Convert effect sizes

Source: R/hedges_g.R

Convert between different effect sized.

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)

Arguments

d, r, f, eta, or, logit

A scalar or vector with effect size(s).
totaln

A vector of total sample size(s).

Value

The requested effect size.

References

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 Unpublished manuscript: George Mason University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

Examples

# convert from d to Hedges' g or odds ratio
hedges_g(d = 0.75, totaln = 50)
#> [1] 0.7382199
odds_ratio(d = .3)
#> [1] 1.723126

# convert from odds ratio to eta_squared
eta_squared(or = 2.3)
#> [1] 0.08707408

# convert from f or r to d
cohens_d(f = .3)
#> [1] 0.6
cohens_d(r = .25)
#> [1] 0.5163978

# functions are vectorized
hedges_g(c(0.75, .3), c(50, 70))
#> [1] 0.7382199 0.2966790
cohens_f(r = c(.1, .2, .3))
#> [1] 0.1005038 0.2041241 0.3144855