esc_mean_sd {esc} | R Documentation |
Compute effect size from mean and either group-based standard deviations or full sample standard deviation.
esc_mean_sd( grp1m, grp1sd, grp1n, grp2m, grp2sd, grp2n, totalsd, r, es.type = c("d", "g", "or", "logit", "r", "cox.or", "cox.log"), study = NULL )
grp1m |
The mean of the first group. |
grp1sd |
The standard deviation of the first group. |
grp1n |
The sample size of the first group. |
grp2m |
The mean of the second group. |
grp2sd |
The standard deviation of the second group. |
grp2n |
The sample size of the second group. |
totalsd |
The full sample standard deviation. Either |
r |
Correlation for within-subject designs (paired samples, repeated measures). |
es.type |
Type of effect size that should be returned.
|
study |
Optional string with the study name. Using |
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 Unpublished manuscript: George Mason University
# with standard deviations for each group esc_mean_sd( grp1m = 7, grp1sd = 2, grp1n = 50, grp2m = 9, grp2sd = 3, grp2n = 60, es.type = "logit" ) # effect-size d, within-subjects design esc_mean_sd( grp1m = 7, grp1sd = 2, grp1n = 50, grp2m = 9, grp2sd = 3, grp2n = 60, r = .7 ) # with full sample standard deviations esc_mean_sd(grp1m = 7, grp1n = 50, grp2m = 9, grp2n = 60, totalsd = 4)