View source: R/es_from_REGRESSION.R
es_from_beta_unstd | R Documentation |
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
)
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 |
reverse_beta_unstd |
a logical value indicating whether the direction of the generated effect sizes should be flipped. |
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.
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 | |
Lipsey, M. W., & Wilson, D. B. (2001). Practical meta-analysis. Sage Publications, Inc.
es_from_beta_unstd(beta_unstd = 2.1, sd_dv = 0.98, n_exp = 20, n_nexp = 22)
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