Computes the Cohen's d and Hedges'g effect size statistics.
cohen.d(d, ...) ## S3 method for class 'formula': cohen.d((formula,data=list(),...)) ## S3 method for class 'default': cohen.d((d,f,pooled=TRUE,paired=FALSE, na.rm=FALSE, hedges.correction=FALSE, conf.level=0.95,noncentral=FALSE, ...))
- a numeric vector giving either the data values (if
fis a factor) or the treatment group values (if
fis a numeric vector)
- either a factor with two levels or a numeric vector of values
- a logical indicating whether compute pooled standard deviation or the whole sample standard deviation
- a logical indicating whether to consider the values as paired
- logical indicating whether
NAs should be removed before computation; if
paired==TRUEthen all incomplete pairs are removed.
- logical indicating whether apply the Hedges correction
- confidence level of the confidence interval
- a formula of the form
y ~ f, where
yis a numeric variable giving the data values and
fa factor with two levels giving the corresponding groups
- an optional matrix or data frame containing the variables in the formula
formula. By default the variables are taken from
- logical indicating whether to use non-central t distributions for computing the confidence interval.
- further arguments to be passed to or from methods.
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
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
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:
- the statistics estimate
- the confidence interval of the statistic
- the estimated variance of the statistic
- the confidence level used to compute the confidence interval
- a qualitative assessment of the magnitude of effect size
- the method used for computing the effect size, either
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.
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)
Documentation reproduced from package effsize, version 0.6.2. License: GPL-2