# The following the example is the t-test for dependent means, where we compared # GPA's from high school to GPA's from UW # Load in the survey data survey <-read.csv("http://www.courses.washington.edu/psy315/datasets/Psych315W19survey.csv") # First find the UW GPA's for the male students x <- survey\$GPA_UW[survey\$gender == "Male"] # Then find the high school GPA's for the male students y <- survey\$GPA_HS[survey\$gender == "Male"] # Remove the pairs that have a NA in either x or y: goodvals = !is.na(x) & !is.na(y) x <- x[goodvals] y <- y[goodvals] # run the t-test. Use 'paired = TRUE' because x and y are dependent out <- t.test(x,y, paired = TRUE, alternative = "two.sided", var.equal = TRUE) # The p-pvalue is: out\$p.value # Displaying the result in APA format: sprintf('t(%g) = %4.2f, p = %5.4f',out\$parameter,out\$statistic,out\$p.value) mx <- mean(x) my <- mean(y) s = sd(x-y) n <- length(x) #effect size d <- abs(mx-my)/s d # Find observed power from d, alpha and n out <- power.t.test(n =n, d = d, sig.level = .05, power = NULL, alternative = "two.sided", type = "one.sample") out\$power # Example 2: Is there a significant difference between male student's heights and their # father's heights? # First find the heights of the male students x <- survey\$height[survey\$gender == "Male"] # Then find the heights of their fathers y <- survey\$pheight[survey\$gender == "Male"] # Remove the pairs that have a NA in either x or y: goodvals = !is.na(x) & !is.na(y) x <- x[goodvals] y <- y[goodvals] # run the t-test. Use 'paired = TRUE' because x and y are dependent out <- t.test(x,y, paired = TRUE, alternative = "two.sided", var.equal = TRUE) # The p-pvalue is: out\$p.value # Displaying the result in APA format: sprintf('t(%g) = %4.2f, p = %5.4f',out\$parameter,out\$statistic,out\$p.value)