| cohen.d {effsize} | 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).
When paired is set, the effect size is computed using the
approach suggested in (Gibbons et al. 1993).
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 variance of the d is computed using the conversion formula reported 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 intervals that are based on central and noncentral distributions. Educational and Psychological Measurement, 61, 633-649.
Gibbons, R. D., Hedeker, D. R., & Davis, J. M. (1993). Estimation of effect size from a series of experiments involving paired comparisons. Journal of Educational Statistics, 18, 271-279.
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)