`dataFrame.columns.stat — hedgesG()`

Description

The hedgesG() method of the stat object computes Hedges' g effect size for two selected columns.

Signature

dataFrame.columns(...columnNames).stat.hedgesG({ comparison: 'independent', estimator: 'sample' })

Scope: columns
Family: stat
Returns: number

Arguments

...columnNames ( string[] ): The name of the columns from which to compute Hedges' g effect size
options (object): Hedges' g computation options.

Options

comparison (string)

The comparison method used to compute Hedges' g.

independent (default)
paired

estimator (string)

The variance estimator used in the calculation.

population
sample (default)

Returns

effectSize (number): The computed Hedges' g. effect size.

Notes

The method requires exactly two selected columns.
The independent comparison computes Hedges' g using two independent samples.
The paired comparison computes Hedges' g using paired differences between observations.
Hedges' g is a bias-corrected version of Cohen's d.
Hedges' g is generally preferred for small sample sizes.
Positive values indicate that the mean of the first selected column is greater than the mean of the second selected column.
Negative values indicate that the mean of the second selected column is greater than the mean of the first selected column.
Common interpretation guidelines for the absolute value of Hedges' g are:
- < 0.2 : negligible
- 0.2 – 0.5 : small
- 0.5 – 0.8 : medium
- ≥ 0.8 : large

Example

// calculate the Hedges' g effect size between 2 columns of the dataFrame
var effectSize = dataFrame.columns('groupA', 'groupB').stat.hedgesG();

// log the effect size
notebook.log(effectSize);