esc (version 0.5.1)

esc_rpb: Compute effect size from Point-Biserial Correlation

Description

Compute effect size from Point-Biserial Correlation.

Usage

esc_rpb(
  r,
  p,
  totaln,
  grp1n,
  grp2n,
  es.type = c("d", "g", "or", "logit", "f", "eta", "cox.or", "cox.log"),
  study = NULL
)

Arguments

r

The point-biserial r-value. One of r or p must be specified.

p

The p-value of the point-biserial correlation. One of r or p must be specified.

totaln

Total sample size. Either totaln, or grp1n and grp2n must be specified.

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.

Value

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.

References

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 Unpublished manuscript: George Mason University

Examples

# NOT RUN {
# unequal sample size
esc_rpb(r = .3, grp1n = 100, grp2n = 150)

# equal sample size
esc_rpb(r = .3, totaln = 200)

# unequal sample size, with p-value
esc_rpb(p = 0.03, grp1n = 100, grp2n = 150)

# equal sample size, with p-value
esc_rpb(p = 0.03, totaln = 200)

# }