d.prop: d for Independent Proportions
In MOTE: Effect Size and Confidence Interval Calculator

Description Usage Arguments Details Value Examples

This function displays d and central confidence interval calculated from differences in independent proportions. Independent proportions are two percentages that are from different groups of participants.

1	d.prop(p1, p2, n1, n2, a = 0.05)

`p1`	proportion for group one
`p2`	proportion for group two
`n1`	sample size group one
`n2`	sample size group two
`a`	significance level

To calculate z, the proportion of group two is substracted from group one, which is then divided by the standard error.

z = (p1 - p2) / se

To calculate d, the proportion of group two is divided by the standard error of group two which is then subtracted from the proportion of group one divided by the standard error of group one.

z1 = p1 / se1

z2 = p2 / se2

d = z1 - z2

Learn more on our example page.

`d`	effect size
`dlow`	lower level confidence interval d value
`dhigh`	upper level confidence interval d value
`p1`	proportion of group one
`se1`	standard error of the proportion of group one
`z1`	z-statistic group one
`z1low`	lower level confidence interval of z
`z1high`	upper level confidence interval of z
`p2`	proportion of group two
`se2`	standard error of the proportion of group two
`z2`	z-statistic of group two
`z2low`	lower level confidence interval of z
`z2high`	upper level confidence interval of z
`n1`	sample size group one
`n2`	sample size group two
`z`	z-statistic for the differences
`ppooled`	pooled proportion to calculate standard error
`se`	standard error
`p`	p-value for the differences
`estimate`	the d statistic and confidence interval in APA style for markdown printing
`statistic`	the t-statistic in APA style for markdown printing

#Several researchers were examining the data on the number
#of students who retake a course after they receive a D, F,
#or withdraw from the course. They randomly sampled form
#a large university two groups of students: traditional
#(less than 25 years old) and non-traditional (25 and older).
#Each group included 100 participants. About 25% of students
#of the traditional group reported they would retake a course,
#while the non-traditional group showed about 35% would
#retake the course.

#You can type in the numbers directly as shown below,
#or refer to your dataset within the function.

    d.prop(p1 = .25, p2 = .35, n1 = 100, n2 = 100, a = .05)

    d.prop(.25, .35, 100, 100, .05)