J {CohensdpLibrary} R Documentation

## The correction factor J for a standardized mean difference.

### Description

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).

### Usage

J(statistics, design)


### Arguments

 statistics 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. design the design of the measures ("within", "between", or "single");

### Details

This function decreases the degrees of freedom by 1 in within-subject design when the population rho is unknown.

### Value

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.

### References

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.

### Examples


# 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