EmoVar Analysis (Study 3) - Leadership Activation vs. Inhibition.ipynb

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       "      <td>113017</td>\n",
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       "      <td>102141329</td>\n",
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       "<p>20 rows × 25 columns</p>\n",
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      "text/plain": [
       "      date      time      group    subject     build  blocknum  trialnum  \\\n",
       "0   113017  05:49:20  336745360  102141329  5.0.10.0         1         1   \n",
       "1   113017  05:49:20  336745360  102141329  5.0.10.0         2         1   \n",
       "2   113017  05:49:20  336745360  102141329  5.0.10.0         2         2   \n",
       "3   113017  05:49:20  336745360  102141329  5.0.10.0         2         3   \n",
       "4   113017  05:49:20  336745360  102141329  5.0.10.0         2         4   \n",
       "5   113017  05:49:20  336745360  102141329  5.0.10.0         2         5   \n",
       "6   113017  05:49:20  336745360  102141329  5.0.10.0         2         6   \n",
       "7   113017  05:49:20  336745360  102141329  5.0.10.0         2         7   \n",
       "8   113017  05:49:20  336745360  102141329  5.0.10.0         2         8   \n",
       "9   113017  05:49:20  336745360  102141329  5.0.10.0         2         9   \n",
       "10  113017  05:49:20  336745360  102141329  5.0.10.0         2        10   \n",
       "11  113017  05:49:20  336745360  102141329  5.0.10.0         2        11   \n",
       "12  113017  05:49:20  336745360  102141329  5.0.10.0         2        12   \n",
       "13  113017  05:49:20  336745360  102141329  5.0.10.0         2        13   \n",
       "14  113017  05:49:20  336745360  102141329  5.0.10.0         2        14   \n",
       "15  113017  05:49:20  336745360  102141329  5.0.10.0         2        15   \n",
       "16  113017  05:49:20  336745360  102141329  5.0.10.0         2        16   \n",
       "17  113017  05:49:20  336745360  102141329  5.0.10.0         2        17   \n",
       "18  113017  05:49:20  336745360  102141329  5.0.10.0         2        18   \n",
       "19  113017  05:49:20  336745360  102141329  5.0.10.0         2        19   \n",
       "\n",
       "         blockcode       trialcode  pretrialpause       ...        latency  \\\n",
       "0       expt_intro      expt_intro              0       ...           8696   \n",
       "1   lv1_male_block  lv1_male_trial              0       ...            500   \n",
       "2   lv1_male_block  lv1_male_trial              0       ...            500   \n",
       "3   lv1_male_block  lv1_male_trial              0       ...            500   \n",
       "4   lv1_male_block  lv1_male_trial              0       ...            500   \n",
       "5   lv1_male_block  lv1_male_trial              0       ...            500   \n",
       "6   lv1_male_block  lv1_male_trial              0       ...            500   \n",
       "7   lv1_male_block  lv1_male_trial              0       ...            500   \n",
       "8   lv1_male_block  lv1_male_trial              0       ...            500   \n",
       "9   lv1_male_block  lv1_male_trial              0       ...            500   \n",
       "10  lv1_male_block  lv1_male_trial              0       ...            500   \n",
       "11  lv1_male_block  lv1_male_trial              0       ...            500   \n",
       "12  lv1_male_block  lv1_male_trial              0       ...            500   \n",
       "13  lv1_male_block  lv1_male_trial              0       ...            500   \n",
       "14  lv1_male_block  lv1_male_trial              0       ...            500   \n",
       "15  lv1_male_block  lv1_male_trial              0       ...            500   \n",
       "16  lv1_male_block  lv1_male_trial              0       ...            500   \n",
       "17  lv1_male_block  lv1_male_trial              0       ...            500   \n",
       "18  lv1_male_block  lv1_male_trial              0       ...            500   \n",
       "19  lv1_male_block  lv1_male_trial              0       ...            500   \n",
       "\n",
       "    stimulusnumber1                                      stimulusitem1  \\\n",
       "0                 1  Welcome to the study!    During this session, ...   \n",
       "1               133                                WM-029_-04_crop.jpg   \n",
       "2               124                                WM-029_-03_crop.jpg   \n",
       "3               143                                WM-029_-04_crop.jpg   \n",
       "4               141                                WM-029_-06_crop.jpg   \n",
       "5               148                                 WM-029_04_crop.jpg   \n",
       "6               122                                WM-029_-05_crop.jpg   \n",
       "7               130                                 WM-029_06_crop.jpg   \n",
       "8               142                                WM-029_-05_crop.jpg   \n",
       "9               121                                WM-029_-06_crop.jpg   \n",
       "10              138                                 WM-029_04_crop.jpg   \n",
       "11              125                                WM-029_-02_crop.jpg   \n",
       "12              128                                 WM-029_04_crop.jpg   \n",
       "13              134                                WM-029_-03_crop.jpg   \n",
       "14              145                                WM-029_-02_crop.jpg   \n",
       "15              123                                WM-029_-04_crop.jpg   \n",
       "16              146                                 WM-029_02_crop.jpg   \n",
       "17              140                                 WM-029_06_crop.jpg   \n",
       "18              139                                 WM-029_05_crop.jpg   \n",
       "19              132                                WM-029_-05_crop.jpg   \n",
       "\n",
       "    stimulusonset1  stimulusnumber2  stimulusitem2  stimulusonset2  \\\n",
       "0                5                1   cbs_logo.jpg               5   \n",
       "1                0                0              0               0   \n",
       "2                0                0              0               0   \n",
       "3                0                0              0               0   \n",
       "4                0                0              0               0   \n",
       "5                0                0              0               0   \n",
       "6                0                0              0               0   \n",
       "7                0                0              0               0   \n",
       "8                0                0              0               0   \n",
       "9                0                0              0               0   \n",
       "10               0                0              0               0   \n",
       "11               0                0              0               0   \n",
       "12               0                0              0               0   \n",
       "13               0                0              0               0   \n",
       "14               0                0              0               0   \n",
       "15               0                0              0               0   \n",
       "16               0                0              0               0   \n",
       "17               0                0              0               0   \n",
       "18               0                0              0               0   \n",
       "19               0                0              0               0   \n",
       "\n",
       "   stimulusnumber3                                      stimulusitem3  \\\n",
       "0                1  If you need to exit the study early, you can d...   \n",
       "1                0                                                  0   \n",
       "2                0                                                  0   \n",
       "3                0                                                  0   \n",
       "4                0                                                  0   \n",
       "5                0                                                  0   \n",
       "6                0                                                  0   \n",
       "7                0                                                  0   \n",
       "8                0                                                  0   \n",
       "9                0                                                  0   \n",
       "10               0                                                  0   \n",
       "11               0                                                  0   \n",
       "12               0                                                  0   \n",
       "13               0                                                  0   \n",
       "14               0                                                  0   \n",
       "15               0                                                  0   \n",
       "16               0                                                  0   \n",
       "17               0                                                  0   \n",
       "18               0                                                  0   \n",
       "19               0                                                  0   \n",
       "\n",
       "    stimulusonset3  \n",
       "0                5  \n",
       "1                0  \n",
       "2                0  \n",
       "3                0  \n",
       "4                0  \n",
       "5                0  \n",
       "6                0  \n",
       "7                0  \n",
       "8                0  \n",
       "9                0  \n",
       "10               0  \n",
       "11               0  \n",
       "12               0  \n",
       "13               0  \n",
       "14               0  \n",
       "15               0  \n",
       "16               0  \n",
       "17               0  \n",
       "18               0  \n",
       "19               0  \n",
       "\n",
       "[20 rows x 25 columns]"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# \"Why, when, and how variability in emotion expression makes people look authentic and leader-like\"\n",
    "# Analysis script for Study 3\n",
    "# Evan W. Carr, 05-01-2018 (Columbia Business School)\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "import statsmodels.stats.mediation as smm\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('amanic3_exptrun_final_clean_17_11_30.csv')\n",
    "rawDemog = pd.read_csv('demographics_survey_survey_17_11_30.csv')\n",
    "rawJobs = pd.read_csv('job_survey_survey_17_11_30.csv')\n",
    "\n",
    "rawData.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>subject</th>\n",
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       "      <th>205</th>\n",
       "      <td>904746232</td>\n",
       "      <td>87</td>\n",
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       "      <th>151</th>\n",
       "      <td>650345568</td>\n",
       "      <td>160</td>\n",
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       "      <td>11432141</td>\n",
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       "      <td>102686385</td>\n",
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       "      <td>458</td>\n",
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       "      <td>672920673</td>\n",
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       "      <td>679348880</td>\n",
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       "      <td>458</td>\n",
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       "      <td>458</td>\n",
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       "      <td>458</td>\n",
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      ],
      "text/plain": [
       "       subject  counts\n",
       "112  474822642      12\n",
       "125  532063191      17\n",
       "113  484746718      21\n",
       "82   329502311      37\n",
       "232  994211854      37\n",
       "99   399120862      37\n",
       "14    45797134      37\n",
       "98   398685616      37\n",
       "166  715551666      37\n",
       "78   317152610      37\n",
       "222  961290475      38\n",
       "144  621169369      39\n",
       "110  465249040      45\n",
       "214  932904243      46\n",
       "217  942818513      48\n",
       "41   147055104      75\n",
       "52   198741392      75\n",
       "186  819540818      75\n",
       "126  533213513      75\n",
       "205  904746232      87\n",
       "151  650345568     160\n",
       "6     11432141     228\n",
       "30   102686385     347\n",
       "231  991603667     379\n",
       "102  417262918     381\n",
       "88   346153982     391\n",
       "27    81733899     417\n",
       "114  492883120     419\n",
       "24    76819037     419\n",
       "121  521944326     438\n",
       "158  676459104     458\n",
       "157  672920673     458\n",
       "159  679348880     458\n",
       "0        48008     458\n",
       "161  681292350     458\n",
       "162  687705745     458\n",
       "156  672681242     458\n",
       "163  696829878     458\n",
       "164  697231167     458\n",
       "165  704745743     458"
      ]
     },
     "execution_count": 237,
     "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(40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([474822642, 532063191, 484746718, 329502311, 994211854, 399120862,\n",
       "        45797134, 398685616, 715551666, 317152610, 961290475, 621169369,\n",
       "       465249040, 932904243, 942818513, 147055104, 198741392, 819540818,\n",
       "       533213513, 904746232, 650345568,  11432141, 102686385, 991603667,\n",
       "       417262918, 346153982,  81733899, 492883120,  76819037, 521944326], dtype=int64)"
      ]
     },
     "execution_count": 238,
     "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": 239,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>lv1_male_block</td>\n",
       "      <td>lv1_male_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WM-029_-03_crop.jpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>102141329</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>lv1_male_block</td>\n",
       "      <td>lv1_male_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WM-029_-04_crop.jpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>102141329</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>lv1_male_block</td>\n",
       "      <td>lv1_male_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WM-029_-06_crop.jpg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     subject  blocknum  trialnum       blockcode       trialcode  response  \\\n",
       "0  102141329         1         1      expt_intro      expt_intro      57.0   \n",
       "1  102141329         2         1  lv1_male_block  lv1_male_trial       0.0   \n",
       "2  102141329         2         2  lv1_male_block  lv1_male_trial       0.0   \n",
       "3  102141329         2         3  lv1_male_block  lv1_male_trial       0.0   \n",
       "4  102141329         2         4  lv1_male_block  lv1_male_trial       0.0   \n",
       "\n",
       "   latency                                      stimulusitem1  \n",
       "0     8696  Welcome to the study!    During this session, ...  \n",
       "1      500                                WM-029_-04_crop.jpg  \n",
       "2      500                                WM-029_-03_crop.jpg  \n",
       "3      500                                WM-029_-04_crop.jpg  \n",
       "4      500                                WM-029_-06_crop.jpg  "
      ]
     },
     "execution_count": 239,
     "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": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34.31730769230769"
      ]
     },
     "execution_count": 240,
     "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": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.245199222217513"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demogSummary['age_response'].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Female    115\n",
       "Male       93\n",
       "Name: gender_response, dtype: int64"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demogSummary['gender_response'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\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>102141329</td>\n",
       "      <td>Yes</td>\n",
       "      <td>8</td>\n",
       "      <td>lower-management</td>\n",
       "      <td>Government</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>979861234</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>non-management</td>\n",
       "      <td>Construction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>249446064</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>non-management</td>\n",
       "      <td>High Tech</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>624134831</td>\n",
       "      <td>Yes</td>\n",
       "      <td>17</td>\n",
       "      <td>middle-management</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>920783273</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>lower-management</td>\n",
       "      <td>Manufacturing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>70403218</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>non-management</td>\n",
       "      <td>Service</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>398606346</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4</td>\n",
       "      <td>lower-management</td>\n",
       "      <td>Educational</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>539316507</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5</td>\n",
       "      <td>non-management</td>\n",
       "      <td>Medical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>179849058</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4</td>\n",
       "      <td>top-management</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>216039920</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5</td>\n",
       "      <td>non-management</td>\n",
       "      <td>Non-profit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2265064</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4</td>\n",
       "      <td>non-management</td>\n",
       "      <td>High Tech</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>687705745</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>non-management</td>\n",
       "      <td>Consumer Goods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>583730540</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>non-management</td>\n",
       "      <td>Service</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>723878180</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5</td>\n",
       "      <td>non-management</td>\n",
       "      <td>Manufacturing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>121360282</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>lower-management</td>\n",
       "      <td>Service</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>535240816</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>non-management</td>\n",
       "      <td>Government</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>619864243</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5</td>\n",
       "      <td>non-management</td>\n",
       "      <td>Service</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>69516753</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3</td>\n",
       "      <td>middle-management</td>\n",
       "      <td>Consumer Goods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>126231437</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5</td>\n",
       "      <td>middle-management</td>\n",
       "      <td>Educational</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>594518924</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4</td>\n",
       "      <td>middle-management</td>\n",
       "      <td>High Tech</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      subject currently_employed_response  years_at_job_response  \\\n",
       "0   102141329                         Yes                      8   \n",
       "1   979861234                         Yes                      2   \n",
       "2   249446064                         Yes                      1   \n",
       "3   624134831                         Yes                     17   \n",
       "4   920783273                         Yes                      2   \n",
       "5    70403218                         Yes                      2   \n",
       "6   398606346                         Yes                      4   \n",
       "7   539316507                         Yes                      5   \n",
       "8   179849058                         Yes                      4   \n",
       "9   216039920                         Yes                      5   \n",
       "10    2265064                         Yes                      4   \n",
       "11  687705745                         Yes                      2   \n",
       "12  583730540                         Yes                      1   \n",
       "13  723878180                         Yes                      5   \n",
       "14  121360282                         Yes                      1   \n",
       "15  535240816                         Yes                      2   \n",
       "16  619864243                         Yes                      5   \n",
       "17   69516753                         Yes                      3   \n",
       "18  126231437                         Yes                      5   \n",
       "19  594518924                         Yes                      4   \n",
       "\n",
       "   mgr_status_response industry_type_response  \n",
       "0     lower-management             Government  \n",
       "1       non-management           Construction  \n",
       "2       non-management              High Tech  \n",
       "3    middle-management                Finance  \n",
       "4     lower-management          Manufacturing  \n",
       "5       non-management                Service  \n",
       "6     lower-management            Educational  \n",
       "7       non-management                Medical  \n",
       "8       top-management                Finance  \n",
       "9       non-management             Non-profit  \n",
       "10      non-management              High Tech  \n",
       "11      non-management         Consumer Goods  \n",
       "12      non-management                Service  \n",
       "13      non-management          Manufacturing  \n",
       "14    lower-management                Service  \n",
       "15      non-management             Government  \n",
       "16      non-management                Service  \n",
       "17   middle-management         Consumer Goods  \n",
       "18   middle-management            Educational  \n",
       "19   middle-management              High Tech  "
      ]
     },
     "execution_count": 243,
     "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": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "98.5781990521327"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Are most of the subjects currently employed?  (Yes, ~99% have a job)\n",
    "100 * (len(cleanJobs.loc[cleanJobs['currently_employed_response']=='Yes']) / cleanJobs.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "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>5.100000</td>\n",
       "      <td>4.307418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>middle-management</td>\n",
       "      <td>6.807018</td>\n",
       "      <td>5.138922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>non-management</td>\n",
       "      <td>4.687500</td>\n",
       "      <td>5.595699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>top-management</td>\n",
       "      <td>12.733333</td>\n",
       "      <td>21.818952</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              5.100000   4.307418\n",
       "1   middle-management              6.807018   5.138922\n",
       "2      non-management              4.687500   5.595699\n",
       "3      top-management             12.733333  21.818952"
      ]
     },
     "execution_count": 245,
     "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 4-13 years across levels, with lots of variability for top-management)\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": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>14.903846</td>\n",
       "      <td>5.935484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Educational</td>\n",
       "      <td>12.019231</td>\n",
       "      <td>4.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>High Tech</td>\n",
       "      <td>10.576923</td>\n",
       "      <td>5.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Medical</td>\n",
       "      <td>8.653846</td>\n",
       "      <td>9.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Consumer Goods</td>\n",
       "      <td>8.173077</td>\n",
       "      <td>6.176471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Government</td>\n",
       "      <td>7.692308</td>\n",
       "      <td>6.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Finance</td>\n",
       "      <td>7.211538</td>\n",
       "      <td>6.466667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>6.250000</td>\n",
       "      <td>6.076923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Non-profit</td>\n",
       "      <td>5.288462</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Construction</td>\n",
       "      <td>4.326923</td>\n",
       "      <td>4.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Corporate</td>\n",
       "      <td>4.326923</td>\n",
       "      <td>4.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Media</td>\n",
       "      <td>2.884615</td>\n",
       "      <td>7.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Arts</td>\n",
       "      <td>2.403846</td>\n",
       "      <td>5.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Legal</td>\n",
       "      <td>1.923077</td>\n",
       "      <td>3.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Agriculture</td>\n",
       "      <td>1.442308</td>\n",
       "      <td>4.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Recreational</td>\n",
       "      <td>0.961538</td>\n",
       "      <td>9.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>0.961538</td>\n",
       "      <td>5.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             index  percent_of_sbj  mean_yrs\n",
       "0          Service       14.903846  5.935484\n",
       "1      Educational       12.019231  4.400000\n",
       "2        High Tech       10.576923  5.818182\n",
       "3          Medical        8.653846  9.222222\n",
       "4   Consumer Goods        8.173077  6.176471\n",
       "5       Government        7.692308  6.125000\n",
       "6          Finance        7.211538  6.466667\n",
       "7    Manufacturing        6.250000  6.076923\n",
       "8       Non-profit        5.288462  5.000000\n",
       "9     Construction        4.326923  4.222222\n",
       "10       Corporate        4.326923  4.333333\n",
       "11           Media        2.884615  7.833333\n",
       "12            Arts        2.403846  5.800000\n",
       "13           Legal        1.923077  3.750000\n",
       "14     Agriculture        1.442308  4.333333\n",
       "15    Recreational        0.961538  9.500000\n",
       "16  Transportation        0.961538  5.500000"
      ]
     },
     "execution_count": 246,
     "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 high-tech -- all > 10% of our subject pool, \n",
    "# and all industry types have average number of years > 3)\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": 247,
   "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>102141329</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>8696</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>102141329</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>lv1_male_block</td>\n",
       "      <td>lv1_male_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WM-029_-04_crop.jpg</td>\n",
       "      <td>lv1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>102141329</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>lv1_male_block</td>\n",
       "      <td>lv1_male_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WM-029_-03_crop.jpg</td>\n",
       "      <td>lv1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>102141329</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>lv1_male_block</td>\n",
       "      <td>lv1_male_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WM-029_-04_crop.jpg</td>\n",
       "      <td>lv1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>102141329</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>lv1_male_block</td>\n",
       "      <td>lv1_male_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WM-029_-06_crop.jpg</td>\n",
       "      <td>lv1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     subject  blocknum  trialnum       blockcode       trialcode  response  \\\n",
       "0  102141329         1         1      expt_intro      expt_intro      57.0   \n",
       "1  102141329         2         1  lv1_male_block  lv1_male_trial       0.0   \n",
       "2  102141329         2         2  lv1_male_block  lv1_male_trial       0.0   \n",
       "3  102141329         2         3  lv1_male_block  lv1_male_trial       0.0   \n",
       "4  102141329         2         4  lv1_male_block  lv1_male_trial       0.0   \n",
       "\n",
       "   latency                                      stimulusitem1 model_block  \n",
       "0     8696  Welcome to the study!    During this session, ...        expt  \n",
       "1      500                                WM-029_-04_crop.jpg         lv1  \n",
       "2      500                                WM-029_-03_crop.jpg         lv1  \n",
       "3      500                                WM-029_-04_crop.jpg         lv1  \n",
       "4      500                                WM-029_-06_crop.jpg         lv1  "
      ]
     },
     "execution_count": 247,
     "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": 248,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>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>102141329</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>8696</td>\n",
       "      <td>Welcome to the study!    During this session, ...</td>\n",
       "      <td>expt</td>\n",
       "      <td>expt</td>\n",
       "      <td>intro</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>102141329</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>lv1_male_block</td>\n",
       "      <td>lv1_male_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WM-029_-04_crop.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>lv1</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>102141329</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>lv1_male_block</td>\n",
       "      <td>lv1_male_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WM-029_-03_crop.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>lv1</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>102141329</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>lv1_male_block</td>\n",
       "      <td>lv1_male_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WM-029_-04_crop.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>lv1</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>102141329</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>lv1_male_block</td>\n",
       "      <td>lv1_male_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WM-029_-06_crop.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>lv1</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>102141329</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>lv1_male_block</td>\n",
       "      <td>lv1_male_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WM-029_04_crop.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>lv1</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>102141329</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>lv1_male_block</td>\n",
       "      <td>lv1_male_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WM-029_-05_crop.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>lv1</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>102141329</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>lv1_male_block</td>\n",
       "      <td>lv1_male_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WM-029_06_crop.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>lv1</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>102141329</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>lv1_male_block</td>\n",
       "      <td>lv1_male_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WM-029_-05_crop.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>lv1</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>102141329</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>lv1_male_block</td>\n",
       "      <td>lv1_male_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WM-029_-06_crop.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>lv1</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>102141329</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>lv1_male_block</td>\n",
       "      <td>lv1_male_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WM-029_04_crop.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>lv1</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>102141329</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>lv1_male_block</td>\n",
       "      <td>lv1_male_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WM-029_-02_crop.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>lv1</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>102141329</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>lv1_male_block</td>\n",
       "      <td>lv1_male_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WM-029_04_crop.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>lv1</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>102141329</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>lv1_male_block</td>\n",
       "      <td>lv1_male_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WM-029_-03_crop.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>lv1</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>102141329</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>lv1_male_block</td>\n",
       "      <td>lv1_male_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WM-029_-02_crop.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>lv1</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>102141329</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>lv1_male_block</td>\n",
       "      <td>lv1_male_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WM-029_-04_crop.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>lv1</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>102141329</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>lv1_male_block</td>\n",
       "      <td>lv1_male_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WM-029_02_crop.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>lv1</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>102141329</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>lv1_male_block</td>\n",
       "      <td>lv1_male_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WM-029_06_crop.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>lv1</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>102141329</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>lv1_male_block</td>\n",
       "      <td>lv1_male_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WM-029_05_crop.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>lv1</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>102141329</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>lv1_male_block</td>\n",
       "      <td>lv1_male_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WM-029_-05_crop.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>lv1</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>102141329</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>lv1_male_block</td>\n",
       "      <td>lv1_male_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WM-029_03_crop.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>lv1</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>102141329</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>lv1_male_block</td>\n",
       "      <td>lv1_male_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WM-029_-03_crop.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>lv1</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>102141329</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>lv1_male_block</td>\n",
       "      <td>lv1_male_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WM-029_03_crop.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>lv1</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>102141329</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>lv1_male_block</td>\n",
       "      <td>lv1_male_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WM-029_02_crop.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>lv1</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>102141329</td>\n",
       "      <td>2</td>\n",
       "      <td>24</td>\n",
       "      <td>lv1_male_block</td>\n",
       "      <td>lv1_male_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WM-029_03_crop.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>lv1</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>102141329</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>lv1_male_block</td>\n",
       "      <td>lv1_male_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WM-029_-06_crop.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>lv1</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>102141329</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>lv1_male_block</td>\n",
       "      <td>lv1_male_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WM-029_06_crop.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>lv1</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>102141329</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>lv1_male_block</td>\n",
       "      <td>lv1_male_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WM-029_05_crop.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>lv1</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>102141329</td>\n",
       "      <td>2</td>\n",
       "      <td>28</td>\n",
       "      <td>lv1_male_block</td>\n",
       "      <td>lv1_male_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WM-029_02_crop.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>lv1</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>102141329</td>\n",
       "      <td>2</td>\n",
       "      <td>29</td>\n",
       "      <td>lv1_male_block</td>\n",
       "      <td>lv1_male_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WM-029_-02_crop.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>lv1</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>102141329</td>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "      <td>lv1_male_block</td>\n",
       "      <td>lv1_male_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WM-029_05_crop.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>lv1</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>102141329</td>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>lv1_male_block</td>\n",
       "      <td>inhibition_openminded_slider</td>\n",
       "      <td>57.0</td>\n",
       "      <td>24246</td>\n",
       "      <td>cbs_logo.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>inhibition</td>\n",
       "      <td>openminded</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>102141329</td>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>lv1_male_block</td>\n",
       "      <td>inhibition_truefeelings_slider</td>\n",
       "      <td>64.0</td>\n",
       "      <td>24246</td>\n",
       "      <td>cbs_logo.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>inhibition</td>\n",
       "      <td>truefeelings</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>102141329</td>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>lv1_male_block</td>\n",
       "      <td>inhibition_truethoughts_slider</td>\n",
       "      <td>73.0</td>\n",
       "      <td>24246</td>\n",
       "      <td>cbs_logo.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>inhibition</td>\n",
       "      <td>truethoughts</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>102141329</td>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>lv1_male_block</td>\n",
       "      <td>activation_takeaction_slider</td>\n",
       "      <td>83.0</td>\n",
       "      <td>24246</td>\n",
       "      <td>cbs_logo.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>activation</td>\n",
       "      <td>takeaction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>102141329</td>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>lv1_male_block</td>\n",
       "      <td>activation_thinkquick_slider</td>\n",
       "      <td>47.0</td>\n",
       "      <td>24246</td>\n",
       "      <td>cbs_logo.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>activation</td>\n",
       "      <td>thinkquick</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>102141329</td>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>lv1_male_block</td>\n",
       "      <td>activation_authoritative_slider</td>\n",
       "      <td>45.0</td>\n",
       "      <td>24246</td>\n",
       "      <td>cbs_logo.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>activation</td>\n",
       "      <td>authoritative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>102141329</td>\n",
       "      <td>2</td>\n",
       "      <td>32</td>\n",
       "      <td>lv1_male_block</td>\n",
       "      <td>power_overall_slider</td>\n",
       "      <td>58.0</td>\n",
       "      <td>6513</td>\n",
       "      <td>cbs_logo.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>power</td>\n",
       "      <td>power</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>102141329</td>\n",
       "      <td>2</td>\n",
       "      <td>33</td>\n",
       "      <td>lv1_male_block</td>\n",
       "      <td>authenticity_overall_slider</td>\n",
       "      <td>60.0</td>\n",
       "      <td>3190</td>\n",
       "      <td>cbs_logo.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>authenticity</td>\n",
       "      <td>authenticity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>102141329</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>lv3_male_block</td>\n",
       "      <td>lv3_male_trial</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>WM-009_02_crop.jpg</td>\n",
       "      <td>lv</td>\n",
       "      <td>lv3</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      subject  blocknum  trialnum       blockcode  \\\n",
       "0   102141329         1         1      expt_intro   \n",
       "1   102141329         2         1  lv1_male_block   \n",
       "2   102141329         2         2  lv1_male_block   \n",
       "3   102141329         2         3  lv1_male_block   \n",
       "4   102141329         2         4  lv1_male_block   \n",
       "5   102141329         2         5  lv1_male_block   \n",
       "6   102141329         2         6  lv1_male_block   \n",
       "7   102141329         2         7  lv1_male_block   \n",
       "8   102141329         2         8  lv1_male_block   \n",
       "9   102141329         2         9  lv1_male_block   \n",
       "10  102141329         2        10  lv1_male_block   \n",
       "11  102141329         2        11  lv1_male_block   \n",
       "12  102141329         2        12  lv1_male_block   \n",
       "13  102141329         2        13  lv1_male_block   \n",
       "14  102141329         2        14  lv1_male_block   \n",
       "15  102141329         2        15  lv1_male_block   \n",
       "16  102141329         2        16  lv1_male_block   \n",
       "17  102141329         2        17  lv1_male_block   \n",
       "18  102141329         2        18  lv1_male_block   \n",
       "19  102141329         2        19  lv1_male_block   \n",
       "20  102141329         2        20  lv1_male_block   \n",
       "21  102141329         2        21  lv1_male_block   \n",
       "22  102141329         2        22  lv1_male_block   \n",
       "23  102141329         2        23  lv1_male_block   \n",
       "24  102141329         2        24  lv1_male_block   \n",
       "25  102141329         2        25  lv1_male_block   \n",
       "26  102141329         2        26  lv1_male_block   \n",
       "27  102141329         2        27  lv1_male_block   \n",
       "28  102141329         2        28  lv1_male_block   \n",
       "29  102141329         2        29  lv1_male_block   \n",
       "30  102141329         2        30  lv1_male_block   \n",
       "31  102141329         2        31  lv1_male_block   \n",
       "32  102141329         2        31  lv1_male_block   \n",
       "33  102141329         2        31  lv1_male_block   \n",
       "34  102141329         2        31  lv1_male_block   \n",
       "35  102141329         2        31  lv1_male_block   \n",
       "36  102141329         2        31  lv1_male_block   \n",
       "37  102141329         2        32  lv1_male_block   \n",
       "38  102141329         2        33  lv1_male_block   \n",
       "39  102141329         3         1  lv3_male_block   \n",
       "\n",
       "                          trialcode  response  latency  \\\n",
       "0                        expt_intro      57.0     8696   \n",
       "1                    lv1_male_trial       0.0      500   \n",
       "2                    lv1_male_trial       0.0      500   \n",
       "3                    lv1_male_trial       0.0      500   \n",
       "4                    lv1_male_trial       0.0      500   \n",
       "5                    lv1_male_trial       0.0      500   \n",
       "6                    lv1_male_trial       0.0      500   \n",
       "7                    lv1_male_trial       0.0      500   \n",
       "8                    lv1_male_trial       0.0      500   \n",
       "9                    lv1_male_trial       0.0      500   \n",
       "10                   lv1_male_trial       0.0      500   \n",
       "11                   lv1_male_trial       0.0      500   \n",
       "12                   lv1_male_trial       0.0      500   \n",
       "13                   lv1_male_trial       0.0      500   \n",
       "14                   lv1_male_trial       0.0      500   \n",
       "15                   lv1_male_trial       0.0      500   \n",
       "16                   lv1_male_trial       0.0      500   \n",
       "17                   lv1_male_trial       0.0      500   \n",
       "18                   lv1_male_trial       0.0      500   \n",
       "19                   lv1_male_trial       0.0      500   \n",
       "20                   lv1_male_trial       0.0      500   \n",
       "21                   lv1_male_trial       0.0      500   \n",
       "22                   lv1_male_trial       0.0      500   \n",
       "23                   lv1_male_trial       0.0      500   \n",
       "24                   lv1_male_trial       0.0      500   \n",
       "25                   lv1_male_trial       0.0      500   \n",
       "26                   lv1_male_trial       0.0      500   \n",
       "27                   lv1_male_trial       0.0      500   \n",
       "28                   lv1_male_trial       0.0      500   \n",
       "29                   lv1_male_trial       0.0      500   \n",
       "30                   lv1_male_trial       0.0      500   \n",
       "31     inhibition_openminded_slider      57.0    24246   \n",
       "32   inhibition_truefeelings_slider      64.0    24246   \n",
       "33   inhibition_truethoughts_slider      73.0    24246   \n",
       "34     activation_takeaction_slider      83.0    24246   \n",
       "35     activation_thinkquick_slider      47.0    24246   \n",
       "36  activation_authoritative_slider      45.0    24246   \n",
       "37             power_overall_slider      58.0     6513   \n",
       "38      authenticity_overall_slider      60.0     3190   \n",
       "39                   lv3_male_trial       0.0      500   \n",
       "\n",
       "                                        stimulusitem1 model_block  \\\n",
       "0   Welcome to the study!    During this session, ...        expt   \n",
       "1                                 WM-029_-04_crop.jpg          lv   \n",
       "2                                 WM-029_-03_crop.jpg          lv   \n",
       "3                                 WM-029_-04_crop.jpg          lv   \n",
       "4                                 WM-029_-06_crop.jpg          lv   \n",
       "5                                  WM-029_04_crop.jpg          lv   \n",
       "6                                 WM-029_-05_crop.jpg          lv   \n",
       "7                                  WM-029_06_crop.jpg          lv   \n",
       "8                                 WM-029_-05_crop.jpg          lv   \n",
       "9                                 WM-029_-06_crop.jpg          lv   \n",
       "10                                 WM-029_04_crop.jpg          lv   \n",
       "11                                WM-029_-02_crop.jpg          lv   \n",
       "12                                 WM-029_04_crop.jpg          lv   \n",
       "13                                WM-029_-03_crop.jpg          lv   \n",
       "14                                WM-029_-02_crop.jpg          lv   \n",
       "15                                WM-029_-04_crop.jpg          lv   \n",
       "16                                 WM-029_02_crop.jpg          lv   \n",
       "17                                 WM-029_06_crop.jpg          lv   \n",
       "18                                 WM-029_05_crop.jpg          lv   \n",
       "19                                WM-029_-05_crop.jpg          lv   \n",
       "20                                 WM-029_03_crop.jpg          lv   \n",
       "21                                WM-029_-03_crop.jpg          lv   \n",
       "22                                 WM-029_03_crop.jpg          lv   \n",
       "23                                 WM-029_02_crop.jpg          lv   \n",
       "24                                 WM-029_03_crop.jpg          lv   \n",
       "25                                WM-029_-06_crop.jpg          lv   \n",
       "26                                 WM-029_06_crop.jpg          lv   \n",
       "27                                 WM-029_05_crop.jpg          lv   \n",
       "28                                 WM-029_02_crop.jpg          lv   \n",
       "29                                WM-029_-02_crop.jpg          lv   \n",
       "30                                 WM-029_05_crop.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                                 WM-009_02_crop.jpg          lv   \n",
       "\n",
       "      overallDim    specificDim  \n",
       "0           expt          intro  \n",
       "1            lv1           male  \n",
       "2            lv1           male  \n",
       "3            lv1           male  \n",
       "4            lv1           male  \n",
       "5            lv1           male  \n",
       "6            lv1           male  \n",
       "7            lv1           male  \n",
       "8            lv1           male  \n",
       "9            lv1           male  \n",
       "10           lv1           male  \n",
       "11           lv1           male  \n",
       "12           lv1           male  \n",
       "13           lv1           male  \n",
       "14           lv1           male  \n",
       "15           lv1           male  \n",
       "16           lv1           male  \n",
       "17           lv1           male  \n",
       "18           lv1           male  \n",
       "19           lv1           male  \n",
       "20           lv1           male  \n",
       "21           lv1           male  \n",
       "22           lv1           male  \n",
       "23           lv1           male  \n",
       "24           lv1           male  \n",
       "25           lv1           male  \n",
       "26           lv1           male  \n",
       "27           lv1           male  \n",
       "28           lv1           male  \n",
       "29           lv1           male  \n",
       "30           lv1           male  \n",
       "31    inhibition     openminded  \n",
       "32    inhibition   truefeelings  \n",
       "33    inhibition   truethoughts  \n",
       "34    activation     takeaction  \n",
       "35    activation     thinkquick  \n",
       "36    activation  authoritative  \n",
       "37         power          power  \n",
       "38  authenticity   authenticity  \n",
       "39           lv3           male  "
      ]
     },
     "execution_count": 248,
     "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:\n",
    "trialcodeSplit = lambda x: x.str.split('_', 3, expand=True)\n",
    "    \n",
    "cleanData['overallDim'] = trialcodeSplit(cleanData['trialcode'])[0]\n",
    "cleanData['specificDim'] = trialcodeSplit(cleanData['trialcode'])[1]\n",
    "\n",
    "cleanData.loc[cleanData['trialcode']=='power_overall_slider', 'specificDim'] = 'power'\n",
    "cleanData.loc[cleanData['trialcode']=='authenticity_overall_slider', 'specificDim'] = 'authenticity'\n",
    "\n",
    "cleanData.head(40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\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>subject</th>\n",
       "      <th>blocknum</th>\n",
       "      <th>stimulusitem1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>48008</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>48008</td>\n",
       "      <td>2</td>\n",
       "      <td>[WF-016_03.jpg, WF-016_-04.jpg, WF-016_04.jpg,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>48008</td>\n",
       "      <td>3</td>\n",
       "      <td>[WM-003_04_crop.jpg, WM-003_05_crop.jpg, WM-00...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48008</td>\n",
       "      <td>4</td>\n",
       "      <td>[WM-006_-02_crop.jpg, WM-006_05_crop.jpg, WM-0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>48008</td>\n",
       "      <td>5</td>\n",
       "      <td>[WF-027_08.jpg, WF-027_-03.jpg, WF-027_-06.jpg...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>48008</td>\n",
       "      <td>6</td>\n",
       "      <td>[WF-015_03.jpg, WF-015_-06.jpg, WF-015_-04.jpg...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>48008</td>\n",
       "      <td>7</td>\n",
       "      <td>[WF-011_-10.jpg, WF-011_16.jpg, WF-011_-15.jpg...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>48008</td>\n",
       "      <td>8</td>\n",
       "      <td>[WM-009_02_crop.jpg, WM-009_04_crop.jpg, WM-00...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>48008</td>\n",
       "      <td>9</td>\n",
       "      <td>[WF-003_02.jpg, WF-003_05.jpg, WF-003_-12.jpg,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>48008</td>\n",
       "      <td>10</td>\n",
       "      <td>[WM-033_05_crop.jpg, WM-033_02_crop.jpg, WM-03...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   subject  blocknum                                      stimulusitem1\n",
       "0    48008         1  Welcome to the study!    During this session, ...\n",
       "1    48008         2  [WF-016_03.jpg, WF-016_-04.jpg, WF-016_04.jpg,...\n",
       "2    48008         3  [WM-003_04_crop.jpg, WM-003_05_crop.jpg, WM-00...\n",
       "3    48008         4  [WM-006_-02_crop.jpg, WM-006_05_crop.jpg, WM-0...\n",
       "4    48008         5  [WF-027_08.jpg, WF-027_-03.jpg, WF-027_-06.jpg...\n",
       "5    48008         6  [WF-015_03.jpg, WF-015_-06.jpg, WF-015_-04.jpg...\n",
       "6    48008         7  [WF-011_-10.jpg, WF-011_16.jpg, WF-011_-15.jpg...\n",
       "7    48008         8  [WM-009_02_crop.jpg, WM-009_04_crop.jpg, WM-00...\n",
       "8    48008         9  [WF-003_02.jpg, WF-003_05.jpg, WF-003_-12.jpg,...\n",
       "9    48008        10  [WM-033_05_crop.jpg, WM-033_02_crop.jpg, WM-03..."
      ]
     },
     "execution_count": 249,
     "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', 'inhibition', 'activation']\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": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <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",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>48008</td>\n",
       "      <td>1</td>\n",
       "      <td>W</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48008</td>\n",
       "      <td>2</td>\n",
       "      <td>WF-016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>48008</td>\n",
       "      <td>3</td>\n",
       "      <td>WM-003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48008</td>\n",
       "      <td>4</td>\n",
       "      <td>WM-006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>48008</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>48008</td>\n",
       "      <td>6</td>\n",
       "      <td>WF-015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>48008</td>\n",
       "      <td>7</td>\n",
       "      <td>WF-011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>48008</td>\n",
       "      <td>8</td>\n",
       "      <td>WM-009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>48008</td>\n",
       "      <td>9</td>\n",
       "      <td>WF-003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>48008</td>\n",
       "      <td>10</td>\n",
       "      <td>WM-033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>48008</td>\n",
       "      <td>11</td>\n",
       "      <td>WF-009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>48008</td>\n",
       "      <td>12</td>\n",
       "      <td>WM-029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>48008</td>\n",
       "      <td>13</td>\n",
       "      <td>WM-016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>48008</td>\n",
       "      <td>16</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2265064</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     48008         1        W\n",
       "1     48008         2   WF-016\n",
       "2     48008         3   WM-003\n",
       "3     48008         4   WM-006\n",
       "4     48008         5   WF-027\n",
       "5     48008         6   WF-015\n",
       "6     48008         7   WF-011\n",
       "7     48008         8   WM-009\n",
       "8     48008         9   WF-003\n",
       "9     48008        10   WM-033\n",
       "10    48008        11   WF-009\n",
       "11    48008        12   WM-029\n",
       "12    48008        13   WM-016\n",
       "13    48008        16        A\n",
       "14  2265064         1        W"
      ]
     },
     "execution_count": 250,
     "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": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>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>102141329</td>\n",
       "      <td>57.0</td>\n",
       "      <td>24246</td>\n",
       "      <td>lv</td>\n",
       "      <td>inhibition</td>\n",
       "      <td>openminded</td>\n",
       "      <td>WM-029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>102141329</td>\n",
       "      <td>64.0</td>\n",
       "      <td>24246</td>\n",
       "      <td>lv</td>\n",
       "      <td>inhibition</td>\n",
       "      <td>truefeelings</td>\n",
       "      <td>WM-029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>102141329</td>\n",
       "      <td>73.0</td>\n",
       "      <td>24246</td>\n",
       "      <td>lv</td>\n",
       "      <td>inhibition</td>\n",
       "      <td>truethoughts</td>\n",
       "      <td>WM-029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>102141329</td>\n",
       "      <td>83.0</td>\n",
       "      <td>24246</td>\n",
       "      <td>lv</td>\n",
       "      <td>activation</td>\n",
       "      <td>takeaction</td>\n",
       "      <td>WM-029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>102141329</td>\n",
       "      <td>47.0</td>\n",
       "      <td>24246</td>\n",
       "      <td>lv</td>\n",
       "      <td>activation</td>\n",
       "      <td>thinkquick</td>\n",
       "      <td>WM-029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>102141329</td>\n",
       "      <td>45.0</td>\n",
       "      <td>24246</td>\n",
       "      <td>lv</td>\n",
       "      <td>activation</td>\n",
       "      <td>authoritative</td>\n",
       "      <td>WM-029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>102141329</td>\n",
       "      <td>58.0</td>\n",
       "      <td>6513</td>\n",
       "      <td>lv</td>\n",
       "      <td>power</td>\n",
       "      <td>power</td>\n",
       "      <td>WM-029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>102141329</td>\n",
       "      <td>60.0</td>\n",
       "      <td>3190</td>\n",
       "      <td>lv</td>\n",
       "      <td>authenticity</td>\n",
       "      <td>authenticity</td>\n",
       "      <td>WM-029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>102141329</td>\n",
       "      <td>84.0</td>\n",
       "      <td>21603</td>\n",
       "      <td>lv</td>\n",
       "      <td>inhibition</td>\n",
       "      <td>openminded</td>\n",
       "      <td>WM-009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>102141329</td>\n",
       "      <td>79.0</td>\n",
       "      <td>21603</td>\n",
       "      <td>lv</td>\n",
       "      <td>inhibition</td>\n",
       "      <td>truefeelings</td>\n",
       "      <td>WM-009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>102141329</td>\n",
       "      <td>79.0</td>\n",
       "      <td>21603</td>\n",
       "      <td>lv</td>\n",
       "      <td>inhibition</td>\n",
       "      <td>truethoughts</td>\n",
       "      <td>WM-009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>102141329</td>\n",
       "      <td>93.0</td>\n",
       "      <td>21603</td>\n",
       "      <td>lv</td>\n",
       "      <td>activation</td>\n",
       "      <td>takeaction</td>\n",
       "      <td>WM-009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>102141329</td>\n",
       "      <td>68.0</td>\n",
       "      <td>21603</td>\n",
       "      <td>lv</td>\n",
       "      <td>activation</td>\n",
       "      <td>thinkquick</td>\n",
       "      <td>WM-009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>102141329</td>\n",
       "      <td>84.0</td>\n",
       "      <td>21603</td>\n",
       "      <td>lv</td>\n",
       "      <td>activation</td>\n",
       "      <td>authoritative</td>\n",
       "      <td>WM-009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>102141329</td>\n",
       "      <td>62.0</td>\n",
       "      <td>3280</td>\n",
       "      <td>lv</td>\n",
       "      <td>authenticity</td>\n",
       "      <td>authenticity</td>\n",
       "      <td>WM-009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>102141329</td>\n",
       "      <td>64.0</td>\n",
       "      <td>3306</td>\n",
       "      <td>lv</td>\n",
       "      <td>power</td>\n",
       "      <td>power</td>\n",
       "      <td>WM-009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>102141329</td>\n",
       "      <td>38.0</td>\n",
       "      <td>14334</td>\n",
       "      <td>lv</td>\n",
       "      <td>inhibition</td>\n",
       "      <td>openminded</td>\n",
       "      <td>WF-011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>102141329</td>\n",
       "      <td>71.0</td>\n",
       "      <td>14334</td>\n",
       "      <td>lv</td>\n",
       "      <td>inhibition</td>\n",
       "      <td>truefeelings</td>\n",
       "      <td>WF-011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>102141329</td>\n",
       "      <td>77.0</td>\n",
       "      <td>14334</td>\n",
       "      <td>lv</td>\n",
       "      <td>inhibition</td>\n",
       "      <td>truethoughts</td>\n",
       "      <td>WF-011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>102141329</td>\n",
       "      <td>87.0</td>\n",
       "      <td>14334</td>\n",
       "      <td>lv</td>\n",
       "      <td>activation</td>\n",
       "      <td>takeaction</td>\n",
       "      <td>WF-011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>102141329</td>\n",
       "      <td>38.0</td>\n",
       "      <td>14334</td>\n",
       "      <td>lv</td>\n",
       "      <td>activation</td>\n",
       "      <td>thinkquick</td>\n",
       "      <td>WF-011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>102141329</td>\n",
       "      <td>87.0</td>\n",
       "      <td>14334</td>\n",
       "      <td>lv</td>\n",
       "      <td>activation</td>\n",
       "      <td>authoritative</td>\n",
       "      <td>WF-011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>102141329</td>\n",
       "      <td>55.0</td>\n",
       "      <td>3887</td>\n",
       "      <td>lv</td>\n",
       "      <td>authenticity</td>\n",
       "      <td>authenticity</td>\n",
       "      <td>WF-011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>102141329</td>\n",
       "      <td>59.0</td>\n",
       "      <td>3922</td>\n",
       "      <td>lv</td>\n",
       "      <td>power</td>\n",
       "      <td>power</td>\n",
       "      <td>WF-011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>102141329</td>\n",
       "      <td>73.0</td>\n",
       "      <td>9868</td>\n",
       "      <td>hv</td>\n",
       "      <td>inhibition</td>\n",
       "      <td>openminded</td>\n",
       "      <td>WM-033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>102141329</td>\n",
       "      <td>57.0</td>\n",
       "      <td>9868</td>\n",
       "      <td>hv</td>\n",
       "      <td>inhibition</td>\n",
       "      <td>truefeelings</td>\n",
       "      <td>WM-033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>102141329</td>\n",
       "      <td>59.0</td>\n",
       "      <td>9868</td>\n",
       "      <td>hv</td>\n",
       "      <td>inhibition</td>\n",
       "      <td>truethoughts</td>\n",
       "      <td>WM-033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>102141329</td>\n",
       "      <td>44.0</td>\n",
       "      <td>9868</td>\n",
       "      <td>hv</td>\n",
       "      <td>activation</td>\n",
       "      <td>takeaction</td>\n",
       "      <td>WM-033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>102141329</td>\n",
       "      <td>42.0</td>\n",
       "      <td>9868</td>\n",
       "      <td>hv</td>\n",
       "      <td>activation</td>\n",
       "      <td>thinkquick</td>\n",
       "      <td>WM-033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>102141329</td>\n",
       "      <td>37.0</td>\n",
       "      <td>9868</td>\n",
       "      <td>hv</td>\n",
       "      <td>activation</td>\n",
       "      <td>authoritative</td>\n",
       "      <td>WM-033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>102141329</td>\n",
       "      <td>58.0</td>\n",
       "      <td>3834</td>\n",
       "      <td>hv</td>\n",
       "      <td>authenticity</td>\n",
       "      <td>authenticity</td>\n",
       "      <td>WM-033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>102141329</td>\n",
       "      <td>35.0</td>\n",
       "      <td>3597</td>\n",
       "      <td>hv</td>\n",
       "      <td>power</td>\n",
       "      <td>power</td>\n",
       "      <td>WM-033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>102141329</td>\n",
       "      <td>37.0</td>\n",
       "      <td>14743</td>\n",
       "      <td>hv</td>\n",
       "      <td>inhibition</td>\n",
       "      <td>openminded</td>\n",
       "      <td>WM-016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>102141329</td>\n",
       "      <td>35.0</td>\n",
       "      <td>14743</td>\n",
       "      <td>hv</td>\n",
       "      <td>inhibition</td>\n",
       "      <td>truefeelings</td>\n",
       "      <td>WM-016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>102141329</td>\n",
       "      <td>38.0</td>\n",
       "      <td>14743</td>\n",
       "      <td>hv</td>\n",
       "      <td>inhibition</td>\n",
       "      <td>truethoughts</td>\n",
       "      <td>WM-016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>102141329</td>\n",
       "      <td>43.0</td>\n",
       "      <td>14743</td>\n",
       "      <td>hv</td>\n",
       "      <td>activation</td>\n",
       "      <td>takeaction</td>\n",
       "      <td>WM-016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>102141329</td>\n",
       "      <td>39.0</td>\n",
       "      <td>14743</td>\n",
       "      <td>hv</td>\n",
       "      <td>activation</td>\n",
       "      <td>thinkquick</td>\n",
       "      <td>WM-016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>102141329</td>\n",
       "      <td>45.0</td>\n",
       "      <td>14743</td>\n",
       "      <td>hv</td>\n",
       "      <td>activation</td>\n",
       "      <td>authoritative</td>\n",
       "      <td>WM-016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>102141329</td>\n",
       "      <td>42.0</td>\n",
       "      <td>5099</td>\n",
       "      <td>hv</td>\n",
       "      <td>power</td>\n",
       "      <td>power</td>\n",
       "      <td>WM-016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>102141329</td>\n",
       "      <td>53.0</td>\n",
       "      <td>4331</td>\n",
       "      <td>hv</td>\n",
       "      <td>authenticity</td>\n",
       "      <td>authenticity</td>\n",
       "      <td>WM-016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       subject  response  latency model_block    overallDim    specificDim  \\\n",
       "31   102141329      57.0    24246          lv    inhibition     openminded   \n",
       "32   102141329      64.0    24246          lv    inhibition   truefeelings   \n",
       "33   102141329      73.0    24246          lv    inhibition   truethoughts   \n",
       "34   102141329      83.0    24246          lv    activation     takeaction   \n",
       "35   102141329      47.0    24246          lv    activation     thinkquick   \n",
       "36   102141329      45.0    24246          lv    activation  authoritative   \n",
       "37   102141329      58.0     6513          lv         power          power   \n",
       "38   102141329      60.0     3190          lv  authenticity   authenticity   \n",
       "69   102141329      84.0    21603          lv    inhibition     openminded   \n",
       "70   102141329      79.0    21603          lv    inhibition   truefeelings   \n",
       "71   102141329      79.0    21603          lv    inhibition   truethoughts   \n",
       "72   102141329      93.0    21603          lv    activation     takeaction   \n",
       "73   102141329      68.0    21603          lv    activation     thinkquick   \n",
       "74   102141329      84.0    21603          lv    activation  authoritative   \n",
       "75   102141329      62.0     3280          lv  authenticity   authenticity   \n",
       "76   102141329      64.0     3306          lv         power          power   \n",
       "107  102141329      38.0    14334          lv    inhibition     openminded   \n",
       "108  102141329      71.0    14334          lv    inhibition   truefeelings   \n",
       "109  102141329      77.0    14334          lv    inhibition   truethoughts   \n",
       "110  102141329      87.0    14334          lv    activation     takeaction   \n",
       "111  102141329      38.0    14334          lv    activation     thinkquick   \n",
       "112  102141329      87.0    14334          lv    activation  authoritative   \n",
       "113  102141329      55.0     3887          lv  authenticity   authenticity   \n",
       "114  102141329      59.0     3922          lv         power          power   \n",
       "145  102141329      73.0     9868          hv    inhibition     openminded   \n",
       "146  102141329      57.0     9868          hv    inhibition   truefeelings   \n",
       "147  102141329      59.0     9868          hv    inhibition   truethoughts   \n",
       "148  102141329      44.0     9868          hv    activation     takeaction   \n",
       "149  102141329      42.0     9868          hv    activation     thinkquick   \n",
       "150  102141329      37.0     9868          hv    activation  authoritative   \n",
       "151  102141329      58.0     3834          hv  authenticity   authenticity   \n",
       "152  102141329      35.0     3597          hv         power          power   \n",
       "183  102141329      37.0    14743          hv    inhibition     openminded   \n",
       "184  102141329      35.0    14743          hv    inhibition   truefeelings   \n",
       "185  102141329      38.0    14743          hv    inhibition   truethoughts   \n",
       "186  102141329      43.0    14743          hv    activation     takeaction   \n",
       "187  102141329      39.0    14743          hv    activation     thinkquick   \n",
       "188  102141329      45.0    14743          hv    activation  authoritative   \n",
       "189  102141329      42.0     5099          hv         power          power   \n",
       "190  102141329      53.0     4331          hv  authenticity   authenticity   \n",
       "\n",
       "    model_id  \n",
       "31    WM-029  \n",
       "32    WM-029  \n",
       "33    WM-029  \n",
       "34    WM-029  \n",
       "35    WM-029  \n",
       "36    WM-029  \n",
       "37    WM-029  \n",
       "38    WM-029  \n",
       "69    WM-009  \n",
       "70    WM-009  \n",
       "71    WM-009  \n",
       "72    WM-009  \n",
       "73    WM-009  \n",
       "74    WM-009  \n",
       "75    WM-009  \n",
       "76    WM-009  \n",
       "107   WF-011  \n",
       "108   WF-011  \n",
       "109   WF-011  \n",
       "110   WF-011  \n",
       "111   WF-011  \n",
       "112   WF-011  \n",
       "113   WF-011  \n",
       "114   WF-011  \n",
       "145   WM-033  \n",
       "146   WM-033  \n",
       "147   WM-033  \n",
       "148   WM-033  \n",
       "149   WM-033  \n",
       "150   WM-033  \n",
       "151   WM-033  \n",
       "152   WM-033  \n",
       "183   WM-016  \n",
       "184   WM-016  \n",
       "185   WM-016  \n",
       "186   WM-016  \n",
       "187   WM-016  \n",
       "188   WM-016  \n",
       "189   WM-016  \n",
       "190   WM-016  "
      ]
     },
     "execution_count": 251,
     "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": 252,
   "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>model_block</th>\n",
       "      <th>model_id</th>\n",
       "      <th>response_authenticity</th>\n",
       "      <th>response_authoritative</th>\n",
       "      <th>response_openminded</th>\n",
       "      <th>response_power</th>\n",
       "      <th>response_takeaction</th>\n",
       "      <th>response_thinkquick</th>\n",
       "      <th>response_truefeelings</th>\n",
       "      <th>response_truethoughts</th>\n",
       "      <th>index_inhibition</th>\n",
       "      <th>index_activation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>48008</td>\n",
       "      <td>hv</td>\n",
       "      <td>WF-003</td>\n",
       "      <td>87.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>81.666667</td>\n",
       "      <td>51.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48008</td>\n",
       "      <td>hv</td>\n",
       "      <td>WF-011</td>\n",
       "      <td>78.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>37.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>48008</td>\n",
       "      <td>hv</td>\n",
       "      <td>WF-027</td>\n",
       "      <td>87.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>64.666667</td>\n",
       "      <td>53.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48008</td>\n",
       "      <td>hv</td>\n",
       "      <td>WM-016</td>\n",
       "      <td>29.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>56.666667</td>\n",
       "      <td>60.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>48008</td>\n",
       "      <td>hv</td>\n",
       "      <td>WM-029</td>\n",
       "      <td>93.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>26.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>48008</td>\n",
       "      <td>hv</td>\n",
       "      <td>WM-033</td>\n",
       "      <td>63.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>47.666667</td>\n",
       "      <td>41.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>48008</td>\n",
       "      <td>lv</td>\n",
       "      <td>WF-009</td>\n",
       "      <td>92.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>40.666667</td>\n",
       "      <td>33.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>48008</td>\n",
       "      <td>lv</td>\n",
       "      <td>WF-015</td>\n",
       "      <td>95.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>64.666667</td>\n",
       "      <td>55.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>48008</td>\n",
       "      <td>lv</td>\n",
       "      <td>WF-016</td>\n",
       "      <td>44.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>68.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>48008</td>\n",
       "      <td>lv</td>\n",
       "      <td>WM-003</td>\n",
       "      <td>27.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>75.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>48008</td>\n",
       "      <td>lv</td>\n",
       "      <td>WM-006</td>\n",
       "      <td>46.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>46.333333</td>\n",
       "      <td>46.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>48008</td>\n",
       "      <td>lv</td>\n",
       "      <td>WM-009</td>\n",
       "      <td>70.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>60.666667</td>\n",
       "      <td>54.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2265064</td>\n",
       "      <td>hv</td>\n",
       "      <td>WF-011</td>\n",
       "      <td>8.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>40.666667</td>\n",
       "      <td>28.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2265064</td>\n",
       "      <td>hv</td>\n",
       "      <td>WF-015</td>\n",
       "      <td>6.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>41.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2265064</td>\n",
       "      <td>hv</td>\n",
       "      <td>WF-016</td>\n",
       "      <td>8.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>22.333333</td>\n",
       "      <td>32.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2265064</td>\n",
       "      <td>hv</td>\n",
       "      <td>WM-003</td>\n",
       "      <td>18.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>31.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2265064</td>\n",
       "      <td>hv</td>\n",
       "      <td>WM-006</td>\n",
       "      <td>84.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>72.333333</td>\n",
       "      <td>63.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2265064</td>\n",
       "      <td>hv</td>\n",
       "      <td>WM-029</td>\n",
       "      <td>36.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>17.666667</td>\n",
       "      <td>74.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2265064</td>\n",
       "      <td>lv</td>\n",
       "      <td>WF-003</td>\n",
       "      <td>28.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>40.666667</td>\n",
       "      <td>42.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2265064</td>\n",
       "      <td>lv</td>\n",
       "      <td>WF-009</td>\n",
       "      <td>100.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>85.666667</td>\n",
       "      <td>65.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    subject model_block model_id  response_authenticity  \\\n",
       "0     48008          hv   WF-003                   87.0   \n",
       "1     48008          hv   WF-011                   78.0   \n",
       "2     48008          hv   WF-027                   87.0   \n",
       "3     48008          hv   WM-016                   29.0   \n",
       "4     48008          hv   WM-029                   93.0   \n",
       "5     48008          hv   WM-033                   63.0   \n",
       "6     48008          lv   WF-009                   92.0   \n",
       "7     48008          lv   WF-015                   95.0   \n",
       "8     48008          lv   WF-016                   44.0   \n",
       "9     48008          lv   WM-003                   27.0   \n",
       "10    48008          lv   WM-006                   46.0   \n",
       "11    48008          lv   WM-009                   70.0   \n",
       "12  2265064          hv   WF-011                    8.0   \n",
       "13  2265064          hv   WF-015                    6.0   \n",
       "14  2265064          hv   WF-016                    8.0   \n",
       "15  2265064          hv   WM-003                   18.0   \n",
       "16  2265064          hv   WM-006                   84.0   \n",
       "17  2265064          hv   WM-029                   36.0   \n",
       "18  2265064          lv   WF-003                   28.0   \n",
       "19  2265064          lv   WF-009                  100.0   \n",
       "\n",
       "    response_authoritative  response_openminded  response_power  \\\n",
       "0                     34.0                 81.0            37.0   \n",
       "1                     27.0                 42.0            60.0   \n",
       "2                     42.0                 60.0            66.0   \n",
       "3                     84.0                 32.0            53.0   \n",
       "4                     22.0                 74.0            31.0   \n",
       "5                     46.0                 69.0            37.0   \n",
       "6                     24.0                 89.0            56.0   \n",
       "7                     57.0                 56.0            75.0   \n",
       "8                     87.0                 32.0            53.0   \n",
       "9                     91.0                  9.0            41.0   \n",
       "10                    48.0                 55.0            38.0   \n",
       "11                    56.0                 57.0            76.0   \n",
       "12                    79.0                  0.0            34.0   \n",
       "13                   100.0                  7.0            12.0   \n",
       "14                    98.0                 12.0             3.0   \n",
       "15                    62.0                 18.0            85.0   \n",
       "16                    19.0                 76.0            44.0   \n",
       "17                   100.0                 33.0            64.0   \n",
       "18                    29.0                 41.0             7.0   \n",
       "19                    33.0                 90.0            78.0   \n",
       "\n",
       "    response_takeaction  response_thinkquick  response_truefeelings  \\\n",
       "0                  58.0                 63.0                   76.0   \n",
       "1                  41.0                 43.0                   42.0   \n",
       "2                  55.0                 62.0                   69.0   \n",
       "3                  56.0                 40.0                   66.0   \n",
       "4                  38.0                 19.0                   40.0   \n",
       "5                  37.0                 41.0                   38.0   \n",
       "6                  37.0                 38.0                   18.0   \n",
       "7                  55.0                 55.0                   68.0   \n",
       "8                  59.0                 60.0                   91.0   \n",
       "9                  58.0                 76.0                   81.0   \n",
       "10                 46.0                 44.0                   43.0   \n",
       "11                 52.0                 54.0                   57.0   \n",
       "12                  0.0                  6.0                   41.0   \n",
       "13                 10.0                 13.0                   89.0   \n",
       "14                  0.0                  0.0                   22.0   \n",
       "15                 11.0                 21.0                   38.0   \n",
       "16                 91.0                 79.0                   69.0   \n",
       "17                 62.0                 60.0                   11.0   \n",
       "18                 71.0                 26.0                   61.0   \n",
       "19                 82.0                 81.0                   84.0   \n",
       "\n",
       "    response_truethoughts  index_inhibition  index_activation  \n",
       "0                    88.0         81.666667         51.666667  \n",
       "1                    39.0         41.000000         37.000000  \n",
       "2                    65.0         64.666667         53.000000  \n",
       "3                    72.0         56.666667         60.000000  \n",
       "4                    39.0         51.000000         26.333333  \n",
       "5                    36.0         47.666667         41.333333  \n",
       "6                    15.0         40.666667         33.000000  \n",
       "7                    70.0         64.666667         55.666667  \n",
       "8                    87.0         70.000000         68.666667  \n",
       "9                    84.0         58.000000         75.000000  \n",
       "10                   41.0         46.333333         46.000000  \n",
       "11                   68.0         60.666667         54.000000  \n",
       "12                   81.0         40.666667         28.333333  \n",
       "13                   93.0         63.000000         41.000000  \n",
       "14                   33.0         22.333333         32.666667  \n",
       "15                   55.0         37.000000         31.333333  \n",
       "16                   72.0         72.333333         63.000000  \n",
       "17                    9.0         17.666667         74.000000  \n",
       "18                   20.0         40.666667         42.000000  \n",
       "19                   83.0         85.666667         65.333333  "
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate wide version of dataframe for later mediation analyses:\n",
    "cleanRatings_wide = (cleanRatings\n",
    "                     .drop(['latency', 'overallDim'], axis=1)\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['index_inhibition'] = cleanRatings_wide.loc[:, ['response_openminded',\n",
    "                                                                  'response_truefeelings',\n",
    "                                                                  'response_truethoughts']].mean(axis=1)\n",
    "cleanRatings_wide['index_activation'] = cleanRatings_wide.loc[:, ['response_authoritative',\n",
    "                                                                  'response_takeaction',\n",
    "                                                                  'response_thinkquick']].mean(axis=1)\n",
    "\n",
    "cleanRatings_wide.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {
    "collapsed": true
   },
   "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": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>specificDim</th>\n",
       "      <th>authenticity</th>\n",
       "      <th>authoritative</th>\n",
       "      <th>openminded</th>\n",
       "      <th>power</th>\n",
       "      <th>takeaction</th>\n",
       "      <th>thinkquick</th>\n",
       "      <th>truefeelings</th>\n",
       "      <th>truethoughts</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>authenticity</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.364274</td>\n",
       "      <td>0.447831</td>\n",
       "      <td>0.514005</td>\n",
       "      <td>0.577753</td>\n",
       "      <td>0.506427</td>\n",
       "      <td>0.423380</td>\n",
       "      <td>0.378435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>authoritative</th>\n",
       "      <td>0.364274</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.025673</td>\n",
       "      <td>0.658613</td>\n",
       "      <td>0.611381</td>\n",
       "      <td>0.568262</td>\n",
       "      <td>0.351821</td>\n",
       "      <td>0.386854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>openminded</th>\n",
       "      <td>0.447831</td>\n",
       "      <td>0.025673</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.213034</td>\n",
       "      <td>0.218010</td>\n",
       "      <td>0.281587</td>\n",
       "      <td>0.132097</td>\n",
       "      <td>0.134599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>power</th>\n",
       "      <td>0.514005</td>\n",
       "      <td>0.658613</td>\n",
       "      <td>0.213034</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.602983</td>\n",
       "      <td>0.587151</td>\n",
       "      <td>0.427462</td>\n",
       "      <td>0.435331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>takeaction</th>\n",
       "      <td>0.577753</td>\n",
       "      <td>0.611381</td>\n",
       "      <td>0.218010</td>\n",
       "      <td>0.602983</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.653436</td>\n",
       "      <td>0.438821</td>\n",
       "      <td>0.424774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thinkquick</th>\n",
       "      <td>0.506427</td>\n",
       "      <td>0.568262</td>\n",
       "      <td>0.281587</td>\n",
       "      <td>0.587151</td>\n",
       "      <td>0.653436</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.371758</td>\n",
       "      <td>0.288275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>truefeelings</th>\n",
       "      <td>0.423380</td>\n",
       "      <td>0.351821</td>\n",
       "      <td>0.132097</td>\n",
       "      <td>0.427462</td>\n",
       "      <td>0.438821</td>\n",
       "      <td>0.371758</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.822032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>truethoughts</th>\n",
       "      <td>0.378435</td>\n",
       "      <td>0.386854</td>\n",
       "      <td>0.134599</td>\n",
       "      <td>0.435331</td>\n",
       "      <td>0.424774</td>\n",
       "      <td>0.288275</td>\n",
       "      <td>0.822032</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "specificDim    authenticity  authoritative  openminded     power  takeaction  \\\n",
       "specificDim                                                                    \n",
       "authenticity       1.000000       0.364274    0.447831  0.514005    0.577753   \n",
       "authoritative      0.364274       1.000000    0.025673  0.658613    0.611381   \n",
       "openminded         0.447831       0.025673    1.000000  0.213034    0.218010   \n",
       "power              0.514005       0.658613    0.213034  1.000000    0.602983   \n",
       "takeaction         0.577753       0.611381    0.218010  0.602983    1.000000   \n",
       "thinkquick         0.506427       0.568262    0.281587  0.587151    0.653436   \n",
       "truefeelings       0.423380       0.351821    0.132097  0.427462    0.438821   \n",
       "truethoughts       0.378435       0.386854    0.134599  0.435331    0.424774   \n",
       "\n",
       "specificDim    thinkquick  truefeelings  truethoughts  \n",
       "specificDim                                            \n",
       "authenticity     0.506427      0.423380      0.378435  \n",
       "authoritative    0.568262      0.351821      0.386854  \n",
       "openminded       0.281587      0.132097      0.134599  \n",
       "power            0.587151      0.427462      0.435331  \n",
       "takeaction       0.653436      0.438821      0.424774  \n",
       "thinkquick       1.000000      0.371758      0.288275  \n",
       "truefeelings     0.371758      1.000000      0.822032  \n",
       "truethoughts     0.288275      0.822032      1.000000  "
      ]
     },
     "execution_count": 254,
     "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": 255,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\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>authenticity</th>\n",
       "      <th>authoritative</th>\n",
       "      <th>openminded</th>\n",
       "      <th>power</th>\n",
       "      <th>takeaction</th>\n",
       "      <th>thinkquick</th>\n",
       "      <th>truefeelings</th>\n",
       "      <th>truethoughts</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>authenticity</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.388502</td>\n",
       "      <td>0.614373</td>\n",
       "      <td>0.508198</td>\n",
       "      <td>0.492506</td>\n",
       "      <td>0.493270</td>\n",
       "      <td>0.489476</td>\n",
       "      <td>0.507781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>authoritative</th>\n",
       "      <td>0.388502</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.194991</td>\n",
       "      <td>0.806009</td>\n",
       "      <td>0.819994</td>\n",
       "      <td>0.727259</td>\n",
       "      <td>0.272366</td>\n",
       "      <td>0.246633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>openminded</th>\n",
       "      <td>0.614373</td>\n",
       "      <td>0.194991</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.331302</td>\n",
       "      <td>0.333542</td>\n",
       "      <td>0.416609</td>\n",
       "      <td>0.390092</td>\n",
       "      <td>0.402637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>power</th>\n",
       "      <td>0.508198</td>\n",
       "      <td>0.806009</td>\n",
       "      <td>0.331302</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.762565</td>\n",
       "      <td>0.734050</td>\n",
       "      <td>0.314648</td>\n",
       "      <td>0.284571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>takeaction</th>\n",
       "      <td>0.492506</td>\n",
       "      <td>0.819994</td>\n",
       "      <td>0.333542</td>\n",
       "      <td>0.762565</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.818267</td>\n",
       "      <td>0.360495</td>\n",
       "      <td>0.376131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thinkquick</th>\n",
       "      <td>0.493270</td>\n",
       "      <td>0.727259</td>\n",
       "      <td>0.416609</td>\n",
       "      <td>0.734050</td>\n",
       "      <td>0.818267</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.303519</td>\n",
       "      <td>0.336476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>truefeelings</th>\n",
       "      <td>0.489476</td>\n",
       "      <td>0.272366</td>\n",
       "      <td>0.390092</td>\n",
       "      <td>0.314648</td>\n",
       "      <td>0.360495</td>\n",
       "      <td>0.303519</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.787208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>truethoughts</th>\n",
       "      <td>0.507781</td>\n",
       "      <td>0.246633</td>\n",
       "      <td>0.402637</td>\n",
       "      <td>0.284571</td>\n",
       "      <td>0.376131</td>\n",
       "      <td>0.336476</td>\n",
       "      <td>0.787208</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "specificDim    authenticity  authoritative  openminded     power  takeaction  \\\n",
       "specificDim                                                                    \n",
       "authenticity       1.000000       0.388502    0.614373  0.508198    0.492506   \n",
       "authoritative      0.388502       1.000000    0.194991  0.806009    0.819994   \n",
       "openminded         0.614373       0.194991    1.000000  0.331302    0.333542   \n",
       "power              0.508198       0.806009    0.331302  1.000000    0.762565   \n",
       "takeaction         0.492506       0.819994    0.333542  0.762565    1.000000   \n",
       "thinkquick         0.493270       0.727259    0.416609  0.734050    0.818267   \n",
       "truefeelings       0.489476       0.272366    0.390092  0.314648    0.360495   \n",
       "truethoughts       0.507781       0.246633    0.402637  0.284571    0.376131   \n",
       "\n",
       "specificDim    thinkquick  truefeelings  truethoughts  \n",
       "specificDim                                            \n",
       "authenticity     0.493270      0.489476      0.507781  \n",
       "authoritative    0.727259      0.272366      0.246633  \n",
       "openminded       0.416609      0.390092      0.402637  \n",
       "power            0.734050      0.314648      0.284571  \n",
       "takeaction       0.818267      0.360495      0.376131  \n",
       "thinkquick       1.000000      0.303519      0.336476  \n",
       "truefeelings     0.303519      1.000000      0.787208  \n",
       "truethoughts     0.336476      0.787208      1.000000  "
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "specDimMat_lv.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "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": 257,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Send datasets with ratings to R: \n",
    "%R -i cleanRatings_wide,authenticityData,powerData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {
    "collapsed": true
   },
   "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",
    "\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 \n",
    "                + (1|subject) + (1|model_id), data=rbind(authenticityData, powerData))\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 activation/inhibition ~ variability (controlling for other index):\n",
    "activation_m1 <- lmer(index_activation ~ model_block + index_inhibition \n",
    "                      + (1|subject) + (1|model_id), data=cleanRatings_wide)\n",
    "activation_m1_summary <- as.data.frame(summary(activation_m1)$coefficients)\n",
    "\n",
    "inhibition_m1 <- lmer(index_inhibition ~ model_block + index_activation \n",
    "                      + (1|subject) + (1|model_id), data=cleanRatings_wide)\n",
    "inhibition_m1_summary <- as.data.frame(summary(inhibition_m1)$coefficients)\n",
    "\n",
    "# Also want to predict overall dimensions of authenticity vs. power by indices ...\n",
    "# Build separate models where authenticity/power ~ activation vs. inhibition indices (controlling for variability):\n",
    "authenticity_m2 <- lmer(response_authenticity ~ model_block \n",
    "                        + index_inhibition + index_activation\n",
    "                        + (1|subject) + (1|model_id), data=cleanRatings_wide)\n",
    "authenticity_m2_summary <- as.data.frame(summary(authenticity_m2)$coefficients)\n",
    "\n",
    "power_m2 <- lmer(response_power ~ model_block\n",
    "                 + index_inhibition + index_activation\n",
    "                 + (1|subject) + (1|model_id), data=cleanRatings_wide)\n",
    "power_m2_summary <- as.data.frame(summary(power_m2)$coefficients)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "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 activation_m1_summary,inhibition_m1_summary,authenticity_m2_summary,power_m2_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
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       "                   Sum Sq       Mean Sq  NumDF        DenDF    F.value  \\\n",
       "model_block  24803.011215  24803.011215      1  2277.342856  58.925218   \n",
       "\n",
       "                   Pr(>F)  \n",
       "model_block  2.398082e-14  "
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     "execution_count": 260,
     "metadata": {},
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   "source": [
    "pandas2ri.ri2py(authenticity_m1_anova)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
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       "      <td>9906.185346</td>\n",
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       "                  Sum Sq      Mean Sq  NumDF        DenDF  F.value  \\\n",
       "model_block  9906.185346  9906.185346      1  2276.850282   24.781   \n",
       "\n",
       "                   Pr(>F)  \n",
       "model_block  6.904123e-07  "
      ]
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     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "pandas2ri.ri2py(power_m1_anova)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
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       "      <td>32997.142572</td>\n",
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       "      <td>4771.894631</td>\n",
       "      <td>77.769445</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>overallDim</th>\n",
       "      <td>8949.490385</td>\n",
       "      <td>8949.490385</td>\n",
       "      <td>1</td>\n",
       "      <td>4769.904437</td>\n",
       "      <td>21.092642</td>\n",
       "      <td>0.000004</td>\n",
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       "    <tr>\n",
       "      <th>model_block:overallDim</th>\n",
       "      <td>2229.346154</td>\n",
       "      <td>2229.346154</td>\n",
       "      <td>1</td>\n",
       "      <td>4769.904437</td>\n",
       "      <td>5.254243</td>\n",
       "      <td>0.021937</td>\n",
       "    </tr>\n",
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      ],
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       "                              Sum Sq       Mean Sq  NumDF        DenDF  \\\n",
       "model_block             32997.142572  32997.142572      1  4771.894631   \n",
       "overallDim               8949.490385   8949.490385      1  4769.904437   \n",
       "model_block:overallDim   2229.346154   2229.346154      1  4769.904437   \n",
       "\n",
       "                          F.value    Pr(>F)  \n",
       "model_block             77.769445  0.000000  \n",
       "overallDim              21.092642  0.000004  \n",
       "model_block:overallDim   5.254243  0.021937  "
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pandas2ri.ri2py(both_m1_anova)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>activation</th>\n",
       "      <td>-1.843985</td>\n",
       "      <td>0.682829</td>\n",
       "      <td>2288.894836</td>\n",
       "      <td>-2.700506</td>\n",
       "      <td>6.974409e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inhibition</th>\n",
       "      <td>-5.020623</td>\n",
       "      <td>0.629534</td>\n",
       "      <td>2282.241952</td>\n",
       "      <td>-7.975140</td>\n",
       "      <td>2.220446e-15</td>\n",
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      "text/plain": [
       "            Estimate  Std. Error           df   t value      Pr(>|t|)\n",
       "activation -1.843985    0.682829  2288.894836 -2.700506  6.974409e-03\n",
       "inhibition -5.020623    0.629534  2282.241952 -7.975140  2.220446e-15"
      ]
     },
     "execution_count": 263,
     "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 index (activation vs. inhibition) \n",
    "# is predicted by variability (controlling for alternative index):\n",
    "\n",
    "modelSummary1 = pd.concat([activation_m1_summary.loc[['model_blocklv'], :].rename({'model_blocklv': 'activation'}),\n",
    "                           inhibition_m1_summary.loc[['model_blocklv'], :].rename({'model_blocklv': 'inhibition'})],\n",
    "                          axis=0)\n",
    "\n",
    "modelSummary1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>activation</th>\n",
       "      <td>0.214046</td>\n",
       "      <td>0.021666</td>\n",
       "      <td>2459.256350</td>\n",
       "      <td>9.879203</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Authenticity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>activation</th>\n",
       "      <td>0.775968</td>\n",
       "      <td>0.016956</td>\n",
       "      <td>2414.707924</td>\n",
       "      <td>45.763229</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Power</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inhibition</th>\n",
       "      <td>0.483483</td>\n",
       "      <td>0.023358</td>\n",
       "      <td>2485.745068</td>\n",
       "      <td>20.698689</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Authenticity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inhibition</th>\n",
       "      <td>0.060888</td>\n",
       "      <td>0.018299</td>\n",
       "      <td>2472.353081</td>\n",
       "      <td>3.327446</td>\n",
       "      <td>0.000889</td>\n",
       "      <td>Power</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Estimate  Std. Error           df    t value  Pr(>|t|)  \\\n",
       "activation  0.214046    0.021666  2459.256350   9.879203  0.000000   \n",
       "activation  0.775968    0.016956  2414.707924  45.763229  0.000000   \n",
       "inhibition  0.483483    0.023358  2485.745068  20.698689  0.000000   \n",
       "inhibition  0.060888    0.018299  2472.353081   3.327446  0.000889   \n",
       "\n",
       "                      DV  \n",
       "activation  Authenticity  \n",
       "activation         Power  \n",
       "inhibition  Authenticity  \n",
       "inhibition         Power  "
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Variability was a stronger predictor of disinhibition than activation ...\n",
    "# Supports and extends Study 2 findings, where variability was most closely associated \n",
    "# with increased perceptions of genuineness and openness (which, in turn, led to more \n",
    "# favorable impressions of authenticity). This also reveals that increased authenticity \n",
    "# from high-variability impacts leadership perceptions, making someone in a leadership \n",
    "# role appear more uninhibited (rather than more prone to action).\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 activation/inhibition indices (controlling for variability):\n",
    "\n",
    "authenticity_m2_stats = (authenticity_m2_summary\n",
    "                         .drop(['(Intercept)', 'model_blocklv'], axis=0)\n",
    "                         .rename({\n",
    "                             'index_inhibition': 'inhibition',\n",
    "                             'index_activation': 'activation'})\n",
    "                         .assign(DV = ['Authenticity']*2))\n",
    "\n",
    "power_m2_stats = (power_m2_summary\n",
    "                  .drop(['(Intercept)', 'model_blocklv'], axis=0)\n",
    "                  .rename({\n",
    "                      'index_inhibition': 'inhibition',\n",
    "                      'index_activation': 'activation'})\n",
    "                  .assign(DV = ['Power']*2))\n",
    "\n",
    "modelSummary2 = pd.concat([authenticity_m2_stats, power_m2_stats], axis=0).sort_index()\n",
    "modelSummary2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>rating_type</th>\n",
       "      <th>slider_mean</th>\n",
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       "      <td>Authenticity</td>\n",
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       "      <td>1.3274</td>\n",
       "      <td>High</td>\n",
       "      <td>17.4629</td>\n",
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       "      <th>1</th>\n",
       "      <td>Authenticity</td>\n",
       "      <td>55.9770</td>\n",
       "      <td>1.3274</td>\n",
       "      <td>Low</td>\n",
       "      <td>10.9770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Power</td>\n",
       "      <td>58.4485</td>\n",
       "      <td>1.3274</td>\n",
       "      <td>High</td>\n",
       "      <td>13.4485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Power</td>\n",
       "      <td>54.6357</td>\n",
       "      <td>1.3274</td>\n",
       "      <td>Low</td>\n",
       "      <td>9.6357</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    rating_type  slider_mean  slider_sem variability  slider_mean_45\n",
       "0  Authenticity      62.4629      1.3274        High         17.4629\n",
       "1  Authenticity      55.9770      1.3274         Low         10.9770\n",
       "2         Power      58.4485      1.3274        High         13.4485\n",
       "3         Power      54.6357      1.3274         Low          9.6357"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As expected, when predicting authenticity, disinhibition was a stronger predictor than activation.\n",
    "# The reverse was true when predicting power, where activation was a stronger predictor than disinhibition.\n",
    "# This helps to explain why high-variability gives a greater boost to authenticity over power ratings,\n",
    "# given that variability more strongly influences perceptions of disinhibition (which, in turn, has \n",
    "# greater effects on authenticity than 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": 266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ORCRzDHIiIpljkBMRyRyDnIhI5hjkREQyxyAnIpI5k84jb25uxldffYUvv/wSBQUFAIBe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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a9836d1da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (182942363361)>"
      ]
     },
     "execution_count": 266,
     "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'])\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')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "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>model_var</th>\n",
       "      <th>index_code</th>\n",
       "      <th>authenticity</th>\n",
       "      <th>power</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>48008</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>62.333333</td>\n",
       "      <td>56.500000</td>\n",
       "      <td>55.388889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48008</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>62.333333</td>\n",
       "      <td>56.500000</td>\n",
       "      <td>56.722222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>48008</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>72.833333</td>\n",
       "      <td>47.333333</td>\n",
       "      <td>44.888889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48008</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>72.833333</td>\n",
       "      <td>47.333333</td>\n",
       "      <td>57.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2265064</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>56.333333</td>\n",
       "      <td>55.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2265064</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>56.333333</td>\n",
       "      <td>62.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2265064</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>26.666667</td>\n",
       "      <td>40.333333</td>\n",
       "      <td>45.055556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2265064</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>26.666667</td>\n",
       "      <td>40.333333</td>\n",
       "      <td>42.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5889681</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>62.500000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>51.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5889681</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>62.500000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>50.277778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5889681</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>64.833333</td>\n",
       "      <td>55.500000</td>\n",
       "      <td>63.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5889681</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>64.833333</td>\n",
       "      <td>55.500000</td>\n",
       "      <td>57.722222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>8914742</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>65.500000</td>\n",
       "      <td>47.166667</td>\n",
       "      <td>42.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>8914742</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>65.500000</td>\n",
       "      <td>47.166667</td>\n",
       "      <td>63.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>8914742</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>61.500000</td>\n",
       "      <td>73.500000</td>\n",
       "      <td>72.888889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>8914742</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>61.500000</td>\n",
       "      <td>73.500000</td>\n",
       "      <td>63.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>9519457</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>64.166667</td>\n",
       "      <td>62.333333</td>\n",
       "      <td>61.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>9519457</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>64.166667</td>\n",
       "      <td>62.333333</td>\n",
       "      <td>53.722222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>9519457</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.500000</td>\n",
       "      <td>54.666667</td>\n",
       "      <td>55.388889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>9519457</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>53.500000</td>\n",
       "      <td>54.666667</td>\n",
       "      <td>54.777778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>9738636</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>78.833333</td>\n",
       "      <td>82.833333</td>\n",
       "      <td>88.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>9738636</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>78.833333</td>\n",
       "      <td>82.833333</td>\n",
       "      <td>69.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>9738636</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>46.333333</td>\n",
       "      <td>72.888889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>9738636</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>46.333333</td>\n",
       "      <td>80.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>12480188</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>56.833333</td>\n",
       "      <td>56.166667</td>\n",
       "      <td>55.444444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>12480188</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>56.833333</td>\n",
       "      <td>56.166667</td>\n",
       "      <td>58.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>12480188</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>72.333333</td>\n",
       "      <td>59.833333</td>\n",
       "      <td>63.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>12480188</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>72.333333</td>\n",
       "      <td>59.833333</td>\n",
       "      <td>68.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>21878078</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>68.166667</td>\n",
       "      <td>70.500000</td>\n",
       "      <td>74.777778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>21878078</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>68.166667</td>\n",
       "      <td>70.500000</td>\n",
       "      <td>68.388889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>802</th>\n",
       "      <td>978012734</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>51.500000</td>\n",
       "      <td>50.888889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>803</th>\n",
       "      <td>978012734</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>51.500000</td>\n",
       "      <td>50.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>804</th>\n",
       "      <td>979861234</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36.500000</td>\n",
       "      <td>67.833333</td>\n",
       "      <td>65.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>805</th>\n",
       "      <td>979861234</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>36.500000</td>\n",
       "      <td>67.833333</td>\n",
       "      <td>39.388889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>806</th>\n",
       "      <td>979861234</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>70.500000</td>\n",
       "      <td>58.666667</td>\n",
       "      <td>57.444444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>807</th>\n",
       "      <td>979861234</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>70.500000</td>\n",
       "      <td>58.666667</td>\n",
       "      <td>65.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>808</th>\n",
       "      <td>980837463</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>52.833333</td>\n",
       "      <td>49.666667</td>\n",
       "      <td>56.277778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>809</th>\n",
       "      <td>980837463</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>52.833333</td>\n",
       "      <td>49.666667</td>\n",
       "      <td>56.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>810</th>\n",
       "      <td>980837463</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>58.500000</td>\n",
       "      <td>67.500000</td>\n",
       "      <td>62.055556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>811</th>\n",
       "      <td>980837463</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>58.500000</td>\n",
       "      <td>67.500000</td>\n",
       "      <td>57.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>812</th>\n",
       "      <td>995324330</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>44.666667</td>\n",
       "      <td>71.500000</td>\n",
       "      <td>80.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>813</th>\n",
       "      <td>995324330</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>44.666667</td>\n",
       "      <td>71.500000</td>\n",
       "      <td>41.944444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>814</th>\n",
       "      <td>995324330</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>74.666667</td>\n",
       "      <td>58.166667</td>\n",
       "      <td>57.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>815</th>\n",
       "      <td>995324330</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>74.666667</td>\n",
       "      <td>58.166667</td>\n",
       "      <td>60.944444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>816</th>\n",
       "      <td>997537214</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>54.166667</td>\n",
       "      <td>70.666667</td>\n",
       "      <td>71.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>817</th>\n",
       "      <td>997537214</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>54.166667</td>\n",
       "      <td>70.666667</td>\n",
       "      <td>49.277778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>818</th>\n",
       "      <td>997537214</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>60.500000</td>\n",
       "      <td>72.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>819</th>\n",
       "      <td>997537214</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>60.500000</td>\n",
       "      <td>57.444444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>820</th>\n",
       "      <td>998308846</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.166667</td>\n",
       "      <td>43.166667</td>\n",
       "      <td>61.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>821</th>\n",
       "      <td>998308846</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10.166667</td>\n",
       "      <td>43.166667</td>\n",
       "      <td>38.444444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>822</th>\n",
       "      <td>998308846</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>44.666667</td>\n",
       "      <td>62.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>823</th>\n",
       "      <td>998308846</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>44.666667</td>\n",
       "      <td>50.944444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>824</th>\n",
       "      <td>999676207</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>47.500000</td>\n",
       "      <td>60.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>825</th>\n",
       "      <td>999676207</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>47.500000</td>\n",
       "      <td>60.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>826</th>\n",
       "      <td>999676207</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>58.333333</td>\n",
       "      <td>50.833333</td>\n",
       "      <td>80.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>827</th>\n",
       "      <td>999676207</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>58.333333</td>\n",
       "      <td>50.833333</td>\n",
       "      <td>51.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>828</th>\n",
       "      <td>999763028</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>73.166667</td>\n",
       "      <td>63.666667</td>\n",
       "      <td>64.277778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>829</th>\n",
       "      <td>999763028</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>73.166667</td>\n",
       "      <td>63.666667</td>\n",
       "      <td>56.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>830</th>\n",
       "      <td>999763028</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>62.166667</td>\n",
       "      <td>66.500000</td>\n",
       "      <td>63.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>831</th>\n",
       "      <td>999763028</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>62.166667</td>\n",
       "      <td>66.500000</td>\n",
       "      <td>55.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>832 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       subject  model_var  index_code  authenticity      power     rating\n",
       "0        48008          0           0     62.333333  56.500000  55.388889\n",
       "1        48008          0           1     62.333333  56.500000  56.722222\n",
       "2        48008          1           0     72.833333  47.333333  44.888889\n",
       "3        48008          1           1     72.833333  47.333333  57.111111\n",
       "4      2265064          0           0     74.000000  56.333333  55.833333\n",
       "5      2265064          0           1     74.000000  56.333333  62.666667\n",
       "6      2265064          1           0     26.666667  40.333333  45.055556\n",
       "7      2265064          1           1     26.666667  40.333333  42.166667\n",
       "8      5889681          0           0     62.500000  49.000000  51.222222\n",
       "9      5889681          0           1     62.500000  49.000000  50.277778\n",
       "10     5889681          1           0     64.833333  55.500000  63.666667\n",
       "11     5889681          1           1     64.833333  55.500000  57.722222\n",
       "12     8914742          0           0     65.500000  47.166667  42.222222\n",
       "13     8914742          0           1     65.500000  47.166667  63.555556\n",
       "14     8914742          1           0     61.500000  73.500000  72.888889\n",
       "15     8914742          1           1     61.500000  73.500000  63.500000\n",
       "16     9519457          0           0     64.166667  62.333333  61.833333\n",
       "17     9519457          0           1     64.166667  62.333333  53.722222\n",
       "18     9519457          1           0     53.500000  54.666667  55.388889\n",
       "19     9519457          1           1     53.500000  54.666667  54.777778\n",
       "20     9738636          0           0     78.833333  82.833333  88.500000\n",
       "21     9738636          0           1     78.833333  82.833333  69.166667\n",
       "22     9738636          1           0     78.000000  46.333333  72.888889\n",
       "23     9738636          1           1     78.000000  46.333333  80.500000\n",
       "24    12480188          0           0     56.833333  56.166667  55.444444\n",
       "25    12480188          0           1     56.833333  56.166667  58.333333\n",
       "26    12480188          1           0     72.333333  59.833333  63.000000\n",
       "27    12480188          1           1     72.333333  59.833333  68.111111\n",
       "28    21878078          0           0     68.166667  70.500000  74.777778\n",
       "29    21878078          0           1     68.166667  70.500000  68.388889\n",
       "..         ...        ...         ...           ...        ...        ...\n",
       "802  978012734          1           0     51.000000  51.500000  50.888889\n",
       "803  978012734          1           1     51.000000  51.500000  50.333333\n",
       "804  979861234          0           0     36.500000  67.833333  65.555556\n",
       "805  979861234          0           1     36.500000  67.833333  39.388889\n",
       "806  979861234          1           0     70.500000  58.666667  57.444444\n",
       "807  979861234          1           1     70.500000  58.666667  65.333333\n",
       "808  980837463          0           0     52.833333  49.666667  56.277778\n",
       "809  980837463          0           1     52.833333  49.666667  56.111111\n",
       "810  980837463          1           0     58.500000  67.500000  62.055556\n",
       "811  980837463          1           1     58.500000  67.500000  57.111111\n",
       "812  995324330          0           0     44.666667  71.500000  80.555556\n",
       "813  995324330          0           1     44.666667  71.500000  41.944444\n",
       "814  995324330          1           0     74.666667  58.166667  57.500000\n",
       "815  995324330          1           1     74.666667  58.166667  60.944444\n",
       "816  997537214          0           0     54.166667  70.666667  71.500000\n",
       "817  997537214          0           1     54.166667  70.666667  49.277778\n",
       "818  997537214          1           0     79.000000  60.500000  72.222222\n",
       "819  997537214          1           1     79.000000  60.500000  57.444444\n",
       "820  998308846          0           0     10.166667  43.166667  61.000000\n",
       "821  998308846          0           1     10.166667  43.166667  38.444444\n",
       "822  998308846          1           0     24.000000  44.666667  62.500000\n",
       "823  998308846          1           1     24.000000  44.666667  50.944444\n",
       "824  999676207          0           0     65.000000  47.500000  60.555556\n",
       "825  999676207          0           1     65.000000  47.500000  60.555556\n",
       "826  999676207          1           0     58.333333  50.833333  80.833333\n",
       "827  999676207          1           1     58.333333  50.833333  51.111111\n",
       "828  999763028          0           0     73.166667  63.666667  64.277778\n",
       "829  999763028          0           1     73.166667  63.666667  56.222222\n",
       "830  999763028          1           0     62.166667  66.500000  63.333333\n",
       "831  999763028          1           1     62.166667  66.500000  55.666667\n",
       "\n",
       "[832 rows x 6 columns]"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run some mediation analyses to examine whether the effect of variability on power/authenticity \n",
    "# was mediated by our leadership indices of activation/disinhibition:\n",
    "#    - IV = variability (0=low; 1=high)\n",
    "#    - DVs = power/authenticity (in separate models)\n",
    "#    - MED = leadership ratings\n",
    "#    - MOD = activation/disinhibition index for rating\n",
    "\n",
    "modMedData = (cleanRatings_wide\n",
    "              .drop(['model_id', 'response_authoritative', 'response_openminded', 'response_takeaction',\n",
    "                    'response_thinkquick', 'response_truefeelings', 'response_truethoughts'], axis=1)\n",
    "              .rename(index=str, columns={\n",
    "                  'response_authenticity': 'authenticity',\n",
    "                  'response_power': 'power',\n",
    "                  'index_inhibition': 'inhibition',\n",
    "                  'index_activation': 'activation'\n",
    "              }))\n",
    "\n",
    "# 0 = low variability; 1 = high variability\n",
    "modMedData['model_var'] = 0\n",
    "modMedData.loc[modMedData['model_block']=='hv', ['model_var']] = 1\n",
    "modMedData = modMedData.drop(['model_block'], axis=1)\n",
    "\n",
    "# Convert activation/inhibition indices to long format for mediation:\n",
    "modMedData = pd.melt(modMedData, id_vars=['subject', 'authenticity', 'power', 'model_var'], \n",
    "                     value_vars=['inhibition', 'activation'],\n",
    "                     var_name='index',\n",
    "                     value_name='rating')\n",
    "\n",
    "# 0 = activation; 1 = inhibition\n",
    "modMedData['index_code'] = 0\n",
    "modMedData.loc[modMedData['index']=='inhibition', ['index_code']] = 1\n",
    "modMedData = (modMedData\n",
    "              .drop(['index'], axis=1)\n",
    "              .groupby(['subject', 'model_var', 'index_code'])\n",
    "              .agg({\n",
    "                  'authenticity': np.mean,\n",
    "                  'power': np.mean,\n",
    "                  'rating': np.mean})\n",
    "              .reset_index())\n",
    "\n",
    "modMedData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\carr3\\Anaconda3\\lib\\site-packages\\patsy\\util.py:652: DeprecationWarning: pandas.core.common.is_categorical_dtype is deprecated. import from the public API: pandas.api.types.is_categorical_dtype instead\n",
      "  return safe_is_pandas_categorical_dtype(data.dtype)\n",
      "C:\\Users\\carr3\\Anaconda3\\lib\\site-packages\\patsy\\util.py:679: DeprecationWarning: pandas.core.common.is_categorical_dtype is deprecated. import from the public API: pandas.api.types.is_categorical_dtype instead\n",
      "  if safe_is_pandas_categorical_dtype(dt1):\n"
     ]
    }
   ],
   "source": [
    "# Run mediation models (number bootstrapped samples = 1000 per test) ...\n",
    "\n",
    "# MEDIATION 1:  DV = authenticity, MOD = index_code 0 (activation):\n",
    "medFit_1 = sm.OLS.from_formula('rating ~ model_var', data=modMedData.loc[modMedData['index_code']==0])\n",
    "outFit_1 = sm.OLS.from_formula('authenticity ~ rating + model_var', data=modMedData.loc[modMedData['index_code']==0])\n",
    "medTest_1 = smm.Mediation(outFit_1, medFit_1, 'model_var', 'rating').fit(n_rep=1000).summary()\n",
    "\n",
    "# MEDIATION 2:  DV = authenticity, MOD = index_code 1 (inhibition):\n",
    "medFit_2 = sm.OLS.from_formula('rating ~ model_var', data=modMedData.loc[modMedData['index_code']==1])\n",
    "outFit_2 = sm.OLS.from_formula('authenticity ~ rating + model_var', data=modMedData.loc[modMedData['index_code']==1])\n",
    "medTest_2 = smm.Mediation(outFit_2, medFit_2, 'model_var', 'rating').fit(n_rep=1000).summary()\n",
    "\n",
    "# MEDIATION 3:  DV = power, MOD = index_code 0 (activation):\n",
    "medFit_3 = sm.OLS.from_formula('rating ~ model_var', data=modMedData.loc[modMedData['index_code']==0])\n",
    "outFit_3 = sm.OLS.from_formula('power ~ rating + model_var', data=modMedData.loc[modMedData['index_code']==0])\n",
    "medTest_3 = smm.Mediation(outFit_3, medFit_3, 'model_var', 'rating').fit(n_rep=1000).summary()\n",
    "\n",
    "# MEDIATION 4:  DV = power, MOD = index_code 1 (inhibition):\n",
    "medFit_4 = sm.OLS.from_formula('rating ~ model_var', data=modMedData.loc[modMedData['index_code']==1])\n",
    "outFit_4 = sm.OLS.from_formula('power ~ rating + model_var', data=modMedData.loc[modMedData['index_code']==1])\n",
    "medTest_4 = smm.Mediation(outFit_4, medFit_4, 'model_var', 'rating').fit(n_rep=1000).summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "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>Estimate</th>\n",
       "      <th>Lower CI bound</th>\n",
       "      <th>Upper CI bound</th>\n",
       "      <th>P-value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ACME (control)</th>\n",
       "      <td>2.313390</td>\n",
       "      <td>0.732057</td>\n",
       "      <td>3.924954</td>\n",
       "      <td>0.008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACME (treated)</th>\n",
       "      <td>2.313390</td>\n",
       "      <td>0.732057</td>\n",
       "      <td>3.924954</td>\n",
       "      <td>0.008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADE (control)</th>\n",
       "      <td>4.095518</td>\n",
       "      <td>1.783714</td>\n",
       "      <td>6.134034</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADE (treated)</th>\n",
       "      <td>4.095518</td>\n",
       "      <td>1.783714</td>\n",
       "      <td>6.134034</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total effect</th>\n",
       "      <td>6.408908</td>\n",
       "      <td>3.615538</td>\n",
       "      <td>8.974276</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prop. mediated (control)</th>\n",
       "      <td>0.353419</td>\n",
       "      <td>0.155246</td>\n",
       "      <td>0.593576</td>\n",
       "      <td>0.008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prop. mediated (treated)</th>\n",
       "      <td>0.353419</td>\n",
       "      <td>0.155246</td>\n",
       "      <td>0.593576</td>\n",
       "      <td>0.008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACME (average)</th>\n",
       "      <td>2.313390</td>\n",
       "      <td>0.732057</td>\n",
       "      <td>3.924954</td>\n",
       "      <td>0.008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADE (average)</th>\n",
       "      <td>4.095518</td>\n",
       "      <td>1.783714</td>\n",
       "      <td>6.134034</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prop. mediated (average)</th>\n",
       "      <td>0.353419</td>\n",
       "      <td>0.155246</td>\n",
       "      <td>0.593576</td>\n",
       "      <td>0.008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Estimate  Lower CI bound  Upper CI bound  P-value\n",
       "ACME (control)            2.313390        0.732057        3.924954    0.008\n",
       "ACME (treated)            2.313390        0.732057        3.924954    0.008\n",
       "ADE (control)             4.095518        1.783714        6.134034    0.000\n",
       "ADE (treated)             4.095518        1.783714        6.134034    0.000\n",
       "Total effect              6.408908        3.615538        8.974276    0.000\n",
       "Prop. mediated (control)  0.353419        0.155246        0.593576    0.008\n",
       "Prop. mediated (treated)  0.353419        0.155246        0.593576    0.008\n",
       "ACME (average)            2.313390        0.732057        3.924954    0.008\n",
       "ADE (average)             4.095518        1.783714        6.134034    0.000\n",
       "Prop. mediated (average)  0.353419        0.155246        0.593576    0.008"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# With activation as mediator to authenticity (DV), average causal mediation effect (ACME)\n",
    "# is estimated around 2.3 [95% CI: 0.7, 3.9] ...\n",
    "medTest_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Estimate</th>\n",
       "      <th>Lower CI bound</th>\n",
       "      <th>Upper CI bound</th>\n",
       "      <th>P-value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ACME (control)</th>\n",
       "      <td>4.707634</td>\n",
       "      <td>2.761188</td>\n",
       "      <td>6.798653</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACME (treated)</th>\n",
       "      <td>4.707634</td>\n",
       "      <td>2.761188</td>\n",
       "      <td>6.798653</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADE (control)</th>\n",
       "      <td>1.676370</td>\n",
       "      <td>-0.301651</td>\n",
       "      <td>3.770527</td>\n",
       "      <td>0.114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADE (treated)</th>\n",
       "      <td>1.676370</td>\n",
       "      <td>-0.301651</td>\n",
       "      <td>3.770527</td>\n",
       "      <td>0.114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total effect</th>\n",
       "      <td>6.384004</td>\n",
       "      <td>3.764467</td>\n",
       "      <td>9.170397</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prop. mediated (control)</th>\n",
       "      <td>0.736395</td>\n",
       "      <td>0.489797</td>\n",
       "      <td>1.072033</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prop. mediated (treated)</th>\n",
       "      <td>0.736395</td>\n",
       "      <td>0.489797</td>\n",
       "      <td>1.072033</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACME (average)</th>\n",
       "      <td>4.707634</td>\n",
       "      <td>2.761188</td>\n",
       "      <td>6.798653</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADE (average)</th>\n",
       "      <td>1.676370</td>\n",
       "      <td>-0.301651</td>\n",
       "      <td>3.770527</td>\n",
       "      <td>0.114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prop. mediated (average)</th>\n",
       "      <td>0.736395</td>\n",
       "      <td>0.489797</td>\n",
       "      <td>1.072033</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Estimate  Lower CI bound  Upper CI bound  P-value\n",
       "ACME (control)            4.707634        2.761188        6.798653    0.000\n",
       "ACME (treated)            4.707634        2.761188        6.798653    0.000\n",
       "ADE (control)             1.676370       -0.301651        3.770527    0.114\n",
       "ADE (treated)             1.676370       -0.301651        3.770527    0.114\n",
       "Total effect              6.384004        3.764467        9.170397    0.000\n",
       "Prop. mediated (control)  0.736395        0.489797        1.072033    0.000\n",
       "Prop. mediated (treated)  0.736395        0.489797        1.072033    0.000\n",
       "ACME (average)            4.707634        2.761188        6.798653    0.000\n",
       "ADE (average)             1.676370       -0.301651        3.770527    0.114\n",
       "Prop. mediated (average)  0.736395        0.489797        1.072033    0.000"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# When mediator is changed to inhibition (with DV=authenticity), ACME effect\n",
    "# gets stronger = 4.7 [2.8, 6.8] ...\n",
    "medTest_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Estimate</th>\n",
       "      <th>Lower CI bound</th>\n",
       "      <th>Upper CI bound</th>\n",
       "      <th>P-value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ACME (control)</th>\n",
       "      <td>3.201206</td>\n",
       "      <td>0.907754</td>\n",
       "      <td>5.368760</td>\n",
       "      <td>0.008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACME (treated)</th>\n",
       "      <td>3.201206</td>\n",
       "      <td>0.907754</td>\n",
       "      <td>5.368760</td>\n",
       "      <td>0.008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADE (control)</th>\n",
       "      <td>0.362525</td>\n",
       "      <td>-1.107037</td>\n",
       "      <td>1.800090</td>\n",
       "      <td>0.588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADE (treated)</th>\n",
       "      <td>0.362525</td>\n",
       "      <td>-1.107037</td>\n",
       "      <td>1.800090</td>\n",
       "      <td>0.588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total effect</th>\n",
       "      <td>3.563731</td>\n",
       "      <td>0.924096</td>\n",
       "      <td>6.117682</td>\n",
       "      <td>0.006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prop. mediated (control)</th>\n",
       "      <td>0.897827</td>\n",
       "      <td>0.487617</td>\n",
       "      <td>1.621150</td>\n",
       "      <td>0.006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prop. mediated (treated)</th>\n",
       "      <td>0.897827</td>\n",
       "      <td>0.487617</td>\n",
       "      <td>1.621150</td>\n",
       "      <td>0.006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACME (average)</th>\n",
       "      <td>3.201206</td>\n",
       "      <td>0.907754</td>\n",
       "      <td>5.368760</td>\n",
       "      <td>0.008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADE (average)</th>\n",
       "      <td>0.362525</td>\n",
       "      <td>-1.107037</td>\n",
       "      <td>1.800090</td>\n",
       "      <td>0.588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prop. mediated (average)</th>\n",
       "      <td>0.897827</td>\n",
       "      <td>0.487617</td>\n",
       "      <td>1.621150</td>\n",
       "      <td>0.006</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Estimate  Lower CI bound  Upper CI bound  P-value\n",
       "ACME (control)            3.201206        0.907754        5.368760    0.008\n",
       "ACME (treated)            3.201206        0.907754        5.368760    0.008\n",
       "ADE (control)             0.362525       -1.107037        1.800090    0.588\n",
       "ADE (treated)             0.362525       -1.107037        1.800090    0.588\n",
       "Total effect              3.563731        0.924096        6.117682    0.006\n",
       "Prop. mediated (control)  0.897827        0.487617        1.621150    0.006\n",
       "Prop. mediated (treated)  0.897827        0.487617        1.621150    0.006\n",
       "ACME (average)            3.201206        0.907754        5.368760    0.008\n",
       "ADE (average)             0.362525       -1.107037        1.800090    0.588\n",
       "Prop. mediated (average)  0.897827        0.487617        1.621150    0.006"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# When DV is changed to power, the reverse happens:  Activation has a stronger\n",
    "# ACME (3.2 [1.0, 5.4]) than inhibition (3.0 [1.8, 4.5]) ...\n",
    "medTest_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "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>Estimate</th>\n",
       "      <th>Lower CI bound</th>\n",
       "      <th>Upper CI bound</th>\n",
       "      <th>P-value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ACME (control)</th>\n",
       "      <td>3.041678</td>\n",
       "      <td>1.766339</td>\n",
       "      <td>4.467569</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACME (treated)</th>\n",
       "      <td>3.041678</td>\n",
       "      <td>1.766339</td>\n",
       "      <td>4.467569</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADE (control)</th>\n",
       "      <td>0.571149</td>\n",
       "      <td>-1.780008</td>\n",
       "      <td>2.718927</td>\n",
       "      <td>0.620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADE (treated)</th>\n",
       "      <td>0.571149</td>\n",
       "      <td>-1.780008</td>\n",
       "      <td>2.718927</td>\n",
       "      <td>0.620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total effect</th>\n",
       "      <td>3.612827</td>\n",
       "      <td>1.076665</td>\n",
       "      <td>6.127839</td>\n",
       "      <td>0.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prop. mediated (control)</th>\n",
       "      <td>0.823233</td>\n",
       "      <td>0.487837</td>\n",
       "      <td>2.456184</td>\n",
       "      <td>0.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prop. mediated (treated)</th>\n",
       "      <td>0.823233</td>\n",
       "      <td>0.487837</td>\n",
       "      <td>2.456184</td>\n",
       "      <td>0.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACME (average)</th>\n",
       "      <td>3.041678</td>\n",
       "      <td>1.766339</td>\n",
       "      <td>4.467569</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADE (average)</th>\n",
       "      <td>0.571149</td>\n",
       "      <td>-1.780008</td>\n",
       "      <td>2.718927</td>\n",
       "      <td>0.620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prop. mediated (average)</th>\n",
       "      <td>0.823233</td>\n",
       "      <td>0.487837</td>\n",
       "      <td>2.456184</td>\n",
       "      <td>0.004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Estimate  Lower CI bound  Upper CI bound  P-value\n",
       "ACME (control)            3.041678        1.766339        4.467569    0.000\n",
       "ACME (treated)            3.041678        1.766339        4.467569    0.000\n",
       "ADE (control)             0.571149       -1.780008        2.718927    0.620\n",
       "ADE (treated)             0.571149       -1.780008        2.718927    0.620\n",
       "Total effect              3.612827        1.076665        6.127839    0.004\n",
       "Prop. mediated (control)  0.823233        0.487837        2.456184    0.004\n",
       "Prop. mediated (treated)  0.823233        0.487837        2.456184    0.004\n",
       "ACME (average)            3.041678        1.766339        4.467569    0.000\n",
       "ADE (average)             0.571149       -1.780008        2.718927    0.620\n",
       "Prop. mediated (average)  0.823233        0.487837        2.456184    0.004"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "medTest_4"
   ]
  }
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