evaluation/ux_evaluation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# UX Evaluation of \"Can I Joke on you?\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "UsageError: Line magic function `%` not found.\n"
     ]
    }
   ],
   "source": [
    "# install needed dependencies\n",
    "% pip install pandas matplotlib seaborn numpy pingouin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-27T14:38:07.208570100Z",
     "start_time": "2023-12-27T14:38:07.189517900Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# load dependencies\n",
    "import os \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import json\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-27T14:48:33.003116200Z",
     "start_time": "2023-12-27T14:48:31.811568400Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 403
    },
    "executionInfo": {
     "elapsed": 467,
     "status": "error",
     "timestamp": 1703685935851,
     "user": {
      "displayName": "Jens De Bock",
      "userId": "08772086773480223779"
     },
     "user_tz": -60
    },
    "id": "fbykrjkOjZWq",
    "outputId": "8c8bd4c8-16e8-41fe-a87b-e9e0d797beac"
   },
   "outputs": [],
   "source": [
    "# load data\n",
    "# A = with recommender\n",
    "# B = without recommender\n",
    "\n",
    "base_path = \"./questionnaires/\"\n",
    "\n",
    "with_recommender_file_path = base_path + \"A.csv\"\n",
    "without_recommender_file_path = base_path + \"B.csv\"\n",
    "A_data = pd.read_csv(with_recommender_file_path)\n",
    "B_data = pd.read_csv(without_recommender_file_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Date', 'Birthdate', 'Gender', 'Latest Education', 'Dead - Alive', 'Stagnant - Lively', 'Mechanical - Organic', 'Artificial - Lifelike', 'Inert - Interactive', 'Apathetic - Responsive', 'Dislike - Like', 'Unfriendly - Friendly', 'Unkind - Kind', 'Unpleasant - Pleasant', 'Awful - Nice', 'Incompetent - Competent', 'Ignorant - Knowledgeable', 'Irresponsible - Responsible', 'Unintelligent - Intelligent', 'Foolish - Sensible', 'Anxious - Relaxed', 'Agitated - Calm', 'Quiescent - Surprised', 'Feedback']\n"
     ]
    }
   ],
   "source": [
    "# change labels in dfs\n",
    "\n",
    "# animacy_labels = [[\"Dead\", \"Alive\"], [\"Stagnant\", \"Lively\"], [\"Mechanical\", \"Organic\"], [\"Artificial\", \"Lifelike\"], [\"Inert\", \"Interactive\"], [\"Apathetic\", \"Responsive\"]]\n",
    "# likeability_labels = [[\"Dislike\", \"Like\"], [\"Unfriendly\", \"Friendly\"], [\"Unkind\", \"Kind\"], [\"Unpleasant\", \"Pleasant\"], [\"Awful\", \"Nice\"]]\n",
    "# percived_intelligence_labels = [[\"Incompetent\", \"Competent\"], [\"Ignorant\", \"Knowledgeable\"], [\"Irresponsible\", \"Responsible\"], [\"Unintelligent\", \"Intelligent\"], [\"Foolish\", \"Sensible\"]]\n",
    "# percived_safety_labels = [[\"Anxious\", \"Relaxed\"], [\"Agitated\", \"Calm\"], [\"Quiescent\", \"Surprised\"]]\n",
    "\n",
    "animacy_labels = [\"Dead - Alive\", \"Stagnant - Lively\", \"Mechanical - Organic\", \"Artificial - Lifelike\", \"Inert - Interactive\", \"Apathetic - Responsive\"]\n",
    "likeability_labels = [\"Dislike - Like\", \"Unfriendly - Friendly\", \"Unkind - Kind\", \"Unpleasant - Pleasant\", \"Awful - Nice\"]\n",
    "percived_intelligence_labels = [\"Incompetent - Competent\", \"Ignorant - Knowledgeable\", \"Irresponsible - Responsible\", \"Unintelligent - Intelligent\", \"Foolish - Sensible\"]\n",
    "percived_safety_labels = [\"Anxious - Relaxed\", \"Agitated - Calm\", \"Quiescent - Surprised\"]\n",
    "\n",
    "all_labels = [\"Date\", \"Birthdate\", \"Gender\", \"Latest Education\"] + animacy_labels + likeability_labels + percived_intelligence_labels + percived_safety_labels + [\"Feedback\"]\n",
    "\n",
    "print(all_labels)\n",
    "\n",
    "A_data.columns = all_labels\n",
    "B_data.columns = all_labels\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Validity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        vertical-align: top;\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Marca de temps</th>\n",
       "      <th>Birthdate</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Latest Education</th>\n",
       "      <th>GODSPEED 2: ANIMACY</th>\n",
       "      <th>GODSPEED 2: ANIMACY.1</th>\n",
       "      <th>GODSPEED 2: ANIMACY.2</th>\n",
       "      <th>GODSPEED 2: ANIMACY.3</th>\n",
       "      <th>GODSPEED 2: ANIMACY.4</th>\n",
       "      <th>GODSPEED 2: ANIMACY.5</th>\n",
       "      <th>...</th>\n",
       "      <th>GODSPEED 3: LIKEABILITY.4</th>\n",
       "      <th>GODSPEED 4: PERCEIVED INTELLIGENCE</th>\n",
       "      <th>GODSPEED 4: PERCEIVED INTELLIGENCE.1</th>\n",
       "      <th>GODSPEED 4: PERCEIVED INTELLIGENCE.2</th>\n",
       "      <th>GODSPEED 4: PERCEIVED INTELLIGENCE.3</th>\n",
       "      <th>GODSPEED 4: PERCEIVED INTELLIGENCE.4</th>\n",
       "      <th>GODSPEED 5: PERCEIVED SAFETY</th>\n",
       "      <th>GODSPEED 5: PERCEIVED SAFETY.1</th>\n",
       "      <th>GODSPEED 5: PERCEIVED SAFETY.2</th>\n",
       "      <th>Additional Feedback :)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023/12/17 2:45:54 p. m. CET</td>\n",
       "      <td>2001-01-26</td>\n",
       "      <td>Male</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023/12/18 2:03:42 p. m. CET</td>\n",
       "      <td>1999-05-20</td>\n",
       "      <td>Male</td>\n",
       "      <td>Master</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023/12/19 1:57:22 p. m. CET</td>\n",
       "      <td>2002-09-10</td>\n",
       "      <td>Male</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023/12/19 2:39:46 p. m. CET</td>\n",
       "      <td>2000-01-18</td>\n",
       "      <td>Male</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023/12/19 7:14:13 p. m. CET</td>\n",
       "      <td>1997-08-25</td>\n",
       "      <td>Male</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2023/12/19 8:02:03 p. m. CET</td>\n",
       "      <td>2003-04-25</td>\n",
       "      <td>Female</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2023/12/19 8:47:53 p. m. CET</td>\n",
       "      <td>1971-09-22</td>\n",
       "      <td>Female</td>\n",
       "      <td>Master</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2023/12/20 10:07:42 a. m. CET</td>\n",
       "      <td>2000-03-12</td>\n",
       "      <td>Female</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2023/12/20 10:20:29 a. m. CET</td>\n",
       "      <td>2001-10-31</td>\n",
       "      <td>Male</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2023/12/21 6:42:21 p. m. CET</td>\n",
       "      <td>1998-07-10</td>\n",
       "      <td>Male</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2023/12/22 8:12:20 a. m. CET</td>\n",
       "      <td>1997-06-29</td>\n",
       "      <td>Male</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2023/12/22 9:44:54 a. m. CET</td>\n",
       "      <td>2000-01-28</td>\n",
       "      <td>Female</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2023/12/22 10:55:02 a. m. CET</td>\n",
       "      <td>1996-03-24</td>\n",
       "      <td>Male</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2023/12/22 3:13:25 p. m. CET</td>\n",
       "      <td>1998-12-08</td>\n",
       "      <td>Male</td>\n",
       "      <td>Master</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>Thanks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2023/12/23 11:49:21 a. m. CET</td>\n",
       "      <td>1972-03-14</td>\n",
       "      <td>Male</td>\n",
       "      <td>Master</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2023/12/23 4:59:34 p. m. CET</td>\n",
       "      <td>2000-03-08</td>\n",
       "      <td>Male</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2023/12/27 2:29:16 p. m. CET</td>\n",
       "      <td>2002-04-16</td>\n",
       "      <td>Female</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2023/12/29 2:55:29 p. m. CET</td>\n",
       "      <td>2001-09-18</td>\n",
       "      <td>Male</td>\n",
       "      <td>Master</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2023/12/29 10:09:16 p. m. CET</td>\n",
       "      <td>1999-01-01</td>\n",
       "      <td>Male</td>\n",
       "      <td>Master</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Bro... I can't answer accurately</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2024/01/06 2:36:02 p. m. CET</td>\n",
       "      <td>2000-02-08</td>\n",
       "      <td>Male</td>\n",
       "      <td>Master</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2024/01/06 3:30:41 p. m. CET</td>\n",
       "      <td>2001-08-31</td>\n",
       "      <td>Male</td>\n",
       "      <td>Master</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Marca de temps   Birthdate  Gender Latest Education  \\\n",
       "0    2023/12/17 2:45:54 p. m. CET  2001-01-26    Male         Bachelor   \n",
       "1    2023/12/18 2:03:42 p. m. CET  1999-05-20    Male           Master   \n",
       "2    2023/12/19 1:57:22 p. m. CET  2002-09-10    Male         Bachelor   \n",
       "3    2023/12/19 2:39:46 p. m. CET  2000-01-18    Male         Bachelor   \n",
       "4    2023/12/19 7:14:13 p. m. CET  1997-08-25    Male         Bachelor   \n",
       "5    2023/12/19 8:02:03 p. m. CET  2003-04-25  Female         Bachelor   \n",
       "6    2023/12/19 8:47:53 p. m. CET  1971-09-22  Female           Master   \n",
       "7   2023/12/20 10:07:42 a. m. CET  2000-03-12  Female         Bachelor   \n",
       "8   2023/12/20 10:20:29 a. m. CET  2001-10-31    Male         Bachelor   \n",
       "9    2023/12/21 6:42:21 p. m. CET  1998-07-10    Male         Bachelor   \n",
       "10   2023/12/22 8:12:20 a. m. CET  1997-06-29    Male         Bachelor   \n",
       "11   2023/12/22 9:44:54 a. m. CET  2000-01-28  Female         Bachelor   \n",
       "12  2023/12/22 10:55:02 a. m. CET  1996-03-24    Male         Bachelor   \n",
       "13   2023/12/22 3:13:25 p. m. CET  1998-12-08    Male           Master   \n",
       "14  2023/12/23 11:49:21 a. m. CET  1972-03-14    Male           Master   \n",
       "15   2023/12/23 4:59:34 p. m. CET  2000-03-08    Male         Bachelor   \n",
       "16   2023/12/27 2:29:16 p. m. CET  2002-04-16  Female         Bachelor   \n",
       "17   2023/12/29 2:55:29 p. m. CET  2001-09-18    Male           Master   \n",
       "18  2023/12/29 10:09:16 p. m. CET  1999-01-01    Male           Master   \n",
       "19   2024/01/06 2:36:02 p. m. CET  2000-02-08    Male           Master   \n",
       "20   2024/01/06 3:30:41 p. m. CET  2001-08-31    Male           Master   \n",
       "\n",
       "    GODSPEED 2: ANIMACY  GODSPEED 2: ANIMACY.1  GODSPEED 2: ANIMACY.2  \\\n",
       "0                     3                      3                      2   \n",
       "1                     1                      1                      1   \n",
       "2                     4                      3                      1   \n",
       "3                     2                      1                      1   \n",
       "4                     4                      3                      2   \n",
       "5                     1                      2                      2   \n",
       "6                     4                      4                      3   \n",
       "7                     3                      3                      2   \n",
       "8                     2                      3                      3   \n",
       "9                     2                      3                      3   \n",
       "10                    3                      2                      1   \n",
       "11                    3                      4                      3   \n",
       "12                    4                      4                      3   \n",
       "13                    4                      4                      1   \n",
       "14                    2                      2                      1   \n",
       "15                    4                      3                      1   \n",
       "16                    2                      2                      2   \n",
       "17                    5                      4                      5   \n",
       "18                    3                      3                      3   \n",
       "19                    3                      3                      3   \n",
       "20                    3                      3                      3   \n",
       "\n",
       "    GODSPEED 2: ANIMACY.3  GODSPEED 2: ANIMACY.4  GODSPEED 2: ANIMACY.5  ...  \\\n",
       "0                       3                      3                      4  ...   \n",
       "1                       1                      1                      1  ...   \n",
       "2                       1                      3                      2  ...   \n",
       "3                       2                      3                      2  ...   \n",
       "4                       4                      3                      2  ...   \n",
       "5                       3                      3                      3  ...   \n",
       "6                       3                      3                      3  ...   \n",
       "7                       3                      3                      3  ...   \n",
       "8                       2                      3                      3  ...   \n",
       "9                       2                      3                      3  ...   \n",
       "10                      1                      2                      1  ...   \n",
       "11                      4                      3                      4  ...   \n",
       "12                      2                      4                      3  ...   \n",
       "13                      4                      5                      5  ...   \n",
       "14                      1                      2                      2  ...   \n",
       "15                      2                      3                      3  ...   \n",
       "16                      2                      2                      3  ...   \n",
       "17                      2                      2                      3  ...   \n",
       "18                      3                      3                      3  ...   \n",
       "19                      3                      3                      3  ...   \n",
       "20                      3                      3                      3  ...   \n",
       "\n",
       "    GODSPEED 3: LIKEABILITY.4  GODSPEED 4: PERCEIVED INTELLIGENCE  \\\n",
       "0                           4                                   4   \n",
       "1                           3                                   2   \n",
       "2                           4                                   4   \n",
       "3                           4                                   1   \n",
       "4                           5                                   3   \n",
       "5                           3                                   3   \n",
       "6                           4                                   3   \n",
       "7                           5                                   4   \n",
       "8                           4                                   4   \n",
       "9                           3                                   3   \n",
       "10                          2                                   3   \n",
       "11                          5                                   5   \n",
       "12                          4                                   3   \n",
       "13                          5                                   3   \n",
       "14                          3                                   3   \n",
       "15                          2                                   3   \n",
       "16                          3                                   4   \n",
       "17                          4                                   2   \n",
       "18                          3                                   3   \n",
       "19                          3                                   3   \n",
       "20                          3                                   3   \n",
       "\n",
       "    GODSPEED 4: PERCEIVED INTELLIGENCE.1  \\\n",
       "0                                      4   \n",
       "1                                      2   \n",
       "2                                      4   \n",
       "3                                      2   \n",
       "4                                      4   \n",
       "5                                      3   \n",
       "6                                      3   \n",
       "7                                      3   \n",
       "8                                      4   \n",
       "9                                      3   \n",
       "10                                     1   \n",
       "11                                     4   \n",
       "12                                     4   \n",
       "13                                     1   \n",
       "14                                     3   \n",
       "15                                     2   \n",
       "16                                     4   \n",
       "17                                     2   \n",
       "18                                     3   \n",
       "19                                     3   \n",
       "20                                     3   \n",
       "\n",
       "    GODSPEED 4: PERCEIVED INTELLIGENCE.2  \\\n",
       "0                                      3   \n",
       "1                                      1   \n",
       "2                                      2   \n",
       "3                                      3   \n",
       "4                                      4   \n",
       "5                                      3   \n",
       "6                                      3   \n",
       "7                                      3   \n",
       "8                                      4   \n",
       "9                                      3   \n",
       "10                                     3   \n",
       "11                                     3   \n",
       "12                                     2   \n",
       "13                                     1   \n",
       "14                                     2   \n",
       "15                                     1   \n",
       "16                                     3   \n",
       "17                                     2   \n",
       "18                                     3   \n",
       "19                                     3   \n",
       "20                                     3   \n",
       "\n",
       "    GODSPEED 4: PERCEIVED INTELLIGENCE.3  \\\n",
       "0                                      4   \n",
       "1                                      1   \n",
       "2                                      3   \n",
       "3                                      1   \n",
       "4                                      4   \n",
       "5                                      2   \n",
       "6                                      3   \n",
       "7                                      3   \n",
       "8                                      3   \n",
       "9                                      2   \n",
       "10                                     3   \n",
       "11                                     4   \n",
       "12                                     3   \n",
       "13                                     4   \n",
       "14                                     2   \n",
       "15                                     4   \n",
       "16                                     4   \n",
       "17                                     3   \n",
       "18                                     3   \n",
       "19                                     3   \n",
       "20                                     3   \n",
       "\n",
       "    GODSPEED 4: PERCEIVED INTELLIGENCE.4  GODSPEED 5: PERCEIVED SAFETY  \\\n",
       "0                                      3                             5   \n",
       "1                                      2                             5   \n",
       "2                                      1                             4   \n",
       "3                                      3                             4   \n",
       "4                                      3                             5   \n",
       "5                                      3                             2   \n",
       "6                                      3                             4   \n",
       "7                                      3                             3   \n",
       "8                                      3                             4   \n",
       "9                                      2                             4   \n",
       "10                                     3                             4   \n",
       "11                                     3                             4   \n",
       "12                                     2                             5   \n",
       "13                                     2                             4   \n",
       "14                                     2                             5   \n",
       "15                                     3                             3   \n",
       "16                                     3                             4   \n",
       "17                                     3                             4   \n",
       "18                                     3                             3   \n",
       "19                                     3                             3   \n",
       "20                                     3                             3   \n",
       "\n",
       "    GODSPEED 5: PERCEIVED SAFETY.1  GODSPEED 5: PERCEIVED SAFETY.2  \\\n",
       "0                                4                               2   \n",
       "1                                4                               2   \n",
       "2                                4                               2   \n",
       "3                                5                               2   \n",
       "4                                4                               3   \n",
       "5                                2                               3   \n",
       "6                                4                               3   \n",
       "7                                5                               4   \n",
       "8                                4                               4   \n",
       "9                                5                               3   \n",
       "10                               3                               2   \n",
       "11                               5                               3   \n",
       "12                               5                               4   \n",
       "13                               4                               5   \n",
       "14                               5                               3   \n",
       "15                               2                               4   \n",
       "16                               4                               4   \n",
       "17                               2                               3   \n",
       "18                               3                               3   \n",
       "19                               3                               3   \n",
       "20                               3                               3   \n",
       "\n",
       "               Additional Feedback :)  \n",
       "0                                 NaN  \n",
       "1                                 NaN  \n",
       "2                                 NaN  \n",
       "3                                 NaN  \n",
       "4                                 NaN  \n",
       "5                                 NaN  \n",
       "6                                 NaN  \n",
       "7                                 NaN  \n",
       "8                                 NaN  \n",
       "9                                 NaN  \n",
       "10                                NaN  \n",
       "11                                NaN  \n",
       "12                                NaN  \n",
       "13                             Thanks  \n",
       "14                                NaN  \n",
       "15                                NaN  \n",
       "16                                NaN  \n",
       "17                                NaN  \n",
       "18  Bro... I can't answer accurately   \n",
       "19                                NaN  \n",
       "20                                NaN  \n",
       "\n",
       "[21 rows x 24 columns]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2500x500 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2500x500 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Function to plot bar charts for a given category for both A_data and B_data\n",
    "def plot_category(category_columns, A_data, B_data, title, num_cols=3):\n",
    "    # Plotting subplots for each category\n",
    "    fig, axs = plt.subplots(1, len(category_columns), figsize=(5 * len(category_columns), 5), sharex=True)\n",
    "\n",
    "    # Plot questions for A_data\n",
    "    for i, category in enumerate(category_columns):\n",
    "        axs[i].bar(A_data[category].value_counts().index - 0.2, A_data[category].value_counts().values, alpha=0.7, width=0.4, label='A', color='blue')\n",
    "\n",
    "    # Plot questions for B_data\n",
    "    for i, category in enumerate(category_columns):\n",
    "        axs[i].bar(B_data[category].value_counts().index + 0.2, B_data[category].value_counts().values, alpha=0.7, width=0.4, label='B', color='orange')\n",
    "\n",
    "        axs[i].set_title(category)\n",
    "        axs[i].legend()\n",
    "\n",
    "    plt.tight_layout(rect=[0, 0.03, 1, 0.95])\n",
    "    plt.suptitle(title, fontsize=16)\n",
    "    plt.show()\n",
    "\n",
    "# Visualize animacy for both A_data and B_data\n",
    "plot_category(animacy_labels, A_data, B_data, \"Animacy\")\n",
    "\n",
    "# Visualize likeability for both A_data and B_data\n",
    "plot_category(likeability_labels, A_data, B_data, \"Likeability\")\n",
    "\n",
    "# Visualize perceived intelligence for both A_data and B_data\n",
    "plot_category(percived_intelligence_labels, A_data, B_data, \"Perceived Intelligence\")\n",
    "\n",
    "# Visualize perceived safety for both A_data and B_data\n",
    "plot_category(percived_safety_labels, A_data, B_data, \"Perceived Safety\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2500x500 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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nn32mqlWrysvLS7Vr17acP5dufN++8cYbkqTy5ctbxss3355s4cKFevDBB+Xt7a3ChQurVatW2r179x0dk61x4KZNm/Tggw+qUKFCllz98ccf28zt9sRiz/WKo0ePWgrko6OjLced2zO/854433sCAPcKe87HS/Zdz7XF1hht3bp1Cg0NlZ+fnzw9PRUUFKSnnnoq05hSkl3XFO7GP//8I0lZzvDu4mJdXmPPmD8710QOHz6sTp06KTAwUGazWaVLl1bz5s21Z88eSx9btw2Xbpz3sOe6ty32nssHnAFFbnAq169f1+OPP67mzZvryy+/VK9evfTBBx9o3Lhxlj4pKSkKDw/XqFGj1K5dOy1dulSzZ89WkyZNdPz4cUk3ismeeOIJvffee3ruuee0atUqDR48WHPmzNGjjz6aqWhu7969ioyM1JAhQ7RkyRIVLVpUHTp00DvvvKOPP/5YY8eO1bx585SYmKh27drp33//zRR3mzZt1Lx5cy1btkz9+vXTf//7Xz377LNW/Xr37q2BAweqRYsWWrZsmaZMmaJ9+/apSZMmlhPOU6ZMUdOmTeXv768tW7ZYHtKN5NS+fXu9++676tKli1atWqV3331Xa9euVWhoaKa49u7dq9dee02DBg3Sl19+qVq1aumFF17Q999/L+nGrVjGjh0rSfrPf/5j2VfGC9x36ptvvpEkPfHEE3b137hxox599FElJiZq5syZmj9/vnx8fBQREaGFCxdm6v/iiy+qaNGiWrBggd5++23FxcXppZdeUtu2bVW7dm0tXrxY3bt31/vvv6/JkydnWv+jjz7S6tWrFRsbq7lz58rFxUXh4eFWP9Z+++03NWjQQL/++qvef/99rVy5Um3btlX//v0tU57XrVtXs2bNkiS9/fbbltfxxRdflCStX79eTZs21YULFzRt2jR9+eWXqlOnjp599lmbBYUvvvii3N3dFRcXp/Hjx2vDhg3q1q2bZfnSpUt1//33KyQkxLKvpUuX2vUaA0BumT59ul5++WXVqlVLS5Ys0dtvv63o6Girk6c3+89//qO1a9cqNjZW8+bN0+XLl9WmTRslJiZa+sTFxal9+/YqUqSI5s+fr5kzZ+r8+fMKDQ3Vpk2bMm2zQ4cOeuCBB/T5559r2rRpkm4MICtXrqzY2FitWbNG48aN0+nTp9WgQQP9/fffmbbhLN/BX3zxhdq1a6cGDRpo3bp1KlGihM1+CQkJ6tq1q7p166bly5crPDxckZGRmjt3rqXP2bNn1aRJE33zzTcaNWqUli9frhYtWuj1119Xv379LP1atGih77//3lIY/tdff+nXX3+Vp6en1q5da+n37bffqnTp0qpZs2aW8c+dO1ePPfaYihQpojlz5mjRokXy9fVVq1atrAbH3333ndq3by8fHx8tWLBAEyZM0KJFiyx5NV1aWpoiIiIUFxenIUOGaOnSpWrUqJHNqe3tyd3SjT+aOHfunF5//XUtW7ZM8+fP10MPPaQOHTro008/zbTd5cuX68MPP9TIkSO1ePFilStXTp07d9bixYuzfB2k7P8OAIDcdu3aNT3++ON69NFH9eWXX1p9Ny5btkxTp07ViBEjtGbNGj388MN2f6+OHz9eUVFR6ty5s1atWqWFCxfqhRdeyFTAJElPPvmkHnjgAS1evFhRUVFatmyZWrVqZfXHSX369NGQIUPUsmVLLV++XKNGjdLq1avVpEmTTDncnnyYnfgySkhIUKdOndS1a1d9+eWX6tixo0aPHn3b27klJCSoYcOGWrNmjUaMGKGvv/5aL7zwgmJiYvTSSy/ddr854ciRI5KkSpUqWdrszU2DBw/W1KlT1b9/f61evVqfffaZnn76actJ/3T2nBO5k/M0tzqfkZ34bNmzZ48GDhyoQYMGaenSpWrSpIkGDBiQZbF9Onv/PQCAM3rrrbd07Ngxffzxx5o+fboOHjyoiIgIu4q2V61apY8++kgjR47UF198IV9fXz355JOWP95+8cUX9X//93+SbvxhVPp4uW7dupKksWPHqnPnzqpWrZoWLVqkzz77TBcvXrT81rhbP//8s1q2bKkrV65ozpw5mjZtmnbt2qUxY8Zk6pudWG53vSIgIECrV6+WJL3wwguW4x4+fPhdH9Pt8J4433sCAPlBamqqUlJSrB7p7D0fn93rubdy9OhRtW3bVh4eHvrkk0+0evVqvfvuu/L29ta1a9es+tpzTeFu1a9fX+7u7howYIDmzZun06dPZ9k3u2N+e+Jv06aNdu7cqfHjx2vt2rWaOnWqQkJC7Dp3Yc91b1vsPZcPOA0DcIBZs2YZkozt27db2rp3725IMhYtWmTVt02bNkblypUtzz/99FNDkjFjxowst7969WpDkjF+/Hir9oULFxqSjOnTp1vaypUrZ3h6ehonTpywtO3Zs8eQZAQEBBiXL1+2tC9btsyQZCxfvjxT3JMmTbLa15gxYwxJxqZNmwzDMIwtW7YYkoz333/fql98fLzh6elpvPnmm5a2tm3bGuXKlct0XPPnzzckGV988YVV+/bt2w1JxpQpU6yOq1ChQsaxY8csbf/++6/h6+tr9O7d29L2+eefG5KM9evXZ9rf3WrdurUhybh69apd/Rs3bmyUKlXKuHjxoqUtJSXFqFGjhlGmTBkjLS3NMIz/fX7+7//+z2r9J554wpBkTJw40aq9Tp06Rt26dS3Pjxw5YkgyAgMDjX///dfSnpSUZPj6+hotWrSwtLVq1cooU6aMkZiYaLXNfv36GYUKFTLOnTtnGMb/3oNZs2ZlOq4qVaoYISEhxvXr163a27VrZwQEBBipqalWx9W3b1+rfuPHjzckGadPn7a0Va9e3WjWrFmmfQFAXsiYx1NTUw1/f3+jUaNGVv2OHTtmuLu7W+W09O/gmjVrGikpKZb2bdu2GZKM+fPnW7YZGBho1KxZ0/I9aRiGcfHiRaNUqVJGkyZNLG3vvPOOIckYMWLEbWNPSUkxLl26ZHh7e1vlbmf5Dk4/FknG/fffb5WnMmrWrJkhyfjpp5+s2qtVq2a0atXK8nzo0KE2+/Xp08cwmUzG/v37DcMwjG+//daQZHz//feGYRjG3LlzDR8fH6Nv375GWFiYZb2KFSsaXbp0sTxPf+2OHDliGIZhXL582fD19TUiIiKs9peammrUrl3baNiwoaWtUaNGWebjm4cqq1atMiQZU6dOtdpmTEyMIcl45513LG325u6MUlJSjOvXrxsvvPCCERISYrVMkuHp6WkkJCRY9a9SpYrxwAMPWNrWr1+f6XeVvb8DACCn3Wrc/cknn2TqL8koWrRopu9Je79X27VrZ9SpU+eWMaXnuUGDBlm1z5s3z5BkzJ071zAMw/j9999t5uWffvrJkGS89dZbljZ786E98WXMaTdv/8svv7Tq+9JLLxkuLi5WY+6MOal3795G4cKFrfoYhmG89957hiRj3759t4wnO9Jj37p1q3H9+nXj4sWLxurVqw1/f3/jkUcescpD9uamGjVqGE888cQt92vvOZHsnqex53yGPfGlf+ZuVq5cOcNkMhl79uyxam/ZsqVRpEgRy3mg9N+tN4/z7/R3BgDkpPTvtrNnz9pcnnHMmj5OadOmjVW/RYsWGZKMLVu2WNq6d++e6by0JKN06dJGUlKSpS0hIcFwcXExYmJiLG0TJkzIlEcNwzCOHz9uuLm5ZTqXe/HiRcPf39945plnMh3bzZo1a5ZpDJ4x5z799NOGt7e31WuSmppqVKtWzSqm7MRi7/WKs2fPZoonJzVr1syoXr26VRvviWPfEwDIj9LHjLYe169fz9b5eHuv59o6V5oxryxevNiQlGl8djN7rynklJkzZxqFCxe2vD4BAQHG888/bzlvns7eMb+98f/999+GJCM2NvaW8WXMw9m57n035/IBZ8FMbnAqJpNJERERVm21atWyuqXY119/rUKFCqlXr15ZbmfdunWSlGkK6qefflre3t6Zqo7r1Kmj++67z/K8atWqkm5M93nzrTTT2zPe4kySunbtavW8S5cukm78hbQkrVy5UiaTSd26dbOqjvf391ft2rWznOnmZitXrlSxYsUUERFhtY06derI398/0zbq1KmjoKAgy/NChQqpUqVKNuO3V8bqfsMw7nhbN7t8+bJ++ukndezYUYULF7a0u7q66rnnntOJEycyTZ/frl07q+fp70/GmeiqVq1q85g7dOigQoUKWZ6n/5XB999/r9TUVF29elXfffednnzySXl5eVkdd5s2bXT16tXbToN76NAh/fHHH5bPR8ZtnD59OtNxPf7441bPa9WqJcn25w4AnMH+/fstt4q+WVBQkJo2bWpznbZt28rV1dXyPON33f79+3Xq1Ck999xzVlOAFy5cWE899ZS2bt2aabryp556KtN+Ll26pCFDhuiBBx6Qm5ub3NzcVLhwYV2+fFm///57pv45/R2clpZm9d1vz1/Ip8dx+PDhTLf6yMjf318NGzbMFPPN8a5bt07VqlXL1K9Hjx4yDMPyu6lp06YqVKiQvv32W0myzBTbunVrbd68WVeuXFF8fLwOHjyoFi1aZBnT5s2bde7cOXXv3t3q2NPS0tS6dWtt375dly9f1uXLl7V9+/Ys8/HNNm7cKEmZPmOdO3e2ep7d3P3555+radOmKly4sNzc3OTu7q6ZM2fa/Gw0b95cpUuXtjx3dXXVs88+q0OHDmW6dV66O/kdAAB5wVbOlKRHH31UxYsXtzzPzvdqw4YNtXfvXvXt21dr1qxRUlJSlvvPOH5+5pln5ObmZhk/p/8345i+YcOGqlq1aqYxvT35MDvxZeTj45PpN0KXLl2UlpZmNbNYRitXrlRYWJgCAwOtXrvw8HBJ/8tvttzpb4jGjRvL3d1dPj4+at26tYoXL64vv/xSbm5ukrKXmxo2bKivv/5aQ4cO1YYNGzLNHn+z250TuZPzNLc7n5Gd+DKqXr26ateunSnmpKQk7dq1y+Y6OXGOAAAc6W7Gu2FhYfLx8bE8L126tEqVKmXXumvWrFFKSoqef/55q+/OQoUKqVmzZnadF7+d9Bllbp4F3cXFJdMYMrux2HO9IrsyzqBz8+3Vs4P35H/u9j0BgILk008/1fbt260ebm5udp+Pv5PrubdSp04deXh46OWXX9acOXMsM5LacrtrClnJ7rXtXr166cSJE4qLi1P//v1VtmxZzZ07V82aNdOECRMs/bI75r9d/L6+vqpQoYImTJigiRMnavfu3dn6nXC769622HsuH3AmFLnBqXh5eVl9+UqS2WzW1atXLc/Pnj2rwMDATPe8vtk///wjNzc3lSxZ0qrdZDLJ398/060rfH19rZ57eHjcsv3meCTJzc1Nfn5+Vm3+/v6WWKQbt/wyDEOlS5eWu7u71WPr1q02b5mW0V9//aULFy7Iw8Mj0zYSEhIybSNjTNKN1zM7J35vdvTo0Uz7vdVJ+fQT0um3R7mV8+fPyzAMm/c4DwwMlKS7et8yvmfS/96jjG3Xrl3TpUuX9M8//yglJUWTJ0/OdNxt2rSRpNu+b+m3oX399dczbaNv3742t5HxfTObzZJ0x+8bAOS29O/nmwuA0tlqk27/XZe+zazyQlpams6fP2/Vbqtvly5d9NFHH+nFF1/UmjVrtG3bNm3fvl0lS5a0+b2a09/BvXr1svrub968uV3rzZgxQz169NC4ceP05ptvZtnPnlz/zz//2JVfCxUqpKZNm1qK3L777ju1bNlSoaGhSk1N1Q8//GC5bemtitzSc1/Hjh0z5b5x48bJMAydO3dO58+fV1paWpb5+Gbpv+0y5viMn6/s5O4lS5bomWee0X333ae5c+dqy5Yt2r59u3r16pWt3w03v4ZZvRbZ+R0AALnNy8tLRYoUsbksY77IzvdqZGSk3nvvPW3dulXh4eHy8/NT8+bNtWPHjkz7yfidmj6mTv8+vd3vgIzfu/bkw+zEl5Gt3zO3ywHSjTywYsWKTK9d9erVJd06B4wcOdJqnQoVKtw2Tul/FyzWrVun3r176/fff7cqCs9Obvrwww81ZMgQLVu2TGFhYfL19dUTTzyhgwcPWu3TnnMi2T1PY897am98ttxJXs+JcwQAkBPSC5ezuliZkpIid3f3TO13M969m/PM6bmnQYMGmb4/Fy5cmCPfnf/8849d5ySyG4s91yuyq0KFClb7HTly5B1th/fkf+72PQGAgqRq1aqqX7++1UOy/3z8nVzPvZUKFSro22+/ValSpfTqq6+qQoUKqlChgiZNmpSp753+lsmYX+bMmXPbuIoWLarOnTtr0qRJ+umnn/Tzzz+rdOnSGjZsmOXWodkd898ufpPJpO+++06tWrXS+PHjVbduXZUsWVL9+/fXxYsXbxvz7a5722LvuXzAmbg5OgAgu0qWLKlNmzYpLS0ty0I3Pz8/paSk6OzZs1YnUA3DUEJCgho0aJCjMaWkpOiff/6xSk4JCQmWWCSpRIkSMplM+uGHHyxJ62a22jIqUaKE/Pz8tHr1apvLb/7LrdwQGBio7du3W7VVrlw5y/6tWrXS9OnTtWzZMg0dOvSW2y5evLhcXFxs3tv81KlTkmT1V185If09ytjm4eGhwoULy93d3fKXB6+++qrNbZQvX/6W+0iPOTIyUh06dLDZ51avIQDkB+m5Ln1AdDNb37XZ2WZWecHFxcVqphnpxiDwZomJiVq5cqXeeecdqzyUnJycZwOzqKgo9evXz/Lc3lzt4uKimTNnymQyacKECUpLS9N77713RzH4+fnZnV+bN2+uESNGaNu2bTpx4oRatmwpHx8fNWjQQGvXrtWpU6dUqVIllS1bNsv9pW9v8uTJaty4sc0+pUuX1vXr12UymbLMxxmPISUlRefOnbMqdMvYr3jx4nbn7rlz56p8+fJauHCh1WcnOTnZ5nq3itPWxQWJ3wEAnFPGfHmrZdn5XnVzc9PgwYM1ePBgXbhwQd9++63eeusttWrVSvHx8VazpCckJFjNpp5xTH3z74AyZcpY7e/UqVN3NDbMTnwZ3eo3TlY5QLqRB2rVqqUxY8bYXJ5+AcCWl19+2Wr2cnvOGUj/u2Ah3ZjhJTU1VR9//LEWL16sjh07Zis3eXt7Kzo6WtHR0frrr78ss6ZFRETojz/+sPS355xIbpynsTc+W+4kr2fn3wMA5Kb0IqGTJ09mKhgyDEOnT5+25AJnkJ57Fi9erHLlyuXKPvz8/Ow6J5EXsdzOihUrrMadt/o9kFt4TwAAGdl7Pt4wjBy/nvvwww/r4YcfVmpqqnbs2KHJkydr4MCBKl26tDp16nQHR2Mt47XtOxm3Va9eXZ06dVJsbKwOHDighg0b3tWYPyvlypXTzJkzJUkHDhzQokWLFBUVpWvXrmnatGm3XPd2171tsfdcPuBMKHJDvhMeHq758+dr9uzZWd6ytHnz5ho/frzmzp2rQYMGWdq/+OILXb582e5ZVLJj3rx56t+/v+V5XFycpBu3PJVu3Frz3Xff1cmTJzNNyZ1RVn9x1a5dOy1YsECpqalq1KhRjsSdnb/Y8/DwyNYJkvbt26tmzZqKiYlRu3btVKNGjUx91qxZo4cfflje3t5q1KiRlixZovfee0+enp6SbtyiZe7cuSpTpowqVapk977tsWTJEk2YMMHyl18XL17UihUr9PDDD8vV1VVeXl4KCwvT7t27VatWLctMcbZk9TpWrlxZFStW1N69ezV27Ngci/1uZuQDgJxWuXJl+fv7a9GiRRo8eLCl/fjx49q8efMdDeYqV66s++67T3FxcXr99dctF90vX76sL774Qg8++OAtL0ZLNy7UG4aR6aLwxx9/bPctv2zJzndwcHCwgoOD72g/6YVurq6uev/995WWlqaJEydmezvNmzdXTEyMdu3apbp161raP/30U5lMJoWFhVnaWrRoobfeekvDhw9XmTJlVKVKFUv78uXLlZCQkOUt7tI1bdpUxYoV02+//WZV4JeRh4eHGjZsmGU+vlmzZs00fvx4LVy4UH369LG0L1iwwKpfdnK3yWSSh4eHVUFHQkKCvvzyS5v9v/vuO/3111+WQX1qaqoWLlyoChUqZCrASJdbvwMAIK9k53v1ZsWKFVPHjh118uRJDRw4UEePHlW1atUsy+fNm6d69epZni9atEgpKSmW8fOjjz4q6UZB8s3FT9u3b9fvv/+uYcOG3dVx3S6+jC5evKjly5db3eYtLi5OLi4ueuSRR7Jcr127dvrqq69UoUKFTMX5txMYGJgjF73Hjx+vL774QiNGjFCHDh3uODeVLl1aPXr00N69exUbG6srV65Y/Ra73TmR3D5Pc7v4Mtq3b5/27t1rdcvSuLg4+fj4WP1eutmd/nsAgJz26KOPymQyaeHChZm+s1avXq2kpKRbzr6dW7I6P9qqVSu5ubnpzz//vO148k41a9ZMX331lf7++2/Lxdq0tDR9/vnnuR5LdmeAr1mzZo7s1x68J5w/BwB7Zed8fG5dz3V1dVWjRo1UpUoVzZs3T7t27cqRIrfsXNv+559/5OPjY3O8l/7HVOlj9bsZ89ujUqVKevvtt/XFF19o165dt+1/u+vetth7Lh9wJhS5Id/p3LmzZs2apVdeeUX79+9XWFiY0tLS9NNPP6lq1arq1KmTWrZsqVatWmnIkCFKSkpS06ZN9fPPP+udd95RSEiInnvuuRyNycPDQ++//74uXbqkBg0aaPPmzRo9erTCw8P10EMPSbqRJF5++WX17NlTO3bs0COPPCJvb2+dPn1amzZtUs2aNS0XbWvWrKklS5Zo6tSpqlevnlxcXFS/fn116tRJ8+bNU5s2bTRgwAA1bNhQ7u7uOnHihNavX6/27dvrySefzFbs6YVn06dPl4+PjwoVKqTy5cvf8q/R7eXq6qqlS5fqscce04MPPqg+ffooLCxM3t7eOnbsmBYvXqwVK1ZYbjcXExOjli1bKiwsTK+//ro8PDw0ZcoU/frrr5o/f/4tZxy40/hatmypwYMHKy0tTePGjVNSUpKio6MtfSZNmqSHHnpIDz/8sPr06aPg4GBdvHhRhw4d0ooVK7Ru3TpJN6bT9fT01Lx581S1alUVLlzYclHiv//9r8LDw9WqVSv16NFD9913n86dO6fff/9du3btyjSwt0fNmjW1YMECLVy4UPfff78KFSqUpydIAOBmLi4uio6OVu/evdWxY0f16tVLFy5cUHR0tAICAm55i/FbbXP8+PHq2rWr2rVrp969eys5OVkTJkzQhQsX9O677952G0WKFNEjjzyiCRMmqESJEgoODtbGjRs1c+ZMFStW7A6O9Ia8/A42mUyaPn26TCaTPvjgAxmGoQ8++CBb2xg0aJA+/fRTtW3bViNHjlS5cuW0atUqTZkyRX369LE66VCvXj0VL15c33zzjXr27Glpb9GihUaNGmX5/1spXLiwJk+erO7du+vcuXPq2LGjSpUqpbNnz2rv3r06e/aspk6dKkkaNWqUWrdurZYtW+q1115Tamqqxo0bJ29vb6vZ9lq3bq2mTZvqtddeU1JSkurVq6ctW7bo008/lSSrz5i9ubtdu3ZasmSJ+vbtq44dOyo+Pl6jRo1SQECAzduclShRQo8++qiGDx8ub29vTZkyRX/88UemQruMcuN3AADkJXu/VyMiIlSjRg3Vr19fJUuW1LFjxxQbG6ty5cqpYsWKVttcsmSJ3Nzc1LJlS+3bt0/Dhw9X7dq1LX8QVrlyZb388suaPHmyXFxcFB4erqNHj2r48OEqW7asVZGUvbITX0Z+fn7q06ePjh8/rkqVKumrr77SjBkz1KdPHwUFBWW53siRI7V27Vo1adJE/fv3V+XKlXX16lUdPXpUX331laZNm5ZloXROKV68uCIjI/Xmm28qLi5O3bp1szs3NWrUSO3atVOtWrVUvHhx/f777/rss88y/bGBPedEcuM8jb3x2RIYGKjHH39cUVFRCggI0Ny5c7V27VqNGzfuluva++8BAHJThQo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      "text/plain": [
       "<Figure size 2500x500 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Function to plot bar charts for a given category for both A_data and B_data\n",
    "def plot_category(category_columns, A_data, B_data, title, num_cols=3):\n",
    "    # Plotting subplots for each category\n",
    "    fig, axs = plt.subplots(1, len(category_columns), figsize=(5 * len(category_columns), 5), sharex=True)\n",
    "\n",
    "    # Plot questions for A_data\n",
    "    for i, category in enumerate(category_columns):\n",
    "        axs[i].bar(A_data[category].value_counts().index, A_data[category].value_counts().values, alpha=0.7, label='A', color='blue')\n",
    "\n",
    "    # Plot questions for B_data\n",
    "    for i, category in enumerate(category_columns):\n",
    "        axs[i].bar(B_data[category].value_counts().index, B_data[category].value_counts().values, alpha=0.7, label='B', color='orange')\n",
    "\n",
    "        axs[i].set_title(category)\n",
    "        axs[i].legend()\n",
    "\n",
    "    plt.tight_layout(rect=[0, 0.03, 1, 0.95])\n",
    "    plt.suptitle(title, fontsize=16)\n",
    "    plt.show()\n",
    "\n",
    "# Visualize animacy for both A_data and B_data\n",
    "plot_category(animacy_labels, A_data, B_data, \"Animacy\")\n",
    "\n",
    "# Visualize likeability for both A_data and B_data\n",
    "plot_category(likeability_labels, A_data, B_data, \"Likeability\")\n",
    "\n",
    "# Visualize perceived intelligence for both A_data and B_data\n",
    "plot_category(percived_intelligence_labels, A_data, B_data, \"Perceived Intelligence\")\n",
    "\n",
    "# Visualize perceived safety for both A_data and B_data\n",
    "plot_category(percived_safety_labels, A_data, B_data, \"Perceived Safety\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### T-Statistic and Significance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            T-statistic   P-value Significant\n",
      "Dead - Alive                        0.0       1.0          No\n",
      "Stagnant - Lively             -0.171499  0.864696          No\n",
      "Mechanical - Organic          -0.289278  0.773862          No\n",
      "Artificial - Lifelike          0.832178  0.410249          No\n",
      "Inert - Interactive            0.639693  0.526019          No\n",
      "Apathetic - Responsive         0.347629  0.729941          No\n",
      "Dislike - Like                 0.146964  0.883899          No\n",
      "Unfriendly - Friendly          0.300489  0.765361          No\n",
      "Unkind - Kind                  0.353553   0.72553          No\n",
      "Unpleasant - Pleasant          1.442541  0.156935          No\n",
      "Awful - Nice                        0.0       1.0          No\n",
      "Incompetent - Competent        1.344663   0.18631          No\n",
      "Ignorant - Knowledgeable       0.886581  0.380604          No\n",
      "Irresponsible - Responsible    0.863868  0.392811          No\n",
      "Unintelligent - Intelligent    1.502088  0.140928          No\n",
      "Foolish - Sensible             1.775317  0.083455          No\n",
      "Anxious - Relaxed             -0.196116  0.845512          No\n",
      "Agitated - Calm               -0.834493  0.408959          No\n",
      "Quiescent - Surprised               0.4  0.691285          No\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import ttest_ind\n",
    "\n",
    "# Define your question labels\n",
    "all_question_labels = animacy_labels + likeability_labels + percived_intelligence_labels + percived_safety_labels\n",
    "\n",
    "# Create an empty DataFrame to store results\n",
    "comparison_results = pd.DataFrame(index=all_question_labels, columns=['T-statistic', 'P-value', 'Significant'])\n",
    "\n",
    "# Loop through each question label and perform t-test\n",
    "for question_label in all_question_labels:\n",
    "    group_a_responses = A_data[question_label]\n",
    "    group_b_responses = B_data[question_label]\n",
    "\n",
    "    t_stat, p_value = ttest_ind(group_a_responses, group_b_responses)\n",
    "\n",
    "    # Store results in the DataFrame\n",
    "    comparison_results.loc[question_label, 'T-statistic'] = t_stat\n",
    "    comparison_results.loc[question_label, 'P-value'] = p_value\n",
    "    comparison_results.loc[question_label, 'Significant'] = 'Yes' if p_value < 0.05 else 'No'\n",
    "\n",
    "# Display the comparison results\n",
    "print(comparison_results)\n",
    "\n",
    "# no question has a significant difference between the two groups\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cronbach's Alpha Reliability\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A: Cronbach's Alpha:\n",
      "Cronbach's Alpha for ['Dead - Alive', 'Stagnant - Lively', 'Mechanical - Organic', 'Artificial - Lifelike', 'Inert - Interactive', 'Apathetic - Responsive']: 0.8335\n",
      "Cronbach's Alpha for ['Dislike - Like', 'Unfriendly - Friendly', 'Unkind - Kind', 'Unpleasant - Pleasant', 'Awful - Nice']: 0.9052\n",
      "Cronbach's Alpha for ['Incompetent - Competent', 'Ignorant - Knowledgeable', 'Irresponsible - Responsible', 'Unintelligent - Intelligent', 'Foolish - Sensible']: 0.7093\n",
      "Cronbach's Alpha for ['Anxious - Relaxed', 'Agitated - Calm', 'Quiescent - Surprised']: 0.3411\n",
      "\n",
      "B: Cronbach's Alpha:\n",
      "Cronbach's Alpha for ['Dead - Alive', 'Stagnant - Lively', 'Mechanical - Organic', 'Artificial - Lifelike', 'Inert - Interactive', 'Apathetic - Responsive']: 0.7766\n",
      "Cronbach's Alpha for ['Dislike - Like', 'Unfriendly - Friendly', 'Unkind - Kind', 'Unpleasant - Pleasant', 'Awful - Nice']: 0.8518\n",
      "Cronbach's Alpha for ['Incompetent - Competent', 'Ignorant - Knowledgeable', 'Irresponsible - Responsible', 'Unintelligent - Intelligent', 'Foolish - Sensible']: 0.7572\n",
      "Cronbach's Alpha for ['Anxious - Relaxed', 'Agitated - Calm', 'Quiescent - Surprised']: 0.4405\n"
     ]
    }
   ],
   "source": [
    "# evaluate the reliability of the questionnaire\n",
    "\n",
    "import pingouin as pg\n",
    "\n",
    "# Define your category labels\n",
    "categories = [animacy_labels, likeability_labels, percived_intelligence_labels, percived_safety_labels]\n",
    "\n",
    "# Loop through each category and calculate Cronbach's Alpha\n",
    "print(f\"A: Cronbach's Alpha:\")\n",
    "for category in categories:\n",
    "    category_data = A_data[category]  # Assuming A_data contains your Likert scale responses for each category\n",
    "    alpha_result = pg.cronbach_alpha(category_data)\n",
    "    print(f\"Cronbach's Alpha for {category}: {alpha_result[0]:.4f}\")\n",
    "\n",
    "print(f\"\\nB: Cronbach's Alpha:\")\n",
    "for category in categories:\n",
    "    category_data = B_data[category]  # Assuming A_data contains your Likert scale responses for each category\n",
    "    alpha_result = pg.cronbach_alpha(category_data)\n",
    "    print(f\"Cronbach's Alpha for {category}: {alpha_result[0]:.4f}\")\n",
    "\n",
    "# Cronbach's Alpha\n",
    "# >= 0.9 = excellent\n",
    "# 0.8 - 0.9 = good\n",
    "# 0.7 - 0.8 = acceptable\n",
    "# 0.6 - 0.7 = questionable\n",
    "# 0.5 - 0.6 = poor\n",
    "# < 0.5 = unacceptable\n",
    "\n",
    "# except for the perceived safety category, all categories have a Cronbach's Alpha of > 0.7, which is good - acceptable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate Mean Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overall Mean Score for Animacy - A: 2.6825\n",
      "Overall Mean Score for Animacy - B: 2.6190\n",
      "\n",
      "Overall Mean Score for Likeability - A: 3.6000\n",
      "Overall Mean Score for Likeability - B: 3.4952\n",
      "\n",
      "Overall Mean Score for Perceived Intelligence - A: 2.8667\n",
      "Overall Mean Score for Perceived Intelligence - B: 2.5238\n",
      "\n",
      "Overall Mean Score for Perceived Safety - A: 3.6032\n",
      "Overall Mean Score for Perceived Safety - B: 3.6667\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# calucalte mean score for comparision as suggested in https://www.bartneck.de/publications/2009/measurementInstrumentsRobots/\n",
    "categories = {\n",
    "    'Animacy': animacy_labels,\n",
    "    'Likeability': likeability_labels,\n",
    "    'Perceived Intelligence': percived_intelligence_labels,\n",
    "    'Perceived Safety': percived_safety_labels\n",
    "}\n",
    "\n",
    "# Loop through each category and calculate mean scores\n",
    "for category, labels in categories.items():\n",
    "    # Select the relevant columns for the current category\n",
    "    category_data_A = A_data[labels]\n",
    "    category_data_B = B_data[labels]\n",
    "\n",
    "    # Calculate mean scores for each respondent\n",
    "    mean_scores_A = category_data_A.mean(axis=1)\n",
    "    mean_scores_B = category_data_B.mean(axis=1)\n",
    "\n",
    "    # Calculate overall mean score for each group\n",
    "    overall_mean_A = mean_scores_A.mean()\n",
    "    overall_mean_B = mean_scores_B.mean()\n",
    "\n",
    "    print(f\"Overall Mean Score for {category} - A: {overall_mean_A:.4f}\")\n",
    "    print(f\"Overall Mean Score for {category} - B: {overall_mean_B:.4f}\\n\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize mean scores\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "# Assuming 'A_data' and 'B_data' are your DataFrames with Likert scale responses\n",
    "categories = {\n",
    "    'Animacy': animacy_labels,\n",
    "    'Likeability': likeability_labels,\n",
    "    'Perceived Intelligence': percived_intelligence_labels,\n",
    "    'Perceived Safety': percived_safety_labels\n",
    "}\n",
    "\n",
    "# Initialize an empty DataFrame to store mean scores\n",
    "mean_scores_df = pd.DataFrame(index=categories.keys(), columns=['With Recommender', 'Without Recommender'])\n",
    "\n",
    "# Loop through each category and calculate mean scores\n",
    "for category, labels in categories.items():\n",
    "    # Select the relevant columns for the current category\n",
    "    category_data_A = A_data[labels]\n",
    "    category_data_B = B_data[labels]\n",
    "\n",
    "    # Calculate mean scores for each respondent\n",
    "    mean_scores_A = category_data_A.mean(axis=1).mean()\n",
    "    mean_scores_B = category_data_B.mean(axis=1).mean()\n",
    "\n",
    "    # Store mean scores in the DataFrame\n",
    "    mean_scores_df.loc[category, 'With Recommender'] = mean_scores_A\n",
    "    mean_scores_df.loc[category, 'Without Recommender'] = mean_scores_B\n",
    "\n",
    "# Plot the mean scores\n",
    "ax = mean_scores_df.plot(kind='bar', rot=0, figsize=(10, 6))\n",
    "ax.set_ylabel('Mean Score')\n",
    "ax.set_title('Mean Scores for Different Categories of GodSpeed Questionnaire')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A: With Recommender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Gender'>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# print(A_data.describe())\n",
    "# print(A_data.sum())\n",
    "\n",
    "A_data[\"Gender\"].value_counts().plot(kind='pie', autopct='%1.1f%%', startangle=90)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Latest Education'>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "A_data[\"Latest Education\"].value_counts().plot(kind='pie', autopct='%1.1f%%', startangle=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "df = B_data\n",
    "\n",
    "df['Birthdate'] = pd.to_datetime(df['Birthdate'], format='%Y-%m-%d')\n",
    "\n",
    "# Calculate ages\n",
    "current_year = datetime.now().year\n",
    "df['Age'] = current_year - df['Birthdate'].dt.year\n",
    "\n",
    "df_filtered = df[df['Age'] <= 35]\n",
    "\n",
    "# Plot the age distribution\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.hist(df_filtered['Age'], bins=range(10, 35), edgecolor='black', alpha=1)\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Age Distribution of Participants')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Dead - Alive  Stagnant - Lively  Mechanical - Organic  \\\n",
      "count     21.000000          21.000000             21.000000   \n",
      "mean       2.952381           2.857143              2.190476   \n",
      "std        1.071270           0.910259              1.077917   \n",
      "min        1.000000           1.000000              1.000000   \n",
      "25%        2.000000           2.000000              1.000000   \n",
      "50%        3.000000           3.000000              2.000000   \n",
      "75%        4.000000           3.000000              3.000000   \n",
      "max        5.000000           4.000000              5.000000   \n",
      "\n",
      "       Artificial - Lifelike  Inert - Interactive  Apathetic - Responsive  \\\n",
      "count              21.000000            21.000000               21.000000   \n",
      "mean                2.428571             2.857143                2.809524   \n",
      "std                 0.978337             0.792825                0.928388   \n",
      "min                 1.000000             1.000000                1.000000   \n",
      "25%                 2.000000             3.000000                2.000000   \n",
      "50%                 2.000000             3.000000                3.000000   \n",
      "75%                 3.000000             3.000000                3.000000   \n",
      "max                 4.000000             5.000000                5.000000   \n",
      "\n",
      "       Dislike - Like  Unfriendly - Friendly  Unkind - Kind  \\\n",
      "count       21.000000              21.000000      21.000000   \n",
      "mean         3.619048               3.619048       3.333333   \n",
      "std          0.973457               1.116969       0.856349   \n",
      "min          1.000000               1.000000       1.000000   \n",
      "25%          3.000000               3.000000       3.000000   \n",
      "50%          4.000000               4.000000       3.000000   \n",
      "75%          4.000000               4.000000       4.000000   \n",
      "max          5.000000               5.000000       5.000000   \n",
      "\n",
      "       Unpleasant - Pleasant  Awful - Nice  Incompetent - Competent  \\\n",
      "count              21.000000     21.000000                21.000000   \n",
      "mean                3.809524      3.619048                 3.142857   \n",
      "std                 0.601585      0.920662                 0.853564   \n",
      "min                 3.000000      2.000000                 1.000000   \n",
      "25%                 3.000000      3.000000                 3.000000   \n",
      "50%                 4.000000      4.000000                 3.000000   \n",
      "75%                 4.000000      4.000000                 4.000000   \n",
      "max                 5.000000      5.000000                 5.000000   \n",
      "\n",
      "       Ignorant - Knowledgeable  Irresponsible - Responsible  \\\n",
      "count                 21.000000                    21.000000   \n",
      "mean                   2.952381                     2.619048   \n",
      "std                    0.973457                     0.864650   \n",
      "min                    1.000000                     1.000000   \n",
      "25%                    2.000000                     2.000000   \n",
      "50%                    3.000000                     3.000000   \n",
      "75%                    4.000000                     3.000000   \n",
      "max                    4.000000                     4.000000   \n",
      "\n",
      "       Unintelligent - Intelligent  Foolish - Sensible  Anxious - Relaxed  \\\n",
      "count                    21.000000           21.000000          21.000000   \n",
      "mean                      2.952381            2.666667           3.904762   \n",
      "std                       0.920662            0.577350           0.830949   \n",
      "min                       1.000000            1.000000           2.000000   \n",
      "25%                       3.000000            2.000000           3.000000   \n",
      "50%                       3.000000            3.000000           4.000000   \n",
      "75%                       4.000000            3.000000           4.000000   \n",
      "max                       4.000000            3.000000           5.000000   \n",
      "\n",
      "       Agitated - Calm  Quiescent - Surprised  \n",
      "count        21.000000              21.000000  \n",
      "mean          3.809524               3.095238  \n",
      "std           1.030488               0.830949  \n",
      "min           2.000000               2.000000  \n",
      "25%           3.000000               3.000000  \n",
      "50%           4.000000               3.000000  \n",
      "75%           5.000000               4.000000  \n",
      "max           5.000000               5.000000  \n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "# Assuming 'A_data' and 'B_data' are your DataFrames with Likert scale responses\n",
    "question_labels = animacy_labels + likeability_labels + percived_intelligence_labels + percived_safety_labels\n",
    "responses = pd.concat([A_data, B_data], keys=['A_data', 'B_data'])\n",
    "\n",
    "# 1. Descriptive Statistics\n",
    "descriptive_stats_A = responses.loc['A_data'].describe()\n",
    "descriptive_stats_B = responses.loc['B_data'].describe()\n",
    "print(descriptive_stats_A[question_labels])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Dead - Alive  Stagnant - Lively  Mechanical - Organic  \\\n",
      "count     21.000000          21.000000             21.000000   \n",
      "mean       2.952381           2.904762              2.285714   \n",
      "std        1.116969           0.889087              1.055597   \n",
      "min        1.000000           2.000000              1.000000   \n",
      "25%        2.000000           2.000000              2.000000   \n",
      "50%        3.000000           3.000000              2.000000   \n",
      "75%        4.000000           4.000000              3.000000   \n",
      "max        5.000000           4.000000              5.000000   \n",
      "\n",
      "       Artificial - Lifelike  Inert - Interactive  Apathetic - Responsive  \\\n",
      "count              21.000000            21.000000               21.000000   \n",
      "mean                2.190476             2.666667                2.714286   \n",
      "std                 0.872872             1.110555                0.845154   \n",
      "min                 1.000000             1.000000                1.000000   \n",
      "25%                 2.000000             2.000000                2.000000   \n",
      "50%                 2.000000             3.000000                3.000000   \n",
      "75%                 3.000000             3.000000                3.000000   \n",
      "max                 4.000000             5.000000                4.000000   \n",
      "\n",
      "       Dislike - Like  Unfriendly - Friendly  Unkind - Kind  \\\n",
      "count       21.000000              21.000000      21.000000   \n",
      "mean         3.571429               3.523810       3.238095   \n",
      "std          1.121224               0.928388       0.889087   \n",
      "min          1.000000               2.000000       2.000000   \n",
      "25%          3.000000               3.000000       3.000000   \n",
      "50%          4.000000               4.000000       3.000000   \n",
      "75%          4.000000               4.000000       4.000000   \n",
      "max          5.000000               5.000000       5.000000   \n",
      "\n",
      "       Unpleasant - Pleasant  Awful - Nice  Incompetent - Competent  \\\n",
      "count              21.000000     21.000000                21.000000   \n",
      "mean                3.523810      3.619048                 2.809524   \n",
      "std                 0.679636      0.497613                 0.749603   \n",
      "min                 2.000000      3.000000                 1.000000   \n",
      "25%                 3.000000      3.000000                 2.000000   \n",
      "50%                 4.000000      4.000000                 3.000000   \n",
      "75%                 4.000000      4.000000                 3.000000   \n",
      "max                 5.000000      4.000000                 4.000000   \n",
      "\n",
      "       Ignorant - Knowledgeable  Irresponsible - Responsible  \\\n",
      "count                 21.000000                    21.000000   \n",
      "mean                   2.666667                     2.380952   \n",
      "std                    1.110555                     0.920662   \n",
      "min                    1.000000                     1.000000   \n",
      "25%                    2.000000                     2.000000   \n",
      "50%                    3.000000                     2.000000   \n",
      "75%                    3.000000                     3.000000   \n",
      "max                    4.000000                     4.000000   \n",
      "\n",
      "       Unintelligent - Intelligent  Foolish - Sensible  Anxious - Relaxed  \\\n",
      "count                    21.000000           21.000000          21.000000   \n",
      "mean                      2.523810            2.238095           3.952381   \n",
      "std                       0.928388            0.943650           0.740013   \n",
      "min                       1.000000            1.000000           3.000000   \n",
      "25%                       2.000000            1.000000           3.000000   \n",
      "50%                       3.000000            2.000000           4.000000   \n",
      "75%                       3.000000            3.000000           4.000000   \n",
      "max                       4.000000            4.000000           5.000000   \n",
      "\n",
      "       Agitated - Calm  Quiescent - Surprised  \n",
      "count        21.000000              21.000000  \n",
      "mean          4.047619               3.000000  \n",
      "std           0.804748               0.707107  \n",
      "min           2.000000               2.000000  \n",
      "25%           4.000000               3.000000  \n",
      "50%           4.000000               3.000000  \n",
      "75%           5.000000               3.000000  \n",
      "max           5.000000               4.000000  \n"
     ]
    }
   ],
   "source": [
    "print(descriptive_stats_B[question_labels])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### B: Without Recommender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date                           2023/12/18 1:54:40 p. m. CET2023/12/19 2:15:36...\n",
      "Gender                         MaleMaleMaleMaleFemaleFemaleFemaleMaleMaleMale...\n",
      "Latest Education               MasterBachelorBachelorBachelorMasterBachelorBa...\n",
      "Dead - Alive                                                                  62\n",
      "Stagnant - Lively                                                             61\n",
      "Mechanical - Organic                                                          48\n",
      "Artificial - Lifelike                                                         46\n",
      "Inert - Interactive                                                           56\n",
      "Apathetic - Responsive                                                        57\n",
      "Dislike - Like                                                                75\n",
      "Unfriendly - Friendly                                                         74\n",
      "Unkind - Kind                                                                 68\n",
      "Unpleasant - Pleasant                                                         74\n",
      "Awful - Nice                                                                  76\n",
      "Incompetent - Competent                                                       59\n",
      "Ignorant - Knowledgeable                                                      56\n",
      "Irresponsible - Responsible                                                   50\n",
      "Unintelligent - Intelligent                                                   53\n",
      "Foolish - Sensible                                                            47\n",
      "Anxious - Relaxed                                                             83\n",
      "Agitated - Calm                                                               85\n",
      "Quiescent - Surprised                                                         63\n",
      "Age                                                                          564\n",
      "dtype: object\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_74336/1648658247.py:2: FutureWarning: The default value of numeric_only in DataFrame.sum is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  print(B_data.sum())\n"
     ]
    }
   ],
   "source": [
    "# print(B_data.describe())\n",
    "print(B_data.sum())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "string indices must be integers, not 'str'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[96], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Visualize animacy\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[43mplot_category\u001b[49m\u001b[43m(\u001b[49m\u001b[43manimacy_labels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mB_data\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mAnimacy\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;66;03m# Visualize likeability\u001b[39;00m\n\u001b[1;32m      5\u001b[0m plot_category(likeability_labels, B_data, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLikeability\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m5\u001b[39m)\n",
      "Cell \u001b[0;32mIn[85], line 12\u001b[0m, in \u001b[0;36mplot_category\u001b[0;34m(category_columns, A_data, B_data, title, num_cols)\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[38;5;66;03m# Plot questions for B_data\u001b[39;00m\n\u001b[1;32m     11\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, category \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(category_columns):\n\u001b[0;32m---> 12\u001b[0m     axs[i]\u001b[38;5;241m.\u001b[39mbar(\u001b[43mB_data\u001b[49m\u001b[43m[\u001b[49m\u001b[43mcategory\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39mvalue_counts()\u001b[38;5;241m.\u001b[39mindex, B_data[category]\u001b[38;5;241m.\u001b[39mvalue_counts()\u001b[38;5;241m.\u001b[39mvalues, alpha\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.7\u001b[39m, label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mB\u001b[39m\u001b[38;5;124m'\u001b[39m, color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124morange\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m     14\u001b[0m     axs[i]\u001b[38;5;241m.\u001b[39mset_title(category)\n\u001b[1;32m     15\u001b[0m     axs[i]\u001b[38;5;241m.\u001b[39mlegend()\n",
      "\u001b[0;31mTypeError\u001b[0m: string indices must be integers, not 'str'"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 3000x500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize animacy\n",
    "plot_category(animacy_labels, B_data, \"Animacy\", 2, 3)\n",
    "\n",
    "# Visualize likeability\n",
    "plot_category(likeability_labels, B_data, \"Likeability\", 1, 5)\n",
    "\n",
    "# Visualize perceived intelligence\n",
    "plot_category(percived_intelligence_labels, B_data, \"Perceived Intelligence\", 2, 3)\n",
    "\n",
    "# Visualize perceived safety\n",
    "plot_category(percived_safety_labels, B_data, \"Perceived Safety\", 1, 3)\n"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "authorship_tag": "ABX9TyNgPn/PQdx6vEeYwM6chSeq",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}