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

 ```1 2``` ```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

`ES.t.one`

`ES.t.two`

Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```## 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.