analyseArne.R

## Setup ###################################################################################################

# To run at the command line, replace this with a function that calls setwd()
# to the local directory where you have the data and R files.
setwd_PROCOAST_pluralistic_ignorance_1()

dF <- read.table("arneData.tsv",
                 header = TRUE,
                 sep = "\t",
                 stringsAsFactors = FALSE)

# We just need permutation_sign_test()
source("analysisFunctions.R")

# Sample size (there is no missing data)
nrow(dF)

## [1] 28

# Percent support
percentSupport <- sum(dF$support>=4) / length(dF$support) * 100
percentSupport

## [1] 82.14286

# Perceived support (percent)
dF$perceivedSupportPercent <- dF$perceivedNormSupport * 10
mean(dF$perceivedSupportPercent)

## [1] 51.07143

# Calculating the EPI difference score
dF$diff <- percentSupport - dF$perceivedSupportPercent

# The EPI difference score is significantly different to zero
permutation_sign_test(dF$diff)

## Permutation Sign Test
## =====================
## 
## Observed mean: 31.0714 
## Alternative hypothesis: two.sided 
## Number of permutations: 99999 
## Number of observations: 28 
## P-value: 0 
## Significance: *** 
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# There is a correlation between support and perceived support
cor.test(dF$support, dF$perceivedNormSupport)

## 
##  Pearson's product-moment correlation
## 
## data:  dF$support and dF$perceivedNormSupport
## t = 2.2746, df = 26, p-value = 0.03141
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.04047101 0.67750019
## sample estimates:
##       cor 
## 0.4073967