wants <- c("effectsize")
has <- wants %in% rownames(installed.packages())
if(any(!has)) install.packages(wants[!has])
One Sample t-test
data: DV
t = 3.7292, df = 99, p-value = 0.0003203
alternative hypothesis: true mean is not equal to 0
95 percent confidence interval:
3.185669 10.430568
sample estimates:
mean of x
6.808118
Cohen's d | 95% CI
------------------------
0.37 | [0.17, 0.58]
- Estimate using pooled SD
Nj <- c(18, 21)
DVm <- rnorm(Nj[1], 180, 10)
DVf <- rnorm(Nj[2], 175, 6)
tIndDf <- data.frame(DV=c(DVm, DVf),
IV=factor(rep(c("f", "m"), Nj)))
Two Sample t-test
data: DV by IV
t = 1.1137, df = 37, p-value = 0.1363
alternative hypothesis: true difference in means is greater than 0
95 percent confidence interval:
-1.230298 Inf
sample estimates:
mean in group f mean in group m
177.0479 174.6580
Welch Two Sample t-test
data: DV by IV
t = 1.1032, df = 34.359, p-value = 0.1388
alternative hypothesis: true difference in means is greater than 0
95 percent confidence interval:
-1.27206 Inf
sample estimates:
mean in group f mean in group m
177.0479 174.6580
Cohen’s \(d\) and Hedge’s \(g\)
Cohen's d | 95% CI
-------------------------
0.36 | [-0.28, 0.99]
- Estimate using pooled SD
Hedge's g | 95% CI
-------------------------
0.35 | [-0.27, 0.97]
- Estimate using pooled SD
- Sample samle bias corrected using Hedges and Olkin's correction.
N <- 20
DVpre <- rnorm(N, mean=90, sd=15)
DVpost <- rnorm(N, mean=100, sd=15)
tDepDf <- data.frame(DV=c(DVpre, DVpost),
IV=factor(rep(0:1, each=N), labels=c("pre", "post")))
Paired t-test
data: DV by IV
t = -2.9918, df = 19, p-value = 0.003748
alternative hypothesis: true difference in means is less than 0
95 percent confidence interval:
-Inf -6.739295
sample estimates:
mean of the differences
-15.96821
Based on data in wide format
Equivalent: one-sample t-test for variable built of pair-wise differences
Cohen's d | 95% CI
--------------------------
-0.67 | [-1.18, -0.18]
- Estimate using pooled SD
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