UEQ

## loadings
library(psychometric)
library(readxl)
data <- as.data.frame(read_excel("UEQ-SK (data).xlsx"))[,1:27]
data <- data[,c(2:ncol(data),1)]

data

## dimensions of UEQ
dimensions <- c("Attractiveness", "Perspicuity", "Efficiency", "Dependability", "Stimulation", "Novelty")

Attractiveness <- c(1,12,14,16,24,25)
Perspicuity    <- c(2,4,13,21)
Efficiency     <- c(9,20,22,23)
Dependability  <- c(8,11,17,19)
Stimulation    <- c(5:7,18)
Novelty        <- c(3,10,15,26)

## the order of the positive and negative term for an item
positive <- c(1,2,6:8,11,13:16,20,22,26)
negative <- setdiff(1:26, positive)

## benchmark table
tabBM <- as.data.frame(matrix(NA, 6, 6))
colnames(tabBM) <- dimensions
rownames(tabBM) <- c("lower border", "bad", "below average", "above average", "good", "excellent")
tabBM[1,] <- -1
tabBM[2,] <- c(0.7, 0.64, 0.54, 0.78, 0.5, 0.3) +1
tabBM[3,] <- c(0.47, 0.44, 0.44, 0.36, 0.49, 0.41)
tabBM[4,] <- c(0.35, 0.48, 0.49, 0.34, 0.32, 0.34)
tabBM[5,] <- c(0.23, 0.34, 0.31, 0.17, 0.24, 0.35)
tabBM[6,] <- c(0.75, 0.6, 0.72, 0.85, 0.95, 1.1)

colors <- c("red", "orange", "green", "forestgreen", "darkgreen")

## define plot function with error bars
error.bar <- function(x, y, upper, lower=upper, length=0.1,...){
    if(length(x) != length(y) | length(y) !=length(lower) | length(lower) != length(upper))
    stop("vectors must be same length")
    arrows(x,y+upper, x, y-lower, angle=90, code=3, length=length, ...)
}

## transformed values
DT <- as.data.frame(matrix(NA,nrow(data), ncol(data)))
colnames(DT) <- colnames(data)
DT[,1] <- data[,1]

for(i in 1:13){
    DT[,positive[i]] <- data[positive[i]] - 4
    DT[,negative[i]] <- 4 - data[negative[i]]
}

## table of means for each respondent
tab <- as.data.frame(matrix(NA, nrow(DT), 6))
colnames(tab) <- dimensions

tab$Attractiveness <- rowMeans(DT[,Attractiveness])
tab$Perspicuity    <- rowMeans(DT[,Perspicuity])
tab$Efficiency     <- rowMeans(DT[,Efficiency])
tab$Dependability  <- rowMeans(DT[,Dependability])
tab$Stimulation    <- rowMeans(DT[,Stimulation])
tab$Novelty        <- rowMeans(DT[,Novelty])

## means of all respondents
scales <- apply(tab, 2, mean)
stDev  <- apply(tab, 2, sd)

scales
stDev

## means for different groups of respondents
scales_KDF <- apply(tab[data$id=="KDF",], 2, mean)
scales_KUF <- apply(tab[data$id=="KUF",], 2, mean)

library(repr)
options(repr.plot.width=8, repr.plot.height=5)
## plot by dimensions
par(mar = c(3, 3, 2, 2))
plot <- barplot(scales, col = "skyblue", ylim = c(-3,3), las = 1)
error.bar(plot,scales, 1.96*stDev/sqrt(10))
abline(h = 0.8, col = "darkgreen", lty = 2)
abline(h = -0.8, col = "red", lty = 2)
abline(h=0)
legend("bottomright", c("positive","neutral", "negative"), lty = 2, col = c("darkgreen", "white", "red"))

options(repr.plot.width=8, repr.plot.height=5)
## plot by grouped dimensions
par(mar = c(3, 3, 2, 2))
barplot(height = c(scales[1], mean(scales[2:4]), mean(scales[5:6])), col = "skyblue", ylim = c(-3,3), las = 1,
        names.arg = c("Attractiveness", "Pragmatic Quality", "Hedonic Quality"))
abline(h = 0.8, col = "darkgreen", lty = 2)
abline(h = -0.8, col = "red", lty = 2)
abline(h=0)
legend("topright", c("positive","neutral", "negative"), lty = 2, col = c("darkgreen", "white", "red"))

options(repr.plot.width=10, repr.plot.height=5)

## benchmark plot
par(mar = c(3, 3, 2, 10), xpd = TRUE)
plot2 <- barplot(as.matrix(tabBM), names.arg = dimensions, ylim = c(-1,2.5), las = 1, 
                 col = c(scales::alpha(c("white", colors), 0.7)), border = "white")

    ## 1st group = KUF (teachers)
    lines(plot2, scales_KUF, lwd = 2)
    points(plot2, scales_KUF, pch = 19)
    ## 2nd group = KDF (doctoral students)
    lines(plot2, scales_KDF, lwd = 2, lty = 2)
    points(plot2, scales_KDF, pch = 17)

legend("topright", inset = c(-0.4, 0.2), bty = "n",
       legend = c(rev(rownames(tabBM)[2:6]), "KUF", "KDF"),
       col = c(scales::alpha(rev(colors), 0.7), "black", "black"),
       pch = c(15, 15, 15, 15, 15, 19, 17), 
       lwd = c(NA, NA, NA, NA, NA, 2, 2),
       lty = c(NA, NA, NA, NA, NA, 1, 2))

## scale consistency - UEQ
alpha(DT[,1:26])

## scale consistencydimensions of UEQ
alpha(DT[,Attractiveness])
alpha(DT[,Perspicuity])
alpha(DT[,Efficiency])
alpha(DT[,Dependability])
alpha(DT[,Stimulation])
alpha(DT[,Novelty])