Standardized Mean Differences
The calculation of Cohen’s d type effect sizes
Aaron R. Caldwell
2024-04-16
The calculation of standardized mean differences (SMDs) can be
helpful in interpreting data and are essential for meta-analysis. In
psychology, effect sizes are very often reported as an SMD rather than
raw units (though either is fine: see Caldwell
and Vigotsky (2020)). In most papers the SMD is reported as
Cohen’s d. The simplest form involves reporting the
mean difference (or mean in the case of a one-sample test) divided by
the standard deviation.
\[
Cohen's \space d = \frac{Mean}{SD}
\]
However, two major problems arise: bias and the calculation of the
denominator. First, the Cohen’s d calculation is biased (meaning the
effect is inflated), and a bias correction (often referred to as Hedges’
g) is applied to provide an unbiased estimate. Second, the denominator
can influence the estimate of the SMD, and there are a multitude of
choices for how to calculate the denominator. To make matters worse, the
calculation (in most cases an approximation) of the confidence intervals
involves the noncentral t distribution. This requires
calculating a non-centrality parameter (lambda: \(\lambda\)), degrees of freedom (\(df\)), or even the standard error (sigma:
\(\sigma\)) for the SMD. None of these
are easy to determine and these calculations are hotly debated in the
statistics literature (Cousineau and
Goulet-Pelletier 2021).
In this package we originally opted to make the default SMD
confidence intervals as the formulation outlined by Goulet-Pelletier and Cousineau (2018). We found
that that these calculations were simple to implement and provided
fairly accurate coverage for the confidence intervals for any type of
SMD (independent, paired, or one sample). However, even the authors have
outlined some issues with the method in a newer publication (Cousineau and Goulet-Pelletier 2021). Other
packages, such as MOTE (Buchanan et
al. 2019) or effectsize (Ben-Shachar, Lüdecke, and Makowski 2020), use a
simpler formulation of the noncentral t-distribution (nct). The default
option in the package is the nct type of confidence intervals. We have
created an argument for all TOST functions (tsum_TOST and
t_TOST) named smd_ci which allow the user to
specify “goulet” (for the Cousineau and
Goulet-Pelletier (2021) method), “nct” (this will approximately
match the results of Buchanan et al.
(2019) and Ben-Shachar, Lüdecke, and
Makowski (2020)), “t” (central t method), or “z” (normal method).
We would strongly recommend using “nct” or “goulet” for any analysis. It
is important to remember that all of these methods are only
approximations of confidence intervals (of varying degrees of
quality) and therefore should be interpreted with caution.
It is my belief that SMDs provide another interesting description of
the sample, and have very limited inferential utility (though exceptions
apply). You may disagree, and if you are basing your inferences on the
SMD, and the associated confidence intervals, we recommend you go with a
bootstrapping approach (see boot_t_TOST) (Kirby and Gerlanc 2013).
In this section we will detail on the calculations that are involved
in calculating the SMD, their associated degrees of freedom,
non-centrality parameter, and variance. If these SMDs are being reported
in a scientific manuscript, we strongly recommend that
the formulas for the SMDs you report be included in the methods
section.
Bias Correction (Hedges)
For all SMD calculative approaches the bias correction was calculated
as the following:
\[
J = \frac{\Gamma(\frac{df}{2})}{\sqrt{\frac{df}{2}} \cdot
\Gamma(\frac{df-1}{2})}
\]
The correction factor is calculated in R as the following:
J <- exp ( lgamma(df/2) - log(sqrt(df/2)) - lgamma((df-1)/2) )
Hedges g (bias corrected Cohen’s d) can then be calculated by
multiplying d by J
\[
g = d \cdot J
\] When the bias correction is not applied, J is equal to 1.
Independent Samples
For independent samples there are three calculative approaches
supported by TOSTER. One the denominator is the pooled
standard deviation (Cohen’s d), the average standard deviation (Cohen’s
d(av)), and the standard deviation of the control group (Glass’s \(\Delta\)). Currently, the d or d(av) is
selected by whether or not variances are assumed to be equal. If the
variances are not assumed to be equal then Cohen’s d(av) will be
returned, and if variances are assumed to be equal then Cohen’s d is
returned. Glass’s delta can be selected by setting the
glass argument to “glass1” or “glass2”.
Variances Assumed Unequal: Cohen’s d(av)
For this calculation, the denominator is simply the square root of
the average variance.
\[
s_{av} = \sqrt \frac {s_{1}^2 + s_{2}^2}{2}
\]
The SMD, Cohen’s d(av), is then calculated as the following:
\[
d_{av} = \frac {\bar{x}_1 - \bar{x}_2} {s_{av}}
\]
Note: the x with the bar above it (pronounced as “x-bar”) refers the
the means of group 1 and 2 respectively.
The degrees of freedom for Cohen’s d(av), derived from Delacre et al. (2021), is the following:
\[
df = \frac{(n_1-1)(n_2-1)(s_1^2+s_2^2)^2}{(n_2-1) \cdot s_1^4+(n_1-1)
\cdot s_2^4}
\]
The non-centrality parameter (\(\lambda\)) is calculated as the
following:
- Under the Cousineau and Goulet-Pelletier
(2021) method (
smd_ci = "goulet"):
\[
\lambda = d_{av} \times \sqrt{\frac{n_1 \cdot
n_2(\sigma^2_1+\sigma^2_2)}{2 \cdot (n_2 \cdot \sigma^2_1+n_1 \cdot
\sigma^2_2)}}
\]
- Under all other methods (nct, t, or z):
\[
\lambda = \frac{2 \cdot (n_2 \cdot \sigma_1^2 + n_1 \cdot \sigma_2^2)}
{n_1 \cdot n_2 \cdot (\sigma_1^2 + \sigma_2^2)}
\] The standard error (\(\sigma\)) of Cohen’s d(av) is calculated as
the following:
\[
\sigma_{SMD} = \sqrt{\frac{df}{df-2} \cdot \frac{2}{\tilde n} (1+d^2
\cdot \frac{\tilde n}{2}) -\frac{d^2}{J^2}}
\]
wherein \(J\) represents the Hedges
correction (calculation above).
Variances Assumed Equal: Cohen’s d
For this calculation, the denominator is simply the pooled standard
deviation.
\[
s_{p} = \sqrt \frac {(n_{1} - 1)s_{1}^2 + (n_{2} - 1)s_{2}^2}{n_{1} +
n_{2} - 2}
\]
\[
d = \frac {\bar{x}_1 - \bar{x}_2} {s_{p}}
\]
The degrees of freedom for Cohen’s d is the following:
\[
df = n_1 + n_2 - 2
\]
The non-centrality parameter (\(\lambda\)) is calculated as the
following:
- Under the Cousineau and Goulet-Pelletier
(2021) method (
smd_ci = "goulet"):
\[
\lambda = d \cdot \sqrt \frac{\tilde n}{2}
\]
wherein, \(\tilde n\) is the
harmonic mean of the 2 sample sizes which is calculated as the
following:
\[
\tilde n = \frac{2 \cdot n_1 \cdot n_2}{n_1 + n_2}
\]
- Under all other methods (nct, t, or z):
\[
\lambda = \frac{1}{n_1} +\frac{1}{n_2}
\]
The standard error (\(\sigma\)) of
Cohen’s d is calculated as the following:
- Under the Cousineau and Goulet-Pelletier
(2021) method (
smd_ci = "goulet"):
\[
\sigma_{SMD} = \sqrt{\frac{df}{df-2} \cdot \frac{2}{\tilde n} (1+d^2
\cdot \frac{\tilde n}{2}) -\frac{d^2}{J}}
\] wherein \(J\) represents the
Hedges correction (calculation above).
- Under all other methods (nct, t, or z):
\[
\sigma_{SMD} = \sqrt{\frac{n_1+n_2}{n_1 \cdot n_2} + \frac{d^2}{2
\cdot(n_1+n_2)} \cdot J^2}
\]
Glass’s Delta
For this calculation, the denominator is simply the standard
deviation of one of the groups (x for
glass = "glass1", or y for
glass = "glass2".
\[
s_{c} = SD_{control \space group}
\]
\[
d = \frac {\bar{x}_1 - \bar{x}_2} {s_{c}}
\]
The degrees of freedom for Glass’s delta is the following:
\[
df = n_c - 1
\]
The non-centrality parameter (\(\lambda\)) is calculated as the
following:
- Under the Cousineau and Goulet-Pelletier
(2021) method (
smd_ci = "goulet"):
\[
\lambda = d \cdot \sqrt \frac{\tilde n}{2}
\]
wherein, \(\tilde n\) is the
harmonic mean of the 2 sample sizes which is calculated as the
following:
\[
\tilde n = \frac{2 \cdot n_1 \cdot n_2}{n_1 + n_2}
\]
- Under all other methods (nct, t, or z):
\[
\lambda = \frac{1}{n_T} + \frac{s_c^2}{n_c \cdot s_c^2}
\]
The standard error (\(\sigma\)) of
Glass’s delta is calculated as the following:
- Under the Cousineau and Goulet-Pelletier
(2021) method (
smd_ci = "goulet"):
\[
\sigma_{SMD} = \sqrt{\frac{df}{df-2} \cdot \frac{2}{\tilde n} (1+d^2
\cdot \frac{\tilde n}{2}) -\frac{d^2}{J}}
\]
wherein \(J\) represents the Hedges
correction (calculation above).
- Under all other methods (nct, t, or z):
\[
\sigma_{SMD} = \sqrt{\frac{1}{\tilde n} \cdot \frac{N - 2}{N - 4} \cdot
(1 + \tilde n \cdot
d ^ 2) -
\frac{d^2}{J^2}}
\]
Paired Samples
For paired samples there are two calculative approaches supported by
TOSTER. One the denominator is the standard deviation of
the change score (Cohen’s d(z)), the correlation corrected effect size
(Cohen’s d(av)), and the standard deviation of the control condition
(Glass’s \(\Delta\)). Currently, the
choice is made by the function based on whether or not the user sets
rm_correction to TRUE. If rm_correction is set
to t TRUE then Cohen’s d(rm) will be returned, and otherwise Cohen’s
d(z) is returned. This can be overridden and Glass’s delta is returned
if the glass argument is set to “glass1” or “glass2”.
Cohen’s d(z): Change Scores
For this calculation, the denominator is the standard deviation of
the difference scores which can be calculated from the standard
deviations of the samples and the correlation between the paired
samples.
\[
s_{diff} = \sqrt{sd_1^2 + sd_2^2 - 2 \cdot r_{12} \cdot sd_1 \cdot
sd_2}
\]
The SMD, Cohen’s d(z), is then calculated as the following:
\[
d_{z} = \frac {\bar{x}_1 - \bar{x}_2} {s_{diff}}
\]
The degrees of freedom for Cohen’s d(z) is the following:
- Under the Cousineau and Goulet-Pelletier
(2021) method (
smd_ci = "goulet"):
\[
df = 2 \cdot (N_{pairs}-1)
\]
- Under all other methods (nct, t, or z):
\[
df = N - 1
\]
The non-centrality parameter (\(\lambda\)) is calculated as the
following:
- Under the Cousineau and Goulet-Pelletier
(2021) method (
smd_ci = "goulet"):
\[
\lambda = d_{z} \cdot \sqrt \frac{N_{pairs}}{2 \cdot (1-r_{12})}
\]
- Under all other methods (nct, t, or z):
\[
\lambda = \frac{1}{n}
\]
The standard error (\(\sigma\)) of
Cohen’s d(z) is calculated as the following:
- Under the Cousineau and Goulet-Pelletier
(2021) method (
smd_ci = "goulet"):
\[
\sigma_{SMD} = \sqrt{\frac{df}{df-2} \cdot \frac{2 \cdot (1-r_{12})}{n}
\cdot (1+d^2 \cdot \frac{n}{2 \cdot (1-r_{12})}) -\frac{d^2}{J^2}}
\space \times \space \sqrt {2 \cdot (1-r_{12})}
\]
- Under all other methods (nct, t, or z):
\[
\sigma_{SMD} = \sqrt{\frac{1}{n} + \frac{d_z^2}{(2 \cdot n)}}
\]
Cohen’s d(rm): Correlation Corrected
For this calculation, the same values for the same calculations above
is adjusted for the correlation between measures. As Goulet-Pelletier and Cousineau (2018) mention,
this is useful for when effect sizes are being compared for studies that
involve between and within subjects designs.
First, the standard deviation of the difference scores are
calculated
\[
s_{diff} = \sqrt{sd_1^2 + sd_2^2 - 2 \cdot r_{12} \cdot sd_1 \cdot
sd_2}
\]
The SMD, Cohen’s d(rm), is then calculated with a small change to the
denominator:
\[
d_{rm} = \frac {\bar{x}_1 - \bar{x}_2}{s_{diff}} \cdot \sqrt {2 \cdot
(1-r_{12})}
\]
The degrees of freedom for Cohen’s d(rm) is the following:
- Under the Cousineau and Goulet-Pelletier
(2021) method (
smd_ci = "goulet"):
\[
df = 2 \cdot (N_{pairs}-1)
\]
- Under all other methods (nct, t, or z):
\[
df = N - 1
\]
The non-centrality parameter (\(\lambda\)) is calculated as the
following:
- Under the Cousineau and Goulet-Pelletier
(2021) method (
smd_ci = "goulet"):
\[
\lambda = d_{rm} \cdot \sqrt \frac{N_{pairs}}{2 \cdot (1-r_{12})}
\]
- Under all other methods (nct, t, or z):
\[
\lambda = \frac{1}{n}
\]
The standard error (\(\sigma\)) of
Cohen’s d(rm) is calculated as the following:
\[
\sigma_{SMD} = \sqrt{\frac{df}{df-2} \cdot \frac{2 \cdot (1-r_{12})}{n}
\cdot (1+d_{rm}^2 \cdot \frac{n}{2 \cdot (1-r_{12})})
-\frac{d_{rm}^2}{J^2}}
\]
Glass’s Delta
For this calculation, the denominator is simply the standard
deviation of one of the groups (x for
glass = "glass1", or y for
glass = "glass2".
\[
s_{c} = SD_{control \space condition}
\]
\[
d = \frac {\bar{x}_1 - \bar{x}_2} {s_{c}}
\]
The degrees of freedom for Glass’s delta is the following:
- Under the Cousineau and Goulet-Pelletier
(2021) method (
smd_ci = "goulet"):
\[
df = 2 \cdot N - 1
\]
- Under all other methods (nct, t, or z):
\[
df = N - 1
\]
The non-centrality parameter (\(\lambda\)) is calculated as the
following:
- Under the Cousineau and Goulet-Pelletier
(2021) method (
smd_ci = "goulet"):
\[
\lambda = d \cdot \sqrt{\frac{N}{2 \cdot (1 - r_{12})}}
\]
- Under all other methods (nct, t, or z):
\[
\lambda = \frac{1}{N}
\]
The standard error (\(\sigma\)) of
Glass’s delta is calculated as the following:
\[
\sigma_{SMD} = \sqrt{J^2 \cdot (\frac{1-r_{12}}{N} + \frac{d^2}{2
\cdot N \cdot J})}
\]
One Sample
For a one-sample situation, the calculations are very straight
forward
For this calculation, the denominator is simply the standard
deviation of the sample.
\[
s={\sqrt {{\frac {1}{N-1}}\sum _{i=1}^{N}\left(x_{i}-{\bar
{x}}\right)^{2}}}
\]
The SMD is then the mean of X divided by the standard deviation.
\[
d = \frac {\bar{x}} {s}
\]
The degrees of freedom for Cohen’s d is the following:
\[
df = N - 1
\]
The non-centrality parameter (\(\lambda\)) is calculated as the
following:
\[
\lambda = d \cdot \sqrt N
\]
The standard error (\(\sigma\)) of
Cohen’s d is calculated as the following:
- Under the Cousineau and Goulet-Pelletier
(2021) method (
smd_ci = "goulet"):
\[
\sigma_{SMD} = \sqrt{\frac{df}{df-2} \cdot \frac{1}{N} (1+d^2 \cdot N)
-\frac{d^2}{J^2}}
\]
- Under all other methods (nct, t, or z):
\[
\sigma_{SMD} = \sqrt{\frac{1}{n} + \frac{d^2}{(2 \cdot n)}}
\]
- Wherein \(J\) represents the Hedges
correction (calculation above).
Confidence Intervals
For the SMDs calculated in this package we use the non-central
t method outlined by Goulet-Pelletier
and Cousineau (2018). These calculations are only approximations
and newer formulations may provide better coverage (Cousineau and Goulet-Pelletier 2021). In any
case, if the calculation of confidence intervals for SMDs is of the
utmost importance then I would strongly recommend using bootstrapping
techniques rather than any calculative approach whenever possible (Kirby and Gerlanc 2013).
The calculations of the confidence intervals in this package involve
a two step process: 1) using the noncentral t-distribution to
calculate the lower and upper bounds of \(\lambda\), and 2) transforming this back to
the effect size estimate.
Cousineau and Goulet-Pelletier (2021)
Method (smd_ci = “goulet”)
Calculate confidence intervals around \(\lambda\).
\[
t_L = t_{(1/2-(1-\alpha)/2,\space df, \space \lambda)} \\
t_U = t_{(1/2+(1-\alpha)/2,\space df, \space \lambda)}
\]
Then transform back to the SMD.
\[
d_L = \frac{t_L}{\lambda} \cdot d \\
d_U = \frac{t_U}{\lambda} \cdot d
\]
The non-central t-method (smd_ci = “nct”)
Calculate the non-centrality parameters necessary to form confidence
intervals wherein the observed t-statistic (\(t_{obs}\)) (note: the standard error is
slightly altered for d_{rm}) is utilized.
\[
t_L = t_{(1-alpha,\space df, \space t_{obs})} \\
t_U = t_{(alpha,\space df, \space t_{obs})}
\] The confidence intervals can then be constructed using the
non-centrality parameter and the bias correction.
\[
d_L = t_L \cdot \sqrt{\lambda} \cdot J \\
d_U = t_U \cdot \sqrt{\lambda} \cdot J
\]
Central t-method
Full warning this method provides sub-optimal coverage.
The limits of the t-distribution at the given alpha-level and degrees
of freedom (qt(1-alpha,df)) are multiplied by the standard
error of the calculated SMD.
\[
CI = SMD \space \pm \space t_{(1-\alpha,df)} \cdot \sigma_{SMD}
\]
Normal method
Full warning this method provides atrocious coverage at most sample
sizes in my opinion.
The limits of the z-distribution at the given alpha-level
(qnorm(1-alpha)) are multiplied by the standard error of
the calculated SMD.
\[
CI = SMD \space \pm \space z_{(1-\alpha)} \cdot \sigma_{SMD}
\]
Just Estimate the SMD
It was requested that a function be provided that only calculates the
SMD. Therefore, I created the smd_calc and
boot_smd_calc functions. The interface is almost the same as
t_TOST but you don’t set an equivalence bound.
smd_calc(formula = extra ~ group,
data = sleep,
paired = TRUE,
smd_ci = "nct",
bias_correction = F)
#> estimate SE lower.ci upper.ci conf.level
#> Cohen's d(z) -1.284558 0.4272053 -2.118017 -0.4146278 0.95
# Setting bootstrap replications low to
## reduce compiling time of vignette
boot_smd_calc(formula = extra ~ group,
data = sleep,
R = 199,
paired = TRUE,
boot_ci = "stud",
bias_correction = F)
#> estimate bias SE lower.ci upper.ci conf.level
#> Cohen's d(z) -1.284558 0.1174692 0.7499641 -1.759249 1.481814 0.95
#> boot_ci
#> Cohen's d(z) stud
Plotting SMDs
Sometimes you may take a different approach to calculating the SMD,
or you may only have the summary statistics from another study. For this
reason, I have included a way to plot the SMD based on just three
values: the estimate of the SMD, the degrees of freedom, and the
non-centrality parameter. So long as all three are reported, or can be
estimated, then a plot of the SMD can be produced.
Two types of plots can be produced: consonance
(type = "c"), consonance density
(type = "cd"), or both (the default option;
(type = c("c","cd")))
plot_smd(d = .43,
df = 58,
lambda = 1.66,
smd_label = "Cohen's d"
)
Comparing SMDs
In some cases, the SMDs between original and replication studies want
to be compared. Rather than looking at whether or not a replication
attempt is significant, a researcher could compare to see how compatible
the SMDs are between the two studies.
For example, say there is original study reports an effect of Cohen’s
dz = 0.95 in a paired samples design with 25 subjects. However, a
replication doubled the sample size, found a non-significant effect at
an SMD of 0.2. Are these two studies compatible? Or, to put it another
way, should the replication be considered a failure to replicate?
We can use the compare_smd function to at least measure
how often we would expect a discrepancy between the original and
replication study if the same underlying effect was being measured (also
assuming no publication bias or differences in protocol).
We can see from the results below that, if the null hypothesis were
true, we would only expect to see a discrepancy in SMDs between studies,
at least this large, ~1% of the time.
compare_smd(smd1 = 0.95,
n1 = 25,
smd2 = 0.23,
n2 = 50,
paired = TRUE)
#>
#> Difference in Cohen's dz (paired)
#>
#> data: Summary Statistics
#> z = 2.5685, p-value = 0.01021
#> alternative hypothesis: true difference in SMDs is not equal to 0
#> sample estimates:
#> difference in SMDs
#> 0.72
Raw Data
The above results are only based on an approximating the differences
between the SMDs. If the raw data is available, then the optimal
solution is the bootstrap the results. This can be accomplished with the
boot_compare_smd function.
For this example, we will simulate some data.
set.seed(4522)
diff_study1 = rnorm(25,.95)
diff_study2 = rnorm(50)
boot_test = boot_compare_smd(x1 = diff_study1,
x2 = diff_study2,
paired = TRUE)
boot_test
#>
#> Bootstrapped Differences in SMDs (paired)
#>
#> data: Bootstrapped
#> z (observed) = 2.887, p-value = 0.006003
#> alternative hypothesis: true difference in SMDs is not equal to 0
#> 95 percent confidence interval:
#> 0.3161207 1.4831351
#> sample estimates:
#> difference in SMDs
#> 0.8058872
# Table of bootstrapped CIs
knitr::kable(boot_test$df_ci, digits = 4)
| Difference in SMD |
0.8059 |
0.3161 |
1.4831 |
| SMD1 |
0.9418 |
0.5403 |
1.5318 |
| SMD2 |
0.1359 |
-0.1420 |
0.4187 |
Visualize
The results of the bootstrapping are stored in the results. So we can
even visualize the differences in SMDs.
library(ggplot2)
list_res = boot_test$boot_res
df1 = data.frame(study = c(rep("original",length(list_res$smd1)),
rep("replication",length(list_res$smd2))),
smd = c(list_res$smd1,list_res$smd2))
ggplot(df1,
aes(fill = study, color =smd, x = smd))+
geom_histogram(aes(y=..density..), alpha=0.5,
position="identity")+
geom_density(alpha=.2) +
labs(y = "", x = "SMD (bootstrapped estimates)") +
theme_classic()
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
df2 = data.frame(diff = list_res$d_diff)
ggplot(df2,
aes(x = diff))+
geom_histogram(aes(y=..density..), alpha=0.5,
position="identity")+
geom_density(alpha=.2) +
labs(y = "", x = "Difference in SMDs (bootstrapped estimates)") +
theme_classic()
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
References
Ben-Shachar, Mattan S., Daniel Lüdecke, and Dominique Makowski. 2020.
“effectsize: Estimation of Effect Size
Indices and Standardized Parameters.” Journal of Open Source
Software 5 (56): 2815.
https://doi.org/10.21105/joss.02815.
Buchanan, Erin M., Amber Gillenwaters, John E. Scofield, and K. D.
Valentine. 2019.
MOTE: Measure of the
Effect: Package to Assist in Effect Size Calculations and Their
Confidence Intervals.
https://github.com/doomlab/MOTE.
Caldwell, Aaron, and Andrew D. Vigotsky. 2020.
“A Case Against
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https://doi.org/10.7717/peerj.10314.
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https://doi.org/10.20982/tqmp.17.1.p051.
Delacre, Marie, Daniel Lakens, Christophe Ley, Limin Liu, and Christophe
Leys. 2021.
“Why Hedges’ gs Based on the Non-Pooled
Standard Deviation Should Be Reported with Welch’s t-Test,” May.
https://doi.org/10.31234/osf.io/tu6mp.
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“A
Review of Effect Sizes and Their Confidence Intervals, Part i: The
Cohen’s d Family.” The Quantitative Methods for
Psychology 14 (4): 242–65.
https://doi.org/10.20982/tqmp.14.4.p242.
Kirby, Kris N., and Daniel Gerlanc. 2013.
“BootES: An
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Behavior Research Methods 45 (4): 905–27.
https://doi.org/10.3758/s13428-013-0330-5.
Lakens, Daniël. 2013. “Calculating and Reporting Effect Sizes to
Facilitate Cumulative Science: A Practical Primer for t-Tests and
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