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

In powerAnalysis: Power Analysis in Experimental Design

Description

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

Usage

ES.t.paired(md = NULL, sd = NULL, n = NULL, t = NULL, se = NULL,
  df = NULL, alternative = c("two.sided", "one.sided"))

Arguments

md	mean difference (e.g., mean(x-y))
sd	standard deviation of mean differences (e.g., sd(x-y))
n	number of paires
t	t statistic
se	standard error of mean differences
df	degree of freedom
alternative	The test is two sided or one sided

See Also

ES.t.one

ES.t.two

Examples

## md, sd -> d
ES.t.paired(md=-0.08062384,sd=1.401886)

## md,se -> d
ES.t.paired(md=-0.08062384,se=0.1982566,n=50)

## t, df -> d
ES.t.paired(t=-0.4067,df=49)

## t, n -> d
ES.t.paired(t=-0.4067,n=50)

Example output

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

              d = 0.05751098
    alternative = two.sided

NOTE: The alternative hypothesis is md != 0
small effect size:  d = 0.2
medium effect size: d = 0.5
large effect size:  d = 0.8


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

              d = 0.05751099
    alternative = two.sided

NOTE: The alternative hypothesis is md != 0
small effect size:  d = 0.2
medium effect size: d = 0.5
large effect size:  d = 0.8


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

              d = 0.0581
    alternative = two.sided

NOTE: The alternative hypothesis is md != 0
small effect size:  d = 0.2
medium effect size: d = 0.5
large effect size:  d = 0.8


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

              d = 0.0581
    alternative = two.sided

NOTE: The alternative hypothesis is md != 0
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.