| h_prop | R Documentation |
This function computes Cohen's h effect size for the difference
between two independent proportions. Cohen's h is defined as a
difference between arcsine-transformed proportions:
h_prop(p1, p2, n1, n2, a = 0.05)
h.prop(p1, p2, n1, n2, a = 0.05)
p1 |
Proportion for group one (between 0 and 1). |
p2 |
Proportion for group two (between 0 and 1). |
n1 |
Sample size for group one. |
n2 |
Sample size for group two. |
a |
Significance level used for confidence intervals. Defaults to 0.05. |
h = 2 \arcsin \sqrt{p_1} - 2 \arcsin \sqrt{p_2}
where p_1 and p_2 are proportions for groups 1 and 2,
respectively.
Using a simple large-sample approximation (via the delta method), the
standard error of h can be taken as:
\mathrm{SE}(h) \approx \sqrt{1 / n_1 + 1 / n_2}
,
which leads to a (1 - \alpha) confidence interval for h:
h \pm z_{1 - \alpha/2} \, \mathrm{SE}(h).
This effect size is commonly recommended for differences in proportions (Cohen, 1988) and is particularly useful for power analysis and meta-analysis when working directly with proportions.
A list containing Cohen's h effect size and related statistics:
‘h' – Cohen’s h.
'hlow', 'hhigh' – lower and upper confidence interval limits.
'h_lower_limit', 'h_upper_limit' – snake_case aliases for the confidence limits.
'p1', 'p2' – input proportions for each group.
'n1', 'n2' – sample sizes for each group, with snake_case aliases 'sample_size_1', 'sample_size_2'.
'z', 'p' – z statistic and p value for the difference in proportions using a pooled-proportion standard error.
'z_value', 'p_value' – snake_case aliases for the z statistic and p value.
'estimate' – APA-style formatted string for Cohen's h and its confidence interval.
'statistic' – APA-style formatted string for the z test of the difference in proportions.
h_prop(p1 = .25, p2 = .35, n1 = 100, n2 = 100, a = .05)
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