esc_beta: Compute effect size from Standardized Regression Coefficient
In esc: Effect Size Computation for Meta Analysis

Description Usage Arguments Value Note References Examples

Compute effect size from Standardized Regression Coefficient.

esc_beta(
  beta,
  sdy,
  grp1n,
  grp2n,
  es.type = c("d", "g", "or", "logit", "r", "f", "eta", "cox.or", "cox.log"),
  study = NULL
)

`beta`	The standardized beta coefficient.
`sdy`	The standard deviation of the dependent variable.
`grp1n`	Treatment group sample size.
`grp2n`	Control group sample size.
`es.type`	Type of effect size that should be returned. `"d"` returns standardized mean difference effect size `d` `"f"` returns effect size Cohen's `f` `"g"` returns adjusted standardized mean difference effect size Hedges' `g` `"or"` returns effect size as odds ratio `"cox.or"` returns effect size as Cox-odds ratio (see `convert_d2or` for details) `"logit"` returns effect size as log odds `"cox.log"` returns effect size as Cox-log odds (see `convert_d2logit` for details) `"r"` returns correlation effect size `r` `"eta"` returns effect size eta squared
`study`	Optional string with the study name. Using `combine_esc` or `as.data.frame` on `esc`-objects will add this as column in the returned data frame.

The effect size es, the standard error se, the variance of the effect size var, the lower and upper confidence limits ci.lo and ci.hi, the weight factor w and the total sample size totaln.

If es.type = "r", Fisher's transformation for the effect size r and their confidence intervals are also returned.

Lipsey MW, Wilson DB. 2001. Practical meta-analysis. Thousand Oaks, Calif: Sage Publications

Wilson DB. 2016. Formulas Used by the "Practical Meta-Analysis Effect Size Calculator". Unpublished manuscript: George Mason University