trial_decision_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e3311414-650d-4c60-844a-24b7bc24ddf3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from scipy.stats import ttest_ind as tt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5a46c31e-2496-4808-866d-3d9961958275",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load data\n",
    "df = pd.read_csv(\"../data/trials_decisions.csv\", sep=\",\", index_col=False)\n",
    "df.index.name = \"Run\"\n",
    "\n",
    "participants = [\"P01\", \"P02\", \"P03\", \"P04\", \"P05\", \"P06\", \"P07\", \"P08\",\"P09\", \"P10\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2d0bc402-7bdd-47da-94c1-e1d9d7552e2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# correct answers of subjects\n",
    "# per subject: 10 runs: one desired answer per run\n",
    "true_answers = [\n",
    "                [1, 2, 1, 2, 1, 2, 1, 1, 1, 1],\n",
    "                [1, 1, 2, 1, 2, 1, 2, 1, 1, 1],\n",
    "                [1, 2, 1, 2, 1, 2, 1, 2, 1, 2],\n",
    "                [2, 1, 1, 1, 2, 2, 1, 1, 1, 1],\n",
    "                [1, 2, 1, 2, 2, 1, 2, 1, 2, 1],\n",
    "                [2, 1, 2, 2, 1, 1, 1, 1, 2, 1],\n",
    "                [2, 1, 1, 1, 1, 1, 1, 1, 1],\n",
    "                [1, 1, 1, 2, 1, 2, 1, 1, 1, 2],\n",
    "                [1, 2, 1, 2, 2, 2, 1, 2, 1, 1],\n",
    "                [2, 1, 1, 1, 1, 2, 2, 1, 2, 1],\n",
    "                ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "83c4258c-f784-492f-9cb9-6447e497f2c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# set up result dfs for \n",
    "    # 1. half vs. 2. half\n",
    "    # yes vs. no trials\n",
    "    \n",
    "res = pd.DataFrame(columns=[\"participants\",\"accuracy\",\"half1\",\"half2\",\"yes\",\"no\",\"total N of trials\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6c3c08b2-12f6-462a-9892-7b6a08517fdb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Participant P01\n",
      "Acc:\t49/100\n",
      "Half 1:\t24/50.0\n",
      "Half 2:\t25/50.0\n",
      "Yes:\t24/50.0\n",
      "No:\t25/50.0\n",
      "\n",
      "Participant P02\n",
      "Acc:\t60/100\n",
      "Half 1:\t28/50.0\n",
      "Half 2:\t32/50.0\n",
      "Yes:\t32/50.0\n",
      "No:\t28/50.0\n",
      "\n",
      "Participant P03\n",
      "Acc:\t91/100\n",
      "Half 1:\t46/50.0\n",
      "Half 2:\t45/50.0\n",
      "Yes:\t45/50.0\n",
      "No:\t46/50.0\n",
      "\n",
      "Participant P04\n",
      "Acc:\t82/100\n",
      "Half 1:\t41/50.0\n",
      "Half 2:\t41/50.0\n",
      "Yes:\t42/50.0\n",
      "No:\t40/50.0\n",
      "\n",
      "Participant P05\n",
      "Acc:\t79/100\n",
      "Half 1:\t37/50.0\n",
      "Half 2:\t42/50.0\n",
      "Yes:\t38/50.0\n",
      "No:\t41/50.0\n",
      "\n",
      "Participant P06\n",
      "Acc:\t69/100\n",
      "Half 1:\t33/50.0\n",
      "Half 2:\t36/50.0\n",
      "Yes:\t34/50.0\n",
      "No:\t35/50.0\n",
      "\n",
      "Participant P07\n",
      "Acc:\t53/90\n",
      "Half 1:\t27/45.0\n",
      "Half 2:\t26/45.0\n",
      "Yes:\t26/45.0\n",
      "No:\t27/45.0\n",
      "\n",
      "Participant P08\n",
      "Acc:\t49/100\n",
      "Half 1:\t24/50.0\n",
      "Half 2:\t25/50.0\n",
      "Yes:\t26/50.0\n",
      "No:\t23/50.0\n",
      "\n",
      "Participant P09\n",
      "Acc:\t74/100\n",
      "Half 1:\t39/50.0\n",
      "Half 2:\t35/50.0\n",
      "Yes:\t36/50.0\n",
      "No:\t38/50.0\n",
      "\n",
      "Participant P10\n",
      "Acc:\t69/100\n",
      "Half 1:\t33/50.0\n",
      "Half 2:\t36/50.0\n",
      "Yes:\t36/50.0\n",
      "No:\t33/50.0\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Anna\\AppData\\Local\\Temp\\ipykernel_118332\\1776440232.py:56: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  res = res.append(row, ignore_index=True)\n",
      "C:\\Users\\Anna\\AppData\\Local\\Temp\\ipykernel_118332\\1776440232.py:56: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  res = res.append(row, ignore_index=True)\n",
      "C:\\Users\\Anna\\AppData\\Local\\Temp\\ipykernel_118332\\1776440232.py:56: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  res = res.append(row, ignore_index=True)\n",
      "C:\\Users\\Anna\\AppData\\Local\\Temp\\ipykernel_118332\\1776440232.py:56: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  res = res.append(row, ignore_index=True)\n",
      "C:\\Users\\Anna\\AppData\\Local\\Temp\\ipykernel_118332\\1776440232.py:56: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  res = res.append(row, ignore_index=True)\n",
      "C:\\Users\\Anna\\AppData\\Local\\Temp\\ipykernel_118332\\1776440232.py:56: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  res = res.append(row, ignore_index=True)\n",
      "C:\\Users\\Anna\\AppData\\Local\\Temp\\ipykernel_118332\\1776440232.py:56: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  res = res.append(row, ignore_index=True)\n",
      "C:\\Users\\Anna\\AppData\\Local\\Temp\\ipykernel_118332\\1776440232.py:56: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  res = res.append(row, ignore_index=True)\n",
      "C:\\Users\\Anna\\AppData\\Local\\Temp\\ipykernel_118332\\1776440232.py:56: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  res = res.append(row, ignore_index=True)\n",
      "C:\\Users\\Anna\\AppData\\Local\\Temp\\ipykernel_118332\\1776440232.py:56: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  res = res.append(row, ignore_index=True)\n"
     ]
    }
   ],
   "source": [
    "# loop over participants\n",
    "for p in participants:\n",
    "    # select columns containing trial decision of participant p\n",
    "    cols = [c for c in df.columns if p in c]    \n",
    "            \n",
    "    # initialize counter per participant\n",
    "    half1_correct = 0\n",
    "    half2_correct = 0\n",
    "    yes_correct = 0\n",
    "    no_correct = 0\n",
    "    total_counter = 0\n",
    "    \n",
    "    # loop over runs (not always equal to 10, see P07)\n",
    "    for r in range(len(true_answers[participants.index(p)])):\n",
    "        dfp = df[cols].values[r]       \n",
    "        \n",
    "        # loop over trials per run\n",
    "        for t in range(10):\n",
    "            # test if the current trial of the current run of the current participant is correct           \n",
    "            # decision should match correct answer in first half of run\n",
    "            if t <= 4:   \n",
    "                if dfp[t] == true_answers[participants.index(p)][r]:\n",
    "                    half1_correct += 1\n",
    "                    # if trial is a correct yes\n",
    "                    if true_answers[participants.index(p)][r] == 1:\n",
    "                         yes_correct += 1\n",
    "                    # if trial is a correct no\n",
    "                    else:\n",
    "                        no_correct += 1\n",
    "            # decision should match opposite answer in second half of run   \n",
    "            if t > 4: \n",
    "                if dfp[t] == true_answers[participants.index(p)][r]:\n",
    "                    half2_correct += 1\n",
    "                    if true_answers[participants.index(p)][r] == 1:\n",
    "                         no_correct += 1\n",
    "                    else:\n",
    "                        yes_correct += 1   \n",
    "            \n",
    "            # count total number of trials per participant\n",
    "            total_counter += 1\n",
    "        \n",
    "    print(f\"Participant {p}\")\n",
    "    print(f\"Acc:\\t{(half1_correct+half2_correct)}/{total_counter}\")\n",
    "    print(f\"Half 1:\\t{half1_correct}/{total_counter/2}\")\n",
    "    print(f\"Half 2:\\t{half2_correct}/{total_counter/2}\")\n",
    "    print(f\"Yes:\\t{yes_correct}/{total_counter/2}\")\n",
    "    print(f\"No:\\t{no_correct}/{total_counter/2}\\n\")\n",
    "    \n",
    "    row = dict(zip(res.columns,[p, \n",
    "                                (half1_correct+half2_correct)/total_counter,\n",
    "                                half1_correct/(total_counter/2), \n",
    "                                half2_correct/(total_counter/2),\n",
    "                                yes_correct/(total_counter/2),\n",
    "                                no_correct/(total_counter/2),\n",
    "                                total_counter]))\n",
    "    res = res.append(row, ignore_index=True)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d31d3867-92a6-4d4c-b2a0-bdac9c028979",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\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>participants</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>half1</th>\n",
       "      <th>half2</th>\n",
       "      <th>yes</th>\n",
       "      <th>no</th>\n",
       "      <th>total N of trials</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P01</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.5</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P02</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.56</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P03</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.92</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P04</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.8</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P05</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.82</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>P06</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.7</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>P07</td>\n",
       "      <td>0.588889</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.577778</td>\n",
       "      <td>0.577778</td>\n",
       "      <td>0.6</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>P08</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.46</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>P09</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.76</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>P10</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.66</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  participants  accuracy half1     half2       yes    no total N of trials\n",
       "0          P01      0.49  0.48       0.5      0.48   0.5               100\n",
       "1          P02       0.6  0.56      0.64      0.64  0.56               100\n",
       "2          P03      0.91  0.92       0.9       0.9  0.92               100\n",
       "3          P04      0.82  0.82      0.82      0.84   0.8               100\n",
       "4          P05      0.79  0.74      0.84      0.76  0.82               100\n",
       "5          P06      0.69  0.66      0.72      0.68   0.7               100\n",
       "6          P07  0.588889   0.6  0.577778  0.577778   0.6                90\n",
       "7          P08      0.49  0.48       0.5      0.52  0.46               100\n",
       "8          P09      0.74  0.78       0.7      0.72  0.76               100\n",
       "9          P10      0.69  0.66      0.72      0.72  0.66               100"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2ffd1d68-8eb9-40ea-a106-fbc8a1ceb59e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Anna\\AppData\\Local\\Temp\\ipykernel_118332\\1064184029.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  res.std()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "accuracy             0.139560\n",
       "half1                0.145831\n",
       "half2                0.138509\n",
       "yes                  0.133794\n",
       "no                   0.149205\n",
       "total N of trials    3.162278\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2287135e-c45a-4ecb-9917-200d3b161975",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"whitegrid\")\n",
    "#res = res.sort_values(by=\"accuracy\", ascending=False)\n",
    "res.plot(x=\"participants\", y=list(res.columns[1:-1]), kind=\"bar\",\n",
    "        layout=(2,2), subplots=False, legend=True, sharey=True, width=.7,\n",
    "        figsize=(6,4), yticks=[0,.2,.4,.6,.8,1],\n",
    "        )\n",
    "plt.grid(axis=\"x\")\n",
    "plt.ylim(0,1)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8f77df30-8c25-4cc9-8508-14bc4e81ebce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.ylim(0,1)\n",
    "splot = sns.barplot(data=res.iloc[:,:-1], ci=\"sd\")\n",
    "for p in splot.patches:\n",
    "    splot.annotate(format(p.get_height(), '.3f'), \n",
    "                   (p.get_x() + p.get_width() / 2., p.get_height()), \n",
    "                   ha = 'center', va = 'center', \n",
    "                   xytext = (0, 9), \n",
    "                   textcoords = 'offset points')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5e4ab525-e574-4acb-a504-2f76996f1a5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-0.34241044872941007, pvalue=0.7360078502009939)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tt(res[\"half1\"], res[\"half2\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "349319bf-ff2d-4b63-aca3-940deb511cb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=0.09116916177104593, pvalue=0.9283651009971122)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tt(res[\"yes\"], res[\"no\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f9174d00-6137-4f14-95ed-949b0978192c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P1\n",
      "Power_divergenceResult(statistic=0.08, pvalue=0.9941243682104942)\n",
      "P2\n",
      "Power_divergenceResult(statistic=4.640000000000002, pvalue=0.2001374153373312)\n",
      "P3\n",
      "Power_divergenceResult(statistic=67.28, pvalue=1.6312990947060236e-14)\n",
      "\t\tEffect size: phi = 0.8202438661763952, large\n",
      "P4\n",
      "Power_divergenceResult(statistic=41.120000000000005, pvalue=6.166984986254534e-09)\n",
      "\t\tEffect size: phi = 0.6412487816752559, large\n",
      "P5\n",
      "Power_divergenceResult(statistic=34.0, pvalue=1.9811876641985663e-07)\n",
      "\t\tEffect size: phi = 0.5830951894845301, large\n",
      "P6\n",
      "Power_divergenceResult(statistic=14.48, pvalue=0.0023195272962100596)\n",
      "\t\tEffect size: phi = 0.380525951808809, medium\n",
      "P7\n",
      "Power_divergenceResult(statistic=3.209876543209875, pvalue=0.3603843958397054)\n",
      "P8\n",
      "Power_divergenceResult(statistic=0.4, pvalue=0.9402424948393607)\n",
      "P9\n",
      "Power_divergenceResult(statistic=23.199999999999996, pvalue=3.668635719472368e-05)\n",
      "\t\tEffect size: phi = 0.4816637831516918, medium\n",
      "P10\n",
      "Power_divergenceResult(statistic=14.8, pvalue=0.001995790028753644)\n",
      "\t\tEffect size: phi = 0.3847076812334269, medium\n",
      "\n",
      "Number of subjects sig. better than chance level (with p <= .05): \n",
      "6\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import chisquare\n",
    "# for sig. chi squared tests\n",
    "counter = 0\n",
    "# for effect size phi/Cramer's V with df=1\n",
    "vals = [.1,.3,.5] \n",
    "labls = [\"small\", \"medium\", \"large\"]\n",
    "\n",
    "chis = list()\n",
    "effectsizes = list()\n",
    "\n",
    "for p in range(len(res)):\n",
    "    observed = [res[\"yes\"][p]*50, 50- res[\"yes\"][p]*50, res[\"no\"][p]*50, 50-res[\"no\"][p]*50] # observed frequencies of TP, FP, TN, FN\n",
    "    chi = chisquare(observed, [25]*4) # [25]*4 are the expected frequencies of TP, FP, TN, FN\n",
    "    print(f\"P{p+1}\") # subject\n",
    "    print(chi)\n",
    "    chis.append(chi)\n",
    "    if chi[1] <= .05: # if significant at alpha = .05\n",
    "        counter +=1\n",
    "        es = np.sqrt(chi[0] / 100) # effect size\n",
    "        idx = max([[.1,.3,.5].index(c) for c in [.1,.3,.5] if es >= c ]) # >=.1 = small, >=.3 = medium, >=.5 = large effect size\n",
    "        print(f\"\\t\\tEffect size: phi = {es}, {labls[idx]}\")\n",
    "        effectsizes.append(es)\n",
    "    else:\n",
    "        effectsizes.append(None)\n",
    "print(f\"\\nNumber of subjects sig. better than chance level (with p <= .05): \\n{counter}\")\n",
    "\n",
    "\n",
    "res = res.assign(chi_square_val=np.array(chis)[:,0])\n",
    "res = res.assign(chi_square_p=np.array(chis)[:,1].round(2))\n",
    "res = res.assign(chi_square_effect_size=effectsizes)\n",
    "#res.prin(effect_size=effectsizes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f3609486-589e-48ee-8f4d-2636797dcf44",
   "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>participants</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>half1</th>\n",
       "      <th>half2</th>\n",
       "      <th>yes</th>\n",
       "      <th>no</th>\n",
       "      <th>total N of trials</th>\n",
       "      <th>chi_square_val</th>\n",
       "      <th>chi_square_p</th>\n",
       "      <th>chi_square_effect_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P01</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.5</td>\n",
       "      <td>100</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.99</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P02</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.56</td>\n",
       "      <td>100</td>\n",
       "      <td>4.640000</td>\n",
       "      <td>0.20</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P03</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.92</td>\n",
       "      <td>100</td>\n",
       "      <td>67.280000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.820244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P04</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.8</td>\n",
       "      <td>100</td>\n",
       "      <td>41.120000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.641249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P05</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.82</td>\n",
       "      <td>100</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.583095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>P06</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.7</td>\n",
       "      <td>100</td>\n",
       "      <td>14.480000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.380526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>P07</td>\n",
       "      <td>0.588889</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.577778</td>\n",
       "      <td>0.577778</td>\n",
       "      <td>0.6</td>\n",
       "      <td>90</td>\n",
       "      <td>3.209877</td>\n",
       "      <td>0.36</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>P08</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.46</td>\n",
       "      <td>100</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.94</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>P09</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.76</td>\n",
       "      <td>100</td>\n",
       "      <td>23.200000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.481664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>P10</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.66</td>\n",
       "      <td>100</td>\n",
       "      <td>14.800000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.384708</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  participants  accuracy half1     half2       yes    no total N of trials  \\\n",
       "0          P01      0.49  0.48       0.5      0.48   0.5               100   \n",
       "1          P02       0.6  0.56      0.64      0.64  0.56               100   \n",
       "2          P03      0.91  0.92       0.9       0.9  0.92               100   \n",
       "3          P04      0.82  0.82      0.82      0.84   0.8               100   \n",
       "4          P05      0.79  0.74      0.84      0.76  0.82               100   \n",
       "5          P06      0.69  0.66      0.72      0.68   0.7               100   \n",
       "6          P07  0.588889   0.6  0.577778  0.577778   0.6                90   \n",
       "7          P08      0.49  0.48       0.5      0.52  0.46               100   \n",
       "8          P09      0.74  0.78       0.7      0.72  0.76               100   \n",
       "9          P10      0.69  0.66      0.72      0.72  0.66               100   \n",
       "\n",
       "   chi_square_val  chi_square_p  chi_square_effect_size  \n",
       "0        0.080000          0.99                     NaN  \n",
       "1        4.640000          0.20                     NaN  \n",
       "2       67.280000          0.00                0.820244  \n",
       "3       41.120000          0.00                0.641249  \n",
       "4       34.000000          0.00                0.583095  \n",
       "5       14.480000          0.00                0.380526  \n",
       "6        3.209877          0.36                     NaN  \n",
       "7        0.400000          0.94                     NaN  \n",
       "8       23.200000          0.00                0.481664  \n",
       "9       14.800000          0.00                0.384708  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "47dfab4e-d38d-4845-8582-a1d866e77857",
   "metadata": {},
   "outputs": [],
   "source": [
    "res.to_csv(\"chi_square.csv\")\n",
    "res.to_excel(\"chi_square.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af8410b0-96cb-4b91-aabd-bc9147913fc2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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