d.prop {MOTE}

d for Independent Proportions

Description

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.

Usage

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

Arguments

p1

proportion for group one

p2

proportion for group two

n1

sample size group one

n2

sample size group two

a

significance level

Details

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.

Value

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

Examples

#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)