es_from_cohen_d_adj function

Convert an adjusted Cohen's d value to several effect size measures

es_from_cohen_d_adj(
  cohen_d_adj,
  n_cov_ancova,
  cov_outcome_r,
  n_exp,
  n_nexp,
  smd_to_cor = "viechtbauer",
  reverse_d
)

Arguments

• cohen_d_adj: Adjusted Cohen's d (i.e., standardized mean difference) value.
• n_cov_ancova: number of covariates
• cov_outcome_r: covariate-outcome correlation (in case of multiple covariates, the multiple correlation)
• 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_d: a logical value indicating whether the direction of 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 1. Cohen's d or Hedges' g'
	https://metaconvert.org/input.html

Details

This function estimates the standard error of an adjusted Cohen's d value and Hedges' g (G), and converts an odds ratio (OR) and correlation coefficients (R/Z).

To estimate the standard error of Cohen's d , the following formula is used (table 12.3 in Cooper):

$$d\_se = \sqrt{\frac{n\_exp+n\_nexp}{n\_exp*n\_nexp} * (1 - cov\_outcome\_r^2) + \frac{cohen\_d\_adj^2}{2*(n\_exp+n\_nexp)}}$$

To estimate other effect size measures , calculations of the es_from_cohen_d() function are used (with the exception of the degree of freedom that is estimated as df = n_exp + n_nexp - 2 - n_cov_ancova).

Examples

es_from_cohen_d_adj(cohen_d_adj = 1, n_cov_ancova = 4, cov_outcome_r = .30, n_exp = 20, n_nexp = 20)

References

Cooper, H., Hedges, L.V., & Valentine, J.C. (Eds.). (2019). The handbook of research synthesis and meta-analysis. Russell Sage Foundation.