cliff.delta: Cliff's Delta effect size for ordinal variables

In effsize: Efficient Effect Size Computation

Description

Computes the Cliff's Delta effect size for ordinal variables with the related confidence interval using efficient algorithms.

Usage

cliff.delta(d, ... )

## S3 method for class 'formula'
cliff.delta(formula, data=list() ,conf.level=.95, 
                                use.unbiased=TRUE, use.normal=FALSE, 
                                return.dm=FALSE, ...)

## Default S3 method:
cliff.delta(d, f, conf.level=.95, 
                         use.unbiased=TRUE, use.normal=FALSE, 
                         return.dm=FALSE, ...)

Arguments

d	a numeric vector giving either the data values (if f is a factor) or the treatment group values (if f is a numeric vector)
f	either a factor with two levels or a numeric vector of values (see Detials)
conf.level	confidence level of the confidence interval
use.unbiased	a logical indicating whether to compute the delta's variance using the "unbiased" estimate formula or the "consistent" estimate
use.normal	logical indicating whether to use the normal or Student-t distribution for the confidence interval estimation
return.dm	logical indicating whether to return the dominance matrix. Warning: the explicit computation of the dominance uses a sub-optimal algorithm both in terms of memory and time
formula	a formula of the form y ~ f, where y is a numeric variable giving the data values and f a factor with two levels giving the corresponding group
data	an optional matrix or data frame containing the variables in the formula formula. By default the variables are taken from environment(formula).
...	further arguments to be passed to or from methods.

Details

Uses the original formula reported in (Cliff 1996).

If the dominance matrix is required i.e. return.dm=TRUE) the full matrix is computed thus using the naive algorithm. Otherwise, if treatment and control are factors then the optimized linear complexity algorithm is used, otherwise the RLE algorithm (with complexity n log n) is used.

Value

A list of class effsize containing the following components:

estimate	the Cliff's delta estimate
conf.int	the confidence interval of the delta
var	the estimated variance of the delta
conf.level	the confidence level used to compute the confidence interval
dm	the dominance matrix used for computation, only if return.dm is TRUE
magnitude	a qualitative assessment of the magnitude of effect size
method	the method used for computing the effect size, always "Cliff's Delta"
variance.estimation	the method used to compute the delta variance estimation, either "unbiased" or "consistent"
CI.distribution	the distribution used to compute the confidence interval, either "Normal" or "Student-t"

The magnitude is assessed using the thresholds provided in (Romano 2006), i.e. |d|<0.147 "negligible", |d|<0.33 "small", |d|<0.474 "medium", otherwise "large"

Author(s)

Marco Torchiano http://softeng.polito.it/torchiano/

References

Norman Cliff (1996). Ordinal methods for behavioral data analysis. Routledge.

J. Romano, J. D. Kromrey, J. Coraggio, J. Skowronek, Appropriate statistics for ordinal level data: Should we really be using t-test and cohen's d for evaluating group differences on the NSSE and other surveys?, in: Annual meeting of the Florida Association of Institutional Research, 2006.

K.Y. Hogarty and J.D.Kromrey (1999). Using SAS to Calculate Tests of Cliff's Delta. Proceedings of the Twenty-Foursth Annual SAS User Group International Conference, Miami Beach, Florida, p 238. Available at: https://support.sas.com/resources/papers/proceedings/proceedings/sugi24/Posters/p238-24.pdf

See Also

cohen.d, print.effsize

Examples

## Example data from Hogarty and Kromrey (1999)
treatment <- c(10,10,20,20,20,30,30,30,40,50)
control <- c(10,20,30,40,40,50)
res = cliff.delta(treatment,control,return.dm=TRUE)
print(res)
print(res$dm)

Example output

Cliff's Delta

delta estimate: -0.25 (small)
95 percent confidence interval:
       inf        sup 
-0.7265846  0.3890062 
   10 20 30 40 40 50
10  0 -1 -1 -1 -1 -1
10  0 -1 -1 -1 -1 -1
20  1  0 -1 -1 -1 -1
20  1  0 -1 -1 -1 -1
20  1  0 -1 -1 -1 -1
30  1  1  0 -1 -1 -1
30  1  1  0 -1 -1 -1
30  1  1  0 -1 -1 -1
40  1  1  1  0  0 -1
50  1  1  1  1  1  0

effsize documentation built on Oct. 23, 2020, 5:15 p.m.