ES.t.two: Calculating effect size (Cohen's d) of independent two-sample...

In powerAnalysis: Power Analysis in Experimental Design

Description

Calculating effect size (Cohen's d) of independent two-sample t test

Usage

ES.t.two(m1 = NULL, m2 = NULL, sd1 = NULL, sd2 = NULL, n1 = NULL,
  n2 = NULL, t = NULL, se1 = NULL, se2 = NULL, df = NULL,
  alternative = c("two.sided", "one.sided"))

Arguments

m1	mean of sample 1
m2	mean of sample 2
sd1	standard deviation of sample 1
sd2	standard deviation of sample 2
n1	number of observations in sample 1
n2	number of observations in sample 2
t	t statistic
se1	standard error of sample 1
se2	standard error of sample 2
df	degree of freedom
alternative	The test is two sided or one sided

See Also

ES.t.one

ES.t.paired

Examples

## mean, sd, n -> d
ES.t.two(m1=13.5,m2=5.5,sd1=4.1833,sd2=3.02765,n1=14,n2=10)

## mean se, n -> d
ES.t.two(m1=13.5,m2=5.5,se1=1.118034,se2=0.9574271,n1=14,n2=10)

## t and n -> d
ES.t.two(n1=14,n2=10,t=5.4349)

## t, df and n -> d
ES.t.two(t = 5.4349, df = 21.982,n1=14,n2=10)

## t and df -> d (assume n1=n2)
ES.t.two(t = 5.4349, df = 21.982)

Example output

     effect size (Cohen's d) of independent two-sample t test 

              d = 2.131182
    alternative = two.sided

NOTE: The alternative hypothesis is m1 != m2
small effect size:  d = 0.2
medium effect size: d = 0.5
large effect size:  d = 0.8


     effect size (Cohen's d) of independent two-sample t test 

              d = 2.225947
    alternative = two.sided

NOTE: The alternative hypothesis is m1 != m2
small effect size:  d = 0.2
medium effect size: d = 0.5
large effect size:  d = 0.8


     effect size (Cohen's d) of independent two-sample t test 

              d = 2.250262
    alternative = two.sided

NOTE: The alternative hypothesis is m1 != m2
small effect size:  d = 0.2
medium effect size: d = 0.5
large effect size:  d = 0.8


     effect size (Cohen's d) of independent two-sample t test 

              d = 2.250262
    alternative = two.sided

NOTE: The alternative hypothesis is m1 != m2
small effect size:  d = 0.2
medium effect size: d = 0.5
large effect size:  d = 0.8


     effect size (Cohen's d) of independent two-sample t test 

              d = 2.318398
    alternative = two.sided

NOTE: The alternative hypothesis is m1 != m2
small effect size:  d = 0.2
medium effect size: d = 0.5
large effect size:  d = 0.8

powerAnalysis documentation built on May 2, 2019, 12:40 p.m.