Conversion from Cohen's d to Common Language Effect Size

Helper function to convert Cohen's d to common language effect size (or vice versa). The result is the probability of superiority for independent samples. It can be interpreted as the probability that a randomly selected observation from Group 1 exceeds a randomly selected observation from Group 2. The rationale is the same for paired-samples and one-sample designs, but the interpretation differs: For paired samples, it can be interpreted as the probability that the difference score (i.e., the score under Condition 1 minus the score under Condition 2) is greater than zero for a randomly selected individual. For a one-sample design, it can be interpreted as the probability that a randomly selected observation is greater than the reference value (e.g., 0).

Usage

d.to.cles(d, design = c("independent", "paired", "one.sample"), verbose = TRUE)

  cles.to.d(cles, design = c("independent", "paired", "one.sample"), verbose = TRUE)

Arguments

d: Cohen's d
design: character; one of the "independent", "paired", or "one.sample". The default is "independent".
cles: common language effect size.
verbose: logical; whether the output should be printed on the console. TRUE by default.

Value

d: Cohen's d
cles: common language effect size.

Examples


d.to.cles(0.20) # small
#>      cles         d 
#> 0.5562315 0.2000000 
d.to.cles(0.50) # medium
#>      cles         d 
#> 0.6381632 0.5000000 
d.to.cles(0.80) # large
#>      cles         d 
#> 0.7141962 0.8000000 

cles.to.d(0.5562315)
#>         d      cles 
#> 0.2000002 0.5562315 
cles.to.d(0.6381632)
#>         d      cles 
#> 0.5000000 0.6381632 
cles.to.d(0.7141962)
#>         d      cles 
#> 0.8000001 0.7141962