J {CohensdpLibrary} | R Documentation |
J()
computes the correction factor to get an unbiased Cohen's $d_p$ in either within-
subject, between-subject design and single-group design. See
Lecoutre (2022 - submitted); Goulet-Pelletier and Cousineau (2018).
J(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 decreases the degrees of freedom by 1 in within-subject design when the population rho is unknown.
The correction factor for unbiasing a Cohen's $d_p$. 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.
Lecoutre B (2022 - submitted).
“A note on the distributions of the sum and ratio of two correlated chi-square distributions.”
submitted, submitted, submitted.
# example in which the means are 114 vs. 101 with sds of 14.3 and 12.5 respectively
J( statistics = list( n1 = 12, n2 = 12 ),
design = "between")
# example in a repeated-measure design
J( statistics = list( n = 12, rho = 0.53 ),
design = "within")
# example with a single-group design where mu is a population parameter
J( statistics = list( n = 12 ),
design = "single")
# The results can be displayed in three modes
res <- J( statistics = list( n = 12 ),
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 )