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

 ```1 2 3``` ```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

`ES.t.one`

`ES.t.paired`

Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14``` ```## 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.