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
l22,gc18,;textualCohensdpLibrary.
J(statistics, design)a list of pre-computed statistics. The statistics to provide
depend on the design:
- for "between": n1, n2, the sample sizes of the two groups;
- for "within": n, and r or rho the correlation between
the measure;
- for "single": n.
the design of the measures ("within", "between", or "single");
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.
# 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.