intercoder reliability Canada

Intercoder Reliability Canada

This file goal is calculating the Intercoder Reliability index Cohen´s K for the sample of Canada

sample <- read_excel("temp random FINAL.xlsx", sheet = "intercoder")

coder1 <- sample$`Coder 1`
coder2 <- sample$`Coder 2`

# Check if coder1 and coder2 are not empty
if (length(coder1) == 0 || length(coder2) == 0) {
  stop("Coder vectors are empty.")
}

# Convert the vectors to factors
coder1_factor <- factor(coder1, levels = c("YES", "NO"))
coder2_factor <- factor(coder2, levels = c("YES", "NO"))

# Check if coder1 and coder2 have the same levels
if (!identical(levels(coder1_factor), levels(coder2_factor))) {
  stop("Coder vectors have different levels.")
}

b <- cbind(coder1, coder2)

index <- gwet.ac1.raw(b, weights = "unweighted", categ.labels = NULL,
                      conflev = 0.95, N = Inf)
print(index)

## $est
##   coeff.name        pa         pe coeff.val coeff.se      conf.int p.value
## 1        AC1 0.9440559 0.06313811   0.94029  0.01252 (0.916,0.965)       0
##       w.name
## 1 unweighted
## 
## $weights
##      [,1] [,2]
## [1,]    1    0
## [2,]    0    1
## 
## $categories
## [1] "NO"  "YES"

acuerdo <- agree(b, tolerance = 1)
print(acuerdo)

##  Percentage agreement (Tolerance=0)
## 
##  Subjects = 429 
##    Raters = 2 
##   %-agree = 94.4

library(vcd)

## Loading required package: grid

cont_table <- table(coder1_factor, coder2_factor)
kappa_result <- Kappa(cont_table)

print(kappa_result)

##             value     ASE     z Pr(>|z|)
## Unweighted 0.1354 0.08616 1.571   0.1161
## Weighted   0.1354 0.08616 1.571   0.1161