compute_dgs: Computes Vector of Standardized Mean Differences

In MAd: Meta-Analysis with Mean Differences

Computes Vector of Standardized Mean Differences

Description

Adds d & g (standardized mean difference) to a data.frame. Required inputs are: n.1 (sample size of group one), m.1 (raw post-mean value of group one), sd.1 (standard deviation of group one), n.2 (sample size of group two), m.2 (raw post-mean value of group two), sd.2 (standard deviation of group two).

Usage

compute_dgs(n.1, m.1, sd.1 , n.2, m.2, sd.2, data, denom = "pooled.sd")

Arguments

n.1	sample size of group one.
m.1	raw post-mean value of group one.
sd.1	standard deviation of group one.
n.2	sample size of group two.
m.2	raw post-mean value of group two.
sd.2	standard deviation of group two.
data	data.frame with above values
denom	Value in the denominator to standardize the means by. pooled.sd will pool together both groups in deriving d. control.sd uses the standard deviation of group two (typically the control condition) to calculate d.

Value

d	Standardized mean difference.
var.d	Variance of d.
se.d	Standard error of d.

Author(s)

AC Del Re & William T. Hoyt

Maintainer: AC Del Re [e-mail]

References

Borenstein (2009). Effect sizes for continuous data. In H. Cooper, L. V. Hedges, & J. C. Valentine (Eds.), The handbook of research synthesis and meta analysis (pp. 279-293). New York: Russell Sage Foundation.

See Also

compute_ds, compute_gs

Examples

id <- c(1:20)
n.1 <- c(10,20,13,22,28,12,12,36,19,12,36,75,33,121,37,14,40,16,14,20)
n.2 <- c(11,22,10,20,25,12,12,36,19,11,34,75,33,120,37,14,40,16,10,21)
m.1 <- c(.68,.56,.23,.64,.49,.4,1.49,.53,.58,1.18,.11,1.27,.26,.40,.49,
         .51,.40,.34,.42,.66)
m.2 <- c(.38,.36,.23,.34,.29,.4,1.9,.33,.28,1.1,.111,.27,.21,.140,.149,
         .51,.140,.134,.42,.16)
sd.1 <- c(.28,.26,.23,.44,.49,.34,.39,.33,.58,.38,.31,.27,.26,.40,
          .49,.51,.140,.134,.42,.46)
sd.2 <- c(.28,.26,.23,.44,.49,.44,.39,.33,.58,.38,.51,.27,.26,.40,
          .49,.51,.140,.134,.142,.36)
mod1 <- c(1,2,3,4,1,2,8,7,5,3,9,7,5,4,3,2,3,5,7,1)
mod2 <- factor(c(rep(c(1,2,3,4),5)))
dfs <- data.frame(id, n.1,m.1, sd.1, n.2, m.2, sd.2, mod1, mod2)

# Example
compute_dgs(n.1, m.1, sd.1, n.2, m.2, sd.2, data = dfs)

MAd documentation built on Aug. 7, 2022, 1:05 a.m.