| cohen.d | R Documentation |
Computes the Cohen's d and Hedges'g effect size statistics.
cohen.d(d, ...)
## S3 method for class 'formula'
cohen.d(formula,data=list(),...)
## Default S3 method:
cohen.d(d,f,pooled=TRUE,paired=FALSE,
na.rm=FALSE, hedges.correction=FALSE,
conf.level=0.95,noncentral=FALSE, ...)
d |
a numeric vector giving either the data values (if |
f |
either a factor with two levels or a numeric vector of values |
pooled |
a logical indicating whether compute pooled standard deviation or the whole sample standard deviation |
paired |
a logical indicating whether to consider the values as paired |
na.rm |
logical indicating whether |
hedges.correction |
logical indicating whether apply the Hedges correction |
conf.level |
confidence level of the confidence interval |
formula |
a formula of the form |
data |
an optional matrix or data frame containing the variables in the formula |
noncentral |
logical indicating whether to use non-central t distributions for computing the confidence interval. |
... |
further arguments to be passed to or from methods. |
When f in the default version is a factor or a character, it must have two values and it identifies the two groups to be compared. Otherwise (e.g. f is numeric), it is considered as a sample to be compare to d.
In the formula version, if f is expected to be a factor, if that is not the case it is coherced to a factor and a warning is issued.
The function computes the value of Cohen's d statistics (Cohen 1988).
If required (hedges.correction==TRUE) the Hedges g statistics is computed instead (Hedges and Holkin, 1985).
The computation of the CI requires the use of non-central Student-t distributions that are used when noncentral==TRUE; otherwise a central distribution is used.
Also a quantification of the effect size magnitude is performed using the thresholds define in Cohen (1992).
The magnitude is assessed using the thresholds provided in (Cohen 1992), i.e. |d|<0.2 "negligible", |d|<0.5 "small", |d|<0.8 "medium", otherwise "large"
The variace of the d is computed using the conversion formula reportead at page 238 of Cooper et al. (2009):
((n1+n2)/(n1*n2) + .5*d^2/df) * ((n1+n2)/df)
A list of class effsize containing the following components:
estimate |
the statistics estimate |
conf.int |
the confidence interval of the statistic |
var |
the estimated variance of the statistic |
conf.level |
the confidence level used to compute the confidence interval |
magnitude |
a qualitative assessment of the magnitude of effect size |
method |
the method used for computing the effect size, either |
Marco Torchiano http://softeng.polito.it/torchiano/
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). New York:Academic Press.
Hedges, L. V. & Olkin, I. (1985). Statistical methods for meta-analysis. Orlando, FL: Academic Press.
Cohen, J. (1992). A power primer. Psychological Bulletin, 112, 155-159.
The Handbook of Research Synthesis and Meta-Analysis (Cooper, Hedges, & Valentine, 2009)
David C. Howell (2010). Confidence Intervals on Effect Size. Available at: https://www.uvm.edu/%7Edhowell/methods7/Supplements/Confidence%20Intervals%20on%20Effect%20Size.pdf
Cumming, G.; Finch, S. (2001). A primer on the understanding, use, and calculation of confidence intervalsthat are based on central and noncentral distributions. Educational and Psychological Measurement, 61, 633-649.
cliff.delta, VD.A, print.effsize
treatment = rnorm(100,mean=10)
control = rnorm(100,mean=12)
d = (c(treatment,control))
f = rep(c("Treatment","Control"),each=100)
## compute Cohen's d
## treatment and control
cohen.d(treatment,control)
## data and factor
cohen.d(d,f)
## formula interface
cohen.d(d ~ f)
## compute Hedges' g
cohen.d(d,f,hedges.correction=TRUE)