es_from_mean_change_pval function

Convert mean changes and standard deviations of two independent groups into standard effect size measures

es_from_mean_change_pval(
  mean_change_exp,
  mean_change_pval_exp,
  mean_change_nexp,
  mean_change_pval_nexp,
  r_pre_post_exp,
  r_pre_post_nexp,
  n_exp,
  n_nexp,
  smd_to_cor = "viechtbauer",
  reverse_mean_change
)

Arguments

• mean_change_exp: mean change of participants in the experimental/exposed group.
• mean_change_pval_exp: p-value of the mean change for participants in the experimental/exposed group.
• mean_change_nexp: mean change of participants in the non-experimental/non-exposed group.
• mean_change_pval_nexp: p-value of the mean change for participants in the non-experimental/non-exposed group.
• r_pre_post_exp: pre-post correlation in the experimental/exposed group
• r_pre_post_nexp: pre-post correlation in the non-experimental/non-exposed group
• 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_mean_change: 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	MD + D + G
converted effect size measure	OR + R + Z
required input data	See 'Section 14. Paired: mean change, and dispersion'
	https://metaconvert.org/input.html

Details

This function converts the mean change and associated p-values of two independent groups into a Cohen's d. The Cohen's d is then converted to other effect size measures.

To start, this function estimates the mean change standard errors from the p-values:

$$t\_exp <- qt(p = mean\_change\_pval\_exp / 2, df = n\_exp - 1, lower.tail = FALSE)$$ $$t\_nexp <- qt(p = mean\_change\_pval\_nexp / 2, df = n\_nexp - 1, lower.tail = FALSE)$$ $$mean\_change\_se\_exp <- |\frac{mean\_change\_exp}{t\_exp}|$$ $$mean\_change\_se\_nexp <- |\frac{mean\_change\_nexp}{t\_nexp}|$$

Then, this function simply internally calls the es_from_means_se_pre_post function but setting:

$$mean\_pre\_exp = mean\_change\_exp$$ $$mean\_pre\_se\_exp = mean\_change\_se\_exp$$ $$mean\_exp = 0$$ $$mean\_se\_exp = 0$$ $$mean\_pre\_nexp = mean\_change\_nexp$$ $$mean\_pre\_se\_nexp = mean\_change\_se\_nexp$$ $$mean\_nexp = 0$$ $$mean\_se\_nexp = 0$$

To know more about other calculations, see es_from_means_sd_pre_post function.

Examples

es_from_mean_change_pval(
  n_exp = 36, n_nexp = 35,
  mean_change_exp = 8.4, mean_change_pval_exp = 0.13,
  mean_change_nexp = 2.43, mean_change_pval_nexp = 0.61,
  r_pre_post_exp = 0.8, r_pre_post_nexp = 0.8
)

References

Bonett, S. B. (2008). Estimating effect sizes from pretest-posttest-control group designs. Organizational Research Methods, 11(2), 364–386. https://doi.org/10.1177/1094428106291059

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