Compute the effect size for t-test. T-test conventional effect sizes, proposed by Cohen, are: 0.2 (small effect), 0.5 (moderate effect) and 0.8 (large effect).
d is calculated as the difference between means or mean minus
mu divided by the estimated standardized deviation.
For independent samples t-test, there are two possibilities implemented. If the t-test did not make a homogeneity of variance assumption, (the Welch test), the variance term will mirror the Welch test, otherwise a pooled estimate is used.
If a paired samples t-test was requested, then effect size desired is based on the standard deviation of the differences.
It can also returns confidence intervals by bootstap.
cohens_d( data, formula, comparisons = NULL, ref.group = NULL, paired = FALSE, mu = 0, var.equal = FALSE, hedges.correction = FALSE, ci = FALSE, conf.level = 0.95, ci.type = "perc", nboot = 1000 )
a data.frame containing the variables in the formula.
a formula of the form
A list of length-2 vectors specifying the groups of
interest to be compared. For example to compare groups "A" vs "B" and "B" vs
"C", the argument is as follow:
a character string specifying the reference group. If specified, for a given grouping variable, each of the group levels will be compared to the reference group (i.e. control group).
a logical indicating whether you want a paired test.
theoretical mean, use for one-sample t-test. Default is 0.
a logical variable indicating whether to treat the two variances as being equal. If TRUE then the pooled variance is used to estimate the variance otherwise the Welch (or Satterthwaite) approximation to the degrees of freedom is used. Used only for unpaired or independent samples test.
logical indicating whether apply the Hedges
correction by multiplying the usual value of Cohen's d by
If TRUE, returns confidence intervals by bootstrap. May be slow.
The level for the confidence interval.
The type of confidence interval to use. Can be any of "norm",
"basic", "perc", or "bca". Passed to
The number of replications to use for bootstrap.
return a data frame with some of the following columns:
.y.: the y variable used in the test.
group1,group2: the compared groups in the pairwise tests.
n,n1,n2: Sample counts.
effsize: estimate of the effect
magnitude: magnitude of effect size.
conf.low,conf.high: lower and upper bound of the effect size
Quantification of the effect size magnitude is performed using the
thresholds defined 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".
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). New York:Academic Press.
Cohen, J. (1992). A power primer. Psychological Bulletin, 112, 155-159.
Hedges, Larry & Olkin, Ingram. (1985). Statistical Methods in Meta-Analysis. 10.2307/1164953.
Navarro, Daniel. 2015. Learning Statistics with R: A Tutorial for Psychology Students and Other Beginners (Version 0.5).
# One-sample t test effect size ToothGrowth %>% cohens_d(len ~ 1, mu = 0)#> # A tibble: 1 x 6 #> .y. group1 group2 effsize n magnitude #> * <chr> <chr> <chr> <dbl> <int> <ord> #> 1 len 1 null model 2.46 60 large# Two indepedent samples t-test effect size ToothGrowth %>% cohens_d(len ~ supp, var.equal = TRUE)#> # A tibble: 1 x 7 #> .y. group1 group2 effsize n1 n2 magnitude #> * <chr> <chr> <chr> <dbl> <int> <int> <ord> #> 1 len OJ VC 0.495 30 30 small# Paired samples effect size df <- data.frame( id = 1:5, pre = c(110, 122, 101, 120, 140), post = c(150, 160, 110, 140, 155) ) df <- df %>% gather(key = "treatment", value = "value", -id) head(df)#> id treatment value #> 1 1 pre 110 #> 2 2 pre 122 #> 3 3 pre 101 #> 4 4 pre 120 #> 5 5 pre 140 #> 6 1 post 150df %>% cohens_d(value ~ treatment, paired = TRUE)#> # A tibble: 1 x 7 #> .y. group1 group2 effsize n1 n2 magnitude #> * <chr> <chr> <chr> <dbl> <int> <int> <ord> #> 1 value post pre 1.75 5 5 large