 % i visualiseringen. Du kan endre på dette ved å klikke på innstillingsikonet til høyre for skjutreglaget. Definisjonen av en “hendelse” eller “respons” er godtyckligt, og skulle kunne definieres som andelen pasienter i remission, for eksempel de som er under en viss terskelverdi med en standardiserat formel. Det er mulig å konvertere Cohens d til en versjon av NNT som ignorerer kontrollgruppas hendelsesfrekvens. Om du vil lese mer om det kan du lese Furukawa & Leucht (2011), som presenterer overbevisende argument for hvorfor det kompliserer tolkninga av NNT.

R-programmeringskode for å beregne NNT fra Cohens d

Fordi mange har spurt om R-kode for formelen ovenfor så viser jeg den her:

Referanser

  • 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.

Referer til denne siden i henhold til din favoritt-siteringsstil. Referansen nedenfor blir automatisk generert med korrekt informasjon.

APA 7

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

BibTex

Meld gjerne inn bugs eller send inn et forskag ved å opprette sak på GitHub, hvis du vil stille et spørsmål GitHub discussions

Nei, det er ingen fare! Denne tjenesten kjøres i din nettleser og og serveren trenger bare sende ut noen små filer.

Dette er bevisst. Du kan lese mer om hva jeg har tenkt i blogginnlegget: Where Cohen went wrong – the proportion of overlap between two normal distributions

Ja, absolutt! Jeg har ikke oppfunnet hvordan tegne to overlappende gauss-kurver. Visualiseringen på den her siden er public domain, noe som medfører at du kan “kopiere, endre, distribuere og fremføre verket, selv for kommersielle formål, uten å spørre om tillatelse.” (see [CC0 1.0 universell Public domain-dedikation](https://creativecommons.org/publicdomain/zero/1.0/deed.no)). Kreditering er ikke et krav, men det vil satt pris på!

Kildekoden for visualiseringen har en MIT-lisens, og tekstene er CC-BY 4.0.

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

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Melinda Rice kjøpte ☕ (1) kaffe

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

@tmoldwin kjøpte ☕ (1) kaffe

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 kjøpte ☕ (1) kaffe

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 kjøpte ☕ (1) kaffe

Incredible visualizations and the best power analysis software on R.

Cameron Proctor kjøpte ☕ (1) kaffe

Great website!

Someone kjøpte ☕ (1) kaffe

Hanah Chapman kjøpte ☕ (1) kaffe

Thank you for this work!!

Someone kjøpte ☕ (1) kaffe

Jayme kjøpte ☕ (1) kaffe

Nice explanation and visual guide of Cohen's d

Bart Comly Boyce kjøpte ☕ (1) kaffe

thank you

Dr. Mitchell Earleywine kjøpte ☕ (1) kaffe

This site is superb!

Florent kjøpte ☕ (1) kaffe

Zampeta kjøpte ☕ (1) kaffe

thank you for sharing your work. 

Mila kjøpte ☕ (1) kaffe

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

Deb kjøpte ☕ (1) kaffe

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 kjøpte ☕ (1) kaffe

@exerpsysing kjøpte ☕ (1) kaffe

Much thanks! Visualizations are key to my learning style! 

Someone kjøpte ☕ (1) kaffe

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Flere visualiseringer

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.