Hedgesgp.RdHedgesgp() computes the unbiased Cohen's d (noted $g_p$) in either within-subject,
between-subject design and single-group design. See
Hedges (1981); Goulet-Pelletier and Cousineau (2018)
.
Hedgesgp(statistics, design)a list of pre-computed statistics. The statistics to provide
depend on the design:
- for "between": m1, m2 the means of the two groups,
s1, s2 the standard deviation of the two groups, and
n1, n2, the sample sizes of the two groups;
- for "within": m1, m2, s1, s2, n, and
r or rho the correlation between the measure;
- for "single": m, s, n and m0 the reference
mean from which m is standardized).
the design of the measures ("within", "between", or "single");
The unbiased Cohen's $d_p$ statistic, commonly called a Hedges' $g_p$. The return value is internally a dpObject which can be displayed with print, explain or summary/summarize.
This function returns the Cohen's d_p statistics corrected for bias but no confidence interval as this estimate is not used to build such interval. This function uses r when rho is unknown.
Goulet-Pelletier J, Cousineau D (2018).
“A review of effect sizes and their confidence intervals, Part I: The Cohen's d family.”
The Quantitative Methods for Psychology, 14(4), 242-265.
doi:10.20982/tqmp.14.4.p242
.
Hedges LV (1981).
“Distribution theory for Glass's estimator of effect size and related estimators.”
journal of Educational Statistics, 6(2), 107--128.
# example in which the means are 114 vs. 101 with sDs of 14.3 and 12.5 respectively
Hedgesgp( statistics = list( m1= 101, m2= 114, s1= 12.5, s2= 14.3, n1= 12, n2= 12 ),
design = "between")
#> >>= CohensdpLibrary 0.5.10=>> There is no confidence interval for an (unbiased) Hedges's gp...
#> [1] -0.9345258
# example in a repeated-measure design
Hedgesgp( statistics = list( m1= 101, m2= 114, s1= 12.5, s2= 14.3, n= 12, rho= 0.53 ),
design = "within")
#> >>= CohensdpLibrary 0.5.10=>> There is no confidence interval for an (unbiased) Hedges's gp...
#> [1] -0.9260117
# example with a single-group design where mu is a population parameter
Hedgesgp( statistics = list( m = 101, m0 = 114, s = 12.5, n = 10 ),
design = "single")
#> >>= CohensdpLibrary 0.5.10=>> There is no confidence interval for an (unbiased) Hedges's gp...
#> [1] -0.9504299
# The results can be displayed in three modes
res <- Hedgesgp( statistics = list( m = 101, m0 = 114, s = 12.5, n = 10 ),
design = "single")
#> >>= CohensdpLibrary 0.5.10=>> There is no confidence interval for an (unbiased) Hedges's gp...
# a raw result of the Cohen's d_p and its confidence interval
res
#> [1] -0.9504299
# a human-readable output
summarize( res )
#> Hedges' gp = -0.950
# ... and a human-readable ouptut with additional explanations
explain( res )
#> Hedges' gp = -0.950
#> sample mean 101.000 is compared to assumed mean 114.000
#> sample standard deviation 12.500 is the denominator