is to test whether some measures of eyetracking for Experimental and Control students are the same. First off, I renamed the variables so that they had Ex or Con at the beginning to make sure there was no confusion. The obvious test to use is a t-test, which is what I'll use below. First though, a barplot of the 8 columns to see the distribution:
Eyetrack <- read.csv("C:/Users/Stephen/Desktop/Reem/Eyetrack.csv")
boxplot(Eyetrack, col = "Wheat", main = "Experimental Total Scores")
t.test(Eyetrack$Exfirst, Eyetrack$Confirst)
Welch Two Sample t-test
data: Eyetrack$Exfirst and Eyetrack$Confirst
t = 8.702, df = 833.7, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
68.7 108.7
sample estimates:
mean of x mean of y
285.4 196.7
t.test(Eyetrack$ExGaze, Eyetrack$ConGaze)
Welch Two Sample t-test
data: Eyetrack$ExGaze and Eyetrack$ConGaze
t = 15.83, df = 552.5, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
1564 2007
sample estimates:
mean of x mean of y
2049.6 263.8
t.test(Eyetrack$ExRead, Eyetrack$Conread)
Welch Two Sample t-test
data: Eyetrack$ExRead and Eyetrack$Conread
t = 11.74, df = 547.3, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
1144 1604
sample estimates:
mean of x mean of y
1425.6 51.6
t.test(Eyetrack$ExTotal, Eyetrack$ConTotal)
Welch Two Sample t-test
data: Eyetrack$ExTotal and Eyetrack$ConTotal
t = 31.25, df = 567.1, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
3222 3654
sample estimates:
mean of x mean of y
3753.4 315.4
All the p values are well below 0.05. Therefore we have to conclude that the means of the variables are different.