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": |

`design` |
the design of the measures ( |

### 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
# a human-readable output
summarize( res )
# ...and a human-readable ouptut with additional explanations
explain( res )
```

*CohensdpLibrary*version 0.5.10 Index]