Compute effect size indices for standardized differences: Cohen's d, Hedges' g and Glass<U+2019>s delta. (This function returns the population estimate.)
Both Cohen's d and Hedges' g are the estimated the standardized difference between the means of two populations. Hedges' g provides a bias correction to Cohen's d for small sample sizes. For sample sizes > 20, the results for both statistics are roughly equivalent. Glass<U+2019>s delta is appropriate when the standard deviations are significantly different between the populations, as it uses only the second group's standard deviation.
cohens_d(
x,
y = NULL,
data = NULL,
pooled_sd = TRUE,
paired = FALSE,
ci = 0.95,
correction
)hedges_g(
x,
y = NULL,
data = NULL,
correction = 1,
pooled_sd = TRUE,
paired = FALSE,
ci = 0.95
)
glass_delta(x, y = NULL, data = NULL, ci = 0.95, correction)
# S3 method for effectsize_difference
print(x, digits = 2, append_CL = FALSE, ...)
A formula, a numeric vector, or a character name of one in data
.
(For print()
the result of one of the standardized difference functions.)
A numeric vector, a grouping (character / factor) vector, a or a
character name of one in data
. Ignored if x
is a formula.
An optional data frame containing the variables.
If TRUE
(default), a sd_pooled()
is used (assuming equal
variance). Else the mean SD from both groups is used instead.
If TRUE
, the values of x
and y
are considered as paired.
This produces an effect size that is equivalent to the one-sample effect
size on x - y
.
Confidence Interval (CI) level
Type of small sample bias correction to apply to produce
Hedges' g. Can be 1
for Hedges and Olkin's original correction
(default) or 2
for Hunter and Schmidt's correction (see McGrath & Meyer,
2006).
Number of significant digits.
Should the Common Language Effect Sizes be printed as well?
Not applicable to Glass' Delta (See d_to_common_language()
)
Not used.
A data frame with the effect size(s) and confidence interval(s).
Confidence intervals are estimated using the Noncentrality parameter method;
These methods searches for a the best non-central parameters (ncp
s) of the
noncentral t-, F- or Chi-squared distribution for the desired
tail-probabilities, and then convert these ncp
s to the corresponding effect
sizes.
Cohen, J. (2013). Statistical power analysis for the behavioral sciences. Routledge.
Hedges, L. V. & Olkin, I. (1985). Statistical methods for meta-analysis. Orlando, FL: Academic Press.
Hunter, J. E., & Schmidt, F. L. (2004). Methods of meta-analysis: Correcting error and bias in research findings. Sage.
McGrath, R. E., & Meyer, G. J. (2006). When effect sizes disagree: the case of r and d. Psychological methods, 11(4), 386.
d_to_common_language()
sd_pooled()
Other effect size indices:
effectsize()
,
eta_squared()
,
phi()
,
standardize_parameters()
# NOT RUN {
cohens_d(iris$Sepal.Length, iris$Sepal.Width)
hedges_g("Sepal.Length", "Sepal.Width", data = iris)
cohens_d(mpg ~ am, data = mtcars)
cohens_d(mpg ~ am, data = mtcars, pooled_sd = FALSE)
hedges_g(mpg ~ am, data = mtcars)
glass_delta(mpg ~ am, data = mtcars)
print(cohens_d(mpg ~ am, data = mtcars), append_CL = TRUE)
# }
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