Effect Size Statistics — cohens

Compute the Cohen's d and Hedges' g effect size statistics.

Usage

cohens_d(
  x,
  y = NULL,
  var.type = "unequal",
  hedges.correction = FALSE,
  hedges.approx = TRUE
)

Arguments

x: Vector of values for the first group (if y specified) or a data.frame with the first column the values and the second column the group names (no y). If using a data.frame, ensure only two groups are given.
y: Vector of values for the second group (if x is a vector), otherwise NULL if x is a data.frame.
var.type: String indicating the variance assumption. Options include 'unequal' (default), 'pooled', 'x', or 'y'.
hedges.correction: Boolean indicating if Hedges correction should be applied
hedges.approx: Boolean if Hedges correction should be approximated or computed using the gamma function

Value

Numeric value for the effect size

References

Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Routledge.

Hedges, Larry & Olkin, Ingram. (1985). Statistical Methods in Meta-Analysis. 10.2307/1164953.

Theriault, R., (2023). rempsyc: Convenience functions for psychology. Journal of Open Source Software, 8(87), 5466.

Examples

x <- rnorm(10, mean = 1, sd = 1)
y <- rnorm(40, mean = 3, sd = 10)
cohens_d(x, y, var.type = "unequal")
#> [1] 0.1905781
cohens_d(x, y, var.type = "pooled", hedges.correction = TRUE)
#> [1] 0.1475207