J.Rd
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 ( |
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.
This function decreases the degrees of freedom by 1 in within-subject design when the population 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
.
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") #> [1] 0.9654507 # example in a repeated-measure design J( statistics = list( n = 12, rho = 0.53 ), design = "within") #> [1] 0.9566549 # example with a single-group design where mu is a population parameter J( statistics = list( n = 12 ), design = "single") #> [1] 0.9299598 # 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 #> [1] 0.9299598 # a human-readable output summarize( res ) #> Correction factor J(11) = 0.930 # ...and a human-readable ouptut with additional explanations explain( res ) #> Correction factor J(11) = 0.930 #> *: degrees of freedom nu = n-1 = 12-1; Hedges, 1981, Journal of Educational Statistics.