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)