{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# UEQ\n",
    "## Loadings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: multilevel\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: nlme\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: MASS\n"
     ]
    }
   ],
   "source": [
    "## loadings\n",
    "library(psychometric)\n",
    "library(readxl)\n",
    "data <- as.data.frame(read_excel(\"UEQ-SK (data).xlsx\"))[,1:27]\n",
    "data <- data[,c(2:ncol(data),1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>obťažujúci pútavý</th><th scope=col>nepochopiteľný pochopiteľný</th><th scope=col>nápaditý tuctový</th><th scope=col>intuitívny neintuitívny</th><th scope=col>hodnotný menejcenný</th><th scope=col>nudný vzrušujúci</th><th scope=col>nezaujímavý zaujímavý</th><th scope=col>nepredvídateľný predvídateľný</th><th scope=col>rýchly pomalý</th><th scope=col>moderný tradičný</th><th scope=col>⋯</th><th scope=col>motivujúci demotivujúci</th><th scope=col>spĺňajúci očakávania nespĺňajúci očakávania</th><th scope=col>neefektívny efektívny</th><th scope=col>jasný mätúci</th><th scope=col>nepraktický praktický</th><th scope=col>prehľadný neprehľadný</th><th scope=col>príťažlivý nepríťažlivý</th><th scope=col>sympatický nesympatický</th><th scope=col>konzervatívny inovatívny</th><th scope=col>id</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>6  </td><td>5  </td><td>1  </td><td>3  </td><td>1  </td><td>6  </td><td>6  </td><td>4  </td><td>3  </td><td>2  </td><td>⋯  </td><td>1  </td><td>2  </td><td>7  </td><td>1  </td><td>7  </td><td>1  </td><td>2  </td><td>2  </td><td>7  </td><td>KDF</td></tr>\n",
       "\t<tr><td>6  </td><td>5  </td><td>2  </td><td>2  </td><td>2  </td><td>4  </td><td>6  </td><td>5  </td><td>2  </td><td>1  </td><td>⋯  </td><td>3  </td><td>3  </td><td>5  </td><td>2  </td><td>5  </td><td>2  </td><td>2  </td><td>2  </td><td>6  </td><td>KDF</td></tr>\n",
       "\t<tr><td>5  </td><td>6  </td><td>1  </td><td>2  </td><td>2  </td><td>4  </td><td>5  </td><td>4  </td><td>2  </td><td>2  </td><td>⋯  </td><td>2  </td><td>2  </td><td>6  </td><td>2  </td><td>7  </td><td>2  </td><td>2  </td><td>1  </td><td>6  </td><td>KDF</td></tr>\n",
       "\t<tr><td>6  </td><td>6  </td><td>5  </td><td>3  </td><td>2  </td><td>4  </td><td>5  </td><td>4  </td><td>4  </td><td>2  </td><td>⋯  </td><td>2  </td><td>3  </td><td>5  </td><td>3  </td><td>6  </td><td>3  </td><td>3  </td><td>2  </td><td>5  </td><td>KDF</td></tr>\n",
       "\t<tr><td>1  </td><td>2  </td><td>1  </td><td>1  </td><td>1  </td><td>1  </td><td>1  </td><td>1  </td><td>1  </td><td>1  </td><td>⋯  </td><td>1  </td><td>1  </td><td>1  </td><td>1  </td><td>1  </td><td>1  </td><td>1  </td><td>1  </td><td>1  </td><td>KUF</td></tr>\n",
       "\t<tr><td>6  </td><td>7  </td><td>2  </td><td>2  </td><td>2  </td><td>6  </td><td>6  </td><td>6  </td><td>2  </td><td>2  </td><td>⋯  </td><td>2  </td><td>2  </td><td>6  </td><td>2  </td><td>6  </td><td>2  </td><td>2  </td><td>2  </td><td>6  </td><td>KUF</td></tr>\n",
       "\t<tr><td>6  </td><td>6  </td><td>4  </td><td>2  </td><td>2  </td><td>5  </td><td>6  </td><td>4  </td><td>3  </td><td>5  </td><td>⋯  </td><td>2  </td><td>2  </td><td>6  </td><td>2  </td><td>6  </td><td>2  </td><td>2  </td><td>4  </td><td>7  </td><td>KUF</td></tr>\n",
       "\t<tr><td>6  </td><td>5  </td><td>4  </td><td>3  </td><td>6  </td><td>4  </td><td>6  </td><td>6  </td><td>5  </td><td>1  </td><td>⋯  </td><td>3  </td><td>2  </td><td>7  </td><td>2  </td><td>6  </td><td>3  </td><td>3  </td><td>3  </td><td>7  </td><td>KUF</td></tr>\n",
       "\t<tr><td>7  </td><td>6  </td><td>3  </td><td>2  </td><td>1  </td><td>5  </td><td>6  </td><td>5  </td><td>2  </td><td>1  </td><td>⋯  </td><td>2  </td><td>1  </td><td>7  </td><td>2  </td><td>7  </td><td>2  </td><td>2  </td><td>1  </td><td>7  </td><td>KUF</td></tr>\n",
       "\t<tr><td>6  </td><td>6  </td><td>2  </td><td>6  </td><td>1  </td><td>6  </td><td>7  </td><td>2  </td><td>3  </td><td>2  </td><td>⋯  </td><td>2  </td><td>1  </td><td>7  </td><td>2  </td><td>7  </td><td>1  </td><td>1  </td><td>1  </td><td>7  </td><td>KUF</td></tr>\n",
       "\t<tr><td>6  </td><td>6  </td><td>2  </td><td>1  </td><td>2  </td><td>5  </td><td>6  </td><td>7  </td><td>2  </td><td>2  </td><td>⋯  </td><td>2  </td><td>1  </td><td>7  </td><td>1  </td><td>6  </td><td>1  </td><td>2  </td><td>1  </td><td>6  </td><td>KUF</td></tr>\n",
       "\t<tr><td>5  </td><td>7  </td><td>2  </td><td>3  </td><td>2  </td><td>6  </td><td>6  </td><td>6  </td><td>2  </td><td>1  </td><td>⋯  </td><td>2  </td><td>3  </td><td>6  </td><td>3  </td><td>6  </td><td>3  </td><td>2  </td><td>2  </td><td>7  </td><td>KUF</td></tr>\n",
       "\t<tr><td>3  </td><td>3  </td><td>2  </td><td>5  </td><td>5  </td><td>3  </td><td>4  </td><td>3  </td><td>2  </td><td>3  </td><td>⋯  </td><td>3  </td><td>4  </td><td>4  </td><td>4  </td><td>5  </td><td>3  </td><td>4  </td><td>4  </td><td>5  </td><td>KUF</td></tr>\n",
       "\t<tr><td>5  </td><td>5  </td><td>3  </td><td>3  </td><td>3  </td><td>4  </td><td>7  </td><td>1  </td><td>2  </td><td>1  </td><td>⋯  </td><td>2  </td><td>3  </td><td>6  </td><td>3  </td><td>6  </td><td>1  </td><td>3  </td><td>3  </td><td>6  </td><td>KUF</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ]
     },
     "execution_count": 3,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "## dimensions of UEQ\n",
    "dimensions <- c(\"Attractiveness\", \"Perspicuity\", \"Efficiency\", \"Dependability\", \"Stimulation\", \"Novelty\")\n",
    "\n",
    "Attractiveness <- c(1,12,14,16,24,25)\n",
    "Perspicuity    <- c(2,4,13,21)\n",
    "Efficiency     <- c(9,20,22,23)\n",
    "Dependability  <- c(8,11,17,19)\n",
    "Stimulation    <- c(5:7,18)\n",
    "Novelty        <- c(3,10,15,26)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "## the order of the positive and negative term for an item\n",
    "positive <- c(1,2,6:8,11,13:16,20,22,26)\n",
    "negative <- setdiff(1:26, positive)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "## benchmark table\n",
    "tabBM <- as.data.frame(matrix(NA, 6, 6))\n",
    "colnames(tabBM) <- dimensions\n",
    "rownames(tabBM) <- c(\"lower border\", \"bad\", \"below average\", \"above average\", \"good\", \"excellent\")\n",
    "tabBM[1,] <- -1\n",
    "tabBM[2,] <- c(0.7, 0.64, 0.54, 0.78, 0.5, 0.3) +1\n",
    "tabBM[3,] <- c(0.47, 0.44, 0.44, 0.36, 0.49, 0.41)\n",
    "tabBM[4,] <- c(0.35, 0.48, 0.49, 0.34, 0.32, 0.34)\n",
    "tabBM[5,] <- c(0.23, 0.34, 0.31, 0.17, 0.24, 0.35)\n",
    "tabBM[6,] <- c(0.75, 0.6, 0.72, 0.85, 0.95, 1.1)\n",
    "\n",
    "colors <- c(\"red\", \"orange\", \"green\", \"forestgreen\", \"darkgreen\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "## define plot function with error bars\n",
    "error.bar <- function(x, y, upper, lower=upper, length=0.1,...){\n",
    "    if(length(x) != length(y) | length(y) !=length(lower) | length(lower) != length(upper))\n",
    "    stop(\"vectors must be same length\")\n",
    "    arrows(x,y+upper, x, y-lower, angle=90, code=3, length=length, ...)\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "## transformed values\n",
    "DT <- as.data.frame(matrix(NA,nrow(data), ncol(data)))\n",
    "colnames(DT) <- colnames(data)\n",
    "DT[,1] <- data[,1]\n",
    "\n",
    "for(i in 1:13){\n",
    "    DT[,positive[i]] <- data[positive[i]] - 4\n",
    "    DT[,negative[i]] <- 4 - data[negative[i]]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "## table of means for each respondent\n",
    "tab <- as.data.frame(matrix(NA, nrow(DT), 6))\n",
    "colnames(tab) <- dimensions\n",
    "\n",
    "tab$Attractiveness <- rowMeans(DT[,Attractiveness])\n",
    "tab$Perspicuity    <- rowMeans(DT[,Perspicuity])\n",
    "tab$Efficiency     <- rowMeans(DT[,Efficiency])\n",
    "tab$Dependability  <- rowMeans(DT[,Dependability])\n",
    "tab$Stimulation    <- rowMeans(DT[,Stimulation])\n",
    "tab$Novelty        <- rowMeans(DT[,Novelty])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl class=dl-horizontal>\n",
       "\t<dt>Attractiveness</dt>\n",
       "\t\t<dd>1.63095238095238</dd>\n",
       "\t<dt>Perspicuity</dt>\n",
       "\t\t<dd>1.19642857142857</dd>\n",
       "\t<dt>Efficiency</dt>\n",
       "\t\t<dd>1.76785714285714</dd>\n",
       "\t<dt>Dependability</dt>\n",
       "\t\t<dd>1.46428571428571</dd>\n",
       "\t<dt>Stimulation</dt>\n",
       "\t\t<dd>1.41071428571429</dd>\n",
       "\t<dt>Novelty</dt>\n",
       "\t\t<dd>1.71428571428571</dd>\n",
       "</dl>\n"
      ]
     },
     "execution_count": 8,
     "metadata": {
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<dl class=dl-horizontal>\n",
       "\t<dt>Attractiveness</dt>\n",
       "\t\t<dd>0.872609240408836</dd>\n",
       "\t<dt>Perspicuity</dt>\n",
       "\t\t<dd>0.915562938582321</dd>\n",
       "\t<dt>Efficiency</dt>\n",
       "\t\t<dd>0.749771027318736</dd>\n",
       "\t<dt>Dependability</dt>\n",
       "\t\t<dd>0.897952493909718</dd>\n",
       "\t<dt>Stimulation</dt>\n",
       "\t\t<dd>0.869389390369995</dd>\n",
       "\t<dt>Novelty</dt>\n",
       "\t\t<dd>0.656748567808626</dd>\n",
       "</dl>\n"
      ]
     },
     "execution_count": 8,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## means of all respondents\n",
    "scales <- apply(tab, 2, mean)\n",
    "stDev  <- apply(tab, 2, sd)\n",
    "\n",
    "scales\n",
    "stDev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "## means for different groups of respondents\n",
    "scales_KDF <- apply(tab[data$id==\"KDF\",], 2, mean)\n",
    "scales_KUF <- apply(tab[data$id==\"KUF\",], 2, mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 26,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "library(repr)\n",
    "options(repr.plot.width=8, repr.plot.height=5)\n",
    "## plot by dimensions\n",
    "par(mar = c(3, 3, 2, 2))\n",
    "plot <- barplot(scales, col = \"skyblue\", ylim = c(-3,3), las = 1)\n",
    "error.bar(plot,scales, 1.96*stDev/sqrt(10))\n",
    "abline(h = 0.8, col = \"darkgreen\", lty = 2)\n",
    "abline(h = -0.8, col = \"red\", lty = 2)\n",
    "abline(h=0)\n",
    "legend(\"bottomright\", c(\"positive\",\"neutral\", \"negative\"), lty = 2, col = c(\"darkgreen\", \"white\", \"red\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 27,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "options(repr.plot.width=8, repr.plot.height=5)\n",
    "## plot by grouped dimensions\n",
    "par(mar = c(3, 3, 2, 2))\n",
    "barplot(height = c(scales[1], mean(scales[2:4]), mean(scales[5:6])), col = \"skyblue\", ylim = c(-3,3), las = 1,\n",
    "        names.arg = c(\"Attractiveness\", \"Pragmatic Quality\", \"Hedonic Quality\"))\n",
    "abline(h = 0.8, col = \"darkgreen\", lty = 2)\n",
    "abline(h = -0.8, col = \"red\", lty = 2)\n",
    "abline(h=0)\n",
    "legend(\"topright\", c(\"positive\",\"neutral\", \"negative\"), lty = 2, col = c(\"darkgreen\", \"white\", \"red\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 28,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "options(repr.plot.width=10, repr.plot.height=5)\n",
    "\n",
    "## benchmark plot\n",
    "par(mar = c(3, 3, 2, 10), xpd = TRUE)\n",
    "plot2 <- barplot(as.matrix(tabBM), names.arg = dimensions, ylim = c(-1,2.5), las = 1, \n",
    "                 col = c(scales::alpha(c(\"white\", colors), 0.7)), border = \"white\")\n",
    "\n",
    "    ## 1st group = KUF (teachers)\n",
    "    lines(plot2, scales_KUF, lwd = 2)\n",
    "    points(plot2, scales_KUF, pch = 19)\n",
    "    ## 2nd group = KDF (doctoral students)\n",
    "    lines(plot2, scales_KDF, lwd = 2, lty = 2)\n",
    "    points(plot2, scales_KDF, pch = 17)\n",
    "\n",
    "legend(\"topright\", inset = c(-0.4, 0.2), bty = \"n\",\n",
    "       legend = c(rev(rownames(tabBM)[2:6]), \"KUF\", \"KDF\"),\n",
    "       col = c(scales::alpha(rev(colors), 0.7), \"black\", \"black\"),\n",
    "       pch = c(15, 15, 15, 15, 15, 19, 17), \n",
    "       lwd = c(NA, NA, NA, NA, NA, 2, 2),\n",
    "       lty = c(NA, NA, NA, NA, NA, 1, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.913657530924084"
      ]
     },
     "execution_count": 14,
     "metadata": {
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "0.77498496692724"
      ]
     },
     "execution_count": 14,
     "metadata": {
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "0.624880513450772"
      ]
     },
     "execution_count": 14,
     "metadata": {
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "0.333944206882509"
      ]
     },
     "execution_count": 14,
     "metadata": {
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "0.564452015900057"
      ]
     },
     "execution_count": 14,
     "metadata": {
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "0.580342268665758"
      ]
     },
     "execution_count": 14,
     "metadata": {
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "0.138004246284501"
      ]
     },
     "execution_count": 14,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## scale consistency - UEQ\n",
    "alpha(DT[,1:26])\n",
    "\n",
    "## scale consistencydimensions of UEQ\n",
    "alpha(DT[,Attractiveness])\n",
    "alpha(DT[,Perspicuity])\n",
    "alpha(DT[,Efficiency])\n",
    "alpha(DT[,Dependability])\n",
    "alpha(DT[,Stimulation])\n",
    "alpha(DT[,Novelty])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "R (R-Project)",
   "language": "r",
   "name": "ir"
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