Description Usage Arguments Value References Examples
Convert between different effect sized.
1 2 3 4 5 6 7 8 9 10 11 12 13 | hedges_g(d, totaln)
eta_squared(d, r, f, or, logit)
cohens_f(d, r, eta, or, logit)
cohens_d(f, r, eta, or, logit)
pearsons_r(d, eta, f, or, logit)
log_odds(d, eta, f, or, r)
odds_ratio(d, eta, f, logit, r)
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d, r, f, eta, or, logit |
A scalar or vector with effect size(s). |
totaln |
A vector of total sample size(s). |
The requested effect size.
Lipsey MW, Wilson DB. 2001. Practical meta-analysis. Thousand Oaks, Calif: Sage Publications
Wilson DB. 2016. Formulas Used by the "Practical Meta-Analysis Effect Size Calculator". Unpublished manuscript: George Mason University
Hedges LV. 1981. Distribution theory for Glass's estimator of effect size and related estimators. Journal of Educational Statistics 6: 107–128.
Borenstein M, Hedges LV, Higgins JPT, Rothstein HR. 2009. Introduction to Meta-Analysis. Chichester, West Sussex, UK: Wiley
Cohen J. 1988. Statistical Power Analysis for the Behavioral Sciences. 2nd ed. Hillsdale, NJ: Erlbaum
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | # convert from d to Hedges' g or odds ratio
hedges_g(d = 0.75, totaln = 50)
odds_ratio(d = .3)
# convert from odds ratio to eta_squared
eta_squared(or = 2.3)
# convert from f or r to d
cohens_d(f = .3)
cohens_d(r = .25)
# functions are vectorized
hedges_g(c(0.75, .3), c(50, 70))
cohens_f(r = c(.1, .2, .3))
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