ImaginedOtherness.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy.stats as stats\n",
    "import statsmodels.formula.api as sm\n",
    "from statsmodels.iolib.summary2 import summary_col\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Correlational Study"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "positive_words = [\n",
    "    'wealthy',\n",
    "    'powerful',\n",
    "    'diverse',\n",
    "    'happy',\n",
    "    'beautiful',\n",
    "    'proud',\n",
    "    'innovative',\n",
    "    'strong',\n",
    "    'bravery',\n",
    "    'family',\n",
    "    'openness',\n",
    "    'independence',\n",
    "    'justice',\n",
    "    'safety',\n",
    "    'leader',\n",
    "    'opportunity'\n",
    "]\n",
    "\n",
    "neutral_words = [\n",
    "    'capitalist',\n",
    "    'politics',\n",
    "    'western',\n",
    "    'constitution',\n",
    "    'state',\n",
    "    'people',\n",
    "    'ocean',\n",
    "    'military',\n",
    "    'mountain',\n",
    "    'city',\n",
    "    'land',\n",
    "    'president',\n",
    "    'coast',\n",
    "    'life',\n",
    "    'eagle',\n",
    "    'work'\n",
    "]\n",
    "\n",
    "negative_words = [\n",
    "    'unequal',\n",
    "    'broken',\n",
    "    'angry',\n",
    "    'unfair',\n",
    "    'dangerous',\n",
    "    'sad',\n",
    "    'expensive',\n",
    "    'ignorant',\n",
    "    'greed',\n",
    "    'racism',\n",
    "    'oppression',\n",
    "    'corruption',\n",
    "    'obesity',\n",
    "    'violence',\n",
    "    'injustice',\n",
    "    'drugs',\n",
    "]\n",
    "\n",
    "words = positive_words + neutral_words + negative_words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Helper Functions\n",
    "\n",
    "def get_vecs(row, order='first', w=words):\n",
    "        \n",
    "        ur_words = set()\n",
    "        \n",
    "        for n in range(1,7):\n",
    "            \n",
    "            search = order+\"_\"+str(n)\n",
    "            \n",
    "            for i in row[search].split(','):\n",
    "                ur_words.add(i)\n",
    "        \n",
    "        return np.array([int(word in ur_words) for word in w])\n",
    "    \n",
    "def get_percent_agree(i, j):\n",
    "    return ((list(i-j).count(0))/len(i))\n",
    "\n",
    "def get_generalized_outgroup(row, w=words):\n",
    "    go = get_vecs(row, order='mp', w=w)\n",
    "    \n",
    "    if row['pid3'] == 'Democrat':\n",
    "        o = get_vecs(row, 'pubs', w=w)\n",
    "    elif row['pid3'] == 'Republican':\n",
    "        o = get_vecs(row, 'dems', w=w)\n",
    "        \n",
    "    return 1 - get_percent_agree(go,o)\n",
    "\n",
    "def get_personal_outgroup(row, w=words):\n",
    "    p = get_vecs(row, w=w)\n",
    "    \n",
    "    if row['pid3'] == 'Democrat':\n",
    "        o = get_vecs(row, 'pubs', w=w)\n",
    "    elif row['pid3'] == 'Republican':\n",
    "        o = get_vecs(row, 'dems', w=w)\n",
    "    \n",
    "    return 1 - get_percent_agree(p,o)\n",
    "\n",
    "def get_personal_ingroup(row, w=words):\n",
    "    p = get_vecs(row, w=w)\n",
    "    \n",
    "    if row['pid3'] == 'Democrat':\n",
    "        i = get_vecs(row, 'dems',w=w)\n",
    "    elif row['pid3'] == 'Republican':\n",
    "        i = get_vecs(row, 'pubs', w=w)\n",
    "        \n",
    "    return 1 - get_percent_agree(p, i)\n",
    "\n",
    "def get_intergroup(row, w=words):\n",
    "    \n",
    "    d = get_vecs(row,order=\"dems\", w=w)\n",
    "    r = get_vecs(row,order=\"pubs\", w=w)\n",
    "    \n",
    "    return 1 - get_percent_agree(d, r)\n",
    "\n",
    "def get_identify(row):\n",
    "    \n",
    "    x1 = {\"Not at all\": 1,\n",
    "          \"Not very important\": 2,\n",
    "          \"Very important\": 3,\n",
    "          \"Extremely important\": 4}[row['sit_1']]\n",
    "    x2 = {\"Not at all\": 1,\n",
    "          \"Not very well\": 2,\n",
    "          \"Very well\": 3,\n",
    "          \"Extremely well\": 4}[row['sit_2']]\n",
    "    x3 = {\"Not at all\": 1,\n",
    "          \"Very little\": 2,\n",
    "          \"Somewhat\": 3,\n",
    "          \"A great deal\": 4}[row['sit_3']]\n",
    "    x4 = {\"Never\": 1,\n",
    "          \"Rarely\": 2,\n",
    "          \"Some of the time\": 3,\n",
    "          \"All of the time\": 4}[row['sit_4']]\n",
    "    \n",
    "    return np.mean([x1,x2,x3,x4])\n",
    "    \n",
    "def get_ideology(row):\n",
    "    \n",
    "    return {'Extremely liberal': 7,\n",
    "            'Liberal': 6,\n",
    "            'Slightly liberal': 5,\n",
    "            'Moderate or middle of the road': 4,\n",
    "            'Slightly conservative': 3,\n",
    "            'Conservative': 2,\n",
    "            'Extremely conservative': 1}[row['ideo7']]\n",
    "\n",
    "def get_pid4(row):\n",
    "    if row['pid3'] == 'Democrat':\n",
    "        if row['strongid'] == 'Strong ${q://QID101/ChoiceGroup/SelectedChoices}':\n",
    "            return 1\n",
    "        elif row['strongid'] == 'Not very strong ${q://QID101/ChoiceGroup/SelectedChoices}':\n",
    "            return 2\n",
    "    elif row['pid3'] == 'Republican':\n",
    "        if row['strongid'] == 'Strong ${q://QID101/ChoiceGroup/SelectedChoices}':\n",
    "            return 4\n",
    "        elif row['strongid'] == 'Not very strong ${q://QID101/ChoiceGroup/SelectedChoices}':\n",
    "            return 3\n",
    "    \n",
    "def get_ap(row):\n",
    "    if row['dem'] == 1:\n",
    "        return row['therm_dems_1'] - row['therm_pubs_1']\n",
    "    else:\n",
    "        return row['therm_pubs_1'] - row['therm_dems_1']\n",
    "      \n",
    "def get_dehum(row):\n",
    "    if row['dem'] == 1:\n",
    "        return row['human_24'] - row['human_23']\n",
    "    else:\n",
    "        return row['human_23'] - row['human_24']\n",
    "    \n",
    "def get_region(s):\n",
    "    south = {'Alabama', 'Arkansas','Delaware','Florida', 'Georgia','Kentucky','Louisiana','Maryland','Mississippi','North Carolina','Oklahoma',\n",
    "            'South Carolina','Tennessee','Texas','Virginia','West Virginia'}\n",
    "    west = {'Arizona', 'California', 'Colorado','Idaho','Montana','Nevada','New Mexico','Oregon','Utah','Washington','Wyoming'}\n",
    "    east = {'Connecticut','Maine','Massachusetts','New Hampshire','New Jersey','New York','Pennsylvania',\n",
    "           'Rhode Island','Vermont'}\n",
    "    midwest = {'Illinois','Indiana','Iowa','Kansas','Michigan','Minnesota','Missouri','Nebraska','North Dakota','Ohio',\n",
    "              'South Dakota','Wisconsin'}\n",
    "    other= {'Alaska','Hawaii'}\n",
    "    foreign = {'Phillipines', 'Vietnam'}\n",
    "    if s in south:\n",
    "        return 'south'\n",
    "    elif s in west:\n",
    "        return 'west'\n",
    "    elif s in east:\n",
    "        return 'east'\n",
    "    elif s in midwest:\n",
    "        return 'midwest'\n",
    "    elif s in other:\n",
    "        return 'other'\n",
    "    elif s in foreign:\n",
    "        return 'foreign'\n",
    "    #else:\n",
    "    #    print(s)\n",
    "    #    return s\n",
    "    \n",
    "def get_race(x):\n",
    "    if x['Ethnicity (Simplified)'] != \"UNK\":\n",
    "        return x['Ethnicity (Simplified)']\n",
    "    else:\n",
    "        return {'White': 'White',\n",
    "                'Black or African American': 'Black',\n",
    "                'White,American Indian or Alaska Native': 'Mixed',\n",
    "                'Asian': 'Asian'}[x['Q309']]\n",
    "    \n",
    "def standardize(x):\n",
    "    return (x - x.mean())/x.std()\n",
    "\n",
    "def alpha(data):\n",
    "    \n",
    "    data = data.dropna()\n",
    "    df_corr = abs(data.corr()).to_numpy()\n",
    "    N = data.shape[1]\n",
    "    \n",
    "    rs = df_corr[np.triu_indices(N, k = 1)]\n",
    "        \n",
    "    return (N * np.mean(rs) / (1 + (N - 1) * np.mean(rs)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "913\n",
      "865\n",
      "774\n",
      "772\n"
     ]
    }
   ],
   "source": [
    "# Load the Data\n",
    "\n",
    "df1 = pd.read_csv(\"CorrelationalStudyData.csv\")\n",
    "print(len(df1))\n",
    "df1 = df1[df1.Progress == 100]\n",
    "print(len(df1))\n",
    "df1 = df1[df1['Duration (in seconds)'] >= df1['Duration (in seconds)'].median()/2]\n",
    "print(len(df1))\n",
    "df1 = df1[df1.pid3 != 'Independent']\n",
    "df1 = df1[df1.pid3 != 'Some other third party (please specify)']\n",
    "print(len(df1))\n",
    "df1 = df1.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merging in Demographic Data from Prolific (Blinded PROLIFIC_IDs)\n",
    "\n",
    "demo_df1 = pd.read_csv('CorrelationalDemos.csv')\n",
    "df1 = demo_df1.merge(df1, how=\"right\", on=\"participant_id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Interpretive Divergences\n",
    "df1['generalized_outgroup'] = df1.apply(get_generalized_outgroup, axis=1)\n",
    "df1['personal_outgroup'] = df1.apply(get_personal_outgroup, axis=1)\n",
    "df1['personal_ingroup'] = df1.apply(get_personal_ingroup, axis=1)\n",
    "df1['intergroup'] = df1.apply(get_intergroup, axis=1)\n",
    "df1['personal_intergroup'] = df1['personal_outgroup'] - df1['personal_ingroup']\n",
    "\n",
    "# Standardized Interpretive Divergences\n",
    "df1['generalized_outgroup_norm'] = standardize(df1.generalized_outgroup)\n",
    "df1['personal_outgroup_norm'] = standardize(df1.personal_outgroup)\n",
    "df1['personal_ingroup_norm'] = standardize(df1.personal_ingroup)\n",
    "df1['intergroup_norm'] = standardize(df1.intergroup)\n",
    "df1['personal_intergroup'] = standardize(df1.personal_intergroup)\n",
    "\n",
    "# Nominal Partisan Identities\n",
    "df1['dem'] = df1.pid3.apply(lambda x: {'Democrat': 1, 'Republican': 0}[x])\n",
    "df1['pid4'] = df1.apply(get_pid4, axis=1)\n",
    "df1['sdem'] = df1['pid4'].apply(lambda x: 1 if x == 1 else 0)\n",
    "df1['wdem'] = df1['pid4'].apply(lambda x: 1 if x == 2 else 0)\n",
    "df1['wpub'] = df1['pid4'].apply(lambda x: 1 if x == 3 else 0)\n",
    "df1['spub'] = df1['pid4'].apply(lambda x: 1 if x == 4 else 0)\n",
    "df1['num_strong'] = df1.strongid.apply(lambda x: 0 if \"Not\" in x else 1)\n",
    "df1['text_strong'] = df1.strongid.apply(lambda x: \"Weak\" if \"Not very\" in x else \"Strong\")\n",
    "\n",
    "# Social Partisan Identity\n",
    "df1['identify'] = df1.apply(get_identify, axis=1)\n",
    "df1['party_identify'] = df1.apply(lambda x: x['identify'] if x['dem'] == 1 else (-1.0 * x['identify']),axis=1)\n",
    "\n",
    "# Political Ideology\n",
    "df1['ideology'] = df1.apply(get_ideology, axis=1)\n",
    "df1['ideo_extreme'] = df1.ideology.apply(lambda x: abs(4 - x))\n",
    "df1['extreme_con'] = df1.ideology.apply(lambda x: 1 if x == 1 else 0)\n",
    "\n",
    "# Other Demographics\n",
    "df1['region'] = df1.Q306.apply(get_region)\n",
    "df1.rename(columns={\"Q310\": \"religion\"}, inplace=True)\n",
    "df1['race'] = df1.apply(get_race,axis=1)\n",
    "\n",
    "# Blatant Outgroup Dehumanization\n",
    "df1['dehuman'] = df1.apply(get_dehum, axis=1)\n",
    "df1['any_dehuman'] = df1.dehuman.apply(lambda x: int(x > 0))\n",
    "          \n",
    "# Affective Polarization\n",
    "df1['ap'] = df1.apply(get_ap, axis=1)\n",
    "df1['out_warm'] = df1.apply(lambda x: x.therm_dems_1 if x.dem==0 else x.therm_pubs_1, axis=1)\n",
    "\n",
    "# Propositional Measures\n",
    "k = {\"Strongly disagree\": 1,\n",
    "     \"Somewhat disagree\": 2,\n",
    "     \"Neither agree nor disagree\": 3,\n",
    "     \"Somewhat agree\": 4,\n",
    "     \"Strongly agree\": 5,\n",
    "     np.nan: np.nan}\n",
    "\n",
    "df1['out_prop_A'] = df1.apply(lambda x: k[x.Q301_4] if x.dem == 1 else k[x.Q301_5], axis=1)\n",
    "df1['in_prop_A'] = df1.apply(lambda x: k[x.Q301_5] if x.dem == 1 else k[x.Q301_4], axis=1)\n",
    "df1['diff_prop_A'] = df1.apply(lambda x: x.out_prop_A - x.in_prop_A, axis=1)\n",
    "\n",
    "df1['out_prop_W'] = df1.apply(lambda x: k[x.Q302_4] if x.dem == 1 else k[x.Q302_5], axis=1)\n",
    "df1['in_prop_W'] = df1.apply(lambda x: k[x.Q302_5] if x.dem == 1 else k[x.Q302_4], axis=1)\n",
    "df1['diff_prop_W'] = df1.apply(lambda x: x.out_prop_W - x.in_prop_W, axis=1)\n",
    "\n",
    "df1['prop'] = df1.apply(lambda x: np.mean([x.out_prop_A, x.out_prop_W]),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.5377752992653202, 4.454539340729727e-59)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How related are affective polarization and outgroup dehumanization?\n",
    "\n",
    "stats.pearsonr(df1.dehuman, df1.ap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.8267195272305428, 1.5966551129450232e-194)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How related is our measure of outgroup dehumanization to an alternative?\n",
    "\n",
    "def get_dehumB(row):\n",
    "    if row['dem'] == 1:\n",
    "        return row['human_23']\n",
    "    else:\n",
    "        return row['human_24']\n",
    "    \n",
    "df1['dehumB'] = df1.apply(get_dehumB, axis=1)\n",
    "\n",
    "stats.pearsonr(df1.dehuman, df1.dehumB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.85"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reliability of propositional measures\n",
    "\n",
    "round(alpha(df1[['out_prop_A','out_prop_W']]),2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Descriptive Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sex\n",
      "--------\n",
      "Sex\n",
      "Female    0.514249\n",
      "Male      0.485751\n",
      "dtype: float64\n",
      "\n",
      "race\n",
      "--------\n",
      "race\n",
      "Asian    0.066062\n",
      "Black    0.110104\n",
      "Mixed    0.036269\n",
      "Other    0.029793\n",
      "White    0.757772\n",
      "dtype: float64\n",
      "\n",
      "religion\n",
      "--------\n",
      "religion\n",
      "Agnostic (not sure if God exists)                                0.137306\n",
      "Atheists (do not believe God exists)                             0.129534\n",
      "Buddhism                                                         0.015544\n",
      "Catholicism (including Roman Catholic)                           0.225389\n",
      "Christian Orthodox                                               0.038860\n",
      "Hinduism                                                         0.003886\n",
      "Islam                                                            0.007772\n",
      "Judaism                                                          0.024611\n",
      "Mormonism                                                        0.010363\n",
      "Other (please specify)                                           0.068653\n",
      "Other Christianity (including non-denominational)                0.111399\n",
      "Protestantism (including Lutheran, Baptist, and Episcopalian)    0.224093\n",
      "dtype: float64\n",
      "\n",
      "region\n",
      "--------\n",
      "region\n",
      "east       0.246114\n",
      "midwest    0.211140\n",
      "other      0.003886\n",
      "south      0.290155\n",
      "west       0.227979\n",
      "dtype: float64\n",
      "\n",
      "pid3\n",
      "--------\n",
      "pid3\n",
      "Democrat      0.505181\n",
      "Republican    0.494819\n",
      "dtype: float64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for d in ['Sex', 'race', 'religion', 'region', 'pid3']:\n",
    "    print(d)\n",
    "    print('--------')\n",
    "    print(df1.groupby(d).size()/len(df1))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pid3\n",
       "Democrat      390\n",
       "Republican    382\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.groupby('pid3').size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "generalized_outgroup     0.40\n",
      "dehuman                 19.07\n",
      "prop                     3.39\n",
      "dem                      0.51\n",
      "num_strong               0.56\n",
      "ideo_extreme             1.86\n",
      "extreme_con              0.09\n",
      "dtype: float64 \n",
      "\n",
      "generalized_outgroup     0.12\n",
      "dehuman                 29.09\n",
      "prop                     1.14\n",
      "dem                      0.50\n",
      "num_strong               0.50\n",
      "ideo_extreme             0.86\n",
      "extreme_con              0.29\n",
      "dtype: float64 \n",
      "\n",
      "                      generalized_outgroup  dehuman  prop   dem  num_strong  \\\n",
      "generalized_outgroup                  1.00     0.19  0.30 -0.13        0.03   \n",
      "dehuman                               0.19     1.00  0.42  0.16        0.32   \n",
      "prop                                  0.30     0.42  1.00 -0.04        0.26   \n",
      "dem                                  -0.13     0.16 -0.04  1.00        0.22   \n",
      "num_strong                            0.03     0.32  0.26  0.22        1.00   \n",
      "ideo_extreme                          0.05     0.25  0.20  0.14        0.52   \n",
      "extreme_con                           0.13     0.14  0.16 -0.31        0.25   \n",
      "\n",
      "                      ideo_extreme  extreme_con  \n",
      "generalized_outgroup          0.05         0.13  \n",
      "dehuman                       0.25         0.14  \n",
      "prop                          0.20         0.16  \n",
      "dem                           0.14        -0.31  \n",
      "num_strong                    0.52         0.25  \n",
      "ideo_extreme                  1.00         0.43  \n",
      "extreme_con                   0.43         1.00   \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Information for Table 1\n",
    "\n",
    "T1 = df1[['generalized_outgroup','dehuman','prop','dem','num_strong','ideo_extreme','extreme_con']]\n",
    "\n",
    "print(round(T1.mean(),2),'\\n')\n",
    "print(round(T1.std(),2),'\\n')\n",
    "print(round(T1.corr(),2),'\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.0\n",
      "0.36\n",
      "0.26\n"
     ]
    }
   ],
   "source": [
    "# Follow-ups on the distribution of dehumanization\n",
    "print(df1.dehuman.median())\n",
    "print(round(len(df1[df1.human_23 == df1.human_24])/len(df1),2))\n",
    "print(round(len(df1[(df1.human_23 == 100) & (df1.human_24 == 100)])/len(df1),2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Main Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>dehuman</td>     <th>  R-squared:         </th> <td>   0.034</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.033</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   27.46</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 01 Mar 2024</td> <th>  Prob (F-statistic):</th> <td>2.07e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>13:55:02</td>     <th>  Log-Likelihood:    </th> <td> -3683.5</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   772</td>      <th>  AIC:               </th> <td>   7371.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   770</td>      <th>  BIC:               </th> <td>   7380.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "              <td></td>                 <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                 <td>   19.0712</td> <td>    1.030</td> <td>   18.523</td> <td> 0.000</td> <td>   17.050</td> <td>   21.092</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>generalized_outgroup_norm</th> <td>    5.3987</td> <td>    1.030</td> <td>    5.240</td> <td> 0.000</td> <td>    3.376</td> <td>    7.421</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>97.457</td> <th>  Durbin-Watson:     </th> <td>   1.848</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td> 139.184</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.903</td> <th>  Prob(JB):          </th> <td>5.98e-31</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.033</td> <th>  Cond. No.          </th> <td>    1.00</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                dehuman   R-squared:                       0.034\n",
       "Model:                            OLS   Adj. R-squared:                  0.033\n",
       "Method:                 Least Squares   F-statistic:                     27.46\n",
       "Date:                Fri, 01 Mar 2024   Prob (F-statistic):           2.07e-07\n",
       "Time:                        13:55:02   Log-Likelihood:                -3683.5\n",
       "No. Observations:                 772   AIC:                             7371.\n",
       "Df Residuals:                     770   BIC:                             7380.\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=============================================================================================\n",
       "                                coef    std err          t      P>|t|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------------------\n",
       "Intercept                    19.0712      1.030     18.523      0.000      17.050      21.092\n",
       "generalized_outgroup_norm     5.3987      1.030      5.240      0.000       3.376       7.421\n",
       "==============================================================================\n",
       "Omnibus:                       97.457   Durbin-Watson:                   1.848\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              139.184\n",
       "Skew:                           0.903   Prob(JB):                     5.98e-31\n",
       "Kurtosis:                       4.033   Cond. No.                         1.00\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Model 1\n",
    "\n",
    "f1 = 'dehuman ~ generalized_outgroup_norm'\n",
    "mod1 = sm.ols(f1, data=df1)\n",
    "res1 = mod1.fit()\n",
    "res1.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>dehuman</td>     <th>  R-squared:         </th> <td>   0.156</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.150</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   28.29</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 01 Mar 2024</td> <th>  Prob (F-statistic):</th> <td>2.30e-26</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>13:55:02</td>     <th>  Log-Likelihood:    </th> <td> -3631.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   772</td>      <th>  AIC:               </th> <td>   7275.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   766</td>      <th>  BIC:               </th> <td>   7303.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     5</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "              <td></td>                 <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                 <td>    2.3648</td> <td>    2.431</td> <td>    0.973</td> <td> 0.331</td> <td>   -2.407</td> <td>    7.137</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>generalized_outgroup_norm</th> <td>    5.3766</td> <td>    0.979</td> <td>    5.493</td> <td> 0.000</td> <td>    3.455</td> <td>    7.298</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dem</th>                       <td>    8.4269</td> <td>    2.197</td> <td>    3.836</td> <td> 0.000</td> <td>    4.114</td> <td>   12.740</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>num_strong</th>                <td>   13.1991</td> <td>    2.323</td> <td>    5.683</td> <td> 0.000</td> <td>    8.640</td> <td>   17.759</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>extreme_con</th>               <td>    7.7561</td> <td>    4.045</td> <td>    1.918</td> <td> 0.056</td> <td>   -0.184</td> <td>   15.696</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ideo_extreme</th>              <td>    2.3018</td> <td>    1.436</td> <td>    1.603</td> <td> 0.109</td> <td>   -0.518</td> <td>    5.121</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>75.093</td> <th>  Durbin-Watson:     </th> <td>   1.855</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td> 101.503</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.752</td> <th>  Prob(JB):          </th> <td>9.10e-23</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.946</td> <th>  Cond. No.          </th> <td>    10.6</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                dehuman   R-squared:                       0.156\n",
       "Model:                            OLS   Adj. R-squared:                  0.150\n",
       "Method:                 Least Squares   F-statistic:                     28.29\n",
       "Date:                Fri, 01 Mar 2024   Prob (F-statistic):           2.30e-26\n",
       "Time:                        13:55:02   Log-Likelihood:                -3631.6\n",
       "No. Observations:                 772   AIC:                             7275.\n",
       "Df Residuals:                     766   BIC:                             7303.\n",
       "Df Model:                           5                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=============================================================================================\n",
       "                                coef    std err          t      P>|t|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------------------\n",
       "Intercept                     2.3648      2.431      0.973      0.331      -2.407       7.137\n",
       "generalized_outgroup_norm     5.3766      0.979      5.493      0.000       3.455       7.298\n",
       "dem                           8.4269      2.197      3.836      0.000       4.114      12.740\n",
       "num_strong                   13.1991      2.323      5.683      0.000       8.640      17.759\n",
       "extreme_con                   7.7561      4.045      1.918      0.056      -0.184      15.696\n",
       "ideo_extreme                  2.3018      1.436      1.603      0.109      -0.518       5.121\n",
       "==============================================================================\n",
       "Omnibus:                       75.093   Durbin-Watson:                   1.855\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              101.503\n",
       "Skew:                           0.752   Prob(JB):                     9.10e-23\n",
       "Kurtosis:                       3.946   Cond. No.                         10.6\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Model 2\n",
    "\n",
    "f2 = f1 + ' + dem + num_strong + extreme_con + ideo_extreme'\n",
    "mod2 = sm.ols(f2, data=df1)\n",
    "res2 = mod2.fit()\n",
    "res2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>dehuman</td>     <th>  R-squared:         </th> <td>   0.156</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.149</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   23.55</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 01 Mar 2024</td> <th>  Prob (F-statistic):</th> <td>1.27e-25</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>13:55:02</td>     <th>  Log-Likelihood:    </th> <td> -3631.5</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   772</td>      <th>  AIC:               </th> <td>   7277.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   765</td>      <th>  BIC:               </th> <td>   7310.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     6</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "              <td></td>                 <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                 <td>    2.4424</td> <td>    2.464</td> <td>    0.991</td> <td> 0.322</td> <td>   -2.395</td> <td>    7.280</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>generalized_outgroup_norm</th> <td>    5.2421</td> <td>    1.195</td> <td>    4.387</td> <td> 0.000</td> <td>    2.896</td> <td>    7.588</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dem</th>                       <td>    8.4304</td> <td>    2.198</td> <td>    3.835</td> <td> 0.000</td> <td>    4.115</td> <td>   12.746</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>num_strong</th>                <td>   13.1980</td> <td>    2.324</td> <td>    5.679</td> <td> 0.000</td> <td>    8.636</td> <td>   17.760</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>extreme_con</th>               <td>    7.7636</td> <td>    4.047</td> <td>    1.918</td> <td> 0.055</td> <td>   -0.181</td> <td>   15.709</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ideo_extreme</th>              <td>    2.2590</td> <td>    1.454</td> <td>    1.554</td> <td> 0.121</td> <td>   -0.594</td> <td>    5.112</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>personal_outgroup_norm</th>    <td>    0.2365</td> <td>    1.204</td> <td>    0.196</td> <td> 0.844</td> <td>   -2.127</td> <td>    2.600</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>75.263</td> <th>  Durbin-Watson:     </th> <td>   1.856</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td> 101.679</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.754</td> <th>  Prob(JB):          </th> <td>8.33e-23</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.943</td> <th>  Cond. No.          </th> <td>    10.6</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                dehuman   R-squared:                       0.156\n",
       "Model:                            OLS   Adj. R-squared:                  0.149\n",
       "Method:                 Least Squares   F-statistic:                     23.55\n",
       "Date:                Fri, 01 Mar 2024   Prob (F-statistic):           1.27e-25\n",
       "Time:                        13:55:02   Log-Likelihood:                -3631.5\n",
       "No. Observations:                 772   AIC:                             7277.\n",
       "Df Residuals:                     765   BIC:                             7310.\n",
       "Df Model:                           6                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=============================================================================================\n",
       "                                coef    std err          t      P>|t|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------------------\n",
       "Intercept                     2.4424      2.464      0.991      0.322      -2.395       7.280\n",
       "generalized_outgroup_norm     5.2421      1.195      4.387      0.000       2.896       7.588\n",
       "dem                           8.4304      2.198      3.835      0.000       4.115      12.746\n",
       "num_strong                   13.1980      2.324      5.679      0.000       8.636      17.760\n",
       "extreme_con                   7.7636      4.047      1.918      0.055      -0.181      15.709\n",
       "ideo_extreme                  2.2590      1.454      1.554      0.121      -0.594       5.112\n",
       "personal_outgroup_norm        0.2365      1.204      0.196      0.844      -2.127       2.600\n",
       "==============================================================================\n",
       "Omnibus:                       75.263   Durbin-Watson:                   1.856\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              101.679\n",
       "Skew:                           0.754   Prob(JB):                     8.33e-23\n",
       "Kurtosis:                       3.943   Cond. No.                         10.6\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Model 3\n",
    "\n",
    "f3 = f2 + ' + personal_outgroup_norm'\n",
    "mod3 = sm.ols(f3, data=df1)\n",
    "res3 = mod3.fit()\n",
    "res3.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>dehuman</td>     <th>  R-squared:         </th> <td>   0.156</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.149</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   23.57</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 01 Mar 2024</td> <th>  Prob (F-statistic):</th> <td>1.22e-25</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>13:55:02</td>     <th>  Log-Likelihood:    </th> <td> -3631.5</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   772</td>      <th>  AIC:               </th> <td>   7277.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   765</td>      <th>  BIC:               </th> <td>   7310.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     6</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "              <td></td>                 <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                 <td>    2.5042</td> <td>    2.464</td> <td>    1.016</td> <td> 0.310</td> <td>   -2.332</td> <td>    7.341</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>generalized_outgroup_norm</th> <td>    5.1897</td> <td>    1.112</td> <td>    4.669</td> <td> 0.000</td> <td>    3.008</td> <td>    7.372</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dem</th>                       <td>    8.3516</td> <td>    2.208</td> <td>    3.782</td> <td> 0.000</td> <td>    4.016</td> <td>   12.687</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>num_strong</th>                <td>   13.1939</td> <td>    2.324</td> <td>    5.677</td> <td> 0.000</td> <td>    8.632</td> <td>   17.756</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>extreme_con</th>               <td>    7.8024</td> <td>    4.049</td> <td>    1.927</td> <td> 0.054</td> <td>   -0.146</td> <td>   15.751</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ideo_extreme</th>              <td>    2.2464</td> <td>    1.445</td> <td>    1.554</td> <td> 0.121</td> <td>   -0.591</td> <td>    5.084</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>intergroup_norm</th>           <td>    0.3948</td> <td>    1.110</td> <td>    0.356</td> <td> 0.722</td> <td>   -1.785</td> <td>    2.574</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>75.038</td> <th>  Durbin-Watson:     </th> <td>   1.857</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td> 101.368</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.752</td> <th>  Prob(JB):          </th> <td>9.73e-23</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.944</td> <th>  Cond. No.          </th> <td>    10.6</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                dehuman   R-squared:                       0.156\n",
       "Model:                            OLS   Adj. R-squared:                  0.149\n",
       "Method:                 Least Squares   F-statistic:                     23.57\n",
       "Date:                Fri, 01 Mar 2024   Prob (F-statistic):           1.22e-25\n",
       "Time:                        13:55:02   Log-Likelihood:                -3631.5\n",
       "No. Observations:                 772   AIC:                             7277.\n",
       "Df Residuals:                     765   BIC:                             7310.\n",
       "Df Model:                           6                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=============================================================================================\n",
       "                                coef    std err          t      P>|t|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------------------\n",
       "Intercept                     2.5042      2.464      1.016      0.310      -2.332       7.341\n",
       "generalized_outgroup_norm     5.1897      1.112      4.669      0.000       3.008       7.372\n",
       "dem                           8.3516      2.208      3.782      0.000       4.016      12.687\n",
       "num_strong                   13.1939      2.324      5.677      0.000       8.632      17.756\n",
       "extreme_con                   7.8024      4.049      1.927      0.054      -0.146      15.751\n",
       "ideo_extreme                  2.2464      1.445      1.554      0.121      -0.591       5.084\n",
       "intergroup_norm               0.3948      1.110      0.356      0.722      -1.785       2.574\n",
       "==============================================================================\n",
       "Omnibus:                       75.038   Durbin-Watson:                   1.857\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              101.368\n",
       "Skew:                           0.752   Prob(JB):                     9.73e-23\n",
       "Kurtosis:                       3.944   Cond. No.                         10.6\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Model 4\n",
    "\n",
    "f4 = f2 + ' + intergroup_norm'\n",
    "mod4 = sm.ols(f4, data=df1)\n",
    "res4 = mod4.fit()\n",
    "res4.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>dehuman</td>     <th>  R-squared:         </th> <td>   0.256</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.250</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   43.69</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 01 Mar 2024</td> <th>  Prob (F-statistic):</th> <td>5.83e-46</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>13:55:02</td>     <th>  Log-Likelihood:    </th> <td> -3569.9</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   769</td>      <th>  AIC:               </th> <td>   7154.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   762</td>      <th>  BIC:               </th> <td>   7186.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     6</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "              <td></td>                 <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                 <td>  -23.9553</td> <td>    3.472</td> <td>   -6.899</td> <td> 0.000</td> <td>  -30.772</td> <td>  -17.139</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>generalized_outgroup_norm</th> <td>    2.6455</td> <td>    0.963</td> <td>    2.747</td> <td> 0.006</td> <td>    0.755</td> <td>    4.536</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dem</th>                       <td>    9.3566</td> <td>    2.076</td> <td>    4.508</td> <td> 0.000</td> <td>    5.282</td> <td>   13.431</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>num_strong</th>                <td>    8.9385</td> <td>    2.232</td> <td>    4.005</td> <td> 0.000</td> <td>    4.557</td> <td>   13.320</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>extreme_con</th>               <td>    6.6832</td> <td>    3.810</td> <td>    1.754</td> <td> 0.080</td> <td>   -0.796</td> <td>   14.163</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ideo_extreme</th>              <td>    1.5516</td> <td>    1.354</td> <td>    1.146</td> <td> 0.252</td> <td>   -1.106</td> <td>    4.209</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>prop</th>                      <td>    8.7719</td> <td>    0.875</td> <td>   10.026</td> <td> 0.000</td> <td>    7.054</td> <td>   10.489</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>41.989</td> <th>  Durbin-Watson:     </th> <td>   1.859</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  55.484</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.488</td> <th>  Prob(JB):          </th> <td>8.95e-13</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.883</td> <th>  Cond. No.          </th> <td>    18.8</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                dehuman   R-squared:                       0.256\n",
       "Model:                            OLS   Adj. R-squared:                  0.250\n",
       "Method:                 Least Squares   F-statistic:                     43.69\n",
       "Date:                Fri, 01 Mar 2024   Prob (F-statistic):           5.83e-46\n",
       "Time:                        13:55:02   Log-Likelihood:                -3569.9\n",
       "No. Observations:                 769   AIC:                             7154.\n",
       "Df Residuals:                     762   BIC:                             7186.\n",
       "Df Model:                           6                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=============================================================================================\n",
       "                                coef    std err          t      P>|t|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------------------\n",
       "Intercept                   -23.9553      3.472     -6.899      0.000     -30.772     -17.139\n",
       "generalized_outgroup_norm     2.6455      0.963      2.747      0.006       0.755       4.536\n",
       "dem                           9.3566      2.076      4.508      0.000       5.282      13.431\n",
       "num_strong                    8.9385      2.232      4.005      0.000       4.557      13.320\n",
       "extreme_con                   6.6832      3.810      1.754      0.080      -0.796      14.163\n",
       "ideo_extreme                  1.5516      1.354      1.146      0.252      -1.106       4.209\n",
       "prop                          8.7719      0.875     10.026      0.000       7.054      10.489\n",
       "==============================================================================\n",
       "Omnibus:                       41.989   Durbin-Watson:                   1.859\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               55.484\n",
       "Skew:                           0.488   Prob(JB):                     8.95e-13\n",
       "Kurtosis:                       3.883   Cond. No.                         18.8\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Model 5\n",
    "\n",
    "f5 = f2 + ' + prop'\n",
    "mod5 = sm.ols(f5, data=df1)\n",
    "res5 = mod5.fit()\n",
    "res5.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "              <td></td>              <th>dehuman I</th> <th>dehuman II</th> <th>dehuman III</th> <th>dehuman IIII</th> <th>dehuman IIIII</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>generalized_outgroup_norm</th>  <td>5.40***</td>    <td>5.38***</td>    <td>5.24***</td>      <td>5.19***</td>      <td>2.65***</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                           <td>(1.03)</td>     <td>(0.98)</td>     <td>(1.20)</td>       <td>(1.11)</td>       <td>(0.96)</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>personal_outgroup_norm</th>        <td></td>           <td></td>         <td>0.24</td>           <td></td>             <td></td>       \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                              <td></td>           <td></td>        <td>(1.20)</td>          <td></td>             <td></td>       \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>intergroup_norm</th>               <td></td>           <td></td>           <td></td>           <td>0.39</td>           <td></td>       \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                              <td></td>           <td></td>           <td></td>          <td>(1.11)</td>          <td></td>       \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>prop</th>                          <td></td>           <td></td>           <td></td>             <td></td>          <td>8.77***</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                              <td></td>           <td></td>           <td></td>             <td></td>          <td>(0.87)</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dem</th>                           <td></td>        <td>8.43***</td>    <td>8.43***</td>      <td>8.35***</td>      <td>9.36***</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                              <td></td>        <td>(2.20)</td>     <td>(2.20)</td>       <td>(2.21)</td>       <td>(2.08)</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>num_strong</th>                    <td></td>       <td>13.20***</td>   <td>13.20***</td>     <td>13.19***</td>      <td>8.94***</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                              <td></td>        <td>(2.32)</td>     <td>(2.32)</td>       <td>(2.32)</td>       <td>(2.23)</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>extreme_con</th>                   <td></td>         <td>7.76*</td>      <td>7.76*</td>        <td>7.80*</td>        <td>6.68*</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                              <td></td>        <td>(4.04)</td>     <td>(4.05)</td>       <td>(4.05)</td>       <td>(3.81)</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ideo_extreme</th>                  <td></td>         <td>2.30</td>       <td>2.26</td>         <td>2.25</td>         <td>1.55</td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                              <td></td>        <td>(1.44)</td>     <td>(1.45)</td>       <td>(1.45)</td>       <td>(1.35)</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                 <td>19.07***</td>     <td>2.36</td>       <td>2.44</td>         <td>2.50</td>       <td>-23.96***</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                           <td>(1.03)</td>     <td>(2.43)</td>     <td>(2.46)</td>       <td>(2.46)</td>       <td>(3.47)</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>R-squared</th>                   <td>0.03</td>       <td>0.16</td>       <td>0.16</td>         <td>0.16</td>         <td>0.26</td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>R-squared Adj.</th>              <td>0.03</td>       <td>0.15</td>       <td>0.15</td>         <td>0.15</td>         <td>0.25</td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>N</th>                         <td>772.0000</td>   <td>772.0000</td>   <td>772.0000</td>     <td>772.0000</td>     <td>769.0000</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>df</th>                        <td>770.0000</td>   <td>766.0000</td>   <td>765.0000</td>     <td>765.0000</td>     <td>762.0000</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>R2</th>                         <td>0.0344</td>     <td>0.1559</td>     <td>0.1559</td>       <td>0.1560</td>       <td>0.2560</td>    \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "\n",
       "=====================================================================================\n",
       "                          dehuman I dehuman II dehuman III dehuman IIII dehuman IIIII\n",
       "-------------------------------------------------------------------------------------\n",
       "generalized_outgroup_norm 5.40***   5.38***    5.24***     5.19***      2.65***      \n",
       "                          (1.03)    (0.98)     (1.20)      (1.11)       (0.96)       \n",
       "personal_outgroup_norm                         0.24                                  \n",
       "                                               (1.20)                                \n",
       "intergroup_norm                                            0.39                      \n",
       "                                                           (1.11)                    \n",
       "prop                                                                    8.77***      \n",
       "                                                                        (0.87)       \n",
       "dem                                 8.43***    8.43***     8.35***      9.36***      \n",
       "                                    (2.20)     (2.20)      (2.21)       (2.08)       \n",
       "num_strong                          13.20***   13.20***    13.19***     8.94***      \n",
       "                                    (2.32)     (2.32)      (2.32)       (2.23)       \n",
       "extreme_con                         7.76*      7.76*       7.80*        6.68*        \n",
       "                                    (4.04)     (4.05)      (4.05)       (3.81)       \n",
       "ideo_extreme                        2.30       2.26        2.25         1.55         \n",
       "                                    (1.44)     (1.45)      (1.45)       (1.35)       \n",
       "Intercept                 19.07***  2.36       2.44        2.50         -23.96***    \n",
       "                          (1.03)    (2.43)     (2.46)      (2.46)       (3.47)       \n",
       "R-squared                 0.03      0.16       0.16        0.16         0.26         \n",
       "R-squared Adj.            0.03      0.15       0.15        0.15         0.25         \n",
       "N                         772.0000  772.0000   772.0000    772.0000     769.0000     \n",
       "df                        770.0000  766.0000   765.0000    765.0000     762.0000     \n",
       "R2                        0.0344    0.1559     0.1559      0.1560       0.2560       \n",
       "=====================================================================================\n",
       "Standard errors in parentheses.\n",
       "* p<.1, ** p<.05, ***p<.01\n",
       "\"\"\""
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Table 2\n",
    "\n",
    "summary_col([res1,res2,res3,res4,res5],stars=True, float_format='%.2f',\n",
    "            regressor_order = [\"generalized_outgroup_norm\",\n",
    "                              \"personal_outgroup_norm\",\n",
    "                              \"intergroup_norm\",\n",
    "                              \"prop\",\n",
    "                              \"dem\",\n",
    "                              \"num_strong\",\n",
    "                              \"extreme_con\",\n",
    "                              \"ideo_extreme\"],\n",
    "           info_dict={\"N\": lambda x: x.nobs,\n",
    "                      \"df\": lambda x: x.df_resid,\n",
    "                      \"R2\": lambda x: x.rsquared})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24.47\n"
     ]
    }
   ],
   "source": [
    "print(19.07 + 5.40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1800x1200 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Figure 4\n",
    "\n",
    "plt.rc('font', size=24) \n",
    "plt.figure(dpi=300)\n",
    "sb.set_style(\"whitegrid\")\n",
    "\n",
    "plt.figure(figsize=(16,10))\n",
    "\n",
    "ax = sb.regplot(x='generalized_outgroup_norm',y='dehuman',\n",
    "           scatter=False,\n",
    "           line_kws={'lw':5, 'color':'black'},\n",
    "           data=df1)\n",
    "\n",
    "for x in set(df1.generalized_outgroup_norm):\n",
    "    small_df = df1[df1.generalized_outgroup_norm == x]\n",
    "    plt.scatter(x, small_df.dehuman.mean(), color=\"black\", alpha=0.7,\n",
    "                edgecolor=\"black\", s=len(small_df)*10)\n",
    "\n",
    "plt.ylabel(\"Outgroup Dehumanization\", fontsize=40)\n",
    "plt.xlabel(\"Imagined Otherness (standardized)\", fontsize=40)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1800x1200 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Figure 4 with full scatter (w/ some noise added to imagined otherness for visibility)\n",
    "\n",
    "plt.rc('font', size=24) \n",
    "plt.figure(dpi=300)\n",
    "sb.set_style(\"whitegrid\")\n",
    "\n",
    "plt.figure(figsize=(16,10))\n",
    "\n",
    "df1['io_noise'] = df1.generalized_outgroup_norm + np.random.uniform(-0.075, 0.075, size=len(df1))\n",
    "\n",
    "sb.regplot(x='generalized_outgroup_norm',y='dehuman',\n",
    "           scatter=False,\n",
    "           line_kws={'lw':5, 'color':'black'},\n",
    "           data=df1)\n",
    "\n",
    "sb.scatterplot(x=\"io_noise\", y=\"dehuman\", data=df1, color=\"black\", alpha=0.4, s=75)\n",
    "\n",
    "for x in set(df1.generalized_outgroup_norm):\n",
    "    small_df = df1[df1.generalized_outgroup_norm == x]\n",
    "    plt.scatter(x, small_df.dehuman.mean(), facecolor='none', alpha=0.7,\n",
    "                edgecolor=\"black\", s=len(small_df)*10, marker=\"s\")\n",
    "\n",
    "plt.ylabel(\"Outgroup Dehumanization\", fontsize=40)\n",
    "plt.xlabel(\"Imagined Otherness (standardized)\", fontsize=40)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Robustness Tests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using single proposition measures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>dehuman</td>     <th>  R-squared:         </th> <td>   0.231</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.225</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   38.23</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 01 Mar 2024</td> <th>  Prob (F-statistic):</th> <td>1.12e-40</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>13:55:03</td>     <th>  Log-Likelihood:    </th> <td> -3591.2</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   771</td>      <th>  AIC:               </th> <td>   7196.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   764</td>      <th>  BIC:               </th> <td>   7229.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     6</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "              <td></td>                 <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                 <td>  -19.0059</td> <td>    3.399</td> <td>   -5.591</td> <td> 0.000</td> <td>  -25.679</td> <td>  -12.333</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>generalized_outgroup_norm</th> <td>    3.0237</td> <td>    0.976</td> <td>    3.098</td> <td> 0.002</td> <td>    1.108</td> <td>    4.940</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dem</th>                       <td>    9.6235</td> <td>    2.108</td> <td>    4.566</td> <td> 0.000</td> <td>    5.486</td> <td>   13.761</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>num_strong</th>                <td>   10.0312</td> <td>    2.253</td> <td>    4.452</td> <td> 0.000</td> <td>    5.608</td> <td>   14.454</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>extreme_con</th>               <td>    7.4554</td> <td>    3.866</td> <td>    1.928</td> <td> 0.054</td> <td>   -0.134</td> <td>   15.045</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ideo_extreme</th>              <td>    1.8149</td> <td>    1.374</td> <td>    1.321</td> <td> 0.187</td> <td>   -0.883</td> <td>    4.513</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>out_prop_A</th>                <td>    7.0209</td> <td>    0.819</td> <td>    8.572</td> <td> 0.000</td> <td>    5.413</td> <td>    8.629</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>56.788</td> <th>  Durbin-Watson:     </th> <td>   1.876</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  76.943</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.601</td> <th>  Prob(JB):          </th> <td>1.96e-17</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.975</td> <th>  Cond. No.          </th> <td>    18.6</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                dehuman   R-squared:                       0.231\n",
       "Model:                            OLS   Adj. R-squared:                  0.225\n",
       "Method:                 Least Squares   F-statistic:                     38.23\n",
       "Date:                Fri, 01 Mar 2024   Prob (F-statistic):           1.12e-40\n",
       "Time:                        13:55:03   Log-Likelihood:                -3591.2\n",
       "No. Observations:                 771   AIC:                             7196.\n",
       "Df Residuals:                     764   BIC:                             7229.\n",
       "Df Model:                           6                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=============================================================================================\n",
       "                                coef    std err          t      P>|t|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------------------\n",
       "Intercept                   -19.0059      3.399     -5.591      0.000     -25.679     -12.333\n",
       "generalized_outgroup_norm     3.0237      0.976      3.098      0.002       1.108       4.940\n",
       "dem                           9.6235      2.108      4.566      0.000       5.486      13.761\n",
       "num_strong                   10.0312      2.253      4.452      0.000       5.608      14.454\n",
       "extreme_con                   7.4554      3.866      1.928      0.054      -0.134      15.045\n",
       "ideo_extreme                  1.8149      1.374      1.321      0.187      -0.883       4.513\n",
       "out_prop_A                    7.0209      0.819      8.572      0.000       5.413       8.629\n",
       "==============================================================================\n",
       "Omnibus:                       56.788   Durbin-Watson:                   1.876\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               76.943\n",
       "Skew:                           0.601   Prob(JB):                     1.96e-17\n",
       "Kurtosis:                       3.975   Cond. No.                         18.6\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Model 5B\n",
    "\n",
    "f5b = f2 + ' + out_prop_A'\n",
    "mod5b = sm.ols(f5b, data=df1)\n",
    "res5b = mod5b.fit()\n",
    "res5b.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>dehuman</td>     <th>  R-squared:         </th> <td>   0.250</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.244</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   42.44</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 01 Mar 2024</td> <th>  Prob (F-statistic):</th> <td>9.10e-45</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>13:55:03</td>     <th>  Log-Likelihood:    </th> <td> -3577.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   770</td>      <th>  AIC:               </th> <td>   7169.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   763</td>      <th>  BIC:               </th> <td>   7201.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     6</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "              <td></td>                 <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                 <td>  -20.7664</td> <td>    3.299</td> <td>   -6.295</td> <td> 0.000</td> <td>  -27.242</td> <td>  -14.291</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>generalized_outgroup_norm</th> <td>    3.0707</td> <td>    0.955</td> <td>    3.214</td> <td> 0.001</td> <td>    1.195</td> <td>    4.946</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dem</th>                       <td>    8.6615</td> <td>    2.078</td> <td>    4.167</td> <td> 0.000</td> <td>    4.581</td> <td>   12.742</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>num_strong</th>                <td>    9.1641</td> <td>    2.234</td> <td>    4.102</td> <td> 0.000</td> <td>    4.779</td> <td>   13.550</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>extreme_con</th>               <td>    6.0857</td> <td>    3.825</td> <td>    1.591</td> <td> 0.112</td> <td>   -1.423</td> <td>   13.595</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ideo_extreme</th>              <td>    1.4992</td> <td>    1.358</td> <td>    1.104</td> <td> 0.270</td> <td>   -1.167</td> <td>    4.166</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>out_prop_W</th>                <td>    7.8482</td> <td>    0.805</td> <td>    9.751</td> <td> 0.000</td> <td>    6.268</td> <td>    9.428</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>38.522</td> <th>  Durbin-Watson:     </th> <td>   1.843</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  49.747</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.466</td> <th>  Prob(JB):          </th> <td>1.58e-11</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.826</td> <th>  Cond. No.          </th> <td>    19.1</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                dehuman   R-squared:                       0.250\n",
       "Model:                            OLS   Adj. R-squared:                  0.244\n",
       "Method:                 Least Squares   F-statistic:                     42.44\n",
       "Date:                Fri, 01 Mar 2024   Prob (F-statistic):           9.10e-45\n",
       "Time:                        13:55:03   Log-Likelihood:                -3577.3\n",
       "No. Observations:                 770   AIC:                             7169.\n",
       "Df Residuals:                     763   BIC:                             7201.\n",
       "Df Model:                           6                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=============================================================================================\n",
       "                                coef    std err          t      P>|t|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------------------\n",
       "Intercept                   -20.7664      3.299     -6.295      0.000     -27.242     -14.291\n",
       "generalized_outgroup_norm     3.0707      0.955      3.214      0.001       1.195       4.946\n",
       "dem                           8.6615      2.078      4.167      0.000       4.581      12.742\n",
       "num_strong                    9.1641      2.234      4.102      0.000       4.779      13.550\n",
       "extreme_con                   6.0857      3.825      1.591      0.112      -1.423      13.595\n",
       "ideo_extreme                  1.4992      1.358      1.104      0.270      -1.167       4.166\n",
       "out_prop_W                    7.8482      0.805      9.751      0.000       6.268       9.428\n",
       "==============================================================================\n",
       "Omnibus:                       38.522   Durbin-Watson:                   1.843\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               49.747\n",
       "Skew:                           0.466   Prob(JB):                     1.58e-11\n",
       "Kurtosis:                       3.826   Cond. No.                         19.1\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Model 5C\n",
    "\n",
    "f5c = f2 + ' + out_prop_W'\n",
    "mod5c = sm.ols(f5c, data=df1)\n",
    "res5c = mod5c.fit()\n",
    "res5c.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using Heteroskedasticity-robust Standard Errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "              <td></td>              <th>dehuman I</th> <th>dehuman II</th> <th>dehuman III</th> <th>dehuman IIII</th> <th>dehuman IIIII</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>generalized_outgroup_norm</th>  <td>5.40***</td>    <td>5.38***</td>    <td>5.24***</td>      <td>5.19***</td>      <td>2.65***</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                           <td>(1.05)</td>     <td>(0.98)</td>     <td>(1.16)</td>       <td>(1.03)</td>       <td>(0.94)</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>personal_outgroup_norm</th>        <td></td>           <td></td>         <td>0.24</td>           <td></td>             <td></td>       \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                              <td></td>           <td></td>        <td>(1.18)</td>          <td></td>             <td></td>       \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>intergroup_norm</th>               <td></td>           <td></td>           <td></td>           <td>0.39</td>           <td></td>       \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                              <td></td>           <td></td>           <td></td>          <td>(0.94)</td>          <td></td>       \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>prop</th>                          <td></td>           <td></td>           <td></td>             <td></td>          <td>8.77***</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                              <td></td>           <td></td>           <td></td>             <td></td>          <td>(0.86)</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dem</th>                           <td></td>        <td>8.43***</td>    <td>8.43***</td>      <td>8.35***</td>      <td>9.36***</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                              <td></td>        <td>(2.20)</td>     <td>(2.20)</td>       <td>(2.19)</td>       <td>(2.04)</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>num_strong</th>                    <td></td>       <td>13.20***</td>   <td>13.20***</td>     <td>13.19***</td>      <td>8.94***</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                              <td></td>        <td>(2.25)</td>     <td>(2.24)</td>       <td>(2.24)</td>       <td>(2.11)</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>extreme_con</th>                   <td></td>         <td>7.76</td>       <td>7.76</td>         <td>7.80*</td>        <td>6.68</td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                              <td></td>        <td>(4.74)</td>     <td>(4.74)</td>       <td>(4.74)</td>       <td>(4.54)</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ideo_extreme</th>                  <td></td>         <td>2.30</td>       <td>2.26</td>         <td>2.25</td>         <td>1.55</td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                              <td></td>        <td>(1.51)</td>     <td>(1.51)</td>       <td>(1.51)</td>       <td>(1.43)</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                 <td>19.07***</td>     <td>2.36</td>       <td>2.44</td>         <td>2.50</td>       <td>-23.96***</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                           <td>(1.03)</td>     <td>(2.25)</td>     <td>(2.27)</td>       <td>(2.26)</td>       <td>(3.08)</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>R-squared</th>                   <td>0.03</td>       <td>0.16</td>       <td>0.16</td>         <td>0.16</td>         <td>0.26</td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>R-squared Adj.</th>              <td>0.03</td>       <td>0.15</td>       <td>0.15</td>         <td>0.15</td>         <td>0.25</td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>N</th>                         <td>772.0000</td>   <td>772.0000</td>   <td>772.0000</td>     <td>772.0000</td>     <td>769.0000</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>df</th>                        <td>770.0000</td>   <td>766.0000</td>   <td>765.0000</td>     <td>765.0000</td>     <td>762.0000</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>R2</th>                         <td>0.0344</td>     <td>0.1559</td>     <td>0.1559</td>       <td>0.1560</td>       <td>0.2560</td>    \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "\n",
       "=====================================================================================\n",
       "                          dehuman I dehuman II dehuman III dehuman IIII dehuman IIIII\n",
       "-------------------------------------------------------------------------------------\n",
       "generalized_outgroup_norm 5.40***   5.38***    5.24***     5.19***      2.65***      \n",
       "                          (1.05)    (0.98)     (1.16)      (1.03)       (0.94)       \n",
       "personal_outgroup_norm                         0.24                                  \n",
       "                                               (1.18)                                \n",
       "intergroup_norm                                            0.39                      \n",
       "                                                           (0.94)                    \n",
       "prop                                                                    8.77***      \n",
       "                                                                        (0.86)       \n",
       "dem                                 8.43***    8.43***     8.35***      9.36***      \n",
       "                                    (2.20)     (2.20)      (2.19)       (2.04)       \n",
       "num_strong                          13.20***   13.20***    13.19***     8.94***      \n",
       "                                    (2.25)     (2.24)      (2.24)       (2.11)       \n",
       "extreme_con                         7.76       7.76        7.80*        6.68         \n",
       "                                    (4.74)     (4.74)      (4.74)       (4.54)       \n",
       "ideo_extreme                        2.30       2.26        2.25         1.55         \n",
       "                                    (1.51)     (1.51)      (1.51)       (1.43)       \n",
       "Intercept                 19.07***  2.36       2.44        2.50         -23.96***    \n",
       "                          (1.03)    (2.25)     (2.27)      (2.26)       (3.08)       \n",
       "R-squared                 0.03      0.16       0.16        0.16         0.26         \n",
       "R-squared Adj.            0.03      0.15       0.15        0.15         0.25         \n",
       "N                         772.0000  772.0000   772.0000    772.0000     769.0000     \n",
       "df                        770.0000  766.0000   765.0000    765.0000     762.0000     \n",
       "R2                        0.0344    0.1559     0.1559      0.1560       0.2560       \n",
       "=====================================================================================\n",
       "Standard errors in parentheses.\n",
       "* p<.1, ** p<.05, ***p<.01\n",
       "\"\"\""
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using White’s (1980) heteroskedasticity robust standard errors\n",
    "\n",
    "cov=\"HC0\"\n",
    "\n",
    "res1r = mod1.fit(cov_type=cov)\n",
    "res2r = mod2.fit(cov_type=cov)\n",
    "res3r = mod3.fit(cov_type=cov)\n",
    "res4r = mod4.fit(cov_type=cov)\n",
    "res5r = mod5.fit(cov_type=cov)\n",
    "\n",
    "summary_col([res1r,res2r,res3r,res4r,res5r],stars=True, float_format='%.2f',\n",
    "            regressor_order = [\"generalized_outgroup_norm\",\n",
    "                              \"personal_outgroup_norm\",\n",
    "                              \"intergroup_norm\",\n",
    "                              \"prop\",\n",
    "                              \"dem\",\n",
    "                              \"num_strong\",\n",
    "                              \"extreme_con\",\n",
    "                              \"ideo_extreme\"],\n",
    "           info_dict={\"N\": lambda x: x.nobs,\n",
    "                      \"df\": lambda x: x.df_resid,\n",
    "                      \"R2\": lambda x: x.rsquared})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculating Bootstrapped Standard Errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bootstrap_reg(f, data=df1, n_reps=10000):\n",
    "    \n",
    "    _betas = {}\n",
    "    \n",
    "    # For first run only, add coefficients to dict\n",
    "    \n",
    "    df_iter = df1.sample(frac=1, replace=True)\n",
    "    m = sm.ols(f, data=df_iter)\n",
    "    r = m.fit()\n",
    "    \n",
    "    for x, b in zip(r.params.index, r.params):\n",
    "        _betas[x] = [b]\n",
    "    \n",
    "    for _ in range(n_reps-1):\n",
    "        \n",
    "        df_iter = df1.sample(frac=1, replace=True)\n",
    "        m = sm.ols(f, data=df_iter)\n",
    "        r = m.fit()\n",
    "    \n",
    "        for x, b in zip(r.params.index, r.params):\n",
    "            _betas[x].append(b)\n",
    "            \n",
    "    return _betas\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept\n",
      "95% CI:  [17.05092558 21.03271325]\n",
      "99% CI:  [16.39591303 21.62025683]\n",
      "99.9% CI:  [15.9130691  22.36097732]\n",
      "\n",
      "generalized_outgroup_norm\n",
      "95% CI:  [3.35247566 7.45407839]\n",
      "99% CI:  [2.5948369  8.17360927]\n",
      "99.9% CI:  [1.75009887 8.95891717]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Checking Model 1\n",
    "\n",
    "np.random.seed(8675309)\n",
    "\n",
    "betas = bootstrap_reg(f1)\n",
    "\n",
    "for x in betas:\n",
    "    print(x)\n",
    "    print('95% CI: ', np.quantile(betas[x], [0.025, 0.975]))\n",
    "    print('99% CI: ', np.quantile(betas[x], [0.005, 0.995]))\n",
    "    print('99.9% CI: ', np.quantile(betas[x], [0.0005, 0.9995]))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept\n",
      "95% CI:  [-2.08773955  6.83487855]\n",
      "99% CI:  [-3.55322561  7.98670511]\n",
      "99.9% CI:  [-5.24441339  9.56458371]\n",
      "\n",
      "generalized_outgroup_norm\n",
      "95% CI:  [3.42554543 7.27070724]\n",
      "99% CI:  [2.77150318 7.87539713]\n",
      "99.9% CI:  [2.05139651 8.77932737]\n",
      "\n",
      "dem\n",
      "95% CI:  [ 4.19454804 12.72221176]\n",
      "99% CI:  [ 2.88836583 14.21962739]\n",
      "99.9% CI:  [ 1.55156301 15.82492822]\n",
      "\n",
      "num_strong\n",
      "95% CI:  [ 8.86691525 17.67243519]\n",
      "99% CI:  [ 7.40759065 18.87240924]\n",
      "99.9% CI:  [ 5.82252366 20.15162814]\n",
      "\n",
      "extreme_con\n",
      "95% CI:  [-1.45162733 17.12523837]\n",
      "99% CI:  [-4.21490534 19.70760784]\n",
      "99.9% CI:  [-7.36147283 23.52168079]\n",
      "\n",
      "ideo_extreme\n",
      "95% CI:  [-0.6893934   5.29946664]\n",
      "99% CI:  [-1.61232836  6.28837683]\n",
      "99.9% CI:  [-2.50313064  7.16013607]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Checking Model 2\n",
    "\n",
    "np.random.seed(8675309)\n",
    "\n",
    "betas = bootstrap_reg(f2)\n",
    "\n",
    "for x in betas:\n",
    "    print(x)\n",
    "    print('95% CI: ', np.quantile(betas[x], [0.025, 0.975]))\n",
    "    print('99% CI: ', np.quantile(betas[x], [0.005, 0.995]))\n",
    "    print('99.9% CI: ', np.quantile(betas[x], [0.0005, 0.9995]))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept\n",
      "95% CI:  [-2.07716848  6.87657576]\n",
      "99% CI:  [-3.42228564  8.18528173]\n",
      "99.9% CI:  [-5.29381406  9.66546762]\n",
      "\n",
      "generalized_outgroup_norm\n",
      "95% CI:  [2.97454033 7.5201524 ]\n",
      "99% CI:  [2.15989805 8.28334907]\n",
      "99.9% CI:  [1.37164877 9.14302715]\n",
      "\n",
      "dem\n",
      "95% CI:  [ 4.19291845 12.71524734]\n",
      "99% CI:  [ 2.88326462 14.23934469]\n",
      "99.9% CI:  [ 1.63706168 15.83058809]\n",
      "\n",
      "num_strong\n",
      "95% CI:  [ 8.85227884 17.65607673]\n",
      "99% CI:  [ 7.32384188 18.86285538]\n",
      "99.9% CI:  [ 5.81800042 20.18791601]\n",
      "\n",
      "extreme_con\n",
      "95% CI:  [-1.41664111 17.15466698]\n",
      "99% CI:  [-4.21366583 19.74372953]\n",
      "99.9% CI:  [-7.49465804 23.78962306]\n",
      "\n",
      "ideo_extreme\n",
      "95% CI:  [-0.75074084  5.29025393]\n",
      "99% CI:  [-1.66788445  6.26602679]\n",
      "99.9% CI:  [-2.57257905  7.00176961]\n",
      "\n",
      "personal_outgroup_norm\n",
      "95% CI:  [-2.1287857   2.56843127]\n",
      "99% CI:  [-2.93110751  3.27279155]\n",
      "99.9% CI:  [-3.59674044  4.23998925]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Checking Model 3\n",
    "\n",
    "np.random.seed(8675309)\n",
    "\n",
    "betas = bootstrap_reg(f3)\n",
    "\n",
    "for x in betas:\n",
    "    print(x)\n",
    "    print('95% CI: ', np.quantile(betas[x], [0.025, 0.975]))\n",
    "    print('99% CI: ', np.quantile(betas[x], [0.005, 0.995]))\n",
    "    print('99.9% CI: ', np.quantile(betas[x], [0.0005, 0.9995]))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept\n",
      "95% CI:  [-1.95302568  6.96304628]\n",
      "99% CI:  [-3.33335156  8.20598411]\n",
      "99.9% CI:  [-5.29352112  9.68911114]\n",
      "\n",
      "generalized_outgroup_norm\n",
      "95% CI:  [3.12076959 7.20950554]\n",
      "99% CI:  [2.49696863 7.91836779]\n",
      "99.9% CI:  [1.71043152 8.69020529]\n",
      "\n",
      "dem\n",
      "95% CI:  [ 4.115219   12.62776743]\n",
      "99% CI:  [ 2.81697266 14.03570329]\n",
      "99.9% CI:  [ 1.40731392 15.70739575]\n",
      "\n",
      "num_strong\n",
      "95% CI:  [ 8.87142562 17.67596461]\n",
      "99% CI:  [ 7.37116111 18.85537744]\n",
      "99.9% CI:  [ 5.79699977 20.06019877]\n",
      "\n",
      "extreme_con\n",
      "95% CI:  [-1.40713408 17.17955127]\n",
      "99% CI:  [-4.17491675 19.80986459]\n",
      "99.9% CI:  [-7.34927245 23.69700002]\n",
      "\n",
      "ideo_extreme\n",
      "95% CI:  [-0.77931639  5.27429684]\n",
      "99% CI:  [-1.70207464  6.25042581]\n",
      "99.9% CI:  [-2.44699685  6.99132523]\n",
      "\n",
      "intergroup_norm\n",
      "95% CI:  [-1.50375179  2.28542361]\n",
      "99% CI:  [-2.1290992   2.81475586]\n",
      "99.9% CI:  [-2.74201227  3.54321608]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Checking Model 4\n",
    "\n",
    "np.random.seed(8675309)\n",
    "\n",
    "betas = bootstrap_reg(f4)\n",
    "\n",
    "for x in betas:\n",
    "    print(x)\n",
    "    print('95% CI: ', np.quantile(betas[x], [0.025, 0.975]))\n",
    "    print('99% CI: ', np.quantile(betas[x], [0.005, 0.995]))\n",
    "    print('99.9% CI: ', np.quantile(betas[x], [0.0005, 0.9995]))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept\n",
      "95% CI:  [-30.13905448 -18.02402834]\n",
      "99% CI:  [-32.06039391 -15.97777295]\n",
      "99.9% CI:  [-34.48917358 -14.1008292 ]\n",
      "\n",
      "generalized_outgroup_norm\n",
      "95% CI:  [0.77258553 4.47469834]\n",
      "99% CI:  [0.16623627 5.07739922]\n",
      "99.9% CI:  [-0.63110044  5.99307941]\n",
      "\n",
      "dem\n",
      "95% CI:  [ 5.37534785 13.31859872]\n",
      "99% CI:  [ 4.23919034 14.71461571]\n",
      "99.9% CI:  [ 3.23663202 16.20850264]\n",
      "\n",
      "num_strong\n",
      "95% CI:  [ 4.8843235  13.08704115]\n",
      "99% CI:  [ 3.4335557  14.22480231]\n",
      "99.9% CI:  [ 2.29299772 15.72488655]\n",
      "\n",
      "extreme_con\n",
      "95% CI:  [-2.15825672 15.81652739]\n",
      "99% CI:  [-5.14280784 18.38934785]\n",
      "99.9% CI:  [-8.0754376  21.61362604]\n",
      "\n",
      "ideo_extreme\n",
      "95% CI:  [-1.30150462  4.4245358 ]\n",
      "99% CI:  [-2.12225013  5.25297146]\n",
      "99.9% CI:  [-3.09258505  6.47714652]\n",
      "\n",
      "prop\n",
      "95% CI:  [ 7.06341544 10.48212415]\n",
      "99% CI:  [ 6.54742972 11.04302464]\n",
      "99.9% CI:  [ 5.9536913  11.68738357]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Checking Model 5\n",
    "\n",
    "np.random.seed(8675309)\n",
    "\n",
    "betas = bootstrap_reg(f5)\n",
    "\n",
    "for x in betas:\n",
    "    print(x)\n",
    "    print('95% CI: ', np.quantile(betas[x], [0.025, 0.975]))\n",
    "    print('99% CI: ', np.quantile(betas[x], [0.005, 0.995]))\n",
    "    print('99.9% CI: ', np.quantile(betas[x], [0.0005, 0.9995]))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Validating the Schema Elicitation Task"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0150922665648976\n"
     ]
    }
   ],
   "source": [
    "# Calculate Cohen's D\n",
    "\n",
    "m1 = df1.personal_ingroup.mean()\n",
    "m2 = df1.personal_outgroup.mean()\n",
    "\n",
    "pooled = pd.concat([df1.personal_ingroup, df1.personal_outgroup])\n",
    "\n",
    "print((m2 - m1)/pooled.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_relResult(statistic=22.640653960624014, pvalue=2.0733363768309447e-87)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Paired sample t test\n",
    "\n",
    "stats.ttest_rel(df1.personal_outgroup, df1.personal_ingroup)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2160x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Figure A1\n",
    "\n",
    "sb.set_style(\"white\")\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(30,15))\n",
    "\n",
    "sb.kdeplot(df1.personal_ingroup, shade=True, label=\"Ingroup\", linewidth=0)\n",
    "sb.kdeplot(df1.personal_outgroup, shade = True, label=\"Outgroup\", linewidth=0)\n",
    "plt.legend(fontsize=62)\n",
    "\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.get_yaxis().set_visible(False)\n",
    "\n",
    "plt.ylabel('')\n",
    "plt.xticks(None,None,fontsize=52)\n",
    "\n",
    "plt.xlabel('Perceived divergence from personal schema',fontsize=64)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SpearmanrResult(correlation=0.15404076608531686, pvalue=1.7176211165989784e-05)\n",
      "(0.14977409513789885, 2.936918761101236e-05)\n"
     ]
    }
   ],
   "source": [
    "# Relationship between social partisan identity strength and personal intergroup divergence\n",
    "\n",
    "print(stats.spearmanr(df1.identify, df1.personal_intergroup))\n",
    "print(stats.pearsonr(df1.identify, df1.personal_intergroup))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ttest_indResult(statistic=-14.023970269179088, pvalue=3.838601152730864e-42)\n",
      "RanksumsResult(statistic=-14.18718876602455, pvalue=1.0997994966774933e-45)\n"
     ]
    }
   ],
   "source": [
    "# Nominal partisan identity and personal intergroup divergence\n",
    "\n",
    "print(stats.ttest_ind(df1.personal_intergroup, df1.num_strong))\n",
    "print(stats.ranksums(df1.personal_intergroup, df1.num_strong))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7DbZ4+Prrr+s0zsbGRlA8zMvLg1KplF0w1tX9+/dFsTZt2tTLtSsUFBTg9u3bSEpKwt27d3H37l2kpaUhLS0NeXl5tX49Y2Nj2WMa6opXIiIiItJdZmamKGZqaiqKSeVXGRkZtdoHOy8vT6s2E9r22FNFajViTVUvhgLQeutPKVJ5ZvVC7/POY8zNzWvlPGVlZUhOTkZCQgJSU1Mrc6F79+7h4cOHsndZISIiaSweEhHRC8vW1ha7du2Cj4+PYMVdfn4+AgICsGnTJvTt21erc9VFwaU6qVm6UqSSb21IFXiqrzhrSKRmKGvjtddeEzwuLy9HXl6e1n1MakqqZ4kuxTW5iouLceTIERw+fBiXL1+u15V8UtsvEREREdHLJzU1VRST6mOube5TE9oWD3Xps1jXpHIKXfMjVWOrfwbPK4+pUNMi7pUrV7B//36cOnUKjx8/rqW7IiIiVeRtak1ERNTAGBsbY/PmzaKksaSkBLNmzRKsSlSnPpIPbXu+VS+OaUuqwNNQ+8wpFAqdk3ipmb/1WUh7+vSpKFYXs5GrOnPmDLy9vbFw4UJcvHhR69fbunVrTJw4sdZm+RIRERHRy+3OnTuimNSWmU+ePKnze5HqvyhFbk+/+iCVp9Ukp5DKBau2+ACeTx5TG9fKzMzEJ598gg8//BAHDx7UOnc3MjLCwIED4enpqdN1iYhedlx5SERELzwrKyusWbMGvr6+gt4FhYWFCAgIwA8//KBxlqOhoaEoFhwcjLFjx9b6/Wqia8FPapVhXSWDNe0RUV5ejuLiYp0KiIWFhaJYTWexyiFV3P3f//5XZ9fbu3cvli1bpnH7HUtLS9jb28PR0RGdO3eGm5tb5fZFUVFRtboFLxERERG9fBITE5GdnS2IGRsbo127dqJjpfKr8PBw9OnTp87u70UiteKvJjmFVC5YvaBY33lMbUhOTsbkyZMlt1ytysjICHZ2dnBwcICzszNcXFzQpUsXGBgYYMOGDYiOjq6nOyYiajxYPCQiokahZ8+e8PX1xbZt2wTx+/fv4/PPP8f69evVjpcqPtXHbFkp+fn5sLKy0mlcddoW1eQWA7Wd5atOYWGhTsXD6p+LoaFhvW5FJLWtbF1tD3vp0iXJwqGRkRH69+8PDw8PODk5oV27dmq3OWqoK1CJiIiI6MVx9uxZUaxr166Sva4bUn7VEEnlFDXZDUdqbPViYX3mMbWhqKgIM2bMkCwcurm5wdPTEy4uLmjfvj1atWqlsud6aWlpXd8qEVGjxOIhERE1GrNmzcJvv/2GpKQkQfynn37C8ePH4e3trXJss2bNRLH09PTavkWtZGRkwMHBQfa4lJQUUczOzk4Uk0qq5DaVlypUyvXw4UPJ912T6n1WWrZsWeN7kUPqh5AHDx7UybUWL14s+mz69++PkJAQrfq7VJBarUlEREREJMeRI0dEsYEDB0oe25Dyq4ZIqq1AamoqnJ2ddTpf9RwYeNbCoKr6zGNqw86dO0Xb5FpYWGDt2rXo0aOH1udpyAVSIqKGrOFt+k1ERKQjQ0NDrFixQrKnxfLly5GXl6dy7BtvvCGKxcXF6Xwvjx8/1nm1140bN3Qad/PmTcHjNm3aSG6HI1U8rN4PQ5PaSPyTk5NljykoKBAVSbt06VLje5HD0dFRFLt165ZO51q+fDm8vb0xdepUhISEYM+ePZXPxcbGin4E6NixI9avXy+rcPjkyRNRsbem284SERER0cvlwoULojyladOmGDJkiOTxUvnV1atXdb5+bm5uvfY5r2tOTk6i2LVr13Q+3/Xr10Wx119/XfC4vvKY2rJ//35RbNOmTbIKh8CzSavVMR8iItKMxUMiImpU3NzcMG7cOFH80aNHWLNmjcpxbdq0QatWrQSx27dv488//9TpPiZMmIAuXbqgX79+GD9+PObOnav1ar3z58/Lvl5aWhoSExMFse7du0seK9V/RO4WObommVXFxMTIHnPmzBnRSryuXbvW+F7ksLGxEa121OW1AMDFixeRmJiI3377Dd99951gNvfly5dFx48dO1b2Fq1SRXC5K02JiIiI6OVVVlaGtWvXiuLDhg2TXEEHoLLfXFUxMTF4+vSp7OsXFxdjyJAh6NKlC7y8vPDRRx9h8eLFL3QByNnZWdST8Ndff9XpNZWVleGXX34RxJo0aQIXFxdBrL7ymNqQkZGBu3fvCmKurq5wc3OTdZ7y8nL897//lYwTEZF6LB4SEVGjExgYKNkzcP/+/ZIFmQo9e/YUPC4tLcXevXtlX//ChQu4ffs2lEolHj58iNjYWFy/fl1yFaCUc+fOyS5aHjhwQJQAvfPOO5LHSvXG09SAvrozZ87IOl7Kzz//LPvHg3379gkeKxQK9OnTp8b3Ilf1wmxaWhpiY2NlnSM1NVVUhO3WrVvlv6VWd9ra2sq6BgAcPHhQFGtMs7aJiIiIqG5t2bJFtGqwSZMm8Pf3VznG0NAQb775piCWm5uLyMhI2dePjIxETk4OSkpKcO/ePcTExCA9PV1lj7sXQdOmTeHu7i6IpaSk4Ny5c7LPFR0dLcrncgtJSgAADUhJREFUXF1dJfPP+shjakNt5UJnz55FRkaGKK6pD+KL/LdFRFRbWDwkIqJGx9jYGAsXLhTFy8vLsWTJEpXbifr4+Ihi27dvR0JCgtbXLi0txapVq0TxESNGaH0OpVIpeQ5VUlNTsWPHDkGsZcuWKotq1tbWomTo9OnTKC4u1up6SUlJopmtusjOzsY333yj9fHHjx/HhQsXBLE+ffqgbdu2Nb4XuT788ENRbM2aNbJW9G3ZskUUGz58eOW/mzQRt6aWu11sdHQ0Tpw4IYpr+1kTERER0ctt165d+Prrr0XxSZMmafweLpVfrV27FpmZmVpfPz8/Hxs2bBDF5eRXDdWECRNEsWXLlsmaYFlYWIiQkBBRfPTo0ZLH10ceUxuqr1oFpLcfVSc/Px8rVqyQfE5TPqSvry94zJ1biOhlxOIhERE1SoMHD0bfvn1F8YSEBGzdulVyjJOTE3r16iWIFRUVYfr06VqvzFuxYoWo30SzZs0wfvx4Le/8maNHj2q16jEnJwczZ84UJZj+/v6SxScAePXVV+Hs7CyIZWVlISIiQuP1cnNzERgYqHGmprb+9a9/aVWIvH79OoKDg0VxX1/fWrkPudzd3dG5c2dB7MqVK/jyyy+1Gh8VFYVDhw4JYm5ubujUqVPlY6mZtREREVonrpcuXUJgYKDkc7psF0VEREREL4+EhATMmDEDy5cvF33/dHNzw8yZMzWew8vLC3Z2doJYdnY2pk+frrYffYXS0lLMnTtXVDSyt7fH0KFDNb+IBq5fv35wcHAQxJKTkzFnzhytJvsVFRVh9uzZou09bWxs8O6770qOqY88pja0bt0aenrCn63j4uJw8+ZNrcY/efIE//d//4eUlBTJ54uKitSOr76lrLYtSIiIGhMWD4mIqNFavHgxXn31VVH8m2++ESVYFZYsWSLa3iUlJQV///vfJVdwVcjJycGcOXMkG8XPmjVL6y1Lq1q6dClWr16tstATFxeHcePGiRIoJycnjBkzRu25vby8RLGVK1fihx9+UDnmwoULGDNmTK30O6xQXl6OGTNmYMeOHSq30oyMjMSkSZNEPzCMGTNGtNVsfQoODhbNiN26dSuCgoKQnZ0tOUapVGL37t0IDAwU/Aijr68vWi0rVfyOj49HUFAQCgsLVd5XdnY2Vq1ahUmTJuHJkyeSxzD5JSIiIiLgWYGuoKCgcjvQHTt2wM/PD8OHD8dPP/0kOr5Vq1bYuHGjVn249fT0sHz5ctGkxri4OIwZM0Ztr/cHDx7g448/Fk00VCgUWLBggWhl2ItIoVBg+fLlopwiKioKPj4+avOuW7duwcfHB6dPnxadMzg4WLLPfYW6zmNqg5mZmai/YVlZGaZNm6b2fSkrK8OJEycwYsQIXLx4UeVxmvIhMzMzweP4+HiNBUciosZGekkCERFRI2BjY4Np06aJZlEWFRUhODgY27ZtE42xs7NDSEgIAgMDBT0EMzMzMWvWLKxfvx5eXl5o27YtjI2NkZ2djbi4OERFRUkWdIYMGYKxY8fq/Bq2bt2Kw4cPY8iQIXB0dISRkRHS09Nx9uxZXLx4UTQL2MzMDOvWrZPc5qWqcePGYfv27Xj8+HFlTKlUYv78+di3bx8GDBgAGxsblJWVISUlBadPn0Z8fHzlsUZGRujVqxeioqJ0fm2tW7fGgwcPUFpaitDQUOzcuRPvvvsu2rZtC0NDQ6SlpeHnn3+WTA47dOiAzz77TOdr1wYXFxd8+umnCA0NFcSPHDmCqKgo9O/fH25ubrCwsEBRURESEhJw6tQpycL1tGnT4OLiIoi1bdsWgwcPFv1oc/ToUZw7dw5DhgxBp06dYGpqiuLiYjx48ABxcXE4d+6cKLG1tLQUbA9VUlKCnJwcmJub1/RtICIiIqIG6v79+3B0dKy183Xo0AGbN29GixYttB7j7u6O2bNni9oypKSkYNKkSXB2dka/fv1gY2ODV155BY8ePUJsbCx+/fVXyXYTfn5+kpPsXlRubm6SOUVcXBzee+89uLu7w9PTE1ZWVlAoFEhPT0d0dDRiY2MldySZPXs2PD091V6zrvOY2jJlyhRMnTpVEHvw4AFGjRqFPn36oHv37rCysgLwbDLv7du3cebMGVGrh+q5EADJPohV2djYCIqPf/75JyZOnIj3338fJiYmyMnJQa9evZ5LCw0iovrC4iERETVqvr6+iIyMxJ07dwTx//znPzhy5Ihkr4yhQ4eivLwc8+fPFxVhkpKSkJSUpNW1Bw4ciNWrV8u+52HDhiEmJqYywcnMzMR3332ncVyzZs2wbds20dZAqo5dsGABFixYIHruypUruHLlisqxBgYG+Oqrr/D777/XqHi4YcMG+Pr6VhYwHzx4gPDwcI3jnJ2dsXXrVslVpfVt0qRJKC4uxtq1awXF5sLCQhw7dgzHjh3TeA4fHx9Mnz5d8rlFixbh2rVrePDggSCenZ2t1ba2BgYGmDNnDmxtbfHJJ58InouLi0P//v01noOIiIiIXm56enoYOXIkFi1apNOOKpMnT4a+vj7CwsJEBa/r16+L2j6oMn78eAQFBcm+fkM3adIkKBQKhIWFCXZjKSsrw8WLF9WuoKugp6eHoKAg+Pn5aX3NusxjakO/fv0wevRo7N+/XxBXKpU4ffq0aNWllN69e2P16tUYPHiwYOJsXFyc2nG9evUSbc9aPU9euHAhPvroIy1eCRHRi4nblhIRUaNmYGCAJUuWQKFQiJ5buXKlyl4b3t7e2L17t6g3oDaaNm2KwMBArF+/XqvtfKqzs7PDzp070aFDB63HdO/eHYcPH5Z1v6NGjUJoaCiMjIy0HmNlZYWdO3fWymxfe3t7bN26FW3atNF6zIgRI7Br1y40b968xtevLVOmTMHGjRthbW0ta5yRkRFCQkKwaNEilce0bNkS27dvR8eOHWXfl5ubGw4cOABfX1+4uLiI/h/QJtkmIiIiopeXkZERvL29cejQIaxcuVKnwmGFSZMmYcuWLVpNdKzOxMQEy5Ytw+LFiyXzusZg4sSJCA8P1+n9ad++PXbv3q114bBCXeYxtWXp0qUYN26c7HHm5uZYunQptm7diubNm8PV1VXw/Pnz59X2gX/33Xfx1ltvqb3G7du3Zd8XEdGLhMVDIiJq9Nzd3fHBBx+I4llZWWpXBrq6uuLQoUPYsGEDevfuLWqaXp2FhQX8/Pxw/Phx+Pv716gPh4ODAw4dOoQZM2ZUbsVSnUKhQI8ePbB582bs2rVLdtIHPCsgnjhxAj4+PrC0tFR5nIWFBaZNm4bjx4+jW7dusq+jSpcuXfDjjz/Cx8dH1FeigoGBAfr06YM9e/Zg9erVNfrRoq4MHDgQP//8M+bNm4dOnTqpPdbc3ByTJ0/GyZMnMXr0aI3ntrOzw4EDBxAUFKSx0GpiYoJBgwYhPDwcEREReOONNwA8K/p6eHgIjj1y5AhycnI0Xp+IiIiIGi89PT289tpraNmyJRwcHODl5QV/f39s2rQJ58+fx7p16zR+v9WWp6cnjh8/jhUrVsDDw0Njq4XWrVsjICAAJ0+e1NjTvTF4++23cezYMYSEhMDNzU1tPtmkSRP07NkT69atQ2RkpM45Wl3mMbVBX18fS5Yswfbt2/H222+rLR7r6+vD1dUV8+fPR1RUFD788MPK44cNGyY49vHjx6KVhVXp6elhy5Yt+OCDD6CnJ/3zOYuHRNTYKcqrrk0nIiIilYqLi3H16lWkp6cjOzsbhYWFMDExgbm5OTp16gR7e3vZM2Hv3bsHLy8vQWz69OkICAiofKxUKnHlyhWkpqbi0aNHMDQ0hI2NTWUfitpSXl6Oa9euITk5GZmZmVAqlWjevDkcHR3RuXNnlUlTbSkuLkZsbCzu37+P7OxsmJmZwdraGm+++SZMTU3r9Nq1LSMjA9euXUNWVhZycnLQtGlTmJub44033oCjo2ONZkynpKQgPj4eWVlZKCwshLGxMczNzfH666/D2dm5RkVrIiIiIqL6UlBQgKtXryIjIwO5ubl4+vQpTExM0KJFCzg7O8PGxuZ53+Jz9fjxY8TFxeHRo0fIzs6GUqmEqakp7Ozs4OzsXCc5Ul3mMbWh4j25d+8e8vPzoaenh+bNm8PS0hJdu3aFiYlJrV8zIyMDly9fxv3791FUVAQjIyNYWlqiffv2lZM1iYgaIxYPiYiIniNtiodERERERERERERE9YXblhIRERERERERERERERERABYPiYiIiIiIiIiIiIiIiOgvLB4SEREREREREREREREREQAWD4mIiIiIiIiIiIiIiIjoLyweEhEREREREREREREREREAFg+JiIiIiIiIiIiIiIiI6C8sHhIRERERERERERERERERABYPiYiIiIiIiIiIiIiIiOgvivLy8vLnfRNERERERERERERERERE9Pxx5SERERERERERERERERERAWDxkIiIiIiIiIiIiIiIiIj+wuIhEREREREREREREREREQFg8ZCIiIiIiIiIiIiIiIiI/sLiIREREREREREREREREREBAP4fKyjrUri+m7kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 2160x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Figure A2\n",
    "\n",
    "plt.figure(figsize=(30,15))\n",
    "sb.boxplot(x=\"dem\", y=\"personal_intergroup\", hue=\"text_strong\", data=df1,\n",
    "          fliersize=25, linewidth=3, whis=1.5, width=0.7)\n",
    "plt.ylabel('Relative Intergroup Divergence', fontsize=52)\n",
    "plt.yticks(None,None,fontsize=52)\n",
    "plt.xticks([0,1],[\"Republican\",\"Democrat\"],fontsize=52)\n",
    "plt.xlabel('')\n",
    "plt.legend(fontsize=48)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "             <td></td>            <th>out_warm I</th> <th>out_warm II</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>personal_outgroup_norm</th>  <td>-7.65***</td>   <td>-6.44***</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                         <td>(0.80)</td>     <td>(0.74)</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dem</th>                         <td></td>       <td>-5.68***</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                            <td></td>        <td>(1.49)</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>num_strong</th>                  <td></td>       <td>-8.20***</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                            <td></td>        <td>(1.74)</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ideo_extreme</th>                <td></td>       <td>-6.30***</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                            <td></td>        <td>(1.00)</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>               <td>27.58***</td>   <td>46.79***</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                         <td>(0.80)</td>     <td>(1.82)</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>R-squared</th>                 <td>0.11</td>       <td>0.26</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>R-squared Adj.</th>            <td>0.11</td>       <td>0.26</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>N</th>                       <td>772.0000</td>   <td>772.0000</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>df</th>                      <td>770.0000</td>   <td>767.0000</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>R2</th>                       <td>0.1069</td>     <td>0.2616</td>   \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "\n",
       "=============================================\n",
       "                       out_warm I out_warm II\n",
       "---------------------------------------------\n",
       "personal_outgroup_norm -7.65***   -6.44***   \n",
       "                       (0.80)     (0.74)     \n",
       "dem                               -5.68***   \n",
       "                                  (1.49)     \n",
       "num_strong                        -8.20***   \n",
       "                                  (1.74)     \n",
       "ideo_extreme                      -6.30***   \n",
       "                                  (1.00)     \n",
       "Intercept              27.58***   46.79***   \n",
       "                       (0.80)     (1.82)     \n",
       "R-squared              0.11       0.26       \n",
       "R-squared Adj.         0.11       0.26       \n",
       "N                      772.0000   772.0000   \n",
       "df                     770.0000   767.0000   \n",
       "R2                     0.1069     0.2616     \n",
       "=============================================\n",
       "Standard errors in parentheses.\n",
       "* p<.1, ** p<.05, ***p<.01\n",
       "\"\"\""
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Is outgroup warmth predicted by personal-outgroup divergence?\n",
    "\n",
    "r1val = sm.ols('out_warm ~ personal_outgroup_norm', df1).fit()\n",
    "r2val = sm.ols('out_warm ~ personal_outgroup_norm + dem + num_strong + ideo_extreme', df1).fit()\n",
    "\n",
    "summary_col([r1val, r2val],stars=True, float_format='%.2f',\n",
    "            regressor_order = [\"personal_outgroup_norm\",\n",
    "                              \"dem\",\n",
    "                              \"num_strong\",\n",
    "                              \"extreme_con\",\n",
    "                              \"ideo_extreme\"],\n",
    "           info_dict={\"N\": lambda x: x.nobs,\n",
    "                      \"df\": lambda x: x.df_resid,\n",
    "                      \"R2\": lambda x: x.rsquared})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Empirical Extensions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Heterogeneity Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                         <td></td>                         <th>dehuman I</th> <th>dehuman II</th> <th>dehuman III</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>generalized_outgroup_norm</th>                        <td>5.53***</td>    <td>4.92***</td>    <td>4.78***</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                                                 <td>(1.03)</td>     <td>(1.06)</td>     <td>(1.01)</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>personal_ingroup_norm</th>                            <td>-2.20**</td>    <td>-2.14**</td>     <td>-0.15</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                                                 <td>(1.03)</td>     <td>(1.03)</td>     <td>(1.00)</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>generalized_outgroup_norm:personal_ingroup_norm</th>     <td></td>        <td>-2.21**</td>    <td>-2.37**</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                                                    <td></td>        <td>(0.98)</td>     <td>(0.92)</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dem</th>                                                 <td></td>           <td></td>        <td>8.63***</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                                                    <td></td>           <td></td>        <td>(2.19)</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>num_strong</th>                                          <td></td>           <td></td>       <td>13.47***</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                                                    <td></td>           <td></td>        <td>(2.35)</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>extreme_con</th>                                         <td></td>           <td></td>         <td>7.08*</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                                                    <td></td>           <td></td>        <td>(4.05)</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ideo_extreme</th>                                        <td></td>           <td></td>         <td>2.11</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                                                    <td></td>           <td></td>        <td>(1.44)</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                                       <td>19.07***</td>   <td>19.20***</td>     <td>2.68</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                                                 <td>(1.03)</td>     <td>(1.03)</td>     <td>(2.44)</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>R-squared</th>                                         <td>0.04</td>       <td>0.05</td>       <td>0.16</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>R-squared Adj.</th>                                    <td>0.04</td>       <td>0.04</td>       <td>0.16</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>N</th>                                               <td>772.0000</td>   <td>772.0000</td>   <td>772.0000</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>df</th>                                              <td>769.0000</td>   <td>768.0000</td>   <td>764.0000</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>R2</th>                                               <td>0.0401</td>     <td>0.0465</td>     <td>0.1631</td>   \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "\n",
       "================================================================================\n",
       "                                                dehuman I dehuman II dehuman III\n",
       "--------------------------------------------------------------------------------\n",
       "generalized_outgroup_norm                       5.53***   4.92***    4.78***    \n",
       "                                                (1.03)    (1.06)     (1.01)     \n",
       "personal_ingroup_norm                           -2.20**   -2.14**    -0.15      \n",
       "                                                (1.03)    (1.03)     (1.00)     \n",
       "generalized_outgroup_norm:personal_ingroup_norm           -2.21**    -2.37**    \n",
       "                                                          (0.98)     (0.92)     \n",
       "dem                                                                  8.63***    \n",
       "                                                                     (2.19)     \n",
       "num_strong                                                           13.47***   \n",
       "                                                                     (2.35)     \n",
       "extreme_con                                                          7.08*      \n",
       "                                                                     (4.05)     \n",
       "ideo_extreme                                                         2.11       \n",
       "                                                                     (1.44)     \n",
       "Intercept                                       19.07***  19.20***   2.68       \n",
       "                                                (1.03)    (1.03)     (2.44)     \n",
       "R-squared                                       0.04      0.05       0.16       \n",
       "R-squared Adj.                                  0.04      0.04       0.16       \n",
       "N                                               772.0000  772.0000   772.0000   \n",
       "df                                              769.0000  768.0000   764.0000   \n",
       "R2                                              0.0401    0.0465     0.1631     \n",
       "================================================================================\n",
       "Standard errors in parentheses.\n",
       "* p<.1, ** p<.05, ***p<.01\n",
       "\"\"\""
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Does the relationship between GO and dehumanization depend on PI? (Table A3)\n",
    "\n",
    "r1app = sm.ols('dehuman ~ generalized_outgroup_norm + personal_ingroup_norm', df1).fit()\n",
    "r2app = sm.ols('dehuman ~ generalized_outgroup_norm*personal_ingroup_norm', df1).fit()\n",
    "r3app = sm.ols('dehuman ~ generalized_outgroup_norm*personal_ingroup_norm + dem + num_strong + extreme_con + ideo_extreme', df1).fit()\n",
    "\n",
    "summary_col([r1app, r2app, r3app],stars=True, float_format='%.2f',\n",
    "            regressor_order = [\"generalized_outgroup_norm\",\n",
    "                              \"personal_ingroup_norm\",\n",
    "                              \"generalized_outgroup_norm:personal_ingroup_norm\",\n",
    "                              \"dem\",\n",
    "                              \"num_strong\",\n",
    "                              \"extreme_con\",\n",
    "                              \"ideo_extreme\"],\n",
    "           info_dict={\"N\": lambda x: x.nobs,\n",
    "                      \"df\": lambda x: x.df_resid,\n",
    "                      \"R2\": lambda x: x.rsquared})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                         <td></td>                         <th>dehuman I</th> <th>dehuman II</th> <th>dehuman III</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>generalized_outgroup_norm</th>                        <td>5.53***</td>    <td>4.92***</td>    <td>4.78***</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                                                 <td>(1.03)</td>     <td>(1.06)</td>     <td>(1.01)</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>personal_ingroup_norm</th>                            <td>-2.20**</td>    <td>-2.14**</td>     <td>-0.15</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                                                 <td>(1.03)</td>     <td>(1.03)</td>     <td>(1.00)</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>generalized_outgroup_norm:personal_ingroup_norm</th>     <td></td>        <td>-2.21**</td>    <td>-2.37**</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                                                    <td></td>        <td>(0.98)</td>     <td>(0.92)</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dem</th>                                                 <td></td>           <td></td>        <td>8.63***</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                                                    <td></td>           <td></td>        <td>(2.19)</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>num_strong</th>                                          <td></td>           <td></td>       <td>13.47***</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                                                    <td></td>           <td></td>        <td>(2.35)</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>extreme_con</th>                                         <td></td>           <td></td>         <td>7.08*</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                                                    <td></td>           <td></td>        <td>(4.05)</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ideo_extreme</th>                                        <td></td>           <td></td>         <td>2.11</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                                                    <td></td>           <td></td>        <td>(1.44)</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                                       <td>19.07***</td>   <td>19.20***</td>     <td>2.68</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                                                 <td>(1.03)</td>     <td>(1.03)</td>     <td>(2.44)</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>R-squared</th>                                         <td>0.04</td>       <td>0.05</td>       <td>0.16</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>R-squared Adj.</th>                                    <td>0.04</td>       <td>0.04</td>       <td>0.16</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>N</th>                                               <td>772.0000</td>   <td>772.0000</td>   <td>772.0000</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>df</th>                                              <td>769.0000</td>   <td>768.0000</td>   <td>764.0000</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>R2</th>                                               <td>0.0401</td>     <td>0.0465</td>     <td>0.1631</td>   \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "\n",
       "================================================================================\n",
       "                                                dehuman I dehuman II dehuman III\n",
       "--------------------------------------------------------------------------------\n",
       "generalized_outgroup_norm                       5.53***   4.92***    4.78***    \n",
       "                                                (1.03)    (1.06)     (1.01)     \n",
       "personal_ingroup_norm                           -2.20**   -2.14**    -0.15      \n",
       "                                                (1.03)    (1.03)     (1.00)     \n",
       "generalized_outgroup_norm:personal_ingroup_norm           -2.21**    -2.37**    \n",
       "                                                          (0.98)     (0.92)     \n",
       "dem                                                                  8.63***    \n",
       "                                                                     (2.19)     \n",
       "num_strong                                                           13.47***   \n",
       "                                                                     (2.35)     \n",
       "extreme_con                                                          7.08*      \n",
       "                                                                     (4.05)     \n",
       "ideo_extreme                                                         2.11       \n",
       "                                                                     (1.44)     \n",
       "Intercept                                       19.07***  19.20***   2.68       \n",
       "                                                (1.03)    (1.03)     (2.44)     \n",
       "R-squared                                       0.04      0.05       0.16       \n",
       "R-squared Adj.                                  0.04      0.04       0.16       \n",
       "N                                               772.0000  772.0000   772.0000   \n",
       "df                                              769.0000  768.0000   764.0000   \n",
       "R2                                              0.0401    0.0465     0.1631     \n",
       "================================================================================\n",
       "Standard errors in parentheses.\n",
       "* p<.1, ** p<.05, ***p<.01\n",
       "\"\"\""
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replicating Table A3 with robust standard errors\n",
    "\n",
    "r1app = sm.ols('dehuman ~ generalized_outgroup_norm + personal_ingroup_norm', df1).fit(cov=\"HC0\")\n",
    "r2app = sm.ols('dehuman ~ generalized_outgroup_norm*personal_ingroup_norm', df1).fit(cov=\"HC0\")\n",
    "r3app = sm.ols('dehuman ~ generalized_outgroup_norm*personal_ingroup_norm + dem + num_strong + extreme_con + ideo_extreme', df1).fit(cov=\"HC0\")\n",
    "\n",
    "summary_col([r1app, r2app, r3app],stars=True, float_format='%.2f',\n",
    "            regressor_order = [\"generalized_outgroup_norm\",\n",
    "                              \"personal_ingroup_norm\",\n",
    "                              \"generalized_outgroup_norm:personal_ingroup_norm\",\n",
    "                              \"dem\",\n",
    "                              \"num_strong\",\n",
    "                              \"extreme_con\",\n",
    "                              \"ideo_extreme\"],\n",
    "           info_dict={\"N\": lambda x: x.nobs,\n",
    "                      \"df\": lambda x: x.df_resid,\n",
    "                      \"R2\": lambda x: x.rsquared})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept\n",
      "95% CI:  [-1.81803926  7.13702734]\n",
      "99% CI:  [-3.216849    8.45725635]\n",
      "99.9% CI:  [-4.93206709  9.90892331]\n",
      "\n",
      "generalized_outgroup_norm\n",
      "95% CI:  [2.82970727 6.71902187]\n",
      "99% CI:  [2.19473593 7.37146867]\n",
      "99.9% CI:  [1.47776849 8.0354801 ]\n",
      "\n",
      "personal_ingroup_norm\n",
      "95% CI:  [-2.16052631  1.92778946]\n",
      "99% CI:  [-2.76864123  2.5270308 ]\n",
      "99.9% CI:  [-3.47920778  3.23260147]\n",
      "\n",
      "generalized_outgroup_norm:personal_ingroup_norm\n",
      "95% CI:  [-4.05913634 -0.70387921]\n",
      "99% CI:  [-4.58969708 -0.1211026 ]\n",
      "99.9% CI:  [-5.3247001  0.5664825]\n",
      "\n",
      "dem\n",
      "95% CI:  [ 4.40727914 12.93768238]\n",
      "99% CI:  [ 3.03782203 14.32615336]\n",
      "99.9% CI:  [ 1.86666017 15.90342915]\n",
      "\n",
      "num_strong\n",
      "95% CI:  [ 8.98349691 18.0906182 ]\n",
      "99% CI:  [ 7.36306277 19.29987229]\n",
      "99.9% CI:  [ 5.96498474 20.52856895]\n",
      "\n",
      "extreme_con\n",
      "95% CI:  [-2.08535202 16.2881563 ]\n",
      "99% CI:  [-4.63326676 19.08737572]\n",
      "99.9% CI:  [-7.54192195 22.31327721]\n",
      "\n",
      "ideo_extreme\n",
      "95% CI:  [-0.90465907  5.13577233]\n",
      "99% CI:  [-1.89045221  6.10077867]\n",
      "99.9% CI:  [-2.72799801  7.09240774]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Replicating model 3 of Table A3 with bootstrapped CIs\n",
    "\n",
    "np.random.seed(8675309)\n",
    "\n",
    "betas = bootstrap_reg('dehuman ~ generalized_outgroup_norm*personal_ingroup_norm + dem + num_strong + extreme_con + ideo_extreme')\n",
    "\n",
    "for x in betas:\n",
    "    print(x)\n",
    "    print('95% CI: ', np.quantile(betas[x], [0.025, 0.975]))\n",
    "    print('99% CI: ', np.quantile(betas[x], [0.005, 0.995]))\n",
    "    print('99.9% CI: ', np.quantile(betas[x], [0.0005, 0.9995]))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Content Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "             <td></td>            <th>dehuman I</th> <th>dehuman II</th> <th>dehuman III</th> <th>dehuman IIII</th> <th>dehuman IIIII</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>go_pos</th>                 <td>17.39***</td>   <td>17.89***</td>    <td>12.05*</td>       <td>13.88**</td>       <td>6.06</td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                        <td>(6.47)</td>     <td>(6.13)</td>     <td>(6.60)</td>       <td>(6.41)</td>       <td>(5.84)</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>personal_outgroup_norm</th>     <td></td>           <td></td>        <td>2.52**</td>          <td></td>             <td></td>       \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                           <td></td>           <td></td>        <td>(1.08)</td>          <td></td>             <td></td>       \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>intergroup_norm</th>            <td></td>           <td></td>           <td></td>          <td>2.18**</td>          <td></td>       \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                           <td></td>           <td></td>           <td></td>          <td>(1.04)</td>          <td></td>       \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>prop</th>                       <td></td>           <td></td>           <td></td>             <td></td>          <td>9.29***</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                           <td></td>           <td></td>           <td></td>             <td></td>          <td>(0.86)</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dem</th>                        <td></td>        <td>7.98***</td>    <td>8.09***</td>      <td>7.63***</td>      <td>9.11***</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                           <td></td>        <td>(2.23)</td>     <td>(2.23)</td>       <td>(2.23)</td>       <td>(2.09)</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>num_strong</th>                 <td></td>       <td>13.23***</td>   <td>13.22***</td>     <td>13.20***</td>      <td>8.72***</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                           <td></td>        <td>(2.36)</td>     <td>(2.35)</td>       <td>(2.35)</td>       <td>(2.24)</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>extreme_con</th>                <td></td>        <td>9.12**</td>     <td>8.84**</td>       <td>9.12**</td>        <td>7.25*</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                           <td></td>        <td>(4.09)</td>     <td>(4.08)</td>       <td>(4.08)</td>       <td>(3.82)</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ideo_extreme</th>               <td></td>         <td>2.32</td>       <td>1.87</td>         <td>2.02</td>         <td>1.54</td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                           <td></td>        <td>(1.46)</td>     <td>(1.47)</td>       <td>(1.46)</td>       <td>(1.36)</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>              <td>12.04***</td>     <td>-4.83</td>      <td>-1.66</td>        <td>-2.45</td>      <td>-27.94***</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                        <td>(2.82)</td>     <td>(3.50)</td>     <td>(3.74)</td>       <td>(3.67)</td>       <td>(3.88)</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>R-squared</th>                <td>0.01</td>       <td>0.13</td>       <td>0.14</td>         <td>0.14</td>         <td>0.25</td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>R-squared Adj.</th>           <td>0.01</td>       <td>0.13</td>       <td>0.13</td>         <td>0.13</td>         <td>0.24</td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>N</th>                      <td>772.0000</td>   <td>772.0000</td>   <td>772.0000</td>     <td>772.0000</td>     <td>769.0000</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>df</th>                     <td>770.0000</td>   <td>766.0000</td>   <td>765.0000</td>     <td>765.0000</td>     <td>762.0000</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>R2</th>                      <td>0.0093</td>     <td>0.1323</td>     <td>0.1385</td>       <td>0.1373</td>       <td>0.2497</td>    \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "\n",
       "==================================================================================\n",
       "                       dehuman I dehuman II dehuman III dehuman IIII dehuman IIIII\n",
       "----------------------------------------------------------------------------------\n",
       "go_pos                 17.39***  17.89***   12.05*      13.88**      6.06         \n",
       "                       (6.47)    (6.13)     (6.60)      (6.41)       (5.84)       \n",
       "personal_outgroup_norm                      2.52**                                \n",
       "                                            (1.08)                                \n",
       "intergroup_norm                                         2.18**                    \n",
       "                                                        (1.04)                    \n",
       "prop                                                                 9.29***      \n",
       "                                                                     (0.86)       \n",
       "dem                              7.98***    8.09***     7.63***      9.11***      \n",
       "                                 (2.23)     (2.23)      (2.23)       (2.09)       \n",
       "num_strong                       13.23***   13.22***    13.20***     8.72***      \n",
       "                                 (2.36)     (2.35)      (2.35)       (2.24)       \n",
       "extreme_con                      9.12**     8.84**      9.12**       7.25*        \n",
       "                                 (4.09)     (4.08)      (4.08)       (3.82)       \n",
       "ideo_extreme                     2.32       1.87        2.02         1.54         \n",
       "                                 (1.46)     (1.47)      (1.46)       (1.36)       \n",
       "Intercept              12.04***  -4.83      -1.66       -2.45        -27.94***    \n",
       "                       (2.82)    (3.50)     (3.74)      (3.67)       (3.88)       \n",
       "R-squared              0.01      0.13       0.14        0.14         0.25         \n",
       "R-squared Adj.         0.01      0.13       0.13        0.13         0.24         \n",
       "N                      772.0000  772.0000   772.0000    772.0000     769.0000     \n",
       "df                     770.0000  766.0000   765.0000    765.0000     762.0000     \n",
       "R2                     0.0093    0.1323     0.1385      0.1373       0.2497       \n",
       "==================================================================================\n",
       "Standard errors in parentheses.\n",
       "* p<.1, ** p<.05, ***p<.01\n",
       "\"\"\""
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Analysis using only Positive words\n",
    "\n",
    "df1['go_pos'] = df1.apply(get_generalized_outgroup, args=([positive_words]), axis=1)\n",
    "\n",
    "f1p = 'dehuman ~ go_pos'\n",
    "f2p = f1p + ' + dem + num_strong + extreme_con + ideo_extreme'\n",
    "f3p = f2p + ' + personal_outgroup_norm'\n",
    "f4p = f2p + ' + intergroup_norm'\n",
    "f5p = f2p + ' + prop'\n",
    "\n",
    "m1p = sm.ols(f1p, data=df1)\n",
    "m2p = sm.ols(f2p, data=df1)\n",
    "m3p = sm.ols(f3p, data=df1)\n",
    "m4p = sm.ols(f4p, data=df1)\n",
    "m5p = sm.ols(f5p, data=df1)\n",
    "\n",
    "r1p = m1p.fit()\n",
    "r2p = m2p.fit()\n",
    "r3p = m3p.fit()\n",
    "r4p = m4p.fit()\n",
    "r5p = m5p.fit()\n",
    "\n",
    "summary_col([r1p,r2p,r3p,r4p,r5p],stars=True, float_format='%.2f',\n",
    "            regressor_order = [\"go_pos\",\n",
    "                              \"personal_outgroup_norm\",\n",
    "                              \"intergroup_norm\",\n",
    "                              \"prop\",\n",
    "                              \"dem\",\n",
    "                              \"num_strong\",\n",
    "                              \"extreme_con\",\n",
    "                              \"ideo_extreme\"],\n",
    "           info_dict={\"N\": lambda x: x.nobs,\n",
    "                      \"df\": lambda x: x.df_resid,\n",
    "                      \"R2\": lambda x: x.rsquared})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "             <td></td>            <th>dehuman I</th> <th>dehuman II</th> <th>dehuman III</th> <th>dehuman IIII</th> <th>dehuman IIIII</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>go_neu</th>                 <td>26.17***</td>   <td>30.74***</td>   <td>28.01***</td>     <td>28.90***</td>     <td>18.34***</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                        <td>(5.88)</td>     <td>(5.59)</td>     <td>(6.22)</td>       <td>(6.12)</td>       <td>(5.39)</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>personal_outgroup_norm</th>     <td></td>           <td></td>         <td>1.10</td>           <td></td>             <td></td>       \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                           <td></td>           <td></td>        <td>(1.10)</td>          <td></td>             <td></td>       \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>intergroup_norm</th>            <td></td>           <td></td>           <td></td>           <td>0.80</td>           <td></td>       \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                           <td></td>           <td></td>           <td></td>          <td>(1.07)</td>          <td></td>       \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>prop</th>                       <td></td>           <td></td>           <td></td>             <td></td>          <td>8.82***</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                           <td></td>           <td></td>           <td></td>             <td></td>          <td>(0.86)</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dem</th>                        <td></td>        <td>9.16***</td>    <td>9.14***</td>      <td>8.97***</td>      <td>9.93***</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                           <td></td>        <td>(2.21)</td>     <td>(2.21)</td>       <td>(2.23)</td>       <td>(2.09)</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>num_strong</th>                 <td></td>       <td>12.78***</td>   <td>12.80***</td>     <td>12.79***</td>      <td>8.65***</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                           <td></td>        <td>(2.32)</td>     <td>(2.32)</td>       <td>(2.33)</td>       <td>(2.22)</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>extreme_con</th>                <td></td>        <td>8.64**</td>     <td>8.55**</td>       <td>8.66**</td>        <td>7.04*</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                           <td></td>        <td>(4.04)</td>     <td>(4.04)</td>       <td>(4.04)</td>       <td>(3.80)</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ideo_extreme</th>               <td></td>        <td>2.84**</td>      <td>2.59*</td>        <td>2.69*</td>        <td>1.84</td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                           <td></td>        <td>(1.44)</td>     <td>(1.46)</td>       <td>(1.45)</td>       <td>(1.35)</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>               <td>9.99***</td>   <td>-9.52***</td>    <td>-8.10**</td>      <td>-8.52**</td>     <td>-31.16***</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                        <td>(2.29)</td>     <td>(3.26)</td>     <td>(3.55)</td>       <td>(3.52)</td>       <td>(3.71)</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>R-squared</th>                <td>0.03</td>       <td>0.16</td>       <td>0.16</td>         <td>0.16</td>         <td>0.26</td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>R-squared Adj.</th>           <td>0.02</td>       <td>0.15</td>       <td>0.15</td>         <td>0.15</td>         <td>0.25</td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>N</th>                      <td>772.0000</td>   <td>772.0000</td>   <td>772.0000</td>     <td>772.0000</td>     <td>769.0000</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>df</th>                     <td>770.0000</td>   <td>766.0000</td>   <td>765.0000</td>     <td>765.0000</td>     <td>762.0000</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>R2</th>                      <td>0.0251</td>     <td>0.1559</td>     <td>0.1570</td>       <td>0.1565</td>       <td>0.2598</td>    \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "\n",
       "==================================================================================\n",
       "                       dehuman I dehuman II dehuman III dehuman IIII dehuman IIIII\n",
       "----------------------------------------------------------------------------------\n",
       "go_neu                 26.17***  30.74***   28.01***    28.90***     18.34***     \n",
       "                       (5.88)    (5.59)     (6.22)      (6.12)       (5.39)       \n",
       "personal_outgroup_norm                      1.10                                  \n",
       "                                            (1.10)                                \n",
       "intergroup_norm                                         0.80                      \n",
       "                                                        (1.07)                    \n",
       "prop                                                                 8.82***      \n",
       "                                                                     (0.86)       \n",
       "dem                              9.16***    9.14***     8.97***      9.93***      \n",
       "                                 (2.21)     (2.21)      (2.23)       (2.09)       \n",
       "num_strong                       12.78***   12.80***    12.79***     8.65***      \n",
       "                                 (2.32)     (2.32)      (2.33)       (2.22)       \n",
       "extreme_con                      8.64**     8.55**      8.66**       7.04*        \n",
       "                                 (4.04)     (4.04)      (4.04)       (3.80)       \n",
       "ideo_extreme                     2.84**     2.59*       2.69*        1.84         \n",
       "                                 (1.44)     (1.46)      (1.45)       (1.35)       \n",
       "Intercept              9.99***   -9.52***   -8.10**     -8.52**      -31.16***    \n",
       "                       (2.29)    (3.26)     (3.55)      (3.52)       (3.71)       \n",
       "R-squared              0.03      0.16       0.16        0.16         0.26         \n",
       "R-squared Adj.         0.02      0.15       0.15        0.15         0.25         \n",
       "N                      772.0000  772.0000   772.0000    772.0000     769.0000     \n",
       "df                     770.0000  766.0000   765.0000    765.0000     762.0000     \n",
       "R2                     0.0251    0.1559     0.1570      0.1565       0.2598       \n",
       "==================================================================================\n",
       "Standard errors in parentheses.\n",
       "* p<.1, ** p<.05, ***p<.01\n",
       "\"\"\""
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Analysis using only Neutral words\n",
    "\n",
    "df1['go_neu'] = df1.apply(get_generalized_outgroup, args=([neutral_words]), axis=1)\n",
    "\n",
    "f1n = 'dehuman ~ go_neu'\n",
    "f2n = f1n + ' + dem + num_strong + extreme_con + ideo_extreme'\n",
    "f3n = f2n + ' + personal_outgroup_norm'\n",
    "f4n = f2n + ' + intergroup_norm'\n",
    "f5n = f2n + ' + prop'\n",
    "\n",
    "m1n = sm.ols(f1n, data=df1)\n",
    "m2n = sm.ols(f2n, data=df1)\n",
    "m3n = sm.ols(f3n, data=df1)\n",
    "m4n = sm.ols(f4n, data=df1)\n",
    "m5n = sm.ols(f5n, data=df1)\n",
    "\n",
    "r1n = m1n.fit()\n",
    "r2n = m2n.fit()\n",
    "r3n = m3n.fit()\n",
    "r4n = m4n.fit()\n",
    "r5n = m5n.fit()\n",
    "\n",
    "summary_col([r1n,r2n,r3n,r4n,r5n],stars=True, float_format='%.2f',\n",
    "            regressor_order = [\"go_neu\",\n",
    "                              \"personal_outgroup_norm\",\n",
    "                              \"intergroup_norm\",\n",
    "                              \"prop\",\n",
    "                              \"dem\",\n",
    "                              \"num_strong\",\n",
    "                              \"extreme_con\",\n",
    "                              \"ideo_extreme\"],\n",
    "           info_dict={\"N\": lambda x: x.nobs,\n",
    "                      \"df\": lambda x: x.df_resid,\n",
    "                      \"R2\": lambda x: x.rsquared})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "             <td></td>            <th>dehuman I</th> <th>dehuman II</th> <th>dehuman III</th> <th>dehuman IIII</th> <th>dehuman IIIII</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>go_neg</th>                 <td>22.94***</td>   <td>17.71***</td>    <td>12.17*</td>       <td>14.05**</td>       <td>6.60</td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                        <td>(6.02)</td>     <td>(5.74)</td>     <td>(6.25)</td>       <td>(6.00)</td>       <td>(5.48)</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>intergroup_norm</th>            <td></td>           <td></td>           <td></td>          <td>2.13**</td>          <td></td>       \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                           <td></td>           <td></td>           <td></td>          <td>(1.04)</td>          <td></td>       \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>prop</th>                       <td></td>           <td></td>           <td></td>             <td></td>          <td>9.27***</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                           <td></td>           <td></td>           <td></td>             <td></td>          <td>(0.86)</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dem</th>                        <td></td>        <td>6.76***</td>    <td>7.25***</td>      <td>6.68***</td>      <td>8.68***</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                           <td></td>        <td>(2.22)</td>     <td>(2.22)</td>       <td>(2.22)</td>       <td>(2.08)</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>num_strong</th>                 <td></td>       <td>13.64***</td>   <td>13.50***</td>     <td>13.52***</td>      <td>8.87***</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                           <td></td>        <td>(2.35)</td>     <td>(2.35)</td>       <td>(2.35)</td>       <td>(2.24)</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>extreme_con</th>                <td></td>         <td>7.83*</td>      <td>7.96*</td>       <td>8.09**</td>        <td>6.78*</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                           <td></td>        <td>(4.11)</td>     <td>(4.10)</td>       <td>(4.11)</td>       <td>(3.84)</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ideo_extreme</th>               <td></td>         <td>2.23</td>       <td>1.83</td>         <td>1.95</td>         <td>1.50</td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                           <td></td>        <td>(1.46)</td>     <td>(1.46)</td>       <td>(1.46)</td>       <td>(1.36)</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>               <td>8.78***</td>     <td>-4.85</td>      <td>-1.80</td>        <td>-2.61</td>      <td>-28.12***</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                        <td>(2.89)</td>     <td>(3.41)</td>     <td>(3.67)</td>       <td>(3.57)</td>       <td>(3.83)</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>personal_outgroup_norm</th>     <td></td>           <td></td>        <td>2.42**</td>          <td></td>             <td></td>       \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                           <td></td>           <td></td>        <td>(1.09)</td>          <td></td>             <td></td>       \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>R-squared</th>                <td>0.02</td>       <td>0.13</td>       <td>0.14</td>         <td>0.14</td>         <td>0.25</td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>R-squared Adj.</th>           <td>0.02</td>       <td>0.13</td>       <td>0.13</td>         <td>0.13</td>         <td>0.24</td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>N</th>                      <td>772.0000</td>   <td>772.0000</td>   <td>772.0000</td>     <td>772.0000</td>     <td>769.0000</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>df</th>                     <td>770.0000</td>   <td>766.0000</td>   <td>765.0000</td>     <td>765.0000</td>     <td>762.0000</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>R2</th>                      <td>0.0185</td>     <td>0.1334</td>     <td>0.1390</td>       <td>0.1382</td>       <td>0.2500</td>    \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "\n",
       "==================================================================================\n",
       "                       dehuman I dehuman II dehuman III dehuman IIII dehuman IIIII\n",
       "----------------------------------------------------------------------------------\n",
       "go_neg                 22.94***  17.71***   12.17*      14.05**      6.60         \n",
       "                       (6.02)    (5.74)     (6.25)      (6.00)       (5.48)       \n",
       "intergroup_norm                                         2.13**                    \n",
       "                                                        (1.04)                    \n",
       "prop                                                                 9.27***      \n",
       "                                                                     (0.86)       \n",
       "dem                              6.76***    7.25***     6.68***      8.68***      \n",
       "                                 (2.22)     (2.22)      (2.22)       (2.08)       \n",
       "num_strong                       13.64***   13.50***    13.52***     8.87***      \n",
       "                                 (2.35)     (2.35)      (2.35)       (2.24)       \n",
       "extreme_con                      7.83*      7.96*       8.09**       6.78*        \n",
       "                                 (4.11)     (4.10)      (4.11)       (3.84)       \n",
       "ideo_extreme                     2.23       1.83        1.95         1.50         \n",
       "                                 (1.46)     (1.46)      (1.46)       (1.36)       \n",
       "Intercept              8.78***   -4.85      -1.80       -2.61        -28.12***    \n",
       "                       (2.89)    (3.41)     (3.67)      (3.57)       (3.83)       \n",
       "personal_outgroup_norm                      2.42**                                \n",
       "                                            (1.09)                                \n",
       "R-squared              0.02      0.13       0.14        0.14         0.25         \n",
       "R-squared Adj.         0.02      0.13       0.13        0.13         0.24         \n",
       "N                      772.0000  772.0000   772.0000    772.0000     769.0000     \n",
       "df                     770.0000  766.0000   765.0000    765.0000     762.0000     \n",
       "R2                     0.0185    0.1334     0.1390      0.1382       0.2500       \n",
       "==================================================================================\n",
       "Standard errors in parentheses.\n",
       "* p<.1, ** p<.05, ***p<.01\n",
       "\"\"\""
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Analysis using only Negative words\n",
    "\n",
    "df1['go_neg'] = df1.apply(get_generalized_outgroup, args=([negative_words]), axis=1)\n",
    "\n",
    "f1neg = 'dehuman ~ go_neg'\n",
    "f2neg = f1neg + ' + dem + num_strong + extreme_con + ideo_extreme'\n",
    "f3neg = f2neg + ' + personal_outgroup_norm'\n",
    "f4neg = f2neg + ' + intergroup_norm'\n",
    "f5neg = f2neg + ' + prop'\n",
    "\n",
    "m1neg = sm.ols(f1neg, data=df1)\n",
    "m2neg = sm.ols(f2neg, data=df1)\n",
    "m3neg = sm.ols(f3neg, data=df1)\n",
    "m4neg = sm.ols(f4neg, data=df1)\n",
    "m5neg = sm.ols(f5neg, data=df1)\n",
    "\n",
    "r1neg = m1neg.fit()\n",
    "r2neg = m2neg.fit()\n",
    "r3neg = m3neg.fit()\n",
    "r4neg = m4neg.fit()\n",
    "r5neg = m5neg.fit()\n",
    "\n",
    "summary_col([r1neg,r2neg,r3neg,r4neg,r5neg],stars=True, float_format='%.2f',\n",
    "            regressor_order = [\"go_neg\",\n",
    "                              \"intergroup_norm\",\n",
    "                              \"prop\",\n",
    "                              \"dem\",\n",
    "                              \"num_strong\",\n",
    "                              \"extreme_con\",\n",
    "                              \"ideo_extreme\"],\n",
    "           info_dict={\"N\": lambda x: x.nobs,\n",
    "                      \"df\": lambda x: x.df_resid,\n",
    "                      \"R2\": lambda x: x.rsquared})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "             <td></td>            <th>dehuman I</th> <th>dehuman II</th> <th>dehuman III</th> <th>dehuman IIII</th> <th>dehuman IIIII</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>go_pos</th>                   <td>4.68</td>       <td>5.26</td>       <td>5.15</td>         <td>5.16</td>         <td>-0.73</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                        <td>(6.93)</td>     <td>(6.52)</td>     <td>(6.69)</td>       <td>(6.59)</td>       <td>(6.18)</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>go_neu</th>                 <td>21.62***</td>   <td>27.03***</td>   <td>26.87***</td>     <td>26.82***</td>     <td>17.94***</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                        <td>(6.27)</td>     <td>(5.96)</td>     <td>(6.34)</td>       <td>(6.30)</td>       <td>(5.70)</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>go_neg</th>                 <td>18.12***</td>    <td>11.46*</td>     <td>11.32*</td>       <td>11.33*</td>        <td>3.97</td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                        <td>(6.17)</td>     <td>(5.86)</td>     <td>(6.20)</td>       <td>(6.01)</td>       <td>(5.59)</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>intergroup_norm</th>            <td></td>           <td></td>           <td></td>           <td>0.12</td>           <td></td>       \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                           <td></td>           <td></td>           <td></td>          <td>(1.11)</td>          <td></td>       \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>prop</th>                       <td></td>           <td></td>           <td></td>             <td></td>          <td>8.73***</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                           <td></td>           <td></td>           <td></td>             <td></td>          <td>(0.87)</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dem</th>                        <td></td>        <td>8.86***</td>    <td>8.87***</td>      <td>8.84***</td>      <td>9.76***</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                           <td></td>        <td>(2.23)</td>     <td>(2.23)</td>       <td>(2.24)</td>       <td>(2.11)</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>num_strong</th>                 <td></td>       <td>12.98***</td>   <td>12.98***</td>     <td>12.98***</td>      <td>8.77***</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                           <td></td>        <td>(2.32)</td>     <td>(2.32)</td>       <td>(2.32)</td>       <td>(2.23)</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>extreme_con</th>                <td></td>         <td>7.83*</td>      <td>7.83*</td>        <td>7.84*</td>        <td>6.77*</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                           <td></td>        <td>(4.05)</td>     <td>(4.05)</td>       <td>(4.06)</td>       <td>(3.82)</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ideo_extreme</th>               <td></td>         <td>2.58*</td>      <td>2.56*</td>        <td>2.56*</td>        <td>1.79</td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                           <td></td>        <td>(1.44)</td>     <td>(1.46)</td>       <td>(1.45)</td>       <td>(1.36)</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                <td>1.56</td>     <td>-14.91***</td>  <td>-14.71***</td>    <td>-14.69***</td>    <td>-32.09***</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                        <td>(3.64)</td>     <td>(4.07)</td>     <td>(4.87)</td>       <td>(4.59)</td>       <td>(4.19)</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>personal_outgroup_norm</th>     <td></td>           <td></td>         <td>0.09</td>           <td></td>             <td></td>       \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                           <td></td>           <td></td>        <td>(1.20)</td>          <td></td>             <td></td>       \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>R-squared</th>                <td>0.04</td>       <td>0.16</td>       <td>0.16</td>         <td>0.16</td>         <td>0.26</td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>R-squared Adj.</th>           <td>0.03</td>       <td>0.15</td>       <td>0.15</td>         <td>0.15</td>         <td>0.25</td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>N</th>                      <td>772.0000</td>   <td>772.0000</td>   <td>772.0000</td>     <td>772.0000</td>     <td>769.0000</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>df</th>                     <td>768.0000</td>   <td>764.0000</td>   <td>763.0000</td>     <td>763.0000</td>     <td>760.0000</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>R2</th>                      <td>0.0378</td>     <td>0.1617</td>     <td>0.1617</td>       <td>0.1617</td>       <td>0.2603</td>    \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "\n",
       "==================================================================================\n",
       "                       dehuman I dehuman II dehuman III dehuman IIII dehuman IIIII\n",
       "----------------------------------------------------------------------------------\n",
       "go_pos                 4.68      5.26       5.15        5.16         -0.73        \n",
       "                       (6.93)    (6.52)     (6.69)      (6.59)       (6.18)       \n",
       "go_neu                 21.62***  27.03***   26.87***    26.82***     17.94***     \n",
       "                       (6.27)    (5.96)     (6.34)      (6.30)       (5.70)       \n",
       "go_neg                 18.12***  11.46*     11.32*      11.33*       3.97         \n",
       "                       (6.17)    (5.86)     (6.20)      (6.01)       (5.59)       \n",
       "intergroup_norm                                         0.12                      \n",
       "                                                        (1.11)                    \n",
       "prop                                                                 8.73***      \n",
       "                                                                     (0.87)       \n",
       "dem                              8.86***    8.87***     8.84***      9.76***      \n",
       "                                 (2.23)     (2.23)      (2.24)       (2.11)       \n",
       "num_strong                       12.98***   12.98***    12.98***     8.77***      \n",
       "                                 (2.32)     (2.32)      (2.32)       (2.23)       \n",
       "extreme_con                      7.83*      7.83*       7.84*        6.77*        \n",
       "                                 (4.05)     (4.05)      (4.06)       (3.82)       \n",
       "ideo_extreme                     2.58*      2.56*       2.56*        1.79         \n",
       "                                 (1.44)     (1.46)      (1.45)       (1.36)       \n",
       "Intercept              1.56      -14.91***  -14.71***   -14.69***    -32.09***    \n",
       "                       (3.64)    (4.07)     (4.87)      (4.59)       (4.19)       \n",
       "personal_outgroup_norm                      0.09                                  \n",
       "                                            (1.20)                                \n",
       "R-squared              0.04      0.16       0.16        0.16         0.26         \n",
       "R-squared Adj.         0.03      0.15       0.15        0.15         0.25         \n",
       "N                      772.0000  772.0000   772.0000    772.0000     769.0000     \n",
       "df                     768.0000  764.0000   763.0000    763.0000     760.0000     \n",
       "R2                     0.0378    0.1617     0.1617      0.1617       0.2603       \n",
       "==================================================================================\n",
       "Standard errors in parentheses.\n",
       "* p<.1, ** p<.05, ***p<.01\n",
       "\"\"\""
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Analysis comparing Positive, Neutral, and Negative terms\n",
    "f1abl = 'dehuman ~ go_pos + go_neu + go_neg'\n",
    "f2abl = f1abl + ' + dem + num_strong + extreme_con + ideo_extreme'\n",
    "f3abl = f2abl + ' + personal_outgroup_norm'\n",
    "f4abl = f2abl + ' + intergroup_norm'\n",
    "f5abl = f2abl + ' + prop'\n",
    "\n",
    "m1abl = sm.ols(f1abl, data=df1)\n",
    "m2abl = sm.ols(f2abl, data=df1)\n",
    "m3abl = sm.ols(f3abl, data=df1)\n",
    "m4abl = sm.ols(f4abl, data=df1)\n",
    "m5abl = sm.ols(f5abl, data=df1)\n",
    "\n",
    "r1abl = m1abl.fit()\n",
    "r2abl = m2abl.fit()\n",
    "r3abl = m3abl.fit()\n",
    "r4abl = m4abl.fit()\n",
    "r5abl = m5abl.fit()\n",
    "\n",
    "summary_col([r1abl,r2abl,r3abl,r4abl,r5abl],stars=True, float_format='%.2f',\n",
    "            regressor_order = [\"go_pos\",\n",
    "                               \"go_neu\",\n",
    "                               \"go_neg\",\n",
    "                              \"intergroup_norm\",\n",
    "                              \"prop\",\n",
    "                              \"dem\",\n",
    "                              \"num_strong\",\n",
    "                              \"extreme_con\",\n",
    "                              \"ideo_extreme\"],\n",
    "           info_dict={\"N\": lambda x: x.nobs,\n",
    "                      \"df\": lambda x: x.df_resid,\n",
    "                      \"R2\": lambda x: x.rsquared})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "             <td></td>            <th>dehuman I</th> <th>dehuman II</th> <th>dehuman III</th> <th>dehuman IIII</th> <th>dehuman IIIII</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>go_pos</th>                   <td>4.68</td>       <td>5.26</td>       <td>5.15</td>         <td>5.16</td>         <td>-0.73</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                        <td>(6.93)</td>     <td>(6.52)</td>     <td>(6.69)</td>       <td>(6.59)</td>       <td>(6.18)</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>go_neu</th>                 <td>21.62***</td>   <td>27.03***</td>   <td>26.87***</td>     <td>26.82***</td>     <td>17.94***</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                        <td>(6.27)</td>     <td>(5.96)</td>     <td>(6.34)</td>       <td>(6.30)</td>       <td>(5.70)</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>go_neg</th>                 <td>18.12***</td>    <td>11.46*</td>     <td>11.32*</td>       <td>11.33*</td>        <td>3.97</td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                        <td>(6.17)</td>     <td>(5.86)</td>     <td>(6.20)</td>       <td>(6.01)</td>       <td>(5.59)</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>intergroup_norm</th>            <td></td>           <td></td>           <td></td>           <td>0.12</td>           <td></td>       \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                           <td></td>           <td></td>           <td></td>          <td>(1.11)</td>          <td></td>       \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>prop</th>                       <td></td>           <td></td>           <td></td>             <td></td>          <td>8.73***</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                           <td></td>           <td></td>           <td></td>             <td></td>          <td>(0.87)</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dem</th>                        <td></td>        <td>8.86***</td>    <td>8.87***</td>      <td>8.84***</td>      <td>9.76***</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                           <td></td>        <td>(2.23)</td>     <td>(2.23)</td>       <td>(2.24)</td>       <td>(2.11)</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>num_strong</th>                 <td></td>       <td>12.98***</td>   <td>12.98***</td>     <td>12.98***</td>      <td>8.77***</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                           <td></td>        <td>(2.32)</td>     <td>(2.32)</td>       <td>(2.32)</td>       <td>(2.23)</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>extreme_con</th>                <td></td>         <td>7.83*</td>      <td>7.83*</td>        <td>7.84*</td>        <td>6.77*</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                           <td></td>        <td>(4.05)</td>     <td>(4.05)</td>       <td>(4.06)</td>       <td>(3.82)</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ideo_extreme</th>               <td></td>         <td>2.58*</td>      <td>2.56*</td>        <td>2.56*</td>        <td>1.79</td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                           <td></td>        <td>(1.44)</td>     <td>(1.46)</td>       <td>(1.45)</td>       <td>(1.36)</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                <td>1.56</td>     <td>-14.91***</td>  <td>-14.71***</td>    <td>-14.69***</td>    <td>-32.09***</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                        <td>(3.64)</td>     <td>(4.07)</td>     <td>(4.87)</td>       <td>(4.59)</td>       <td>(4.19)</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>personal_outgroup_norm</th>     <td></td>           <td></td>         <td>0.09</td>           <td></td>             <td></td>       \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                           <td></td>           <td></td>        <td>(1.20)</td>          <td></td>             <td></td>       \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>R-squared</th>                <td>0.04</td>       <td>0.16</td>       <td>0.16</td>         <td>0.16</td>         <td>0.26</td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>R-squared Adj.</th>           <td>0.03</td>       <td>0.15</td>       <td>0.15</td>         <td>0.15</td>         <td>0.25</td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>N</th>                      <td>772.0000</td>   <td>772.0000</td>   <td>772.0000</td>     <td>772.0000</td>     <td>769.0000</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>df</th>                     <td>768.0000</td>   <td>764.0000</td>   <td>763.0000</td>     <td>763.0000</td>     <td>760.0000</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>R2</th>                      <td>0.0378</td>     <td>0.1617</td>     <td>0.1617</td>       <td>0.1617</td>       <td>0.2603</td>    \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "\n",
       "==================================================================================\n",
       "                       dehuman I dehuman II dehuman III dehuman IIII dehuman IIIII\n",
       "----------------------------------------------------------------------------------\n",
       "go_pos                 4.68      5.26       5.15        5.16         -0.73        \n",
       "                       (6.93)    (6.52)     (6.69)      (6.59)       (6.18)       \n",
       "go_neu                 21.62***  27.03***   26.87***    26.82***     17.94***     \n",
       "                       (6.27)    (5.96)     (6.34)      (6.30)       (5.70)       \n",
       "go_neg                 18.12***  11.46*     11.32*      11.33*       3.97         \n",
       "                       (6.17)    (5.86)     (6.20)      (6.01)       (5.59)       \n",
       "intergroup_norm                                         0.12                      \n",
       "                                                        (1.11)                    \n",
       "prop                                                                 8.73***      \n",
       "                                                                     (0.87)       \n",
       "dem                              8.86***    8.87***     8.84***      9.76***      \n",
       "                                 (2.23)     (2.23)      (2.24)       (2.11)       \n",
       "num_strong                       12.98***   12.98***    12.98***     8.77***      \n",
       "                                 (2.32)     (2.32)      (2.32)       (2.23)       \n",
       "extreme_con                      7.83*      7.83*       7.84*        6.77*        \n",
       "                                 (4.05)     (4.05)      (4.06)       (3.82)       \n",
       "ideo_extreme                     2.58*      2.56*       2.56*        1.79         \n",
       "                                 (1.44)     (1.46)      (1.45)       (1.36)       \n",
       "Intercept              1.56      -14.91***  -14.71***   -14.69***    -32.09***    \n",
       "                       (3.64)    (4.07)     (4.87)      (4.59)       (4.19)       \n",
       "personal_outgroup_norm                      0.09                                  \n",
       "                                            (1.20)                                \n",
       "R-squared              0.04      0.16       0.16        0.16         0.26         \n",
       "R-squared Adj.         0.03      0.15       0.15        0.15         0.25         \n",
       "N                      772.0000  772.0000   772.0000    772.0000     769.0000     \n",
       "df                     768.0000  764.0000   763.0000    763.0000     760.0000     \n",
       "R2                     0.0378    0.1617     0.1617      0.1617       0.2603       \n",
       "==================================================================================\n",
       "Standard errors in parentheses.\n",
       "* p<.1, ** p<.05, ***p<.01\n",
       "\"\"\""
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Table A4\n",
    "\n",
    "f1abl = 'dehuman ~ go_pos + go_neu + go_neg'\n",
    "f2abl = f1abl + ' + dem + num_strong + extreme_con + ideo_extreme'\n",
    "f3abl = f2abl + ' + personal_outgroup_norm'\n",
    "f4abl = f2abl + ' + intergroup_norm'\n",
    "f5abl = f2abl + ' + prop'\n",
    "\n",
    "m1abl = sm.ols(f1abl, data=df1)\n",
    "m2abl = sm.ols(f2abl, data=df1)\n",
    "m3abl = sm.ols(f3abl, data=df1)\n",
    "m4abl = sm.ols(f4abl, data=df1)\n",
    "m5abl = sm.ols(f5abl, data=df1)\n",
    "\n",
    "r1abl = m1abl.fit(cov=\"HC0\")\n",
    "r2abl = m2abl.fit(cov=\"HC0\")\n",
    "r3abl = m3abl.fit(cov=\"HC0\")\n",
    "r4abl = m4abl.fit(cov=\"HC0\")\n",
    "r5abl = m5abl.fit(cov=\"HC0\")\n",
    "\n",
    "summary_col([r1abl,r2abl,r3abl,r4abl,r5abl],stars=True, float_format='%.2f',\n",
    "            regressor_order = [\"go_pos\",\n",
    "                               \"go_neu\",\n",
    "                               \"go_neg\",\n",
    "                              \"intergroup_norm\",\n",
    "                              \"prop\",\n",
    "                              \"dem\",\n",
    "                              \"num_strong\",\n",
    "                              \"extreme_con\",\n",
    "                              \"ideo_extreme\"],\n",
    "           info_dict={\"N\": lambda x: x.nobs,\n",
    "                      \"df\": lambda x: x.df_resid,\n",
    "                      \"R2\": lambda x: x.rsquared})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept\n",
      "95% CI:  [-40.04128192 -24.1025965 ]\n",
      "99% CI:  [-42.70319362 -21.62380368]\n",
      "99.9% CI:  [-45.34048997 -18.84012758]\n",
      "\n",
      "go_pos\n",
      "95% CI:  [-12.44111306  11.09104871]\n",
      "99% CI:  [-16.37020823  14.76362297]\n",
      "99.9% CI:  [-20.50841079  18.32772543]\n",
      "\n",
      "go_neu\n",
      "95% CI:  [ 7.14560608 28.61676755]\n",
      "99% CI:  [ 3.7894151  31.63331481]\n",
      "99.9% CI:  [ 0.3887742  36.17141827]\n",
      "\n",
      "go_neg\n",
      "95% CI:  [-6.73772841 14.64000762]\n",
      "99% CI:  [-10.36785298  17.83731375]\n",
      "99.9% CI:  [-14.31988154  22.1168922 ]\n",
      "\n",
      "dem\n",
      "95% CI:  [ 5.66502669 13.72484059]\n",
      "99% CI:  [ 4.58653935 15.08424245]\n",
      "99.9% CI:  [ 3.23317048 16.41932167]\n",
      "\n",
      "num_strong\n",
      "95% CI:  [ 4.66478214 12.95154267]\n",
      "99% CI:  [ 3.28825894 14.00044357]\n",
      "99.9% CI:  [ 2.22189189 15.37645272]\n",
      "\n",
      "extreme_con\n",
      "95% CI:  [-2.13883765 15.84035772]\n",
      "99% CI:  [-5.00054462 18.41493645]\n",
      "99.9% CI:  [-7.85315533 21.25727935]\n",
      "\n",
      "ideo_extreme\n",
      "95% CI:  [-1.08112032  4.62934053]\n",
      "99% CI:  [-1.84790036  5.52841521]\n",
      "99.9% CI:  [-2.82342068  6.73636629]\n",
      "\n",
      "prop\n",
      "95% CI:  [ 7.03486217 10.42748117]\n",
      "99% CI:  [ 6.54308811 10.9659473 ]\n",
      "99.9% CI:  [ 5.95633403 11.60183592]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Replicating Model 5 of Table A4 with Boostrapped CIs\n",
    "\n",
    "np.random.seed(8675309)\n",
    "\n",
    "betas = bootstrap_reg(f5abl)\n",
    "\n",
    "for x in betas:\n",
    "    print(x)\n",
    "    print('95% CI: ', np.quantile(betas[x], [0.025, 0.975]))\n",
    "    print('99% CI: ', np.quantile(betas[x], [0.005, 0.995]))\n",
    "    print('99.9% CI: ', np.quantile(betas[x], [0.0005, 0.9995]))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Experimental Study"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "400\n",
      "400\n",
      "400\n",
      "398\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-50-63a6d64340d0>:8: FutureWarning: In a future version of pandas all arguments of DataFrame.dropna will be keyword-only.\n",
      "  df2 = df2.dropna(0, subset=[\"consent\", 'cond'])\n"
     ]
    }
   ],
   "source": [
    "# Load the data\n",
    "\n",
    "df2 = pd.read_csv(\"ExperimentalStudyData.csv\")\n",
    "\n",
    "# Drop participants on pre-registered criteria\n",
    "\n",
    "df2 = df2[(df2.cond == \"lowOGOCD\")|(df2.cond == \"highOGOCD\")]\n",
    "print(len(df2))\n",
    "df2 = df2[(df2.pid3 == \"Democrat\")|(df2.pid3 == \"Republican\")]\n",
    "print(len(df2))\n",
    "df2['dem'] = df2['pid3'].apply(lambda x: {'Democrat': 1, 'Republican': 0}[x])\n",
    "df2 = df2.dropna(0, subset=[\"consent\", 'cond'])\n",
    "print(len(df2))\n",
    "df2 = df2[df2.consent == \"Yes, you may use my data\"]\n",
    "print(len(df2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Experimental condition (high==1 means the \"Greater Difference\" condition)\n",
    "df2['high'] = df2.cond.apply(lambda x: 'high' in x)\n",
    "\n",
    "\n",
    "df2['pre_dehuman'] = df2.apply(lambda x: x['pre_dehum_dems'] - x['pre_dehum_pubs'] if x['dem'] else x['pre_dehum_pubs'] - x['pre_dehum_dems'], axis=1)\n",
    "df2['dehuman'] = df2.apply(lambda x: x['post_dehum_dems'] - x['post_dehum_pubs'] if x['dem'] else x['post_dehum_pubs'] - x['post_dehum_dems'], axis=1)\n",
    "df2['delta_dehuman'] = df2.apply(lambda x: x['dehuman'] - x['pre_dehuman'], axis=1)\n",
    "\n",
    "k = {\"Strongly disagree\": 1, \"Somewhat disagree\": 2, \"Neither agree nor disagree\": 3, \"Somewhat agree\": 4, \"Strongly agree\": 5}\n",
    "df2['pre_prop'] = df2.apply(lambda x: k[x['pre_med_A_1']] if x['dem'] == 0 else k[x['pre_med_B_1']], axis=1)\n",
    "df2['post_prop'] = df2.apply(lambda x: k[x['post_mediators_A_1']] if x['dem'] == 0 else k[x['post_mediators_B_1']], axis=1)\n",
    "df2['delta_prop'] = df2.apply(lambda x: x['post_prop'] - x['pre_prop'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "high   dem\n",
       "False  0       99\n",
       "       1      102\n",
       "True   0       98\n",
       "       1       99\n",
       "dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.groupby(['high','dem']).size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Manipulation Check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ttest_indResult(statistic=2.91369191812637, pvalue=0.0037743654132436993)\n",
      "0.28939804753512505\n",
      "[0.08326388 0.41718241]\n"
     ]
    }
   ],
   "source": [
    "a = df2[df2.high==1].delta_prop\n",
    "b = df2[df2.high==0].delta_prop\n",
    "\n",
    "print(stats.ttest_ind(a, b))\n",
    "\n",
    "print((a.mean() - b.mean())/pd.concat([a,b]).std())\n",
    "\n",
    "n_reps = 10000\n",
    "\n",
    "ts = []\n",
    "\n",
    "np.random.seed(8675309)\n",
    "\n",
    "for _ in range(n_reps):\n",
    "        \n",
    "    df_iter = df2.sample(frac=1, replace=True)\n",
    "    \n",
    "    a = df_iter[df_iter.high==1].delta_prop.mean()\n",
    "    b = df_iter[df_iter.high==0].delta_prop.mean()\n",
    "\n",
    "    ts.append(a - b)\n",
    "    \n",
    "print(np.quantile(ts, [0.025,0.975]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RanksumsResult(statistic=2.1208228015461352, pvalue=0.0339367171106029)\n",
      "[0.47280669 3.75034516]\n"
     ]
    }
   ],
   "source": [
    "a = df2[df2.high==1].delta_prop\n",
    "b = df2[df2.high==0].delta_prop\n",
    "\n",
    "print(stats.ranksums(a, b))\n",
    "\n",
    "n_reps = 10000\n",
    "\n",
    "zs = []\n",
    "\n",
    "np.random.seed(8675309)\n",
    "\n",
    "for _ in range(n_reps):\n",
    "        \n",
    "    df_iter = df2.sample(frac=1, replace=True)\n",
    "    \n",
    "    a = df_iter[df_iter.high==1].delta_prop\n",
    "    b = df_iter[df_iter.high==0].delta_prop\n",
    "\n",
    "    zs.append(stats.ranksums(a,b)[0])\n",
    "    \n",
    "print(np.quantile(zs, [0.025,0.975]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.04975124378109453\n",
      "3.487562189054726\n",
      "\n",
      "0.19796954314720813\n",
      "3.66497461928934\n"
     ]
    }
   ],
   "source": [
    "print(df2[df2.high==0].delta_prop.mean())\n",
    "print(df2[df2.high==0].post_prop.mean())\n",
    "print()\n",
    "print(df2[df2.high==1].delta_prop.mean())\n",
    "print(df2[df2.high==1].post_prop.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outcome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ttest_indResult(statistic=2.185089372594498, pvalue=0.029467733454884192)\n",
      "0.21803426457661754\n",
      "[0.23039682 3.62824553]\n"
     ]
    }
   ],
   "source": [
    "a = df2[df2.high==1].delta_dehuman\n",
    "b = df2[df2.high==0].delta_dehuman\n",
    "\n",
    "print(stats.ttest_ind(a, b))\n",
    "\n",
    "print((a.mean() - b.mean())/pd.concat([a,b]).std())\n",
    "\n",
    "n_reps = 10000\n",
    "\n",
    "ts = []\n",
    "\n",
    "np.random.seed(8675309)\n",
    "\n",
    "for _ in range(n_reps):\n",
    "        \n",
    "    df_iter = df2.sample(frac=1, replace=True)\n",
    "    \n",
    "    a = df_iter[df_iter.high==1].delta_dehuman.mean()\n",
    "    b = df_iter[df_iter.high==0].delta_dehuman.mean()\n",
    "\n",
    "    ts.append(a - b)\n",
    "    \n",
    "print(np.quantile(ts, [0.025,0.975]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RanksumsResult(statistic=2.6781542918230015, pvalue=0.007402910832421099)\n",
      "[0.89501867 4.4606781 ]\n"
     ]
    }
   ],
   "source": [
    "a = df2[df2.high==1].delta_dehuman\n",
    "b = df2[df2.high==0].delta_dehuman\n",
    "\n",
    "print(stats.ranksums(a, b))\n",
    "\n",
    "n_reps = 10000\n",
    "\n",
    "zs = []\n",
    "\n",
    "np.random.seed(8675309)\n",
    "\n",
    "for _ in range(n_reps):\n",
    "        \n",
    "    df_iter = df2.sample(frac=1, replace=True)\n",
    "    \n",
    "    a = df_iter[df_iter.high==1].delta_dehuman\n",
    "    b = df_iter[df_iter.high==0].delta_dehuman\n",
    "\n",
    "    zs.append(stats.ranksums(a,b)[0])\n",
    "    \n",
    "print(np.quantile(zs, [0.025,0.975]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 6480x1800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rc('font', size=68) \n",
    "sb.set_style('white')\n",
    "fig, axes = plt.subplots(1, 2, sharex=False, figsize=(90,25))\n",
    "\n",
    "axes[0].set_title('A',fontsize=200, pad=100)\n",
    "sb.histplot(ax=axes[0], x=\"delta_dehuman\",data=df2, bins=40, \n",
    "            legend=False, color=\"grey\")\n",
    "axes[0].axvline(df2[df2.high == 1].delta_dehuman.mean(), label=\"High Othering\", color=\"black\", lw=8)\n",
    "axes[0].axvline(df2[df2.high == 0].delta_dehuman.mean(), label=\"Low Othering\", ls=\"--\", color=\"black\", lw=8)\n",
    "leg = axes[0].legend(title=r\"$\\bf{Condition\\ Mean}$\", loc='upper right', labels=['Greater difference', 'Less difference'],\n",
    "                    fancybox=True, shadow=True, edgecolor=\"black\")\n",
    "leg.get_frame().set_linewidth(2)\n",
    "axes[0].set_yscale(\"log\")\n",
    "axes[0].set_xlabel(\"Change in Outgroup Dehumanization\", fontsize=96)\n",
    "axes[0].set_ylabel(\"Count (logged)\", fontsize=96)\n",
    "\n",
    "\n",
    "axes[1].set_title('B',fontsize=200, pad=100)\n",
    "sb.barplot(ax=axes[1], x='cond', y='delta_dehuman', data=df2,\n",
    "          errwidth=10,capsize=0.05, color=\"grey\")\n",
    "axes[1].set_ylabel(\"Change in Outgroup Dehumanization\", fontsize=68)\n",
    "axes[1].set_xlabel(\"Condition\", fontsize=96, fontweight=\"bold\")\n",
    "axes[1].set_xticks([0,1],['More difference', 'Less difference'])\n",
    "axes[1].set_xticklabels(['Greater difference', 'Less difference'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.89481526378261"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[df2.high==1].delta_dehuman.mean() - df2[df2.high==0].delta_dehuman.mean()"
   ]
  }
 ],
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