EmoVar Analysis (Study 2) - Dimensions of Authenticity vs. Power.ipynb

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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>time</th>\n",
       "      <th>subject</th>\n",
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       "      <th>stimulusitem1</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>110817</td>\n",
       "      <td>2:47:21</td>\n",
       "      <td>397465487</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>expt_intro</td>\n",
       "      <td>expt_intro</td>\n",
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       "      <td>Welcome to the study!    During this session, ...</td>\n",
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       "      <td>500</td>\n",
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       "      <td>lv3_female_block</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
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       "      <td>397465487</td>\n",
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       "      <td>3</td>\n",
       "      <td>lv3_female_block</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
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       "      <td>500</td>\n",
       "      <td>WF-009_-05.jpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>110817</td>\n",
       "      <td>2:47:21</td>\n",
       "      <td>397465487</td>\n",
       "      <td>1</td>\n",
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       "      <td>5</td>\n",
       "      <td>lv3_female_block</td>\n",
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       "      <td>500</td>\n",
       "      <td>WF-009_03.jpg</td>\n",
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       "      <th>6</th>\n",
       "      <td>110817</td>\n",
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       "      <td>500</td>\n",
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       "      <th>8</th>\n",
       "      <td>110817</td>\n",
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       "      <td>397465487</td>\n",
       "      <td>1</td>\n",
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       "      <td>8</td>\n",
       "      <td>lv3_female_block</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WF-009_02.jpg</td>\n",
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       "      <td>397465487</td>\n",
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       "      <td>9</td>\n",
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       "      <th>10</th>\n",
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       "      <td>397465487</td>\n",
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       "      <td>500</td>\n",
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       "      <td>13</td>\n",
       "      <td>lv3_female_block</td>\n",
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       "      <td>17</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
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       "      <th>18</th>\n",
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       "      <td>2:47:21</td>\n",
       "      <td>397465487</td>\n",
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       "      <td>18</td>\n",
       "      <td>lv3_female_block</td>\n",
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       "      <td>500</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>110817</td>\n",
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       "      <td>19</td>\n",
       "      <td>lv3_female_block</td>\n",
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       "      <td>500</td>\n",
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       "      date     time    subject  finished  blocknum  trialnum  \\\n",
       "0   110817  2:47:21  397465487         1         1         1   \n",
       "1   110817  2:47:21  397465487         1         2         1   \n",
       "2   110817  2:47:21  397465487         1         2         2   \n",
       "3   110817  2:47:21  397465487         1         2         3   \n",
       "4   110817  2:47:21  397465487         1         2         4   \n",
       "5   110817  2:47:21  397465487         1         2         5   \n",
       "6   110817  2:47:21  397465487         1         2         6   \n",
       "7   110817  2:47:21  397465487         1         2         7   \n",
       "8   110817  2:47:21  397465487         1         2         8   \n",
       "9   110817  2:47:21  397465487         1         2         9   \n",
       "10  110817  2:47:21  397465487         1         2        10   \n",
       "11  110817  2:47:21  397465487         1         2        11   \n",
       "12  110817  2:47:21  397465487         1         2        12   \n",
       "13  110817  2:47:21  397465487         1         2        13   \n",
       "14  110817  2:47:21  397465487         1         2        14   \n",
       "15  110817  2:47:21  397465487         1         2        15   \n",
       "16  110817  2:47:21  397465487         1         2        16   \n",
       "17  110817  2:47:21  397465487         1         2        17   \n",
       "18  110817  2:47:21  397465487         1         2        18   \n",
       "19  110817  2:47:21  397465487         1         2        19   \n",
       "\n",
       "           blockcode         trialcode  response  latency  \\\n",
       "0         expt_intro        expt_intro      57.0    11670   \n",
       "1   lv3_female_block  lv3_female_trial       0.0      500   \n",
       "2   lv3_female_block  lv3_female_trial       0.0      500   \n",
       "3   lv3_female_block  lv3_female_trial       0.0      500   \n",
       "4   lv3_female_block  lv3_female_trial       0.0      500   \n",
       "5   lv3_female_block  lv3_female_trial       0.0      500   \n",
       "6   lv3_female_block  lv3_female_trial       0.0      500   \n",
       "7   lv3_female_block  lv3_female_trial       0.0      500   \n",
       "8   lv3_female_block  lv3_female_trial       0.0      500   \n",
       "9   lv3_female_block  lv3_female_trial       0.0      500   \n",
       "10  lv3_female_block  lv3_female_trial       0.0      500   \n",
       "11  lv3_female_block  lv3_female_trial       0.0      500   \n",
       "12  lv3_female_block  lv3_female_trial       0.0      500   \n",
       "13  lv3_female_block  lv3_female_trial       0.0      500   \n",
       "14  lv3_female_block  lv3_female_trial       0.0      500   \n",
       "15  lv3_female_block  lv3_female_trial       0.0      500   \n",
       "16  lv3_female_block  lv3_female_trial       0.0      500   \n",
       "17  lv3_female_block  lv3_female_trial       0.0      500   \n",
       "18  lv3_female_block  lv3_female_trial       0.0      500   \n",
       "19  lv3_female_block  lv3_female_trial       0.0      500   \n",
       "\n",
       "                                        stimulusitem1  \n",
       "0   Welcome to the study!    During this session, ...  \n",
       "1                                      WF-009_-06.jpg  \n",
       "2                                      WF-009_-04.jpg  \n",
       "3                                      WF-009_-02.jpg  \n",
       "4                                      WF-009_-05.jpg  \n",
       "5                                       WF-009_03.jpg  \n",
       "6                                      WF-009_-04.jpg  \n",
       "7                                       WF-009_05.jpg  \n",
       "8                                       WF-009_02.jpg  \n",
       "9                                      WF-009_-03.jpg  \n",
       "10                                     WF-009_-02.jpg  \n",
       "11                                      WF-009_04.jpg  \n",
       "12                                     WF-009_-02.jpg  \n",
       "13                                     WF-009_-06.jpg  \n",
       "14                                     WF-009_-05.jpg  \n",
       "15                                      WF-009_06.jpg  \n",
       "16                                      WF-009_04.jpg  \n",
       "17                                      WF-009_04.jpg  \n",
       "18                                     WF-009_-05.jpg  \n",
       "19                                     WF-009_-04.jpg  "
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   ],
   "source": [
    "# \"Why, when, and how variability in emotion expression makes people look authentic and leader-like\"\n",
    "# Analysis script for Study 2\n",
    "# Evan W. Carr, 05-01-2018 (Columbia Business School)\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import statsmodels.formula.api as smf\n",
    "\n",
    "from plotnine import *\n",
    "from rpy2.robjects import r, pandas2ri\n",
    "\n",
    "%matplotlib inline  \n",
    "\n",
    "# Set working directory to location of data files, and read in .csv's:\n",
    "rawData = pd.read_csv('amanic2_exptrun_final_clean_nov132017.csv')\n",
    "rawDemog = pd.read_csv('demographics_survey_survey_17_11_14.csv')\n",
    "rawJobs = pd.read_csv('job_survey_survey_17_11_14.csv')\n",
    "\n",
    "rawData.head(20)"
   ]
  },
  {
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   "metadata": {},
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject</th>\n",
       "      <th>counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>650316385</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>72893364</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>944263546</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>244237017</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>268500279</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>257887556</td>\n",
       "      <td>37</td>\n",
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       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>629976880</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>930295834</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>934593570</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>138819187</td>\n",
       "      <td>39</td>\n",
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       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>720050574</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>445584446</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>769097460</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>77434867</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>195307446</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>975595584</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>281811054</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>188803907</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>201194565</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>777465132</td>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>776767336</td>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>499441303</td>\n",
       "      <td>265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>113720039</td>\n",
       "      <td>265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>190918033</td>\n",
       "      <td>343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>228069227</td>\n",
       "      <td>381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>601864713</td>\n",
       "      <td>381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>662767948</td>\n",
       "      <td>424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>693446730</td>\n",
       "      <td>457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>717841565</td>\n",
       "      <td>458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>689270235</td>\n",
       "      <td>458</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       subject  counts\n",
       "145  650316385      13\n",
       "16    72893364      33\n",
       "215  944263546      34\n",
       "59   244237017      37\n",
       "62   268500279      37\n",
       "60   257887556      37\n",
       "141  629976880      37\n",
       "210  930295834      37\n",
       "211  934593570      37\n",
       "33   138819187      39\n",
       "161  720050574      40\n",
       "97   445584446      65\n",
       "177  769097460      74\n",
       "19    77434867      77\n",
       "47   195307446     100\n",
       "219  975595584     113\n",
       "63   281811054     116\n",
       "41   188803907     129\n",
       "49   201194565     151\n",
       "183  777465132     189\n",
       "182  776767336     189\n",
       "107  499441303     265\n",
       "29   113720039     265\n",
       "42   190918033     343\n",
       "54   228069227     381\n",
       "133  601864713     381\n",
       "148  662767948     424\n",
       "155  693446730     457\n",
       "160  717841565     458\n",
       "152  689270235     458"
      ]
     },
     "execution_count": 435,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# All subjects should have 458 observations (rows) in the dataset ...\n",
    "# Any that have more/less than 458 need to be removed before analysis:\n",
    "rawObsCount = (rawData\n",
    "               .groupby('subject', as_index=False)\n",
    "               .size()\n",
    "               .reset_index(name='counts')\n",
    "               .sort_values(by='counts'))\n",
    "\n",
    "rawObsCount.head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([650316385,  72893364, 944263546, 244237017, 268500279, 257887556,\n",
       "       629976880, 930295834, 934593570, 138819187, 720050574, 445584446,\n",
       "       769097460,  77434867, 195307446, 975595584, 281811054, 188803907,\n",
       "       201194565, 777465132, 776767336, 499441303, 113720039, 190918033,\n",
       "       228069227, 601864713, 662767948, 693446730], dtype=int64)"
      ]
     },
     "execution_count": 436,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create list of subject ID's to exclude:\n",
    "exclSbj = rawObsCount.query('counts != 458')\n",
    "exclSbj = np.array(exclSbj['subject'])\n",
    "exclSbj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "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>subject</th>\n",
       "      <th>blocknum</th>\n",
       "      <th>trialnum</th>\n",
       "      <th>blockcode</th>\n",
       "      <th>trialcode</th>\n",
       "      <th>response</th>\n",
       "      <th>latency</th>\n",
       "      <th>stimulusitem1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>397465487</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>expt_intro</td>\n",
       "      <td>expt_intro</td>\n",
       "      <td>57.0</td>\n",
       "      <td>11670</td>\n",
       "      <td>Welcome to the study!    During this session, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>397465487</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>lv3_female_block</td>\n",
       "      <td>lv3_female_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WF-009_-06.jpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>397465487</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>lv3_female_block</td>\n",
       "      <td>lv3_female_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WF-009_-04.jpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>397465487</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>lv3_female_block</td>\n",
       "      <td>lv3_female_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WF-009_-02.jpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>397465487</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>lv3_female_block</td>\n",
       "      <td>lv3_female_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WF-009_-05.jpg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     subject  blocknum  trialnum         blockcode         trialcode  \\\n",
       "0  397465487         1         1        expt_intro        expt_intro   \n",
       "1  397465487         2         1  lv3_female_block  lv3_female_trial   \n",
       "2  397465487         2         2  lv3_female_block  lv3_female_trial   \n",
       "3  397465487         2         3  lv3_female_block  lv3_female_trial   \n",
       "4  397465487         2         4  lv3_female_block  lv3_female_trial   \n",
       "\n",
       "   response  latency                                      stimulusitem1  \n",
       "0      57.0    11670  Welcome to the study!    During this session, ...  \n",
       "1       0.0      500                                     WF-009_-06.jpg  \n",
       "2       0.0      500                                     WF-009_-04.jpg  \n",
       "3       0.0      500                                     WF-009_-02.jpg  \n",
       "4       0.0      500                                     WF-009_-05.jpg  "
      ]
     },
     "execution_count": 437,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter to important columns, and exclude subjects that didn't finish the study:\n",
    "cleanCols = ['subject', 'blocknum', 'trialnum', 'blockcode', 'trialcode', 'response', 'latency', 'stimulusitem1']\n",
    "cleanData = rawData.loc[~rawData['subject'].isin(exclSbj), cleanCols]\n",
    "cleanData.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35.79187817258883"
      ]
     },
     "execution_count": 438,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get quick demographics summary, without excluded subjects:\n",
    "demogSummary = (rawDemog\n",
    "                .groupby('subject')\n",
    "                .agg({'age_response': np.mean, \"gender_response\": pd.Series.unique})\n",
    "                .reset_index())\n",
    "\n",
    "demogSummary = demogSummary[~demogSummary['subject'].isin(exclSbj)]\n",
    "\n",
    "demogSummary['age_response'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 439,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.04136013153621"
      ]
     },
     "execution_count": 439,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demogSummary['age_response'].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Female    100\n",
       "Male       97\n",
       "Name: gender_response, dtype: int64"
      ]
     },
     "execution_count": 440,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demogSummary['gender_response'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "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>subject</th>\n",
       "      <th>currently_employed_response</th>\n",
       "      <th>years_at_job_response</th>\n",
       "      <th>mgr_status_response</th>\n",
       "      <th>industry_type_response</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>397465487</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>lower-management</td>\n",
       "      <td>Service</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>101955739</td>\n",
       "      <td>Yes</td>\n",
       "      <td>14</td>\n",
       "      <td>middle-management</td>\n",
       "      <td>Medical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>213681965</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3</td>\n",
       "      <td>non-management</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>603341386</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3</td>\n",
       "      <td>non-management</td>\n",
       "      <td>Educational</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>106401510</td>\n",
       "      <td>Yes</td>\n",
       "      <td>6</td>\n",
       "      <td>non-management</td>\n",
       "      <td>Construction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>191376770</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>lower-management</td>\n",
       "      <td>Educational</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>34907686</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>middle-management</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>43864138</td>\n",
       "      <td>Yes</td>\n",
       "      <td>31</td>\n",
       "      <td>top-management</td>\n",
       "      <td>Government</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>644706613</td>\n",
       "      <td>Yes</td>\n",
       "      <td>9</td>\n",
       "      <td>non-management</td>\n",
       "      <td>Educational</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>60909027</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3</td>\n",
       "      <td>lower-management</td>\n",
       "      <td>Medical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>187151687</td>\n",
       "      <td>Yes</td>\n",
       "      <td>7</td>\n",
       "      <td>middle-management</td>\n",
       "      <td>Consumer Goods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>341557005</td>\n",
       "      <td>Yes</td>\n",
       "      <td>8</td>\n",
       "      <td>top-management</td>\n",
       "      <td>Corporate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>468689004</td>\n",
       "      <td>Yes</td>\n",
       "      <td>13</td>\n",
       "      <td>non-management</td>\n",
       "      <td>Educational</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>436570347</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3</td>\n",
       "      <td>non-management</td>\n",
       "      <td>Medical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>405101261</td>\n",
       "      <td>Yes</td>\n",
       "      <td>14</td>\n",
       "      <td>lower-management</td>\n",
       "      <td>Educational</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>56949116</td>\n",
       "      <td>Yes</td>\n",
       "      <td>6</td>\n",
       "      <td>middle-management</td>\n",
       "      <td>Educational</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>716177355</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>lower-management</td>\n",
       "      <td>Government</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>724298534</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>middle-management</td>\n",
       "      <td>Medical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>498330805</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>lower-management</td>\n",
       "      <td>High Tech</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>763975742</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5</td>\n",
       "      <td>top-management</td>\n",
       "      <td>Educational</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      subject currently_employed_response  years_at_job_response  \\\n",
       "0   397465487                         Yes                      2   \n",
       "1   101955739                         Yes                     14   \n",
       "2   213681965                         Yes                      3   \n",
       "3   603341386                         Yes                      3   \n",
       "4   106401510                         Yes                      6   \n",
       "5   191376770                         Yes                      2   \n",
       "6    34907686                         Yes                      1   \n",
       "7    43864138                         Yes                     31   \n",
       "8   644706613                         Yes                      9   \n",
       "9    60909027                         Yes                      3   \n",
       "10  187151687                         Yes                      7   \n",
       "11  341557005                         Yes                      8   \n",
       "12  468689004                         Yes                     13   \n",
       "13  436570347                         Yes                      3   \n",
       "14  405101261                         Yes                     14   \n",
       "15   56949116                         Yes                      6   \n",
       "16  716177355                         Yes                      2   \n",
       "17  724298534                         Yes                      2   \n",
       "18  498330805                         Yes                      2   \n",
       "19  763975742                         Yes                      5   \n",
       "\n",
       "   mgr_status_response industry_type_response  \n",
       "0     lower-management                Service  \n",
       "1    middle-management                Medical  \n",
       "2       non-management                Finance  \n",
       "3       non-management            Educational  \n",
       "4       non-management           Construction  \n",
       "5     lower-management            Educational  \n",
       "6    middle-management                Finance  \n",
       "7       top-management             Government  \n",
       "8       non-management            Educational  \n",
       "9     lower-management                Medical  \n",
       "10   middle-management         Consumer Goods  \n",
       "11      top-management              Corporate  \n",
       "12      non-management            Educational  \n",
       "13      non-management                Medical  \n",
       "14    lower-management            Educational  \n",
       "15   middle-management            Educational  \n",
       "16    lower-management             Government  \n",
       "17   middle-management                Medical  \n",
       "18    lower-management              High Tech  \n",
       "19      top-management            Educational  "
      ]
     },
     "execution_count": 441,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take a look at the data from the job survey.  Questions asked included ...\n",
    "#    - Currently employed? (y/n) -- if so, how many years?\n",
    "#    - Managerial status? (top, middle, lower, non)\n",
    "#    - Job industry\n",
    "\n",
    "cleanJobs = rawJobs[['subject', \n",
    "                     'currently_employed_response', \n",
    "                     'years_at_job_response',\n",
    "                     'mgr_status_response',\n",
    "                     'industry_type_response']]\n",
    "\n",
    "cleanJobs.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 442,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "96.05911330049261"
      ]
     },
     "execution_count": 442,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Are most of the subjects currently employed?  (Yes, ~96% have a job)\n",
    "100 * (len(cleanJobs.loc[cleanJobs['currently_employed_response']=='Yes']) / cleanJobs.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mgr_status_response</th>\n",
       "      <th colspan=\"2\" halign=\"left\">years_at_job_response</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>lower-management</td>\n",
       "      <td>6.433962</td>\n",
       "      <td>6.122184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>middle-management</td>\n",
       "      <td>7.525424</td>\n",
       "      <td>6.568882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>non-management</td>\n",
       "      <td>5.732394</td>\n",
       "      <td>5.621280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>top-management</td>\n",
       "      <td>9.333333</td>\n",
       "      <td>8.510243</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  mgr_status_response years_at_job_response          \n",
       "                                       mean       std\n",
       "0    lower-management              6.433962  6.122184\n",
       "1   middle-management              7.525424  6.568882\n",
       "2      non-management              5.732394  5.621280\n",
       "3      top-management              9.333333  8.510243"
      ]
     },
     "execution_count": 443,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Of the subjects that are currently employed, how long have they been at their jobs, by management type?\n",
    "# (average range between 5-10 years across levels)\n",
    "(cleanJobs.loc[cleanJobs['currently_employed_response']=='Yes']\n",
    " .groupby('mgr_status_response')\n",
    " .agg({'years_at_job_response': [np.mean, np.std]})\n",
    " .reset_index())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 444,
   "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>index</th>\n",
       "      <th>percent_of_sbj</th>\n",
       "      <th>mean_yrs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Service</td>\n",
       "      <td>16.923077</td>\n",
       "      <td>4.454545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Educational</td>\n",
       "      <td>16.410256</td>\n",
       "      <td>8.562500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Medical</td>\n",
       "      <td>11.794872</td>\n",
       "      <td>5.478261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>High Tech</td>\n",
       "      <td>8.717949</td>\n",
       "      <td>5.176471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>8.205128</td>\n",
       "      <td>5.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Finance</td>\n",
       "      <td>7.179487</td>\n",
       "      <td>6.642857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Government</td>\n",
       "      <td>6.153846</td>\n",
       "      <td>10.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Consumer Goods</td>\n",
       "      <td>5.641026</td>\n",
       "      <td>6.545455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Non-profit</td>\n",
       "      <td>4.615385</td>\n",
       "      <td>8.444444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Construction</td>\n",
       "      <td>3.589744</td>\n",
       "      <td>5.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Corporate</td>\n",
       "      <td>3.589744</td>\n",
       "      <td>6.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Media</td>\n",
       "      <td>2.564103</td>\n",
       "      <td>11.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>2.564103</td>\n",
       "      <td>8.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Legal</td>\n",
       "      <td>1.538462</td>\n",
       "      <td>4.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Arts</td>\n",
       "      <td>0.512821</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             index  percent_of_sbj   mean_yrs\n",
       "0          Service       16.923077   4.454545\n",
       "1      Educational       16.410256   8.562500\n",
       "2          Medical       11.794872   5.478261\n",
       "3        High Tech        8.717949   5.176471\n",
       "4    Manufacturing        8.205128   5.875000\n",
       "5          Finance        7.179487   6.642857\n",
       "6       Government        6.153846  10.916667\n",
       "7   Consumer Goods        5.641026   6.545455\n",
       "8       Non-profit        4.615385   8.444444\n",
       "9     Construction        3.589744   5.428571\n",
       "10       Corporate        3.589744   6.714286\n",
       "11           Media        2.564103  11.200000\n",
       "12  Transportation        2.564103   8.800000\n",
       "13           Legal        1.538462   4.333333\n",
       "14            Arts        0.512821   5.000000"
      ]
     },
     "execution_count": 444,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What percentages of our subjects fall into which industries, and how many years have they been there?\n",
    "# (Many subjects work in service, education, and/or medical -- all > 10% of our subject pool, \n",
    "# and all industry types have average number of years > 4)\n",
    "industryPerc = (100 * (cleanJobs.loc[cleanJobs['currently_employed_response']=='Yes', 'industry_type_response']\n",
    "                      .value_counts() \\\n",
    "                      / cleanJobs.loc[cleanJobs['currently_employed_response']=='Yes', 'industry_type_response'].shape[0])\n",
    "                .to_frame()).reset_index().rename(index=str, columns={'industry_type_response': 'percent_of_sbj'})\n",
    "\n",
    "industryYrs = (cleanJobs.loc[cleanJobs['currently_employed_response']=='Yes']\n",
    "               .groupby('industry_type_response')\n",
    "               .agg({'years_at_job_response': np.mean})\n",
    "               .reset_index()\n",
    "               .rename(index=str, columns={'industry_type_response': 'index', 'years_at_job_response': 'mean_yrs'}))\n",
    "\n",
    "industryPerc.merge(industryYrs, on='index', how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 445,
   "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>subject</th>\n",
       "      <th>blocknum</th>\n",
       "      <th>trialnum</th>\n",
       "      <th>blockcode</th>\n",
       "      <th>trialcode</th>\n",
       "      <th>response</th>\n",
       "      <th>latency</th>\n",
       "      <th>stimulusitem1</th>\n",
       "      <th>model_block</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>397465487</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>expt_intro</td>\n",
       "      <td>expt_intro</td>\n",
       "      <td>57.0</td>\n",
       "      <td>11670</td>\n",
       "      <td>Welcome to the study!    During this session, ...</td>\n",
       "      <td>expt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>397465487</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>lv3_female_block</td>\n",
       "      <td>lv3_female_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WF-009_-06.jpg</td>\n",
       "      <td>lv3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>397465487</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>lv3_female_block</td>\n",
       "      <td>lv3_female_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WF-009_-04.jpg</td>\n",
       "      <td>lv3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>397465487</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>lv3_female_block</td>\n",
       "      <td>lv3_female_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WF-009_-02.jpg</td>\n",
       "      <td>lv3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>397465487</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>lv3_female_block</td>\n",
       "      <td>lv3_female_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WF-009_-05.jpg</td>\n",
       "      <td>lv3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     subject  blocknum  trialnum         blockcode         trialcode  \\\n",
       "0  397465487         1         1        expt_intro        expt_intro   \n",
       "1  397465487         2         1  lv3_female_block  lv3_female_trial   \n",
       "2  397465487         2         2  lv3_female_block  lv3_female_trial   \n",
       "3  397465487         2         3  lv3_female_block  lv3_female_trial   \n",
       "4  397465487         2         4  lv3_female_block  lv3_female_trial   \n",
       "\n",
       "   response  latency                                      stimulusitem1  \\\n",
       "0      57.0    11670  Welcome to the study!    During this session, ...   \n",
       "1       0.0      500                                     WF-009_-06.jpg   \n",
       "2       0.0      500                                     WF-009_-04.jpg   \n",
       "3       0.0      500                                     WF-009_-02.jpg   \n",
       "4       0.0      500                                     WF-009_-05.jpg   \n",
       "\n",
       "  model_block  \n",
       "0        expt  \n",
       "1         lv3  \n",
       "2         lv3  \n",
       "3         lv3  \n",
       "4         lv3  "
      ]
     },
     "execution_count": 445,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create column for emotion variability condition ('model_block'):\n",
    "cleanData = (cleanData\n",
    "             .join(cleanData['blockcode'].str.split('_', 1, expand=True)\n",
    "                   .rename(columns={0:'model_block', 1:'junk'})))\n",
    "\n",
    "cleanData = cleanData.drop(['junk'], axis=1)\n",
    "\n",
    "cleanData.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 446,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject</th>\n",
       "      <th>blocknum</th>\n",
       "      <th>trialnum</th>\n",
       "      <th>blockcode</th>\n",
       "      <th>trialcode</th>\n",
       "      <th>response</th>\n",
       "      <th>latency</th>\n",
       "      <th>stimulusitem1</th>\n",
       "      <th>model_block</th>\n",
       "      <th>overallDim</th>\n",
       "      <th>specificDim</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>397465487</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>expt_intro</td>\n",
       "      <td>expt_intro</td>\n",
       "      <td>57.0</td>\n",
       "      <td>11670</td>\n",
       "      <td>Welcome to the study!    During this session, ...</td>\n",
       "      <td>expt</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>397465487</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>lv3_female_block</td>\n",
       "      <td>lv3_female_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WF-009_-06.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>trial</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>397465487</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>lv3_female_block</td>\n",
       "      <td>lv3_female_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WF-009_-04.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>trial</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>397465487</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>lv3_female_block</td>\n",
       "      <td>lv3_female_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WF-009_-02.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>trial</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>397465487</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>lv3_female_block</td>\n",
       "      <td>lv3_female_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WF-009_-05.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>trial</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>397465487</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>lv3_female_block</td>\n",
       "      <td>lv3_female_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WF-009_03.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>trial</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>397465487</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>lv3_female_block</td>\n",
       "      <td>lv3_female_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WF-009_-04.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>trial</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>397465487</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>lv3_female_block</td>\n",
       "      <td>lv3_female_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WF-009_05.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>trial</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>397465487</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>lv3_female_block</td>\n",
       "      <td>lv3_female_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WF-009_02.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>trial</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>397465487</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>lv3_female_block</td>\n",
       "      <td>lv3_female_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WF-009_-03.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>trial</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>397465487</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>lv3_female_block</td>\n",
       "      <td>lv3_female_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WF-009_-02.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>trial</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>397465487</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>lv3_female_block</td>\n",
       "      <td>lv3_female_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WF-009_04.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>trial</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>397465487</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>lv3_female_block</td>\n",
       "      <td>lv3_female_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WF-009_-02.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>trial</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>397465487</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>lv3_female_block</td>\n",
       "      <td>lv3_female_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WF-009_-06.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>trial</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>397465487</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>lv3_female_block</td>\n",
       "      <td>lv3_female_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WF-009_-05.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>trial</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>397465487</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>lv3_female_block</td>\n",
       "      <td>lv3_female_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WF-009_06.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>trial</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>397465487</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>lv3_female_block</td>\n",
       "      <td>lv3_female_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WF-009_04.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>trial</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>397465487</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>lv3_female_block</td>\n",
       "      <td>lv3_female_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WF-009_04.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>trial</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>397465487</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>lv3_female_block</td>\n",
       "      <td>lv3_female_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WF-009_-05.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>trial</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>397465487</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>lv3_female_block</td>\n",
       "      <td>lv3_female_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WF-009_-04.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>trial</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>397465487</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>lv3_female_block</td>\n",
       "      <td>lv3_female_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WF-009_06.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>trial</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>397465487</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>lv3_female_block</td>\n",
       "      <td>lv3_female_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WF-009_-03.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>trial</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>397465487</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>lv3_female_block</td>\n",
       "      <td>lv3_female_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WF-009_06.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>trial</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>397465487</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>lv3_female_block</td>\n",
       "      <td>lv3_female_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WF-009_05.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>trial</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>397465487</td>\n",
       "      <td>2</td>\n",
       "      <td>24</td>\n",
       "      <td>lv3_female_block</td>\n",
       "      <td>lv3_female_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WF-009_05.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>trial</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>397465487</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>lv3_female_block</td>\n",
       "      <td>lv3_female_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WF-009_03.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>trial</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>397465487</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>lv3_female_block</td>\n",
       "      <td>lv3_female_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WF-009_02.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>trial</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>397465487</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>lv3_female_block</td>\n",
       "      <td>lv3_female_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WF-009_03.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>trial</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>397465487</td>\n",
       "      <td>2</td>\n",
       "      <td>28</td>\n",
       "      <td>lv3_female_block</td>\n",
       "      <td>lv3_female_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WF-009_-03.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>trial</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>397465487</td>\n",
       "      <td>2</td>\n",
       "      <td>29</td>\n",
       "      <td>lv3_female_block</td>\n",
       "      <td>lv3_female_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WF-009_-06.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>trial</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>397465487</td>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "      <td>lv3_female_block</td>\n",
       "      <td>lv3_female_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WF-009_02.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>trial</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>397465487</td>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>lv3_female_block</td>\n",
       "      <td>specificDimension_questions_authenticity_genui...</td>\n",
       "      <td>65.0</td>\n",
       "      <td>30487</td>\n",
       "      <td>cbs_logo.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>authenticity</td>\n",
       "      <td>genuineness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>397465487</td>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>lv3_female_block</td>\n",
       "      <td>specificDimension_questions_authenticity_value...</td>\n",
       "      <td>68.0</td>\n",
       "      <td>30487</td>\n",
       "      <td>cbs_logo.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>authenticity</td>\n",
       "      <td>values</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>397465487</td>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>lv3_female_block</td>\n",
       "      <td>specificDimension_questions_authenticity_openn...</td>\n",
       "      <td>67.0</td>\n",
       "      <td>30487</td>\n",
       "      <td>cbs_logo.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>authenticity</td>\n",
       "      <td>openness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>397465487</td>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>lv3_female_block</td>\n",
       "      <td>specificDimension_questions_power_action_slider</td>\n",
       "      <td>53.0</td>\n",
       "      <td>30487</td>\n",
       "      <td>cbs_logo.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>power</td>\n",
       "      <td>action</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>397465487</td>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>lv3_female_block</td>\n",
       "      <td>specificDimension_questions_power_control_slider</td>\n",
       "      <td>69.0</td>\n",
       "      <td>30487</td>\n",
       "      <td>cbs_logo.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>power</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>397465487</td>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>lv3_female_block</td>\n",
       "      <td>specificDimension_questions_power_independence...</td>\n",
       "      <td>54.0</td>\n",
       "      <td>30487</td>\n",
       "      <td>cbs_logo.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>power</td>\n",
       "      <td>independence</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>397465487</td>\n",
       "      <td>2</td>\n",
       "      <td>32</td>\n",
       "      <td>lv3_female_block</td>\n",
       "      <td>power_overall_slider</td>\n",
       "      <td>64.0</td>\n",
       "      <td>6035</td>\n",
       "      <td>cbs_logo.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>power</td>\n",
       "      <td>overall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>397465487</td>\n",
       "      <td>2</td>\n",
       "      <td>33</td>\n",
       "      <td>lv3_female_block</td>\n",
       "      <td>authenticity_overall_slider</td>\n",
       "      <td>76.0</td>\n",
       "      <td>5948</td>\n",
       "      <td>cbs_logo.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>authenticity</td>\n",
       "      <td>overall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>397465487</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>lv1_female_block</td>\n",
       "      <td>lv1_female_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WF-015_-03.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>trial</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>397465487</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>lv1_female_block</td>\n",
       "      <td>lv1_female_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WF-015_-05.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>trial</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>397465487</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>lv1_female_block</td>\n",
       "      <td>lv1_female_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WF-015_-03.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>trial</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>397465487</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>lv1_female_block</td>\n",
       "      <td>lv1_female_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WF-015_04.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>trial</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>397465487</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>lv1_female_block</td>\n",
       "      <td>lv1_female_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WF-015_03.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>trial</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>397465487</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>lv1_female_block</td>\n",
       "      <td>lv1_female_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WF-015_-04.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>trial</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>397465487</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>lv1_female_block</td>\n",
       "      <td>lv1_female_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WF-015_06.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>trial</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>397465487</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>lv1_female_block</td>\n",
       "      <td>lv1_female_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WF-015_04.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>trial</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>397465487</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>lv1_female_block</td>\n",
       "      <td>lv1_female_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WF-015_-05.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>trial</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>397465487</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>lv1_female_block</td>\n",
       "      <td>lv1_female_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WF-015_-06.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>trial</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>397465487</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>lv1_female_block</td>\n",
       "      <td>lv1_female_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WF-015_06.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>trial</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      subject  blocknum  trialnum         blockcode  \\\n",
       "0   397465487         1         1        expt_intro   \n",
       "1   397465487         2         1  lv3_female_block   \n",
       "2   397465487         2         2  lv3_female_block   \n",
       "3   397465487         2         3  lv3_female_block   \n",
       "4   397465487         2         4  lv3_female_block   \n",
       "5   397465487         2         5  lv3_female_block   \n",
       "6   397465487         2         6  lv3_female_block   \n",
       "7   397465487         2         7  lv3_female_block   \n",
       "8   397465487         2         8  lv3_female_block   \n",
       "9   397465487         2         9  lv3_female_block   \n",
       "10  397465487         2        10  lv3_female_block   \n",
       "11  397465487         2        11  lv3_female_block   \n",
       "12  397465487         2        12  lv3_female_block   \n",
       "13  397465487         2        13  lv3_female_block   \n",
       "14  397465487         2        14  lv3_female_block   \n",
       "15  397465487         2        15  lv3_female_block   \n",
       "16  397465487         2        16  lv3_female_block   \n",
       "17  397465487         2        17  lv3_female_block   \n",
       "18  397465487         2        18  lv3_female_block   \n",
       "19  397465487         2        19  lv3_female_block   \n",
       "20  397465487         2        20  lv3_female_block   \n",
       "21  397465487         2        21  lv3_female_block   \n",
       "22  397465487         2        22  lv3_female_block   \n",
       "23  397465487         2        23  lv3_female_block   \n",
       "24  397465487         2        24  lv3_female_block   \n",
       "25  397465487         2        25  lv3_female_block   \n",
       "26  397465487         2        26  lv3_female_block   \n",
       "27  397465487         2        27  lv3_female_block   \n",
       "28  397465487         2        28  lv3_female_block   \n",
       "29  397465487         2        29  lv3_female_block   \n",
       "30  397465487         2        30  lv3_female_block   \n",
       "31  397465487         2        31  lv3_female_block   \n",
       "32  397465487         2        31  lv3_female_block   \n",
       "33  397465487         2        31  lv3_female_block   \n",
       "34  397465487         2        31  lv3_female_block   \n",
       "35  397465487         2        31  lv3_female_block   \n",
       "36  397465487         2        31  lv3_female_block   \n",
       "37  397465487         2        32  lv3_female_block   \n",
       "38  397465487         2        33  lv3_female_block   \n",
       "39  397465487         3         1  lv1_female_block   \n",
       "40  397465487         3         2  lv1_female_block   \n",
       "41  397465487         3         3  lv1_female_block   \n",
       "42  397465487         3         4  lv1_female_block   \n",
       "43  397465487         3         5  lv1_female_block   \n",
       "44  397465487         3         6  lv1_female_block   \n",
       "45  397465487         3         7  lv1_female_block   \n",
       "46  397465487         3         8  lv1_female_block   \n",
       "47  397465487         3         9  lv1_female_block   \n",
       "48  397465487         3        10  lv1_female_block   \n",
       "49  397465487         3        11  lv1_female_block   \n",
       "\n",
       "                                            trialcode  response  latency  \\\n",
       "0                                          expt_intro      57.0    11670   \n",
       "1                                    lv3_female_trial       0.0      500   \n",
       "2                                    lv3_female_trial       0.0      500   \n",
       "3                                    lv3_female_trial       0.0      500   \n",
       "4                                    lv3_female_trial       0.0      500   \n",
       "5                                    lv3_female_trial       0.0      500   \n",
       "6                                    lv3_female_trial       0.0      500   \n",
       "7                                    lv3_female_trial       0.0      500   \n",
       "8                                    lv3_female_trial       0.0      500   \n",
       "9                                    lv3_female_trial       0.0      500   \n",
       "10                                   lv3_female_trial       0.0      500   \n",
       "11                                   lv3_female_trial       0.0      500   \n",
       "12                                   lv3_female_trial       0.0      500   \n",
       "13                                   lv3_female_trial       0.0      500   \n",
       "14                                   lv3_female_trial       0.0      500   \n",
       "15                                   lv3_female_trial       0.0      500   \n",
       "16                                   lv3_female_trial       0.0      500   \n",
       "17                                   lv3_female_trial       0.0      500   \n",
       "18                                   lv3_female_trial       0.0      500   \n",
       "19                                   lv3_female_trial       0.0      500   \n",
       "20                                   lv3_female_trial       0.0      500   \n",
       "21                                   lv3_female_trial       0.0      500   \n",
       "22                                   lv3_female_trial       0.0      500   \n",
       "23                                   lv3_female_trial       0.0      500   \n",
       "24                                   lv3_female_trial       0.0      500   \n",
       "25                                   lv3_female_trial       0.0      500   \n",
       "26                                   lv3_female_trial       0.0      500   \n",
       "27                                   lv3_female_trial       0.0      500   \n",
       "28                                   lv3_female_trial       0.0      500   \n",
       "29                                   lv3_female_trial       0.0      500   \n",
       "30                                   lv3_female_trial       0.0      500   \n",
       "31  specificDimension_questions_authenticity_genui...      65.0    30487   \n",
       "32  specificDimension_questions_authenticity_value...      68.0    30487   \n",
       "33  specificDimension_questions_authenticity_openn...      67.0    30487   \n",
       "34    specificDimension_questions_power_action_slider      53.0    30487   \n",
       "35   specificDimension_questions_power_control_slider      69.0    30487   \n",
       "36  specificDimension_questions_power_independence...      54.0    30487   \n",
       "37                               power_overall_slider      64.0     6035   \n",
       "38                        authenticity_overall_slider      76.0     5948   \n",
       "39                                   lv1_female_trial       0.0      500   \n",
       "40                                   lv1_female_trial       0.0      500   \n",
       "41                                   lv1_female_trial       0.0      500   \n",
       "42                                   lv1_female_trial       0.0      500   \n",
       "43                                   lv1_female_trial       0.0      500   \n",
       "44                                   lv1_female_trial       0.0      500   \n",
       "45                                   lv1_female_trial       0.0      500   \n",
       "46                                   lv1_female_trial       0.0      500   \n",
       "47                                   lv1_female_trial       0.0      500   \n",
       "48                                   lv1_female_trial       0.0      500   \n",
       "49                                   lv1_female_trial       0.0      500   \n",
       "\n",
       "                                        stimulusitem1 model_block  \\\n",
       "0   Welcome to the study!    During this session, ...        expt   \n",
       "1                                      WF-009_-06.jpg          lv   \n",
       "2                                      WF-009_-04.jpg          lv   \n",
       "3                                      WF-009_-02.jpg          lv   \n",
       "4                                      WF-009_-05.jpg          lv   \n",
       "5                                       WF-009_03.jpg          lv   \n",
       "6                                      WF-009_-04.jpg          lv   \n",
       "7                                       WF-009_05.jpg          lv   \n",
       "8                                       WF-009_02.jpg          lv   \n",
       "9                                      WF-009_-03.jpg          lv   \n",
       "10                                     WF-009_-02.jpg          lv   \n",
       "11                                      WF-009_04.jpg          lv   \n",
       "12                                     WF-009_-02.jpg          lv   \n",
       "13                                     WF-009_-06.jpg          lv   \n",
       "14                                     WF-009_-05.jpg          lv   \n",
       "15                                      WF-009_06.jpg          lv   \n",
       "16                                      WF-009_04.jpg          lv   \n",
       "17                                      WF-009_04.jpg          lv   \n",
       "18                                     WF-009_-05.jpg          lv   \n",
       "19                                     WF-009_-04.jpg          lv   \n",
       "20                                      WF-009_06.jpg          lv   \n",
       "21                                     WF-009_-03.jpg          lv   \n",
       "22                                      WF-009_06.jpg          lv   \n",
       "23                                      WF-009_05.jpg          lv   \n",
       "24                                      WF-009_05.jpg          lv   \n",
       "25                                      WF-009_03.jpg          lv   \n",
       "26                                      WF-009_02.jpg          lv   \n",
       "27                                      WF-009_03.jpg          lv   \n",
       "28                                     WF-009_-03.jpg          lv   \n",
       "29                                     WF-009_-06.jpg          lv   \n",
       "30                                      WF-009_02.jpg          lv   \n",
       "31                                       cbs_logo.jpg          lv   \n",
       "32                                       cbs_logo.jpg          lv   \n",
       "33                                       cbs_logo.jpg          lv   \n",
       "34                                       cbs_logo.jpg          lv   \n",
       "35                                       cbs_logo.jpg          lv   \n",
       "36                                       cbs_logo.jpg          lv   \n",
       "37                                       cbs_logo.jpg          lv   \n",
       "38                                       cbs_logo.jpg          lv   \n",
       "39                                     WF-015_-03.jpg          lv   \n",
       "40                                     WF-015_-05.jpg          lv   \n",
       "41                                     WF-015_-03.jpg          lv   \n",
       "42                                      WF-015_04.jpg          lv   \n",
       "43                                      WF-015_03.jpg          lv   \n",
       "44                                     WF-015_-04.jpg          lv   \n",
       "45                                      WF-015_06.jpg          lv   \n",
       "46                                      WF-015_04.jpg          lv   \n",
       "47                                     WF-015_-05.jpg          lv   \n",
       "48                                     WF-015_-06.jpg          lv   \n",
       "49                                      WF-015_06.jpg          lv   \n",
       "\n",
       "      overallDim   specificDim  \n",
       "0           None          None  \n",
       "1          trial          None  \n",
       "2          trial          None  \n",
       "3          trial          None  \n",
       "4          trial          None  \n",
       "5          trial          None  \n",
       "6          trial          None  \n",
       "7          trial          None  \n",
       "8          trial          None  \n",
       "9          trial          None  \n",
       "10         trial          None  \n",
       "11         trial          None  \n",
       "12         trial          None  \n",
       "13         trial          None  \n",
       "14         trial          None  \n",
       "15         trial          None  \n",
       "16         trial          None  \n",
       "17         trial          None  \n",
       "18         trial          None  \n",
       "19         trial          None  \n",
       "20         trial          None  \n",
       "21         trial          None  \n",
       "22         trial          None  \n",
       "23         trial          None  \n",
       "24         trial          None  \n",
       "25         trial          None  \n",
       "26         trial          None  \n",
       "27         trial          None  \n",
       "28         trial          None  \n",
       "29         trial          None  \n",
       "30         trial          None  \n",
       "31  authenticity   genuineness  \n",
       "32  authenticity        values  \n",
       "33  authenticity      openness  \n",
       "34         power        action  \n",
       "35         power       control  \n",
       "36         power  independence  \n",
       "37         power       overall  \n",
       "38  authenticity       overall  \n",
       "39         trial          None  \n",
       "40         trial          None  \n",
       "41         trial          None  \n",
       "42         trial          None  \n",
       "43         trial          None  \n",
       "44         trial          None  \n",
       "45         trial          None  \n",
       "46         trial          None  \n",
       "47         trial          None  \n",
       "48         trial          None  \n",
       "49         trial          None  "
      ]
     },
     "execution_count": 446,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Map emotion variability condition ('model_block') to umbrella factor: \n",
    "varDict = {\n",
    "    'expt': 'expt',\n",
    "    'lv1': 'lv',\n",
    "    'lv2': 'lv',\n",
    "    'lv3': 'lv',\n",
    "    'hv1': 'hv',\n",
    "    'hv2': 'hv',\n",
    "    'hv3': 'hv'\n",
    "}\n",
    "\n",
    "cleanData['model_block'] = cleanData['model_block'].map(varDict)\n",
    "\n",
    "# Need to also map types of slider questions to new factor (overall = split index 2; specific = split index 3):\n",
    "trialcodeSplit = lambda x: x.str.split('_', 4, expand=True)\n",
    "    \n",
    "cleanData['overallDim'] = trialcodeSplit(cleanData['trialcode'])[2]\n",
    "cleanData['specificDim'] = trialcodeSplit(cleanData['trialcode'])[3]\n",
    "\n",
    "cleanData.loc[cleanData['trialcode']=='power_overall_slider', 'overallDim'] = 'power'\n",
    "cleanData.loc[cleanData['trialcode']=='authenticity_overall_slider', 'overallDim'] = 'authenticity'\n",
    "cleanData.loc[cleanData['trialcode']=='power_overall_slider', 'specificDim'] = 'overall'\n",
    "cleanData.loc[cleanData['trialcode']=='authenticity_overall_slider', 'specificDim'] = 'overall'\n",
    "\n",
    "cleanData.head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 447,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject</th>\n",
       "      <th>blocknum</th>\n",
       "      <th>stimulusitem1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8534673</td>\n",
       "      <td>1</td>\n",
       "      <td>Welcome to the study!    During this session, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8534673</td>\n",
       "      <td>2</td>\n",
       "      <td>[WF-003_-03.jpg, WF-003_02.jpg, WF-003_04.jpg,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8534673</td>\n",
       "      <td>3</td>\n",
       "      <td>[WM-033_-08_crop.jpg, WM-033_11_crop.jpg, WM-0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8534673</td>\n",
       "      <td>4</td>\n",
       "      <td>[WF-016_-02.jpg, WF-016_05.jpg, WF-016_-04.jpg...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8534673</td>\n",
       "      <td>5</td>\n",
       "      <td>[WM-009_-09_crop.jpg, WM-009_07_crop.jpg, WM-0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8534673</td>\n",
       "      <td>6</td>\n",
       "      <td>[WF-027_09.jpg, WF-027_-11.jpg, WF-027_-14.jpg...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8534673</td>\n",
       "      <td>7</td>\n",
       "      <td>[WF-011_11.jpg, WF-011_-03.jpg, WF-011_-13.jpg...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8534673</td>\n",
       "      <td>8</td>\n",
       "      <td>[WF-015_16.jpg, WF-015_-16.jpg, WF-015_08.jpg,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8534673</td>\n",
       "      <td>9</td>\n",
       "      <td>[WM-016_09_crop.jpg, WM-016_13_crop.jpg, WM-01...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8534673</td>\n",
       "      <td>10</td>\n",
       "      <td>[WM-006_04_crop.jpg, WM-006_-06_crop.jpg, WM-0...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   subject  blocknum                                      stimulusitem1\n",
       "0  8534673         1  Welcome to the study!    During this session, ...\n",
       "1  8534673         2  [WF-003_-03.jpg, WF-003_02.jpg, WF-003_04.jpg,...\n",
       "2  8534673         3  [WM-033_-08_crop.jpg, WM-033_11_crop.jpg, WM-0...\n",
       "3  8534673         4  [WF-016_-02.jpg, WF-016_05.jpg, WF-016_-04.jpg...\n",
       "4  8534673         5  [WM-009_-09_crop.jpg, WM-009_07_crop.jpg, WM-0...\n",
       "5  8534673         6  [WF-027_09.jpg, WF-027_-11.jpg, WF-027_-14.jpg...\n",
       "6  8534673         7  [WF-011_11.jpg, WF-011_-03.jpg, WF-011_-13.jpg...\n",
       "7  8534673         8  [WF-015_16.jpg, WF-015_-16.jpg, WF-015_08.jpg,...\n",
       "8  8534673         9  [WM-016_09_crop.jpg, WM-016_13_crop.jpg, WM-01...\n",
       "9  8534673        10  [WM-006_04_crop.jpg, WM-006_-06_crop.jpg, WM-0..."
      ]
     },
     "execution_count": 447,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Access and log stimulus model ID codes for inclusion in mixed-effects models (shown later):\n",
    "ratingsList = ['authenticity', 'power']\n",
    "modelSummary = (cleanData\n",
    "                .groupby(['subject', 'blocknum'])\n",
    "                .agg({\"stimulusitem1\": pd.Series.unique})\n",
    "                .reset_index())\n",
    "\n",
    "modelSummary.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 448,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject</th>\n",
       "      <th>blocknum</th>\n",
       "      <th>model_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>8534673</td>\n",
       "      <td>1</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8534673</td>\n",
       "      <td>2</td>\n",
       "      <td>WF-003</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8534673</td>\n",
       "      <td>3</td>\n",
       "      <td>WM-033</td>\n",
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       "      <th>3</th>\n",
       "      <td>8534673</td>\n",
       "      <td>4</td>\n",
       "      <td>WF-016</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8534673</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8534673</td>\n",
       "      <td>6</td>\n",
       "      <td>WF-027</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8534673</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8534673</td>\n",
       "      <td>8</td>\n",
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       "      <th>8</th>\n",
       "      <td>8534673</td>\n",
       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8534673</td>\n",
       "      <td>10</td>\n",
       "      <td>WM-006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>8534673</td>\n",
       "      <td>11</td>\n",
       "      <td>WM-029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>8534673</td>\n",
       "      <td>12</td>\n",
       "      <td>WF-009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>8534673</td>\n",
       "      <td>13</td>\n",
       "      <td>WM-003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>8534673</td>\n",
       "      <td>16</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>8738505</td>\n",
       "      <td>1</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    subject  blocknum model_id\n",
       "0   8534673         1        W\n",
       "1   8534673         2   WF-003\n",
       "2   8534673         3   WM-033\n",
       "3   8534673         4   WF-016\n",
       "4   8534673         5   WM-009\n",
       "5   8534673         6   WF-027\n",
       "6   8534673         7   WF-011\n",
       "7   8534673         8   WF-015\n",
       "8   8534673         9   WM-016\n",
       "9   8534673        10   WM-006\n",
       "10  8534673        11   WM-029\n",
       "11  8534673        12   WF-009\n",
       "12  8534673        13   WM-003\n",
       "13  8534673        16        A\n",
       "14  8738505         1        W"
      ]
     },
     "execution_count": 448,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 'modelSummary' dataframe shows all unique stimuli shown to each subject, by blocknum ...\n",
    "# Not useful yet, but will be if we can split stimulus names apart:\n",
    "modelList = []\n",
    "for i in range(0, modelSummary.shape[0]):\n",
    "    modelID = modelSummary.iloc[i]['stimulusitem1'][0].split('_', 1)[0]\n",
    "    modelList.append(modelID)\n",
    "    \n",
    "modelSummary['model_id'] = modelList\n",
    "modelSummary = modelSummary.drop('stimulusitem1', axis=1)\n",
    "\n",
    "modelSummary.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 449,
   "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>subject</th>\n",
       "      <th>response</th>\n",
       "      <th>latency</th>\n",
       "      <th>model_block</th>\n",
       "      <th>overallDim</th>\n",
       "      <th>specificDim</th>\n",
       "      <th>model_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>397465487</td>\n",
       "      <td>65.0</td>\n",
       "      <td>30487</td>\n",
       "      <td>lv</td>\n",
       "      <td>authenticity</td>\n",
       "      <td>genuineness</td>\n",
       "      <td>WF-009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>397465487</td>\n",
       "      <td>68.0</td>\n",
       "      <td>30487</td>\n",
       "      <td>lv</td>\n",
       "      <td>authenticity</td>\n",
       "      <td>values</td>\n",
       "      <td>WF-009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>397465487</td>\n",
       "      <td>67.0</td>\n",
       "      <td>30487</td>\n",
       "      <td>lv</td>\n",
       "      <td>authenticity</td>\n",
       "      <td>openness</td>\n",
       "      <td>WF-009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>397465487</td>\n",
       "      <td>53.0</td>\n",
       "      <td>30487</td>\n",
       "      <td>lv</td>\n",
       "      <td>power</td>\n",
       "      <td>action</td>\n",
       "      <td>WF-009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>397465487</td>\n",
       "      <td>69.0</td>\n",
       "      <td>30487</td>\n",
       "      <td>lv</td>\n",
       "      <td>power</td>\n",
       "      <td>control</td>\n",
       "      <td>WF-009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>397465487</td>\n",
       "      <td>54.0</td>\n",
       "      <td>30487</td>\n",
       "      <td>lv</td>\n",
       "      <td>power</td>\n",
       "      <td>independence</td>\n",
       "      <td>WF-009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>397465487</td>\n",
       "      <td>64.0</td>\n",
       "      <td>6035</td>\n",
       "      <td>lv</td>\n",
       "      <td>power</td>\n",
       "      <td>overall</td>\n",
       "      <td>WF-009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>397465487</td>\n",
       "      <td>76.0</td>\n",
       "      <td>5948</td>\n",
       "      <td>lv</td>\n",
       "      <td>authenticity</td>\n",
       "      <td>overall</td>\n",
       "      <td>WF-009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>397465487</td>\n",
       "      <td>25.0</td>\n",
       "      <td>18968</td>\n",
       "      <td>lv</td>\n",
       "      <td>authenticity</td>\n",
       "      <td>genuineness</td>\n",
       "      <td>WF-015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>397465487</td>\n",
       "      <td>37.0</td>\n",
       "      <td>18968</td>\n",
       "      <td>lv</td>\n",
       "      <td>authenticity</td>\n",
       "      <td>values</td>\n",
       "      <td>WF-015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>397465487</td>\n",
       "      <td>12.0</td>\n",
       "      <td>18968</td>\n",
       "      <td>lv</td>\n",
       "      <td>authenticity</td>\n",
       "      <td>openness</td>\n",
       "      <td>WF-015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>397465487</td>\n",
       "      <td>77.0</td>\n",
       "      <td>18968</td>\n",
       "      <td>lv</td>\n",
       "      <td>power</td>\n",
       "      <td>action</td>\n",
       "      <td>WF-015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>397465487</td>\n",
       "      <td>78.0</td>\n",
       "      <td>18968</td>\n",
       "      <td>lv</td>\n",
       "      <td>power</td>\n",
       "      <td>control</td>\n",
       "      <td>WF-015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>397465487</td>\n",
       "      <td>95.0</td>\n",
       "      <td>18968</td>\n",
       "      <td>lv</td>\n",
       "      <td>power</td>\n",
       "      <td>independence</td>\n",
       "      <td>WF-015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>397465487</td>\n",
       "      <td>32.0</td>\n",
       "      <td>4871</td>\n",
       "      <td>lv</td>\n",
       "      <td>authenticity</td>\n",
       "      <td>overall</td>\n",
       "      <td>WF-015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>397465487</td>\n",
       "      <td>69.0</td>\n",
       "      <td>3139</td>\n",
       "      <td>lv</td>\n",
       "      <td>power</td>\n",
       "      <td>overall</td>\n",
       "      <td>WF-015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>397465487</td>\n",
       "      <td>9.0</td>\n",
       "      <td>21548</td>\n",
       "      <td>lv</td>\n",
       "      <td>authenticity</td>\n",
       "      <td>genuineness</td>\n",
       "      <td>WM-016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>397465487</td>\n",
       "      <td>72.0</td>\n",
       "      <td>21548</td>\n",
       "      <td>lv</td>\n",
       "      <td>authenticity</td>\n",
       "      <td>values</td>\n",
       "      <td>WM-016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>397465487</td>\n",
       "      <td>4.0</td>\n",
       "      <td>21548</td>\n",
       "      <td>lv</td>\n",
       "      <td>authenticity</td>\n",
       "      <td>openness</td>\n",
       "      <td>WM-016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>397465487</td>\n",
       "      <td>69.0</td>\n",
       "      <td>21548</td>\n",
       "      <td>lv</td>\n",
       "      <td>power</td>\n",
       "      <td>action</td>\n",
       "      <td>WM-016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>397465487</td>\n",
       "      <td>81.0</td>\n",
       "      <td>21548</td>\n",
       "      <td>lv</td>\n",
       "      <td>power</td>\n",
       "      <td>control</td>\n",
       "      <td>WM-016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>397465487</td>\n",
       "      <td>88.0</td>\n",
       "      <td>21548</td>\n",
       "      <td>lv</td>\n",
       "      <td>power</td>\n",
       "      <td>independence</td>\n",
       "      <td>WM-016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>397465487</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8606</td>\n",
       "      <td>lv</td>\n",
       "      <td>authenticity</td>\n",
       "      <td>overall</td>\n",
       "      <td>WM-016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>397465487</td>\n",
       "      <td>85.0</td>\n",
       "      <td>3206</td>\n",
       "      <td>lv</td>\n",
       "      <td>power</td>\n",
       "      <td>overall</td>\n",
       "      <td>WM-016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>397465487</td>\n",
       "      <td>98.0</td>\n",
       "      <td>22388</td>\n",
       "      <td>hv</td>\n",
       "      <td>authenticity</td>\n",
       "      <td>genuineness</td>\n",
       "      <td>WM-009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>397465487</td>\n",
       "      <td>81.0</td>\n",
       "      <td>22388</td>\n",
       "      <td>hv</td>\n",
       "      <td>authenticity</td>\n",
       "      <td>values</td>\n",
       "      <td>WM-009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>397465487</td>\n",
       "      <td>100.0</td>\n",
       "      <td>22388</td>\n",
       "      <td>hv</td>\n",
       "      <td>authenticity</td>\n",
       "      <td>openness</td>\n",
       "      <td>WM-009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>397465487</td>\n",
       "      <td>70.0</td>\n",
       "      <td>22388</td>\n",
       "      <td>hv</td>\n",
       "      <td>power</td>\n",
       "      <td>action</td>\n",
       "      <td>WM-009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>397465487</td>\n",
       "      <td>26.0</td>\n",
       "      <td>22388</td>\n",
       "      <td>hv</td>\n",
       "      <td>power</td>\n",
       "      <td>control</td>\n",
       "      <td>WM-009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>397465487</td>\n",
       "      <td>58.0</td>\n",
       "      <td>22388</td>\n",
       "      <td>hv</td>\n",
       "      <td>power</td>\n",
       "      <td>independence</td>\n",
       "      <td>WM-009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>397465487</td>\n",
       "      <td>63.0</td>\n",
       "      <td>3370</td>\n",
       "      <td>hv</td>\n",
       "      <td>power</td>\n",
       "      <td>overall</td>\n",
       "      <td>WM-009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>397465487</td>\n",
       "      <td>92.0</td>\n",
       "      <td>3289</td>\n",
       "      <td>hv</td>\n",
       "      <td>authenticity</td>\n",
       "      <td>overall</td>\n",
       "      <td>WM-009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>397465487</td>\n",
       "      <td>100.0</td>\n",
       "      <td>13007</td>\n",
       "      <td>hv</td>\n",
       "      <td>authenticity</td>\n",
       "      <td>genuineness</td>\n",
       "      <td>WF-027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>397465487</td>\n",
       "      <td>100.0</td>\n",
       "      <td>13007</td>\n",
       "      <td>hv</td>\n",
       "      <td>authenticity</td>\n",
       "      <td>values</td>\n",
       "      <td>WF-027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>397465487</td>\n",
       "      <td>100.0</td>\n",
       "      <td>13007</td>\n",
       "      <td>hv</td>\n",
       "      <td>authenticity</td>\n",
       "      <td>openness</td>\n",
       "      <td>WF-027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>397465487</td>\n",
       "      <td>100.0</td>\n",
       "      <td>13007</td>\n",
       "      <td>hv</td>\n",
       "      <td>power</td>\n",
       "      <td>action</td>\n",
       "      <td>WF-027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>397465487</td>\n",
       "      <td>75.0</td>\n",
       "      <td>13007</td>\n",
       "      <td>hv</td>\n",
       "      <td>power</td>\n",
       "      <td>control</td>\n",
       "      <td>WF-027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>397465487</td>\n",
       "      <td>91.0</td>\n",
       "      <td>13007</td>\n",
       "      <td>hv</td>\n",
       "      <td>power</td>\n",
       "      <td>independence</td>\n",
       "      <td>WF-027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>397465487</td>\n",
       "      <td>95.0</td>\n",
       "      <td>2596</td>\n",
       "      <td>hv</td>\n",
       "      <td>power</td>\n",
       "      <td>overall</td>\n",
       "      <td>WF-027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>397465487</td>\n",
       "      <td>98.0</td>\n",
       "      <td>2632</td>\n",
       "      <td>hv</td>\n",
       "      <td>authenticity</td>\n",
       "      <td>overall</td>\n",
       "      <td>WF-027</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       subject  response  latency model_block    overallDim   specificDim  \\\n",
       "31   397465487      65.0    30487          lv  authenticity   genuineness   \n",
       "32   397465487      68.0    30487          lv  authenticity        values   \n",
       "33   397465487      67.0    30487          lv  authenticity      openness   \n",
       "34   397465487      53.0    30487          lv         power        action   \n",
       "35   397465487      69.0    30487          lv         power       control   \n",
       "36   397465487      54.0    30487          lv         power  independence   \n",
       "37   397465487      64.0     6035          lv         power       overall   \n",
       "38   397465487      76.0     5948          lv  authenticity       overall   \n",
       "69   397465487      25.0    18968          lv  authenticity   genuineness   \n",
       "70   397465487      37.0    18968          lv  authenticity        values   \n",
       "71   397465487      12.0    18968          lv  authenticity      openness   \n",
       "72   397465487      77.0    18968          lv         power        action   \n",
       "73   397465487      78.0    18968          lv         power       control   \n",
       "74   397465487      95.0    18968          lv         power  independence   \n",
       "75   397465487      32.0     4871          lv  authenticity       overall   \n",
       "76   397465487      69.0     3139          lv         power       overall   \n",
       "107  397465487       9.0    21548          lv  authenticity   genuineness   \n",
       "108  397465487      72.0    21548          lv  authenticity        values   \n",
       "109  397465487       4.0    21548          lv  authenticity      openness   \n",
       "110  397465487      69.0    21548          lv         power        action   \n",
       "111  397465487      81.0    21548          lv         power       control   \n",
       "112  397465487      88.0    21548          lv         power  independence   \n",
       "113  397465487      12.0     8606          lv  authenticity       overall   \n",
       "114  397465487      85.0     3206          lv         power       overall   \n",
       "145  397465487      98.0    22388          hv  authenticity   genuineness   \n",
       "146  397465487      81.0    22388          hv  authenticity        values   \n",
       "147  397465487     100.0    22388          hv  authenticity      openness   \n",
       "148  397465487      70.0    22388          hv         power        action   \n",
       "149  397465487      26.0    22388          hv         power       control   \n",
       "150  397465487      58.0    22388          hv         power  independence   \n",
       "151  397465487      63.0     3370          hv         power       overall   \n",
       "152  397465487      92.0     3289          hv  authenticity       overall   \n",
       "183  397465487     100.0    13007          hv  authenticity   genuineness   \n",
       "184  397465487     100.0    13007          hv  authenticity        values   \n",
       "185  397465487     100.0    13007          hv  authenticity      openness   \n",
       "186  397465487     100.0    13007          hv         power        action   \n",
       "187  397465487      75.0    13007          hv         power       control   \n",
       "188  397465487      91.0    13007          hv         power  independence   \n",
       "189  397465487      95.0     2596          hv         power       overall   \n",
       "190  397465487      98.0     2632          hv  authenticity       overall   \n",
       "\n",
       "    model_id  \n",
       "31    WF-009  \n",
       "32    WF-009  \n",
       "33    WF-009  \n",
       "34    WF-009  \n",
       "35    WF-009  \n",
       "36    WF-009  \n",
       "37    WF-009  \n",
       "38    WF-009  \n",
       "69    WF-015  \n",
       "70    WF-015  \n",
       "71    WF-015  \n",
       "72    WF-015  \n",
       "73    WF-015  \n",
       "74    WF-015  \n",
       "75    WF-015  \n",
       "76    WF-015  \n",
       "107   WM-016  \n",
       "108   WM-016  \n",
       "109   WM-016  \n",
       "110   WM-016  \n",
       "111   WM-016  \n",
       "112   WM-016  \n",
       "113   WM-016  \n",
       "114   WM-016  \n",
       "145   WM-009  \n",
       "146   WM-009  \n",
       "147   WM-009  \n",
       "148   WM-009  \n",
       "149   WM-009  \n",
       "150   WM-009  \n",
       "151   WM-009  \n",
       "152   WM-009  \n",
       "183   WF-027  \n",
       "184   WF-027  \n",
       "185   WF-027  \n",
       "186   WF-027  \n",
       "187   WF-027  \n",
       "188   WF-027  \n",
       "189   WF-027  \n",
       "190   WF-027  "
      ]
     },
     "execution_count": 449,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Subjects only saw real stimuli in blocknum's 2-13 ...\n",
    "# Do an inner join with the main data to get only those blocks, then filter to only when subjects gave ratings:\n",
    "cleanRatings = pd.merge(cleanData, modelSummary, on=('subject', 'blocknum'), how='inner')\n",
    "cleanRatings = cleanRatings.loc[(cleanRatings['model_block'] != 'expt') & (cleanRatings['overallDim'].isin(ratingsList)), :]\n",
    "cleanRatings = cleanRatings.drop(['blocknum', 'trialnum', 'blockcode', 'trialcode', 'stimulusitem1'], axis=1)\n",
    "\n",
    "cleanRatings.head(40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 450,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>subject</th>\n",
       "      <th>model_block</th>\n",
       "      <th>model_id</th>\n",
       "      <th>response_action</th>\n",
       "      <th>response_authenticity</th>\n",
       "      <th>response_control</th>\n",
       "      <th>response_genuineness</th>\n",
       "      <th>response_independence</th>\n",
       "      <th>response_openness</th>\n",
       "      <th>response_power</th>\n",
       "      <th>response_values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8534673</td>\n",
       "      <td>hv</td>\n",
       "      <td>WF-011</td>\n",
       "      <td>34.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8534673</td>\n",
       "      <td>hv</td>\n",
       "      <td>WF-015</td>\n",
       "      <td>98.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8534673</td>\n",
       "      <td>hv</td>\n",
       "      <td>WF-027</td>\n",
       "      <td>72.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8534673</td>\n",
       "      <td>hv</td>\n",
       "      <td>WM-009</td>\n",
       "      <td>75.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8534673</td>\n",
       "      <td>hv</td>\n",
       "      <td>WM-016</td>\n",
       "      <td>91.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8534673</td>\n",
       "      <td>hv</td>\n",
       "      <td>WM-033</td>\n",
       "      <td>30.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8534673</td>\n",
       "      <td>lv</td>\n",
       "      <td>WF-003</td>\n",
       "      <td>13.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8534673</td>\n",
       "      <td>lv</td>\n",
       "      <td>WF-009</td>\n",
       "      <td>63.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8534673</td>\n",
       "      <td>lv</td>\n",
       "      <td>WF-016</td>\n",
       "      <td>67.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8534673</td>\n",
       "      <td>lv</td>\n",
       "      <td>WM-003</td>\n",
       "      <td>64.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>8534673</td>\n",
       "      <td>lv</td>\n",
       "      <td>WM-006</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>8534673</td>\n",
       "      <td>lv</td>\n",
       "      <td>WM-029</td>\n",
       "      <td>69.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>8738505</td>\n",
       "      <td>hv</td>\n",
       "      <td>WF-009</td>\n",
       "      <td>75.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>8738505</td>\n",
       "      <td>hv</td>\n",
       "      <td>WF-011</td>\n",
       "      <td>75.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>8738505</td>\n",
       "      <td>hv</td>\n",
       "      <td>WF-015</td>\n",
       "      <td>60.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>8738505</td>\n",
       "      <td>hv</td>\n",
       "      <td>WM-006</td>\n",
       "      <td>40.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>8738505</td>\n",
       "      <td>hv</td>\n",
       "      <td>WM-016</td>\n",
       "      <td>51.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>8738505</td>\n",
       "      <td>hv</td>\n",
       "      <td>WM-033</td>\n",
       "      <td>75.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>8738505</td>\n",
       "      <td>lv</td>\n",
       "      <td>WF-003</td>\n",
       "      <td>90.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>8738505</td>\n",
       "      <td>lv</td>\n",
       "      <td>WF-016</td>\n",
       "      <td>60.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    subject model_block model_id  response_action  response_authenticity  \\\n",
       "0   8534673          hv   WF-011             34.0                   59.0   \n",
       "1   8534673          hv   WF-015             98.0                  100.0   \n",
       "2   8534673          hv   WF-027             72.0                   86.0   \n",
       "3   8534673          hv   WM-009             75.0                   94.0   \n",
       "4   8534673          hv   WM-016             91.0                   61.0   \n",
       "5   8534673          hv   WM-033             30.0                   61.0   \n",
       "6   8534673          lv   WF-003             13.0                   32.0   \n",
       "7   8534673          lv   WF-009             63.0                   61.0   \n",
       "8   8534673          lv   WF-016             67.0                   38.0   \n",
       "9   8534673          lv   WM-003             64.0                   66.0   \n",
       "10  8534673          lv   WM-006              3.0                   15.0   \n",
       "11  8534673          lv   WM-029             69.0                   60.0   \n",
       "12  8738505          hv   WF-009             75.0                   85.0   \n",
       "13  8738505          hv   WF-011             75.0                   90.0   \n",
       "14  8738505          hv   WF-015             60.0                   85.0   \n",
       "15  8738505          hv   WM-006             40.0                   90.0   \n",
       "16  8738505          hv   WM-016             51.0                   75.0   \n",
       "17  8738505          hv   WM-033             75.0                   88.0   \n",
       "18  8738505          lv   WF-003             90.0                   85.0   \n",
       "19  8738505          lv   WF-016             60.0                   20.0   \n",
       "\n",
       "    response_control  response_genuineness  response_independence  \\\n",
       "0               66.0                  96.0                   39.0   \n",
       "1               96.0                  94.0                  100.0   \n",
       "2               66.0                  83.0                   84.0   \n",
       "3               67.0                  77.0                   67.0   \n",
       "4               80.0                   9.0                   84.0   \n",
       "5               38.0                  62.0                   37.0   \n",
       "6                9.0                  28.0                   19.0   \n",
       "7               62.0                   2.0                   85.0   \n",
       "8               32.0                  27.0                   66.0   \n",
       "9               25.0                   2.0                   69.0   \n",
       "10               5.0                  27.0                   10.0   \n",
       "11              78.0                  37.0                   90.0   \n",
       "12              25.0                  90.0                   20.0   \n",
       "13              20.0                  90.0                   15.0   \n",
       "14              55.0                  88.0                   30.0   \n",
       "15              20.0                  93.0                   20.0   \n",
       "16              50.0                  62.0                   50.0   \n",
       "17              20.0                 100.0                   38.0   \n",
       "18              60.0                   0.0                   95.0   \n",
       "19              50.0                  10.0                   25.0   \n",
       "\n",
       "    response_openness  response_power  response_values  \n",
       "0                99.0            19.0             69.0  \n",
       "1                99.0           100.0             96.0  \n",
       "2               100.0            60.0             82.0  \n",
       "3                94.0            81.0             95.0  \n",
       "4                29.0            88.0             77.0  \n",
       "5                69.0            59.0             64.0  \n",
       "6                24.0             8.0             28.0  \n",
       "7                 3.0            76.0             77.0  \n",
       "8                37.0            73.0             70.0  \n",
       "9                 5.0            13.0             92.0  \n",
       "10               29.0             2.0             32.0  \n",
       "11               38.0            80.0             88.0  \n",
       "12               90.0            10.0             80.0  \n",
       "13               90.0             5.0             85.0  \n",
       "14               80.0            20.0             60.0  \n",
       "15               90.0            10.0             85.0  \n",
       "16               69.0            35.0             50.0  \n",
       "17               73.0            20.0             75.0  \n",
       "18                0.0            75.0             10.0  \n",
       "19               15.0            60.0             60.0  "
      ]
     },
     "execution_count": 450,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate wide version of dataframe for R analyses later:\n",
    "cleanRatings_wide = cleanRatings\n",
    "cleanRatings_wide.loc[(cleanRatings_wide['specificDim']=='overall') & (cleanRatings_wide['overallDim']=='power'), 'specificDim'] = 'power'                    \n",
    "cleanRatings_wide.loc[(cleanRatings_wide['specificDim']=='overall') & (cleanRatings_wide['overallDim']=='authenticity'), 'specificDim'] = 'authenticity'                    \n",
    "cleanRatings_wide = cleanRatings_wide.drop(['latency', 'overallDim'], axis=1)\n",
    "\n",
    "cleanRatings_wide = (cleanRatings_wide\n",
    "                     .set_index(['subject', 'model_block', 'model_id', 'specificDim'])\n",
    "                     .unstack('specificDim')\n",
    "                     .reset_index())\n",
    "\n",
    "def flatten_cols(df):\n",
    "    df.columns = ['_'.join(tuple(map(str, t))).rstrip('_') for t in df.columns.values]\n",
    "    return df\n",
    "\n",
    "cleanRatings_wide = flatten_cols(cleanRatings_wide)\n",
    "\n",
    "cleanRatings_wide.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 451,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create individual datasets for each major DV:\n",
    "authenticityData = cleanRatings.query(\"specificDim == 'authenticity'\")\n",
    "powerData = cleanRatings.query(\"specificDim == 'power'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 452,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>specificDim</th>\n",
       "      <th>action</th>\n",
       "      <th>authenticity</th>\n",
       "      <th>control</th>\n",
       "      <th>genuineness</th>\n",
       "      <th>independence</th>\n",
       "      <th>openness</th>\n",
       "      <th>power</th>\n",
       "      <th>values</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>specificDim</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>action</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.748403</td>\n",
       "      <td>0.580426</td>\n",
       "      <td>0.632949</td>\n",
       "      <td>0.667161</td>\n",
       "      <td>0.494366</td>\n",
       "      <td>0.740937</td>\n",
       "      <td>0.779782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>authenticity</th>\n",
       "      <td>0.748403</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.463610</td>\n",
       "      <td>0.749374</td>\n",
       "      <td>0.570953</td>\n",
       "      <td>0.602121</td>\n",
       "      <td>0.646370</td>\n",
       "      <td>0.788712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>control</th>\n",
       "      <td>0.580426</td>\n",
       "      <td>0.463610</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.405024</td>\n",
       "      <td>0.512661</td>\n",
       "      <td>0.277546</td>\n",
       "      <td>0.644254</td>\n",
       "      <td>0.540068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>genuineness</th>\n",
       "      <td>0.632949</td>\n",
       "      <td>0.749374</td>\n",
       "      <td>0.405024</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.375411</td>\n",
       "      <td>0.669171</td>\n",
       "      <td>0.476464</td>\n",
       "      <td>0.730472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>independence</th>\n",
       "      <td>0.667161</td>\n",
       "      <td>0.570953</td>\n",
       "      <td>0.512661</td>\n",
       "      <td>0.375411</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.405636</td>\n",
       "      <td>0.658979</td>\n",
       "      <td>0.606640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>openness</th>\n",
       "      <td>0.494366</td>\n",
       "      <td>0.602121</td>\n",
       "      <td>0.277546</td>\n",
       "      <td>0.669171</td>\n",
       "      <td>0.405636</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.387991</td>\n",
       "      <td>0.598016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>power</th>\n",
       "      <td>0.740937</td>\n",
       "      <td>0.646370</td>\n",
       "      <td>0.644254</td>\n",
       "      <td>0.476464</td>\n",
       "      <td>0.658979</td>\n",
       "      <td>0.387991</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.641496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>values</th>\n",
       "      <td>0.779782</td>\n",
       "      <td>0.788712</td>\n",
       "      <td>0.540068</td>\n",
       "      <td>0.730472</td>\n",
       "      <td>0.606640</td>\n",
       "      <td>0.598016</td>\n",
       "      <td>0.641496</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "specificDim     action  authenticity   control  genuineness  independence  \\\n",
       "specificDim                                                                 \n",
       "action        1.000000      0.748403  0.580426     0.632949      0.667161   \n",
       "authenticity  0.748403      1.000000  0.463610     0.749374      0.570953   \n",
       "control       0.580426      0.463610  1.000000     0.405024      0.512661   \n",
       "genuineness   0.632949      0.749374  0.405024     1.000000      0.375411   \n",
       "independence  0.667161      0.570953  0.512661     0.375411      1.000000   \n",
       "openness      0.494366      0.602121  0.277546     0.669171      0.405636   \n",
       "power         0.740937      0.646370  0.644254     0.476464      0.658979   \n",
       "values        0.779782      0.788712  0.540068     0.730472      0.606640   \n",
       "\n",
       "specificDim   openness     power    values  \n",
       "specificDim                                 \n",
       "action        0.494366  0.740937  0.779782  \n",
       "authenticity  0.602121  0.646370  0.788712  \n",
       "control       0.277546  0.644254  0.540068  \n",
       "genuineness   0.669171  0.476464  0.730472  \n",
       "independence  0.405636  0.658979  0.606640  \n",
       "openness      1.000000  0.387991  0.598016  \n",
       "power         0.387991  1.000000  0.641496  \n",
       "values        0.598016  0.641496  1.000000  "
      ]
     },
     "execution_count": 452,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate quick correlation tables, split out by variability condition: \n",
    "specDimMat = (cleanRatings\n",
    "              .groupby(['subject', 'model_block', 'specificDim'])\n",
    "              .agg({'response': np.mean})\n",
    "              .reset_index())\n",
    "\n",
    "specDimMat_hv = specDimMat.query(\"model_block == 'hv'\").pivot(index='subject', columns='specificDim', values='response')\n",
    "specDimMat_lv = specDimMat.query(\"model_block == 'lv'\").pivot(index='subject', columns='specificDim', values='response')\n",
    "\n",
    "specDimMat_hv.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 453,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>specificDim</th>\n",
       "      <th>action</th>\n",
       "      <th>authenticity</th>\n",
       "      <th>control</th>\n",
       "      <th>genuineness</th>\n",
       "      <th>independence</th>\n",
       "      <th>openness</th>\n",
       "      <th>power</th>\n",
       "      <th>values</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>specificDim</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>action</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.610218</td>\n",
       "      <td>0.700781</td>\n",
       "      <td>0.467904</td>\n",
       "      <td>0.693902</td>\n",
       "      <td>0.452799</td>\n",
       "      <td>0.773966</td>\n",
       "      <td>0.794871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>authenticity</th>\n",
       "      <td>0.610218</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.446060</td>\n",
       "      <td>0.745335</td>\n",
       "      <td>0.438079</td>\n",
       "      <td>0.684805</td>\n",
       "      <td>0.545598</td>\n",
       "      <td>0.709290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>control</th>\n",
       "      <td>0.700781</td>\n",
       "      <td>0.446060</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.435380</td>\n",
       "      <td>0.572158</td>\n",
       "      <td>0.394810</td>\n",
       "      <td>0.765114</td>\n",
       "      <td>0.642353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>genuineness</th>\n",
       "      <td>0.467904</td>\n",
       "      <td>0.745335</td>\n",
       "      <td>0.435380</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.313665</td>\n",
       "      <td>0.825816</td>\n",
       "      <td>0.485969</td>\n",
       "      <td>0.548976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>independence</th>\n",
       "      <td>0.693902</td>\n",
       "      <td>0.438079</td>\n",
       "      <td>0.572158</td>\n",
       "      <td>0.313665</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.280262</td>\n",
       "      <td>0.665634</td>\n",
       "      <td>0.616200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>openness</th>\n",
       "      <td>0.452799</td>\n",
       "      <td>0.684805</td>\n",
       "      <td>0.394810</td>\n",
       "      <td>0.825816</td>\n",
       "      <td>0.280262</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420903</td>\n",
       "      <td>0.540624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>power</th>\n",
       "      <td>0.773966</td>\n",
       "      <td>0.545598</td>\n",
       "      <td>0.765114</td>\n",
       "      <td>0.485969</td>\n",
       "      <td>0.665634</td>\n",
       "      <td>0.420903</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.713937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>values</th>\n",
       "      <td>0.794871</td>\n",
       "      <td>0.709290</td>\n",
       "      <td>0.642353</td>\n",
       "      <td>0.548976</td>\n",
       "      <td>0.616200</td>\n",
       "      <td>0.540624</td>\n",
       "      <td>0.713937</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "specificDim     action  authenticity   control  genuineness  independence  \\\n",
       "specificDim                                                                 \n",
       "action        1.000000      0.610218  0.700781     0.467904      0.693902   \n",
       "authenticity  0.610218      1.000000  0.446060     0.745335      0.438079   \n",
       "control       0.700781      0.446060  1.000000     0.435380      0.572158   \n",
       "genuineness   0.467904      0.745335  0.435380     1.000000      0.313665   \n",
       "independence  0.693902      0.438079  0.572158     0.313665      1.000000   \n",
       "openness      0.452799      0.684805  0.394810     0.825816      0.280262   \n",
       "power         0.773966      0.545598  0.765114     0.485969      0.665634   \n",
       "values        0.794871      0.709290  0.642353     0.548976      0.616200   \n",
       "\n",
       "specificDim   openness     power    values  \n",
       "specificDim                                 \n",
       "action        0.452799  0.773966  0.794871  \n",
       "authenticity  0.684805  0.545598  0.709290  \n",
       "control       0.394810  0.765114  0.642353  \n",
       "genuineness   0.825816  0.485969  0.548976  \n",
       "independence  0.280262  0.665634  0.616200  \n",
       "openness      1.000000  0.420903  0.540624  \n",
       "power         0.420903  1.000000  0.713937  \n",
       "values        0.540624  0.713937  1.000000  "
      ]
     },
     "execution_count": 453,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "specDimMat_lv.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 454,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The rpy2.ipython extension is already loaded. To reload it, use:\n",
      "  %reload_ext rpy2.ipython\n"
     ]
    }
   ],
   "source": [
    "# Run mixed-effects models on each DV in R:\n",
    "#    - fixed = variability (high vs. low)\n",
    "#    - random = subject & model_id\n",
    "\n",
    "# Push objects to R (statsmodels package in python has trouble fitting certain random-effect structures):\n",
    "\n",
    "pandas2ri.activate()\n",
    "%load_ext rpy2.ipython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 455,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Send datasets with ratings to R: \n",
    "%R -i cleanRatings_wide,authenticityData,powerData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 456,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "\n",
    "# Use R to create mixed-effects models with both subject- and stimulus-level random effects:\n",
    "\n",
    "library(lmerTest)\n",
    "library(dplyr)\n",
    "library(tidyr)\n",
    "\n",
    "authenticity_m1 <- lmer(response ~ model_block + (1|subject) + (1|model_id), data=authenticityData)\n",
    "authenticity_m1_anova <- anova(authenticity_m1)\n",
    "authenticity_m1_lsmeans <- lsmeans(authenticity_m1)\n",
    "             \n",
    "power_m1 <- lmer(response ~ model_block + (1|subject) + (1|model_id), data=powerData)\n",
    "power_m1_anova <- anova(power_m1)\n",
    "power_m1_lsmeans <- lsmeans(power_m1)\n",
    "\n",
    "# Include rating type ('overallDim' = authenticity vs. power) as another factor for overall ratings analysis:\n",
    "both_m1 <- lmer(response ~ model_block * overallDim + (1|subject) + (1|model_id), data=overallDimData)\n",
    "both_m1_anova <- anova(both_m1)\n",
    "both_m1_lsmeans <- lsmeans(both_m1)\n",
    "\n",
    "# Need to figure out which subdimensions are most affected by changes in emotion variability ...\n",
    "# Build separate models where subdimension ~ variability (controlling for other subdimensions):\n",
    "action_m1 <- lmer(response_action ~ model_block \n",
    "                  + response_control \n",
    "                  + response_genuineness \n",
    "                  + response_independence \n",
    "                  + response_openness \n",
    "                  + response_values \n",
    "                  + (1|subject) + (1|model_id), \n",
    "                  data=cleanRatings_wide)\n",
    "action_m1_summary <- as.data.frame(summary(action_m1)$coefficients)\n",
    "\n",
    "control_m1 <- lmer(response_control ~ model_block \n",
    "                   + response_action \n",
    "                   + response_genuineness \n",
    "                   + response_independence \n",
    "                   + response_openness \n",
    "                   + response_values \n",
    "                   + (1|subject) + (1|model_id), \n",
    "                   data=cleanRatings_wide)\n",
    "control_m1_summary <- as.data.frame(summary(control_m1)$coefficients)\n",
    "\n",
    "genuineness_m1 <- lmer(response_genuineness ~ model_block \n",
    "                       + response_action \n",
    "                       + response_control \n",
    "                       + response_independence \n",
    "                       + response_openness \n",
    "                       + response_values \n",
    "                       + (1|subject) + (1|model_id), \n",
    "                       data=cleanRatings_wide)\n",
    "genuineness_m1_summary <- as.data.frame(summary(genuineness_m1)$coefficients)\n",
    "\n",
    "independence_m1 <- lmer(response_independence ~ model_block \n",
    "                        + response_action \n",
    "                        + response_control \n",
    "                        + response_genuineness \n",
    "                        + response_openness \n",
    "                        + response_values \n",
    "                        + (1|subject) + (1|model_id), \n",
    "                        data=cleanRatings_wide)\n",
    "independence_m1_summary <- as.data.frame(summary(independence_m1)$coefficients)\n",
    "\n",
    "openness_m1 <- lmer(response_openness ~ model_block \n",
    "                    + response_action \n",
    "                    + response_control \n",
    "                    + response_genuineness \n",
    "                    + response_independence\n",
    "                    + response_values \n",
    "                    + (1|subject) + (1|model_id), \n",
    "                    data=cleanRatings_wide)\n",
    "openness_m1_summary <- as.data.frame(summary(openness_m1)$coefficients)\n",
    "\n",
    "values_m1 <- lmer(response_values ~ model_block \n",
    "                  + response_action \n",
    "                  + response_control \n",
    "                  + response_genuineness \n",
    "                  + response_independence \n",
    "                  + response_openness \n",
    "                  + (1|subject) + (1|model_id), \n",
    "                  data=cleanRatings_wide)\n",
    "values_m1_summary <- as.data.frame(summary(values_m1)$coefficients)\n",
    "\n",
    "# Also want to predict overall dimensions of authenticity vs. power by subdimensions ...\n",
    "# Build separate models where authenticity/power ~ subdimensions (controlling for variability):\n",
    "authenticity_m2 <- lmer(response_authenticity ~ model_block \n",
    "                        + response_action \n",
    "                        + response_control \n",
    "                        + response_genuineness \n",
    "                        + response_independence \n",
    "                        + response_openness \n",
    "                        + response_values \n",
    "                        + (1|subject) + (1|model_id), \n",
    "                        data=cleanRatings_wide)\n",
    "authenticity_m2_summary <- as.data.frame(summary(authenticity_m2)$coefficients)\n",
    "\n",
    "power_m2 <- lmer(response_power ~ model_block \n",
    "                 + response_action \n",
    "                 + response_control \n",
    "                 + response_genuineness \n",
    "                 + response_independence \n",
    "                 + response_openness \n",
    "                 + response_values \n",
    "                 + (1|subject) + (1|model_id), \n",
    "                 data=cleanRatings_wide)\n",
    "power_m2_summary <- as.data.frame(summary(power_m2)$coefficients)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 457,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Pull model results back to python:\n",
    "%R -o authenticity_m1_anova,authenticity_m1_lsmeans,power_m1_anova,power_m1_lsmeans,both_m1_anova,both_m1_lsmeans\n",
    "%R -o action_m1_summary,control_m1_summary,genuineness_m1_summary,independence_m1_summary,openness_m1_summary,values_m1_summary\n",
    "%R -o authenticity_m2_summary,power_m2_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 458,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>model_block</th>\n",
       "      <td>196202.668966</td>\n",
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      "text/plain": [
       "                    Sum Sq        Mean Sq  NumDF        DenDF     F.value  \\\n",
       "model_block  196202.668966  196202.668966      1  2158.210488  406.849795   \n",
       "\n",
       "             Pr(>F)  \n",
       "model_block     0.0  "
      ]
     },
     "execution_count": 458,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pandas2ri.ri2py(authenticity_m1_anova)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 459,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Sum Sq</th>\n",
       "      <th>Mean Sq</th>\n",
       "      <th>NumDF</th>\n",
       "      <th>DenDF</th>\n",
       "      <th>F.value</th>\n",
       "      <th>Pr(&gt;F)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>model_block</th>\n",
       "      <td>23806.261725</td>\n",
       "      <td>23806.261725</td>\n",
       "      <td>1</td>\n",
       "      <td>2157.745946</td>\n",
       "      <td>58.226712</td>\n",
       "      <td>3.486100e-14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Sum Sq       Mean Sq  NumDF        DenDF    F.value  \\\n",
       "model_block  23806.261725  23806.261725      1  2157.745946  58.226712   \n",
       "\n",
       "                   Pr(>F)  \n",
       "model_block  3.486100e-14  "
      ]
     },
     "execution_count": 459,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pandas2ri.ri2py(power_m1_anova)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 460,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sum Sq</th>\n",
       "      <th>Mean Sq</th>\n",
       "      <th>NumDF</th>\n",
       "      <th>DenDF</th>\n",
       "      <th>F.value</th>\n",
       "      <th>Pr(&gt;F)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>model_block</th>\n",
       "      <td>177991.354577</td>\n",
       "      <td>177991.354577</td>\n",
       "      <td>1</td>\n",
       "      <td>4521.550756</td>\n",
       "      <td>393.958496</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>overallDim</th>\n",
       "      <td>1768.964467</td>\n",
       "      <td>1768.964467</td>\n",
       "      <td>1</td>\n",
       "      <td>4517.083667</td>\n",
       "      <td>3.915351</td>\n",
       "      <td>0.047908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_block:overallDim</th>\n",
       "      <td>41609.280034</td>\n",
       "      <td>41609.280034</td>\n",
       "      <td>1</td>\n",
       "      <td>4517.083667</td>\n",
       "      <td>92.096211</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Sum Sq        Mean Sq  NumDF        DenDF  \\\n",
       "model_block             177991.354577  177991.354577      1  4521.550756   \n",
       "overallDim                1768.964467    1768.964467      1  4517.083667   \n",
       "model_block:overallDim   41609.280034   41609.280034      1  4517.083667   \n",
       "\n",
       "                           F.value    Pr(>F)  \n",
       "model_block             393.958496  0.000000  \n",
       "overallDim                3.915351  0.047908  \n",
       "model_block:overallDim   92.096211  0.000000  "
      ]
     },
     "execution_count": 460,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pandas2ri.ri2py(both_m1_anova)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 461,
   "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>Estimate</th>\n",
       "      <th>Std. Error</th>\n",
       "      <th>df</th>\n",
       "      <th>t value</th>\n",
       "      <th>Pr(&gt;|t|)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>action</th>\n",
       "      <td>-1.318396</td>\n",
       "      <td>0.723538</td>\n",
       "      <td>2262.030910</td>\n",
       "      <td>-1.822153</td>\n",
       "      <td>6.856383e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>control</th>\n",
       "      <td>1.733433</td>\n",
       "      <td>0.837567</td>\n",
       "      <td>2241.777894</td>\n",
       "      <td>2.069604</td>\n",
       "      <td>3.860372e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>genuineness</th>\n",
       "      <td>-12.065748</td>\n",
       "      <td>0.822861</td>\n",
       "      <td>2221.424658</td>\n",
       "      <td>-14.663164</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>independence</th>\n",
       "      <td>1.913597</td>\n",
       "      <td>0.896056</td>\n",
       "      <td>2253.702625</td>\n",
       "      <td>2.135577</td>\n",
       "      <td>3.282139e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>openness</th>\n",
       "      <td>-6.358844</td>\n",
       "      <td>0.912456</td>\n",
       "      <td>2230.379820</td>\n",
       "      <td>-6.968930</td>\n",
       "      <td>4.188871e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>values</th>\n",
       "      <td>0.330294</td>\n",
       "      <td>0.747385</td>\n",
       "      <td>2213.559533</td>\n",
       "      <td>0.441932</td>\n",
       "      <td>6.585813e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Estimate  Std. Error           df    t value      Pr(>|t|)\n",
       "action        -1.318396    0.723538  2262.030910  -1.822153  6.856383e-02\n",
       "control        1.733433    0.837567  2241.777894   2.069604  3.860372e-02\n",
       "genuineness  -12.065748    0.822861  2221.424658 -14.663164  0.000000e+00\n",
       "independence   1.913597    0.896056  2253.702625   2.135577  3.282139e-02\n",
       "openness      -6.358844    0.912456  2230.379820  -6.968930  4.188871e-12\n",
       "values         0.330294    0.747385  2213.559533   0.441932  6.585813e-01"
      ]
     },
     "execution_count": 461,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# High-variability leads to greater ratings of authenticity and power, but the effect is\n",
    "# stronger for authenticity (interaction in combined model) ...\n",
    "\n",
    "# Evaluate 1st group of models, where each subdimension is predicted by \n",
    "# variability (controlling for other subdimensions):\n",
    "\n",
    "modelSummary1 = pd.concat([action_m1_summary.loc[['model_blocklv'], :].rename({'model_blocklv': 'action'}),\n",
    "                          control_m1_summary.loc[['model_blocklv'], :].rename({'model_blocklv': 'control'}),\n",
    "                          genuineness_m1_summary.loc[['model_blocklv'], :].rename({'model_blocklv': 'genuineness'}),\n",
    "                          independence_m1_summary.loc[['model_blocklv'], :].rename({'model_blocklv': 'independence'}),\n",
    "                          openness_m1_summary.loc[['model_blocklv'], :].rename({'model_blocklv': 'openness'}),\n",
    "                          values_m1_summary.loc[['model_blocklv'], :].rename({'model_blocklv': 'values'})],\n",
    "                          axis=0)\n",
    "\n",
    "modelSummary1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 462,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Estimate</th>\n",
       "      <th>Std. Error</th>\n",
       "      <th>df</th>\n",
       "      <th>t value</th>\n",
       "      <th>Pr(&gt;|t|)</th>\n",
       "      <th>DV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>action</th>\n",
       "      <td>0.167279</td>\n",
       "      <td>0.022180</td>\n",
       "      <td>2336.575443</td>\n",
       "      <td>7.541721</td>\n",
       "      <td>6.594725e-14</td>\n",
       "      <td>Authenticity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>action</th>\n",
       "      <td>0.264903</td>\n",
       "      <td>0.019502</td>\n",
       "      <td>2355.811427</td>\n",
       "      <td>13.583225</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Power</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>control</th>\n",
       "      <td>-0.013482</td>\n",
       "      <td>0.018777</td>\n",
       "      <td>2215.428389</td>\n",
       "      <td>-0.718040</td>\n",
       "      <td>4.728083e-01</td>\n",
       "      <td>Authenticity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>control</th>\n",
       "      <td>0.292025</td>\n",
       "      <td>0.016609</td>\n",
       "      <td>2321.673147</td>\n",
       "      <td>17.582708</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Power</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>genuineness</th>\n",
       "      <td>0.321777</td>\n",
       "      <td>0.018492</td>\n",
       "      <td>2265.194915</td>\n",
       "      <td>17.400418</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Authenticity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>genuineness</th>\n",
       "      <td>0.003011</td>\n",
       "      <td>0.016318</td>\n",
       "      <td>2341.166010</td>\n",
       "      <td>0.184538</td>\n",
       "      <td>8.536077e-01</td>\n",
       "      <td>Power</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>independence</th>\n",
       "      <td>0.027345</td>\n",
       "      <td>0.017786</td>\n",
       "      <td>2307.764717</td>\n",
       "      <td>1.537436</td>\n",
       "      <td>1.243238e-01</td>\n",
       "      <td>Authenticity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>independence</th>\n",
       "      <td>0.200169</td>\n",
       "      <td>0.015670</td>\n",
       "      <td>2352.582557</td>\n",
       "      <td>12.773865</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Power</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>openness</th>\n",
       "      <td>0.129145</td>\n",
       "      <td>0.017353</td>\n",
       "      <td>2333.135580</td>\n",
       "      <td>7.442174</td>\n",
       "      <td>1.385558e-13</td>\n",
       "      <td>Authenticity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>openness</th>\n",
       "      <td>-0.010332</td>\n",
       "      <td>0.015258</td>\n",
       "      <td>2353.536530</td>\n",
       "      <td>-0.677147</td>\n",
       "      <td>4.983792e-01</td>\n",
       "      <td>Power</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>values</th>\n",
       "      <td>0.219869</td>\n",
       "      <td>0.021428</td>\n",
       "      <td>2338.678895</td>\n",
       "      <td>10.260944</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Authenticity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>values</th>\n",
       "      <td>0.115354</td>\n",
       "      <td>0.018831</td>\n",
       "      <td>2353.308936</td>\n",
       "      <td>6.125705</td>\n",
       "      <td>1.055074e-09</td>\n",
       "      <td>Power</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Estimate  Std. Error           df    t value      Pr(>|t|)  \\\n",
       "action        0.167279    0.022180  2336.575443   7.541721  6.594725e-14   \n",
       "action        0.264903    0.019502  2355.811427  13.583225  0.000000e+00   \n",
       "control      -0.013482    0.018777  2215.428389  -0.718040  4.728083e-01   \n",
       "control       0.292025    0.016609  2321.673147  17.582708  0.000000e+00   \n",
       "genuineness   0.321777    0.018492  2265.194915  17.400418  0.000000e+00   \n",
       "genuineness   0.003011    0.016318  2341.166010   0.184538  8.536077e-01   \n",
       "independence  0.027345    0.017786  2307.764717   1.537436  1.243238e-01   \n",
       "independence  0.200169    0.015670  2352.582557  12.773865  0.000000e+00   \n",
       "openness      0.129145    0.017353  2333.135580   7.442174  1.385558e-13   \n",
       "openness     -0.010332    0.015258  2353.536530  -0.677147  4.983792e-01   \n",
       "values        0.219869    0.021428  2338.678895  10.260944  0.000000e+00   \n",
       "values        0.115354    0.018831  2353.308936   6.125705  1.055074e-09   \n",
       "\n",
       "                        DV  \n",
       "action        Authenticity  \n",
       "action               Power  \n",
       "control       Authenticity  \n",
       "control              Power  \n",
       "genuineness   Authenticity  \n",
       "genuineness          Power  \n",
       "independence  Authenticity  \n",
       "independence         Power  \n",
       "openness      Authenticity  \n",
       "openness             Power  \n",
       "values        Authenticity  \n",
       "values               Power  "
      ]
     },
     "execution_count": 462,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Genuineness and openness subdimensions (p's < .001) are \n",
    "# most impacted by changes in emotion variability ...\n",
    "\n",
    "# Look at results from 2nd group of models, where the overarching dimensions (authenticity or power)\n",
    "# are predicted by each of the individual subdimensions (controlling for variability):\n",
    "\n",
    "authenticity_m2_stats = (authenticity_m2_summary\n",
    "                           .drop(['(Intercept)', 'model_blocklv'], axis=0)\n",
    "                           .rename({\n",
    "                               'response_action': 'action',\n",
    "                               'response_control': 'control',\n",
    "                               'response_genuineness': 'genuineness',\n",
    "                               'response_independence': 'independence',\n",
    "                               'response_openness': 'openness',\n",
    "                               'response_values': 'values'})\n",
    "                           .assign(DV = ['Authenticity']*6))\n",
    "\n",
    "power_m2_stats = (power_m2_summary\n",
    "                    .drop(['(Intercept)', 'model_blocklv'], axis=0)\n",
    "                    .rename({'response_action': 'action',\n",
    "                             'response_control': 'control',\n",
    "                             'response_genuineness': 'genuineness',\n",
    "                             'response_independence': 'independence',\n",
    "                             'response_openness': 'openness',\n",
    "                             'response_values': 'values'})\n",
    "                    .assign(DV = ['Power']*6))\n",
    "\n",
    "modelSummary2 = pd.concat([authenticity_m2_stats, power_m2_stats], axis=0).sort_index()\n",
    "modelSummary2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 463,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rating_type</th>\n",
       "      <th>slider_mean</th>\n",
       "      <th>slider_sem</th>\n",
       "      <th>variability</th>\n",
       "      <th>slider_mean_45</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Authenticity</td>\n",
       "      <td>66.2183</td>\n",
       "      <td>1.3446</td>\n",
       "      <td>High</td>\n",
       "      <td>21.2183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Authenticity</td>\n",
       "      <td>47.9763</td>\n",
       "      <td>1.3446</td>\n",
       "      <td>Low</td>\n",
       "      <td>2.9763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Power</td>\n",
       "      <td>59.0618</td>\n",
       "      <td>1.3446</td>\n",
       "      <td>High</td>\n",
       "      <td>14.0618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Power</td>\n",
       "      <td>52.6861</td>\n",
       "      <td>1.3446</td>\n",
       "      <td>Low</td>\n",
       "      <td>7.6861</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    rating_type  slider_mean  slider_sem variability  slider_mean_45\n",
       "0  Authenticity      66.2183      1.3446        High         21.2183\n",
       "1  Authenticity      47.9763      1.3446         Low          2.9763\n",
       "2         Power      59.0618      1.3446        High         14.0618\n",
       "3         Power      52.6861      1.3446         Low          7.6861"
      ]
     },
     "execution_count": 463,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Authenticity is most predicted by its relevant subdimensions (i.e., openness, genuineness, &\n",
    "# value-consistency; all p's < .001), and power is most predicted by its relevant subdimensions\n",
    "# (i.e., action, control, and independence; all p's < .001).  Note that value-consistency predicts\n",
    "# BOTH power and authenticity, so not specifically diagnostic of either dimension ...\n",
    "\n",
    "# Overall, emotion variability especially impacts judgments of genuineness and openness ('modelSummary1'),\n",
    "# and those subdimensions are particularly linked to overarching perceptions of authenticity.  This helps\n",
    "# to explain why variability effects on authenticity are stronger than those for power.\n",
    "\n",
    "# We can create a dataset to quick plot the overall variability x rating type interaction:\n",
    "plotData = pd.DataFrame({\n",
    "    'rating_type': ['Authenticity', 'Authenticity', 'Power', 'Power'],\n",
    "    'variability': ['High', 'Low', 'High', 'Low'],\n",
    "    'slider_mean': [pandas2ri.ri2py(both_m1_lsmeans[0]).Estimate[4],\n",
    "                    pandas2ri.ri2py(both_m1_lsmeans[0]).Estimate[5],\n",
    "                    pandas2ri.ri2py(both_m1_lsmeans[0]).Estimate[6],\n",
    "                    pandas2ri.ri2py(both_m1_lsmeans[0]).Estimate[7]],\n",
    "    'slider_sem': [pandas2ri.ri2py(both_m1_lsmeans[0])['Standard Error'][4],\n",
    "                   pandas2ri.ri2py(both_m1_lsmeans[0])['Standard Error'][5],\n",
    "                   pandas2ri.ri2py(both_m1_lsmeans[0])['Standard Error'][6],\n",
    "                   pandas2ri.ri2py(both_m1_lsmeans[0])['Standard Error'][7]]\n",
    "})\n",
    "\n",
    "# Adjust numbers to start at 45 (plotnine isn't great at scaling charts to non-zero y-axes):\n",
    "plotData['slider_mean_45'] = plotData['slider_mean']-45\n",
    "plotData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 466,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vb48vv/wSADBlyhTExsaavTAiIjKOyUGuUCgwd+5czJ071xL1EFEbV1ZWBo1G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\nkjkGORGRzP1/vbIEwou7t9oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2909ef7d8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (176260320411)>"
      ]
     },
     "execution_count": 466,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use plotnine package to plot using ggplot2 (R) style ...\n",
    "# Manually recoding y-axis labels using 'slider_mean_45' variable (starting y-axis at zero doesn't look good):\n",
    "(ggplot(plotData, aes(x='factor(variability)', y='slider_mean_45', fill='factor(variability)')) \n",
    " + geom_errorbar(aes(ymax='slider_mean_45 + slider_sem', ymin='slider_mean_45'), position=position_dodge(width=0.9), width=0.2, color='black')\n",
    " + geom_bar(stat='identity', position=position_dodge(width=0.9), alpha=0.5, color='black')\n",
    " + scale_fill_manual(values=['red', 'deepskyblue'], guide=False)\n",
    " + facet_wrap('~ rating_type', nrow=1)\n",
    " + scale_y_continuous(labels=['45', '50', '55', '60', '65'])\n",
    " + labs(x='Variability', y='Slider Rating (0-100)')\n",
    " + theme_classic()\n",
    " + theme(figure_size = (4, 3.5),\n",
    "         text = element_text(family='Gill Sans MT'),\n",
    "         axis_text_x = element_text(size = 8),\n",
    "         axis_title_x = element_text(size = 9, weight = 'bold'),\n",
    "         axis_text_y = element_text(size = 9),\n",
    "         axis_title_y = element_text(size = 9, weight = 'bold'),\n",
    "         axis_line_x = element_line(color = 'black'),\n",
    "         axis_line_y = element_line(color = 'black'),\n",
    "         strip_text_x = element_text(weight = 'bold', color = 'white', size = 9), \n",
    "         strip_background = element_rect(fill = 'black')))"
   ]
  }
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