compute_ds | R Documentation |
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).
compute_ds(n.1, m.1, sd.1 , n.2, m.2, sd.2 , data, denom = "pooled.sd")
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 |
|
denom |
Value in the denominator to standardize the means by. |
d |
Standardized mean difference. |
var.d |
Variance of d. |
se.d |
Standard error of d. |
AC Del Re & William T. Hoyt
Maintainer: AC Del Re acdelre@gmail.com
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.
compute_ds
,
compute_dgs
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)
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