obj_eval.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Objective Evaluations - Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from scipy.stats import f_oneway"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .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>strategy</th>\n",
       "      <th>topic</th>\n",
       "      <th>method</th>\n",
       "      <th>participant</th>\n",
       "      <th>score</th>\n",
       "      <th>competence</th>\n",
       "      <th>activity</th>\n",
       "      <th>frustration</th>\n",
       "      <th>pleasure</th>\n",
       "      <th>pressure</th>\n",
       "      <th>effort</th>\n",
       "      <th>immersion</th>\n",
       "      <th>physicality</th>\n",
       "      <th>difficulty</th>\n",
       "      <th>familarity</th>\n",
       "      <th>understanding</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AN</td>\n",
       "      <td>AN_G</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AN</td>\n",
       "      <td>AN_G</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AN</td>\n",
       "      <td>AN_G</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "      <td>5</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>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AN</td>\n",
       "      <td>AN_G</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AN</td>\n",
       "      <td>AN_G</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  strategy topic  method  participant  score  competence  activity  \\\n",
       "0       AN  AN_G       1            3      7           3         6   \n",
       "1       AN  AN_G       1            1      7           1         7   \n",
       "2       AN  AN_G       0           16      7           5         4   \n",
       "3       AN  AN_G       0            2      7           4         5   \n",
       "4       AN  AN_G       1            7      4           4         4   \n",
       "\n",
       "   frustration  pleasure  pressure  effort  immersion  physicality  \\\n",
       "0            7         3         2       5          6            2   \n",
       "1            7         1         7       7          7            6   \n",
       "2            4         3         3       3          3            2   \n",
       "3            4         3         3       4          6            3   \n",
       "4            4         4         3       6          4            3   \n",
       "\n",
       "   difficulty  familarity  understanding  \n",
       "0           7           1              3  \n",
       "1           7           1              1  \n",
       "2           5           1              5  \n",
       "3           2           1              5  \n",
       "4           5           2              3  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resp_data = pd.read_csv('questionnaire\\\\response_summary.csv')\n",
    "resp_data.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mix-up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean and Standard Deviation of 'score' by 'method':\n",
      "            mean       std\n",
      "method                    \n",
      "0       5.736111  1.373728\n",
      "1       5.763889  1.294550\n",
      "\n",
      "ANOVA Test Result:\n",
      "F-value: 0.0155924014494345\n",
      "p-value: 0.9008034354500833\n"
     ]
    }
   ],
   "source": [
    "# Group by 'method' and compute mean and std of 'score'\n",
    "grouped_data = resp_data.groupby('method')['score'].agg(['mean', 'std'])\n",
    "\n",
    "# Perform ANOVA test\n",
    "anova_result = f_oneway(*[group['score'] for name, group in resp_data.groupby('method')])\n",
    "\n",
    "# Print the mean and std of 'score' for each 'method'\n",
    "print(\"Mean and Standard Deviation of 'score' by 'method':\")\n",
    "print(grouped_data)\n",
    "\n",
    "# Print the ANOVA test result\n",
    "print(\"\\nANOVA Test Result:\")\n",
    "print(\"F-value:\", anova_result.statistic)\n",
    "print(\"p-value:\", anova_result.pvalue)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add Strategy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import scipy.stats as stats\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(x='strategy', y='score', hue='method', data=resp_data, palette='viridis')\n",
    "\n",
    "# Set plot title and labels\n",
    "plt.title('Scores by Strategy and Method')\n",
    "plt.xlabel('Strategy')\n",
    "plt.ylabel('Score')\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Strategy: AN, F-value: 0.02, p-value: 0.8762\n",
      "Strategy: EE, F-value: 0.53, p-value: 0.4726\n",
      "Strategy: IL, F-value: 0.12, p-value: 0.7369\n",
      "Strategy: LC, F-value: 0.00, p-value: 1.0000\n",
      "Strategy: RQ, F-value: 0.98, p-value: 0.3334\n",
      "Strategy: TP, F-value: 2.95, p-value: 0.0999\n"
     ]
    }
   ],
   "source": [
    "# Function to perform ANOVA for each strategy\n",
    "def anova_per_strategy(df):\n",
    "    strategies = df['strategy'].unique()\n",
    "    results = {}\n",
    "    for strategy in strategies:\n",
    "        subset = df[df['strategy'] == strategy]\n",
    "        groups = [subset[subset['method'] == method]['score'] for method in subset['method'].unique()]\n",
    "        f_val, p_val = stats.f_oneway(*groups)\n",
    "        results[strategy] = {'F-value': f_val, 'p-value': p_val}\n",
    "    return results\n",
    "\n",
    "# Perform ANOVA\n",
    "anova_results = anova_per_strategy(resp_data)\n",
    "\n",
    "# Print the results\n",
    "for strategy, result in anova_results.items():\n",
    "    print(f\"Strategy: {strategy}, F-value: {result['F-value']:.2f}, p-value: {result['p-value']:.4f}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add Person"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(x='participant', y='score', hue='method', data=resp_data, palette='viridis')\n",
    "\n",
    "# Set plot title and labels\n",
    "plt.title('Scores by Participant and Method')\n",
    "plt.xlabel('Participant')\n",
    "plt.ylabel('Score')\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "da",
   "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": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}