Hedgesgp {CohensdpLibrary} | R Documentation |
Hedgesgp()
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)
statistics |
a list of pre-computed statistics. The statistics to provide
depend on the design:
- for "between": |
design |
the design of the measures ( |
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.
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.
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")
# 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")
# 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")
# The results can be displayed in three modes
res <- Hedgesgp( statistics = list( m = 101, m0 = 114, s = 12.5, n = 10 ),
design = "single")
# a raw result of the Cohen's d_p and its confidence interval
res
# a human-readable output
summarize( res )
# ... and a human-readable ouptut with additional explanations
explain( res )