compute_ds {MAd}

Computes Vector of Standardized Mean Differences

Description

Adds d (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_ds(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 values above.

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_dgs

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_ds(n.1, m.1, sd.1, n.2, m.2, sd.2, data = dfs)