es_from_beta_unstd function

Convert an unstandardized regression coefficient and the standard deviation of the dependent variable into several effect size measures

es_from_beta_unstd(
  beta_unstd,
  sd_dv,
  n_exp,
  n_nexp,
  smd_to_cor = "viechtbauer",
  reverse_beta_unstd
)

Arguments

• beta_unstd: an unstandardized regression coefficient value (binary predictor, no other covariables in the model)
• sd_dv: standard deviation of the dependent variable
• n_exp: number of participants in the experimental/exposed group.
• n_nexp: number of participants in the non-experimental/non-exposed group.
• smd_to_cor: formula used to convert the cohen_d value into a coefficient correlation (see details).
• reverse_beta_unstd: a logical value indicating whether the direction of the generated effect sizes should be flipped.

Returns

This function estimates and converts between several effect size measures.

natural effect size measure	D + G
converted effect size measure	OR + R + Z
required input data	See 'Section 13. (Un-)Standardized regression coefficient'
	https://metaconvert.org/input.html

Details

This function estimates a Cohen's d (D) and Hedges' g (G) from an unstandardized linear regression coefficient (coming from a model with only one binary predictor), and the standard deviation of the dependent variable. Odds ratio (OR) and correlation coefficients (R/Z) are then converted from the Cohen's d.

The formula used to obtain the Cohen's d is :

$$N = n\_exp + n\_nexp$$ $$sd\_pooled = \sqrt{\frac{sd\_dv^2 * (N - 1) - unstd\_beta^2 * \frac{n\_exp * n\_nexp}{N}}{N - 2}}$$ $$cohen\_d = \frac{unstd\_beta}{sd\_pooled}$$

To estimate other effect size measures , calculations of the es_from_cohen_d() are applied.

Examples

es_from_beta_unstd(beta_unstd = 2.1, sd_dv = 0.98, n_exp = 20, n_nexp = 22)

References

Lipsey, M. W., & Wilson, D. B. (2001). Practical meta-analysis. Sage Publications, Inc.