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1/replication-package/.ipynb_checkpoints/data_cleaning-checkpoint.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "3b68d946",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "# Define coalesce function for recoding of post-election thermometers\n",
+    "def coalesce(*args):\n",
+    "    return pd.Series(args).bfill().iloc[0]\n",
+    "\n",
+    "# Define a function for recoding values, similar to car::recode in R\n",
+    "def recode(series, recode_dict):\n",
+    "    return series.replace(recode_dict)\n",
+    "\n",
+    "# Initialize workspace\n",
+    "tpnw = pd.read_csv(\"data/tpnw_raw.csv\", index_col=0)\n",
+    "orig_inc = pd.read_csv(\"data/tpnw_orig_income.csv\", index_col=0)\n",
+    "aware = pd.read_csv(\"data/tpnw_aware_raw.csv\", index_col=0)\n",
+    "\n",
+    "# Clean TPNW data\n",
+    "tpnw = tpnw.iloc[2:, :]\n",
+    "orig_inc = orig_inc.iloc[2:, :]\n",
+    "\n",
+    "tpnw = tpnw[tpnw['consent'] == \"1\"]\n",
+    "orig_inc = orig_inc[orig_inc['consent'] == \"1\"]\n",
+    "\n",
+    "# Coalesce income variables\n",
+    "orig_inc['income'] = pd.to_numeric(orig_inc['income'], errors='coerce')\n",
+    "orig_inc['income'] = np.where(orig_inc['income'] < 1000, np.nan, orig_inc['income'])\n",
+    "orig_inc['income'] = pd.cut(orig_inc['income'], bins=[0, 14999, 24999, 49999, 74999, 99999, 149999, 199999, 249999, 499999, 999999, np.inf], labels=range(1, 12))\n",
+    "orig_inc = orig_inc[['pid', 'income']]\n",
+    "orig_inc.columns = ['pid', 'income_old']\n",
+    "\n",
+    "tpnw = pd.merge(tpnw, orig_inc, on=\"pid\", how=\"left\")\n",
+    "tpnw['income'] = tpnw.apply(lambda row: coalesce(row['income'], row['income_old']), axis=1)\n",
+    "\n",
+    "# Note variables\n",
+    "meta = [\"consent\", \"confirmation_code\", \"new_income_q\"]\n",
+    "qualtrics_vars = [\"StartDate\", \"EndDate\", \"Status\", \"Progress\", \"Duration..in.seconds.\", \"Finished\", \"RecordedDate\", \"DistributionChannel\", \"UserLanguage\"]\n",
+    "dynata_vars = [\"pid\", \"psid\"]\n",
+    "\n",
+    "# Convert character variables to appropriate types and clean data\n",
+    "char_vars = qualtrics_vars + dynata_vars + [\"ResponseId\"] + [col for col in tpnw.columns if \"text\" in col.lower()]\n",
+    "char_cols = tpnw.columns.isin(char_vars)\n",
+    "\n",
+    "# Convert all non-character columns to numeric, coercing errors to NaN\n",
+    "tpnw.loc[:, ~char_cols] = tpnw.loc[:, ~char_cols].apply(pd.to_numeric, errors='coerce')\n",
+    "\n",
+    "tpnw_atts = tpnw.columns.isin([\"danger\", \"peace\", \"safe\", \"use_unaccept\", \"always_cheat\", \"cannot_elim\", \"slow_reduc\"])\n",
+    "tpnw.columns.values[tpnw_atts] = [\"tpnw_atts_\" + col for col in tpnw.columns[tpnw_atts]]\n",
+    "\n",
+    "# Coalesce relevant variables\n",
+    "tpnw['female'] = np.where(tpnw['gender'] == 95, np.nan, tpnw['gender'])\n",
+    "tpnw['age'] = 2019 - tpnw['birthyr']\n",
+    "tpnw['income'] = recode(tpnw['income'], {95: np.nan})\n",
+    "tpnw['pid3'] = np.where(tpnw['pid3'] == 0, tpnw['pid_forc'], tpnw['pid3'])\n",
+    "tpnw['ideo'] = recode(tpnw['ideo'], {3: np.nan})\n",
+    "tpnw['educ'] = recode(tpnw['educ'], {95: np.nan})\n",
+    "state_recode = {\n",
+    "    1: 'Alabama', 2: 'Alaska', 4: 'Arizona', 5: 'Arkansas', 6: 'California', 8: 'Colorado', 9: 'Connecticut', \n",
+    "    10: 'Delaware', 11: 'Washington DC', 12: 'Florida', 13: 'Georgia', 15: 'Hawaii', 16: 'Idaho', 17: 'Illinois',\n",
+    "    18: 'Indiana', 19: 'Iowa', 20: 'Kansas', 21: 'Kentucky', 22: 'Louisiana', 23: 'Maine', 24: 'Maryland',\n",
+    "    25: 'Massachusetts', 26: 'Michigan', 27: 'Minnesota', 28: 'Mississippi', 29: 'Missouri', 30: 'Montana',\n",
+    "    31: 'Nebraska', 32: 'Nevada', 33: 'New Hampshire', 34: 'New Jersey', 35: 'New Mexico', 36: 'New York',\n",
+    "    37: 'North Carolina', 38: 'North Dakota', 39: 'Ohio', 40: 'Oklahoma', 41: 'Oregon', 42: 'Pennsylvania',\n",
+    "    44: 'Rhode Island', 45: 'South Carolina', 46: 'South Dakota', 47: 'Tennessee', 48: 'Texas', 49: 'Utah',\n",
+    "    50: 'Vermont', 51: 'Virginia', 53: 'Washington', 54: 'West Virginia', 55: 'Wisconsin', 56: 'Wyoming'\n",
+    "}\n",
+    "tpnw['state'] = recode(tpnw['state'], state_recode)\n",
+    "\n",
+    "# Create regional indicators\n",
+    "tpnw['northeast'] = tpnw['state'].isin(['Connecticut', 'Maine', 'Massachusetts', 'New Hampshire', 'Rhode Island', 'Vermont', 'New Jersey', 'New York', 'Pennsylvania'])\n",
+    "tpnw['midwest'] = tpnw['state'].isin(['Illinois', 'Indiana', 'Michigan', 'Ohio', 'Wisconsin', 'Iowa', 'Kansas', 'Minnesota', 'Missouri', 'Nebraska', 'North Dakota', 'South Dakota'])\n",
+    "tpnw['south'] = tpnw['state'].isin(['Delaware', 'Florida', 'Georgia', 'Maryland', 'North Carolina', 'South Carolina', 'Virginia', 'Washington DC', 'West Virginia', 'Alabama', 'Kentucky', 'Mississippi', 'Tennessee', 'Arkansas', 'Louisiana', 'Oklahoma', 'Texas'])\n",
+    "tpnw['west'] = tpnw['state'].isin(['Arizona', 'Colorado', 'Idaho', 'Montana', 'Nevada', 'New Mexico', 'Utah', 'Wyoming', 'Alaska', 'California', 'Hawaii', 'Oregon', 'Washington'])\n",
+    "\n",
+    "# Recode join_tpnw outcome and attitudinal outcomes\n",
+    "tpnw['join_tpnw'] = recode(tpnw['join_tpnw'], {2: 0})\n",
+    "tpnw['tpnw_atts_danger'] = recode(tpnw['tpnw_atts_danger'], {-2: 2, -1: 1, 1: -1, 2: -2})\n",
+    "tpnw['tpnw_atts_use_unaccept'] = recode(tpnw['tpnw_atts_use_unaccept'], {-2: 2, -1: 1, 1: -1, 2: -2})\n",
+    "tpnw['tpnw_atts_always_cheat'] = recode(tpnw['tpnw_atts_always_cheat'], {-2: 2, -1: 1, 1: -1, 2: -2})\n",
+    "tpnw['tpnw_atts_cannot_elim'] = recode(tpnw['tpnw_atts_cannot_elim'], {-2: 2, -1: 1, 1: -1, 2: -2})\n",
+    "\n",
+    "# Define indicator variables for each treatment arm\n",
+    "tpnw['control'] = tpnw['treatment'] == 0\n",
+    "tpnw['group_cue'] = tpnw['treatment'] == 1\n",
+    "tpnw['security_cue'] = tpnw['treatment'] == 2\n",
+    "tpnw['norms_cue'] = tpnw['treatment'] == 3\n",
+    "tpnw['institutions_cue'] = tpnw['treatment'] == 4\n",
+    "\n",
+    "# Mean imputation for missing values\n",
+    "numeric_cols = tpnw.columns[~tpnw.columns.isin(char_vars + meta + [\"state\", \"pid_forc\", \"income_old\", \"gender\"])]\n",
+    "tpnw[numeric_cols] = tpnw[numeric_cols].apply(lambda x: x.fillna(x.mean()))\n",
+    "\n",
+    "# Save the cleaned dataset\n",
+    "tpnw.to_csv(\"data/tpnw_data.csv\", index=False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "3ae7ce11",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>StartDate</th>\n",
+       "      <th>EndDate</th>\n",
+       "      <th>Status</th>\n",
+       "      <th>Progress</th>\n",
+       "      <th>Duration..in.seconds.</th>\n",
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+       "      <th>ResponseId</th>\n",
+       "      <th>DistributionChannel</th>\n",
+       "      <th>UserLanguage</th>\n",
+       "      <th>...</th>\n",
+       "      <th>age</th>\n",
+       "      <th>northeast</th>\n",
+       "      <th>midwest</th>\n",
+       "      <th>south</th>\n",
+       "      <th>west</th>\n",
+       "      <th>control</th>\n",
+       "      <th>group_cue</th>\n",
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+       "      <td>False</td>\n",
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+       "      <th>2</th>\n",
+       "      <td>2019-08-12 16:55:18</td>\n",
+       "      <td>2019-08-12 16:55:18</td>\n",
+       "      <td>4</td>\n",
+       "      <td>100</td>\n",
+       "      <td>13</td>\n",
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+       "      <td>2019-08-12 16:55:20</td>\n",
+       "      <td>R_4UD1j5073pRw8Kx</td>\n",
+       "      <td>anonymous</td>\n",
+       "      <td>EN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
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+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2019-08-12 16:55:18</td>\n",
+       "      <td>2019-08-12 16:55:18</td>\n",
+       "      <td>4</td>\n",
+       "      <td>100</td>\n",
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+       "      <td>anonymous</td>\n",
+       "      <td>EN</td>\n",
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+       "      <td>False</td>\n",
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+       "      <td>...</td>\n",
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+       "    <tr>\n",
+       "      <th>1214</th>\n",
+       "      <td>2019-08-12 17:59:25</td>\n",
+       "      <td>2019-08-12 18:04:27</td>\n",
+       "      <td>0</td>\n",
+       "      <td>100</td>\n",
+       "      <td>302</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019-08-12 18:04:28</td>\n",
+       "      <td>R_WoOeItZpT8cYDQd</td>\n",
+       "      <td>anonymous</td>\n",
+       "      <td>EN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>51</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1215</th>\n",
+       "      <td>2019-08-12 17:59:11</td>\n",
+       "      <td>2019-08-12 18:04:28</td>\n",
+       "      <td>0</td>\n",
+       "      <td>100</td>\n",
+       "      <td>316</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019-08-12 18:04:29</td>\n",
+       "      <td>R_2attLt3IeEgUz9s</td>\n",
+       "      <td>anonymous</td>\n",
+       "      <td>EN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
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+       "    <tr>\n",
+       "      <th>1216</th>\n",
+       "      <td>2019-08-12 18:00:08</td>\n",
+       "      <td>2019-08-12 18:04:58</td>\n",
+       "      <td>0</td>\n",
+       "      <td>100</td>\n",
+       "      <td>290</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019-08-12 18:04:58</td>\n",
+       "      <td>R_2CKBd1hxhlAU3S7</td>\n",
+       "      <td>anonymous</td>\n",
+       "      <td>EN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>35</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1217</th>\n",
+       "      <td>2019-08-12 17:59:11</td>\n",
+       "      <td>2019-08-12 18:05:39</td>\n",
+       "      <td>0</td>\n",
+       "      <td>100</td>\n",
+       "      <td>387</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019-08-12 18:05:39</td>\n",
+       "      <td>R_1H7g1o2HWZAipRy</td>\n",
+       "      <td>anonymous</td>\n",
+       "      <td>EN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>59</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1218</th>\n",
+       "      <td>2019-08-12 17:59:14</td>\n",
+       "      <td>2019-08-12 18:15:03</td>\n",
+       "      <td>0</td>\n",
+       "      <td>100</td>\n",
+       "      <td>949</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019-08-12 18:15:03</td>\n",
+       "      <td>R_velqedV5Yw40R3j</td>\n",
+       "      <td>anonymous</td>\n",
+       "      <td>EN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>26</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1219 rows × 46 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                StartDate              EndDate Status Progress  \\\n",
+       "0     2019-08-12 16:55:18  2019-08-12 16:55:18      4      100   \n",
+       "1     2019-08-12 16:55:18  2019-08-12 16:55:18      4      100   \n",
+       "2     2019-08-12 16:55:18  2019-08-12 16:55:18      4      100   \n",
+       "3     2019-08-12 16:55:18  2019-08-12 16:55:18      4      100   \n",
+       "4     2019-08-12 16:55:18  2019-08-12 16:55:18      4      100   \n",
+       "...                   ...                  ...    ...      ...   \n",
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+       "1215  2019-08-12 17:59:11  2019-08-12 18:04:28      0      100   \n",
+       "1216  2019-08-12 18:00:08  2019-08-12 18:04:58      0      100   \n",
+       "1217  2019-08-12 17:59:11  2019-08-12 18:05:39      0      100   \n",
+       "1218  2019-08-12 17:59:14  2019-08-12 18:15:03      0      100   \n",
+       "\n",
+       "     Duration..in.seconds. Finished         RecordedDate         ResponseId  \\\n",
+       "0                      503        1  2019-08-12 16:55:20  R_eKatZ6uLJ2ywYpT   \n",
+       "1                      204        1  2019-08-12 16:55:20  R_7ZI2v7y4DbtW1XD   \n",
+       "2                       13        1  2019-08-12 16:55:20  R_4UD1j5073pRw8Kx   \n",
+       "3                       97        1  2019-08-12 16:55:20  R_7UJx1q2BGBgPR0V   \n",
+       "4                      135        1  2019-08-12 16:55:20  R_6VWAec7rMVtbSWp   \n",
+       "...                    ...      ...                  ...                ...   \n",
+       "1214                   302        1  2019-08-12 18:04:28  R_WoOeItZpT8cYDQd   \n",
+       "1215                   316        1  2019-08-12 18:04:29  R_2attLt3IeEgUz9s   \n",
+       "1216                   290        1  2019-08-12 18:04:58  R_2CKBd1hxhlAU3S7   \n",
+       "1217                   387        1  2019-08-12 18:05:39  R_1H7g1o2HWZAipRy   \n",
+       "1218                   949        1  2019-08-12 18:15:03  R_velqedV5Yw40R3j   \n",
+       "\n",
+       "     DistributionChannel UserLanguage  ... age  northeast midwest  south  \\\n",
+       "0              anonymous           EN  ...  65      False   False   True   \n",
+       "1              anonymous           EN  ...  68      False   False   True   \n",
+       "2              anonymous           EN  ...  14      False   False  False   \n",
+       "3              anonymous           EN  ...  37      False    True  False   \n",
+       "4              anonymous           EN  ...  41      False    True  False   \n",
+       "...                  ...          ...  ...  ..        ...     ...    ...   \n",
+       "1214           anonymous           EN  ...  51      False   False  False   \n",
+       "1215           anonymous           EN  ...  23      False    True  False   \n",
+       "1216           anonymous           EN  ...  35       True   False  False   \n",
+       "1217           anonymous           EN  ...  59       True   False  False   \n",
+       "1218           anonymous           EN  ...  26      False    True  False   \n",
+       "\n",
+       "       west  control  group_cue security_cue  norms_cue  institutions_cue  \n",
+       "0     False    False      False        False       True             False  \n",
+       "1     False     True      False        False      False             False  \n",
+       "2     False    False      False        False      False              True  \n",
+       "3     False    False      False        False       True             False  \n",
+       "4     False    False      False         True      False             False  \n",
+       "...     ...      ...        ...          ...        ...               ...  \n",
+       "1214   True    False       True        False      False             False  \n",
+       "1215  False    False      False         True      False             False  \n",
+       "1216  False    False      False        False       True             False  \n",
+       "1217  False    False      False         True      False             False  \n",
+       "1218  False    False      False        False      False              True  \n",
+       "\n",
+       "[1219 rows x 46 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tpnw"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "df57469c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['StartDate', 'EndDate', 'Status', 'Progress', 'Duration..in.seconds.',\n",
+       "       'Finished', 'RecordedDate', 'ResponseId', 'DistributionChannel',\n",
+       "       'UserLanguage', 'consent', 'birthyr', 'gender', 'gender_95_TEXT',\n",
+       "       'state', 'income', 'educ', 'educ_95_TEXT', 'ideo', 'pid3', 'pid_forc',\n",
+       "       'join_tpnw', 'tpnw_atts_danger', 'tpnw_atts_peace', 'tpnw_atts_safe',\n",
+       "       'tpnw_atts_use_unaccept', 'tpnw_atts_always_cheat',\n",
+       "       'tpnw_atts_cannot_elim', 'tpnw_atts_slow_reduc', 'psid', 'pid',\n",
+       "       'new_income_q', 'treatment', 'confirmation_code', 'income_old',\n",
+       "       'female', 'age', 'northeast', 'midwest', 'south', 'west', 'control',\n",
+       "       'group_cue', 'security_cue', 'norms_cue', 'institutions_cue'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tpnw.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "60f8eb35",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0       5.000000\n",
+       "1       6.000000\n",
+       "2       3.979097\n",
+       "3       5.000000\n",
+       "4       5.000000\n",
+       "          ...   \n",
+       "1214    4.000000\n",
+       "1215    5.000000\n",
+       "1216    5.000000\n",
+       "1217    3.000000\n",
+       "1218    6.000000\n",
+       "Name: educ, Length: 1219, dtype: float64"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tpnw['educ']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b0a26a47",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "'''\n",
+    "'join_tpnw' column contains treatment effects on support for the TPNW.\n",
+    "'group_cue', 'security_cue', 'norms_cue', 'institutions_cue' columns are boolean values.\n",
+    "'''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c5becc59",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# for table1:\n",
+    "# bal_covars <- c(\"age\", \"female\", \"northeast\", \"midwest\", \"west\", \n",
+    "# \t\t\t\t\"south\", \"income\", \"educ\", \"ideo\", \"pid3\")\n",
+    "# for figure1:\n",
+    "# 'join_tpnw' column contains treatment effects on support for the TPNW.\n",
+    "# 'group_cue', 'security_cue', 'norms_cue', 'institutions_cue' columns are boolean values."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

1/replication-package/README.txt

@@ -0,0 +1,152 @@
+### README for Herzog, Baron, and Gibbons (Forthcoming), "Anti-Normative 
+### Messaging, Group Cues, and the Nuclear Ban Treaty"; forthcoming at The 
+### Journal of Politics.
+
+### This README details instructions and files pertaining to survey, data, and 
+### analysis code for replication purposes. Please direct inquiries to 
+### [email protected]
+
+## Meta information and instructions.
+# Performance assessments:
+	- Measured total run time (seconds), using
+		R CMD BATCH run_hbg_replication.R 2>&1 replication_out.out
+		- 137.820 (see run_time outfile for machine-specific statistics).
+	- Hardware used.
+		- Lenovo ThinkPad X1 Carbon 5th Generation;
+		- Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz;
+		- Physical Memory Array; Maximum Capacity 16GB.
+	- Operating system used.
+		- Linux Mint 19 Tara Cinnamon 64-bit (4.10.0-38-generic).
+
+# Dependencies:
+1.) R version 3.6.3 (2020-02-09) -- "Holding the Windstock."
+	- Required packages.
+		- plyr
+		- car
+		- anesrake
+		- sandwich
+2.) LaTeX (for typesetting tabular output).
+	- Required and recommended packages.
+		- array
+		- booktabs
+		- float
+		- makecell
+		- multirow
+		- siunitx
+
+# Instructions:
+1.) Set working directory to the replication-file parent directory.
+	- All scripts assume ~/Downloads/hbg_replication as the parent directory.
+2.) For all operating systems, the scripts/run_hbg_replication.R script may be
+	executed in an R instance.
+	- Open the hbg_replication.R script in R.
+	- Run all commands in the console.
+3.) For UNIX/UNIX-like systems (MacOS, Linux, Windows 10 Subsysten for Linux), 
+it is recommended to run the script in a terminal instance.
+	- Enter either
+		R CMD BATCH scripts/run_hbg_replication.R 2>&1 cli_script.out
+	which will produce an outfile containing command-line interface output in 
+	the cli_script.out outfile; or,
+		Rscript scripts/run_hbg_replication.R
+	though Rscript will not echo output.
+4.) Commands may also be run in an interactive R session without use of 
+run_hbg_replication.R, e.g., in RStudio.
+	- Working directory will have to be set manually; in the R console, enter
+		setwd("~/hbg_replication")
+	- The output directory will also need to be created separately; once the
+	working directory has been set to the parent directory, in the R console, 
+	enter
+		dir.create("output")
+
+## Directories and files.
+# ./meta:
+1.) hbg_instrument.pdf
+	- Herzog, Baron, and Gibbons (Forthcoming) survey instrument.
+	- The instrument does not describe randomization; treatment assignment was
+	randomized using Qualtrics embedded data, randomized using Qualtrics'
+	internal "Evenly Present Elements" algorithm. Some answer choice options
+	were also randomized in order to avoid ordering effects; questions employing
+	internal randomization include pid3, join_tpnw, and the row order of the 
+	attitudinal outcomes battery.
+2.) hbg_codebook.txt
+	- Herzog, Baron, and Gibbons codebook.
+		- Details coding values for embedded data and survey questions in 
+		all included data files.
+		- Notes variable recoding values used in cleaned experimental data
+		(tpnw_data.csv), used for analysis.
+3.) hbg_pap.pdf
+	- Herzog, Baron, and Gibbons pre-analysis plan.
+		- Details all analysis decisions, per research design pre-registered 
+		with EGAP prior to collecting experimental data.
+
+# ./data:
+1.) tpnw_aware_raw.csv
+	- Data from Herzog, Baron, and Gibbons YouGov study.
+	- Note that some variables have been excluded as they are used in separate
+	studies.
+2.) tpnw_orig_income.csv
+	- Data from original income coding from Herzog, Baron, and Gibbons.
+	- Note that some variables have been excluded as they are used in separate
+	studies.
+3.) tpnw_raw.csv
+	- Data from Herzog, Baron, and Gibbons experimental survey.
+	- Note that some variables have been excluded as they are used in separate
+	studies.
+
+# ./output (produced by either ../scripts/hbg.sh or 
+# ../scripts/run_hbg_replication.R):
+1.) ./hbg_log.txt
+	- Output for experimental data cleaning and analysis (produced by either 
+	../scripts/hbg.sh or ../scripts/run_hbg_replication.R).
+2.) ./run_time
+	- Output for total run time  (produced by either ../scripts/hbg.sh or 
+	../scripts/run_hbg_replication.R).
+4.) ./fg%.eps
+	- .eps images of figures produced by ../scripts/hbg_replication.R); 
+	inventoried below.
+5.) ./%_tab.tex
+	- .tex files containing LaTeX tables produced by 
+	../scripts/hbg_replication.R; inventoried below.
+
+# ./scripts:
+1.) run_hbg_replication.R
+	- "Run file" to run replication code and produce console and run-time output 
+	in R (all systems); produces
+		- ../output/hbg_log.txt
+			- Output for all analyses and results.
+		- ../output/run_time
+			- Output for total run time.
+2.) helper_functions.R
+	- R source file containing replication code helper functions.
+3.) hbg_cleaning.R
+	- Cleaning script; outputs cleaned experimental dataset including anesrake 
+	weights.
+		- ../data/tpnw_data.csv
+			- Cleaned experimental data.
+		- ../data/tpnw_aware.csv
+			- Cleaned YouGov data.
+4.) hbg_analysis.R
+	- Analysis script; outputs analysis results in graphical, tabular, and RData
+	formats.
+		- ../output/fg1.eps
+			- .eps image of Figure 1.
+		- ../output/%_tab.tex
+			- .tex files containing LaTeX markup of all tables.
+				- balance_tab.tex
+					- Table demonstrating covariate balance across arms.
+				- main_results_tab.tex
+					- Table containing main results.
+				- atts_tab.tex
+					- Table containing attitudinal battery results.
+				- pid_support_tab.tex
+					- Table containing results by partisan identification.
+				- ideo_support_tab.tex
+					- Table containing results by political ideology.
+				- weighted_main_results_tab.tex
+					- Table containing weighted main results.
+		- ../output/hbg_replication_out.RData
+			- .RData file containing all analysis results.
+5.) hbg_group_cue.R
+	- Script to produce group cue graphic.
+		- hbg_fgc1.eps
+			- .eps image of Figure C1.

1/replication-package/data/tpnw_orig_income.csv

@@ -0,0 +1,159 @@
+"","consent","income","pid"
+"1","Online Consent to Participate in a Research Study Purpose:We are conducting a research study to examine attitudes relevant to American nuclear policy. Procedures:Participation in this study will involve completing a short survey which will take you approximately 5 minutes. Risks and Benefits:It is unlikely, but possible, that participants in this study may experience distress over the nature of the questions. Although this study will not benefit you personally, we hope that our results will add to the knowledge about public preferences on this topic.   Confidentiality:All of your responses will be anonymous. When we publish any results from this study, we will do so in a way that does not identify you. We may also share the data with other researchers so that they can check the accuracy of our conclusions but will only do so if we are confident that your anonymity is protected. Voluntary Participation:Participation in this study is completely voluntary. You are free to decline to participate, to end participation at any time for any reason, or to refuse to answer any individual question. Refusing to participate will involve no penalty or loss of benefits or compensation to which you are otherwise entitled. Questions:If you have any questions about this study, you may contact the investigator, Jonathon Baron at [email protected]. If you would like to talk with someone other than the researcher to discuss problems or concerns, to discuss situations in the event that a member of the research team is not available, or to discuss your rights as a research participant, you may contact the Yale University Human Subjects Committee,  (203) 785-4688, [email protected]. Additional information is available at http://www.yale.edu/hrpp/participants/index.html. The IRB Protocol Number is HIC/HSC #2000026191. Additional information is available at http://www.yale.edu/hrpp/participants/index.html. 
+Do you voluntarily consent to participate in this study?","What was your total income in 2018, before taxes?
+This figure should include income from all sources, including salaries, wages, pensions, Social Security, dividends, interest, and all other income.
+Type the number. Your best guess is fine.","pid"
+"2","{""ImportId"":""QID3""}","{""ImportId"":""QID10_TEXT""}","{""ImportId"":""pid""}"
+"3","2","","1502062540"
+"4","2","","1625783733"
+"5","1","","1504516479"
+"6","2","","1562320221"
+"7","2","","1504394326"
+"8","2","","1502966236"
+"9","2","","1507996899"
+"10","1","80000","1612594169"
+"11","1","85000","1507144288"
+"12","1","0","1502947386"
+"13","1","150000","1503517729"
+"14","1","55000","1503260415"
+"15","1","12009","1517622277"
+"16","2","","1502135834"
+"17","1","75000","1504815230"
+"18","2","","1503933642"
+"19","1","25000","1512900692"
+"20","1","70000","1503526516"
+"21","1","50000","1458332659"
+"22","1","300000","1503519684"
+"23","1","45000","1502102790"
+"24","1","35000","1507984745"
+"25","1","185000","1502941026"
+"26","1","180000","1503395421"
+"27","1","124000","1501922011"
+"28","1","108000","1589002432"
+"29","1","112000","1502114324"
+"30","1","95000","1505144365"
+"31","1","1200","1609910274"
+"32","1","69500","1502424215"
+"33","1","40000","1501951157"
+"34","1","10000","1507851231"
+"35","1","90000","1501382953"
+"36","1","50000","1501203924"
+"37","1","69000","1502433491"
+"38","1","4","1620578427"
+"39","1","21000","1559531870"
+"40","1","100.000","1503213021"
+"41","1","80000","1502350937"
+"42","1","21000","1506953783"
+"43","1","85000","1503869686"
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+"45","1","87000","1502054654"
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1/replication-package/meta/hbg_codebook.txt

@@ -0,0 +1,1009 @@
+================================================================================
+RAW EXPERIMENTAL DATA (tpnw_raw.csv)
+================================================================================
+
+--------------------------------------------------------------------------------
+StartDate
+--------------------------------------------------------------------------------
+
+Qualtrics embedded data field (more information is available on Qualtrics' 
+website at https://www.qualtrics.com/support/survey-platform/
+data-and-analysis-module/data/download-data/understanding-your-dataset/)
+
+Date and time that respondent started the survey
+
+--------------------------------------------------------------------------------
+EndDate
+--------------------------------------------------------------------------------
+
+Qualtrics embedded data field (more information is available on Qualtrics' 
+website at https://www.qualtrics.com/support/survey-platform/
+data-and-analysis-module/data/download-data/understanding-your-dataset/)
+
+Date and time that respondent finished the survey
+
+--------------------------------------------------------------------------------
+Status
+--------------------------------------------------------------------------------
+
+Qualtrics embedded data field (more information is available on Qualtrics' 
+website at https://www.qualtrics.com/support/survey-platform/
+data-and-analysis-module/data/download-data/understanding-your-dataset/)
+
+Indicator of the type of response collected.
+
+0  - IP Address: A normal response
+1  - Survey Preview: A preview response
+2  - Survey Test: A test response
+4  - Imported: An imported response
+8  - Spam: A possible spam response
+9  - Preview Spam: A possible spam response submitted through the preview link
+12 - Imported Spam: A possible spam response that was imported
+16 - Offline: A Qualtrics Offline App response
+17 - Offline Preview: Previews submitted through the Qualtrics Offline App. This
+	 feature is deprecated in latest versions of the app
+
+--------------------------------------------------------------------------------
+Progress
+--------------------------------------------------------------------------------
+
+Qualtrics embedded data field (more information is available on Qualtrics' 
+website at https://www.qualtrics.com/support/survey-platform/
+data-and-analysis-module/data/download-data/understanding-your-dataset/)
+
+Indicates the progress a respondent made before finishing the survey.
+
+100 - Respondent completed the survey or was screened out
+
+--------------------------------------------------------------------------------
+Duration..in.seconds.
+--------------------------------------------------------------------------------
+
+Qualtrics embedded data field (more information is available on Qualtrics' 
+website at https://www.qualtrics.com/support/survey-platform/
+data-and-analysis-module/data/download-data/understanding-your-dataset/)
+
+Number of seconds it took a respondent to complete the survey.
+
+--------------------------------------------------------------------------------
+Finished
+--------------------------------------------------------------------------------
+
+Qualtrics embedded data field (more information is available on Qualtrics' 
+website at https://www.qualtrics.com/support/survey-platform/
+data-and-analysis-module/data/download-data/understanding-your-dataset/)
+
+Indicates whether a respondent finished the survey.
+
+1 - Respondent finished the survey or was screened out
+
+--------------------------------------------------------------------------------
+RecordedDate
+--------------------------------------------------------------------------------
+
+Qualtrics embedded data field (more information is available on Qualtrics' 
+website at https://www.qualtrics.com/support/survey-platform/
+data-and-analysis-module/data/download-data/understanding-your-dataset/)
+
+Date that respondent's survey was recorded in Qualtrics.
+
+--------------------------------------------------------------------------------
+ResponseId
+--------------------------------------------------------------------------------
+
+Qualtrics embedded data field (more information is available on Qualtrics' 
+website at https://www.qualtrics.com/support/survey-platform/
+data-and-analysis-module/data/download-data/understanding-your-dataset/)
+
+Character variable indicating unique respondent ID.
+
+--------------------------------------------------------------------------------
+DistributionChannel
+--------------------------------------------------------------------------------
+
+Qualtrics embedded data field (more information is available on Qualtrics' 
+website at https://www.qualtrics.com/support/survey-platform/
+data-and-analysis-module/data/download-data/understanding-your-dataset/)
+
+Character variable indicating method of survey distribution.
+
+"anonymous" - Survey was distributed without collecting respondent data
+
+--------------------------------------------------------------------------------
+UserLanguage
+--------------------------------------------------------------------------------
+
+Qualtrics embedded data field (more information is available on Qualtrics' 
+website at https://www.qualtrics.com/support/survey-platform/
+data-and-analysis-module/data/download-data/understanding-your-dataset/)
+
+Character variable indicating respondent's language code.
+
+"EN" - Respondent took the survey in English
+
+--------------------------------------------------------------------------------
+psid
+--------------------------------------------------------------------------------
+
+Dynata embedded data field
+
+Character variable uniquely identifying a respondent and specific project 
+(project specific ID).
+
+--------------------------------------------------------------------------------
+pid
+--------------------------------------------------------------------------------
+
+Dynata embedded data field
+
+Numeric variable uniquely identifying a panelist (panelist ID).
+
+--------------------------------------------------------------------------------
+consent
+--------------------------------------------------------------------------------
+
+Custom embedded data field
+
+Indicator of whether a respondent consented to participate in the survey.
+
+0 - Respondent did not consent
+1 - Respondent consented
+
+--------------------------------------------------------------------------------
+new_income_q
+--------------------------------------------------------------------------------
+
+Custom embedded data field
+
+Indicator of usage of new income demographic question format.
+
+"" - Old income question
+1  - new income question
+
+--------------------------------------------------------------------------------
+confirmation_code
+--------------------------------------------------------------------------------
+
+Custom embedded data field
+
+Numeric code provided to each respondent for response recording after completing
+the survey.
+
+--------------------------------------------------------------------------------
+treatment
+--------------------------------------------------------------------------------
+
+Custom embedded data field
+
+Numeric variable indicating treatment arm to which a respondent was assigned
+
+0 - Control
+1 - Group Cue
+2 - Security Cue
+3 - Norms Cue
+4 - Institutions Cue
+
+--------------------------------------------------------------------------------
+birthyr
+--------------------------------------------------------------------------------
+
+Demographic question
+
+Respondent's birth year (numeric entry only).
+
+--------------------------------------------------------------------------------
+gender
+--------------------------------------------------------------------------------
+
+Demographic question
+
+Respondent's self-reported gender.
+
+0  - Male
+1  - Female
+95 - Other
+
+--------------------------------------------------------------------------------
+gender_95_TEXT
+--------------------------------------------------------------------------------
+
+Demographic question
+
+Respondent's self-reported gender (if Other; text entry).
+
+--------------------------------------------------------------------------------
+state
+--------------------------------------------------------------------------------
+
+Demographic question
+
+Respondent's state of residence (recoded to character strings in cleaned
+experimental data).
+
+1  - Alabama
+2  - Alaska
+4  - Arizona
+5  - Arkansas
+6  - California
+8  - Colorado
+9  - Connecticut
+10 - Delaware
+11 - District of Columbia
+12 - Florida
+13 - Georgia
+15 - Hawaii
+16 - Idaho
+17 - Illinois
+18 - Indiana
+19 - Iowa
+20 - Kansas
+21 - Kentucky
+22 - Louisiana
+23 - Maine
+24 - Maryland
+25 - Massachusetts
+26 - Michigan
+27 - Minnesota
+28 - Mississippi
+29 - Missouri
+30 - Montana
+31 - Nebraska
+32 - Nevada
+33 - New Hampshire
+34 - New Jersey
+35 - New Mexico
+36 - New York
+37 - North Carolina
+38 - North Dakota
+39 - Ohio
+40 - Oklahoma
+41 - Oregon
+42 - Pennsylvania
+44 - Rhode Island
+45 - South Carolina
+46 - South Dakota
+47 - Tennessee
+48 - Texas
+49 - Utah
+50 - Vermont
+51 - Virginia
+53 - Washington
+54 - West Virginia
+55 - Wisconsin
+56 - Wyoming
+
+--------------------------------------------------------------------------------
+income
+--------------------------------------------------------------------------------
+
+Demographic question
+
+Respondent's self-reported, pre-tax family income.
+
+1  - Less than $15,000
+2  - $15,000 to $24,999
+3  - $25,000 to $49,999
+4  - $50,000 to $74,999
+5  - $75,000 to $99,999
+6  - $100,000 to $149,999
+7  - $150,000 to $199,999
+8  - $200,000 to $249,999
+9  - $250,000 to $499,999
+10 - $500,000 to $999,999
+11 - More than $1 million
+95 - Prefer not to say (recoded to NA in cleaned experimental data)
+
+Note: income is coalesced with income from tpnw_orig_income.csv, described
+below.
+
+--------------------------------------------------------------------------------
+educ
+--------------------------------------------------------------------------------
+
+Demographic question
+
+Respondent's self-reported level of education.
+
+
+1  - Did not graduate from high school
+2  - High school graduate or equivalent (for example: GED)
+3  - Some college, but no degree (yet)
+4  - 2-year college degree
+5  - 4-year college degree
+6  - Postgraduate degree (MA, MBA, MD, JD, PhD, EdD, etc.)
+95 - Other (recoded to NA in cleaned experimental data)
+
+--------------------------------------------------------------------------------
+educ_95_TEXT
+--------------------------------------------------------------------------------
+
+Demographic question
+
+Respondent's self-reported education level (if Other; text entry).
+
+--------------------------------------------------------------------------------
+ideo
+--------------------------------------------------------------------------------
+
+Demographic question
+
+Respondent's self-reported, five-point political ideology.
+
+
+-2 - Very liberal
+-1 - Liberal
+0  - Moderate; middle of the road
+1  - Conservative
+2  - Very conservative
+3  - Haven't thought much about this (recoded to NA in cleaned experimental data)
+
+--------------------------------------------------------------------------------
+pid3
+--------------------------------------------------------------------------------
+
+Demographic question
+
+Three-point partisan identification.
+
+-1 - Democrat
+0  - Independent
+1  - Republican
+
+--------------------------------------------------------------------------------
+pid_forc
+--------------------------------------------------------------------------------
+
+Demographic question
+
+Follow-up question to pid3; displayed only if pid3 skipped or if respondent 
+replied "Independent" to pid3 (coalesced with pid3 in cleaned experimental 
+data).
+
+-1 - Closer to Democratic
+0  - Neither
+1  - Closer to Republican
+
+--------------------------------------------------------------------------------
+join_tpnw
+--------------------------------------------------------------------------------
+
+Outcome question
+
+Respondent's support for joining TPNW.
+
+1 - Yes
+2 - No (recoded to 0 in cleaned experimental data)
+
+--------------------------------------------------------------------------------
+tpnw_atts_danger
+--------------------------------------------------------------------------------
+
+Outcome question
+
+Nuclear weapons are dangerous and present a threat to the world (reverse-coded
+in cleaned experimental data).						
+
+2  - Strongly Agree
+1  - Agree
+-1 - Disagree
+-2 - Strongly disagree
+
+--------------------------------------------------------------------------------
+tpnw_atts_peace
+--------------------------------------------------------------------------------
+
+Outcome question
+
+Nuclear weapons contribute to peace by preventing conflict between countries.						
+
+2  - Strongly Agree
+1  - Agree
+-1 - Disagree
+-2 - Strongly disagree
+
+--------------------------------------------------------------------------------
+tpnw_atts_safe
+--------------------------------------------------------------------------------
+
+Outcome question
+
+Nuclear weapons help to keep my country safe.						
+
+2  - Strongly Agree
+1  - Agree
+-1 - Disagree
+-2 - Strongly disagree
+
+--------------------------------------------------------------------------------
+tpnw_atts_use_unaccept
+--------------------------------------------------------------------------------
+
+Outcome question
+
+It is unacceptable to use nuclear weapons in any situation (reverse-coded
+in cleaned experimental data).												
+
+2  - Strongly Agree
+1  - Agree
+-1 - Disagree
+-2 - Strongly disagree
+
+--------------------------------------------------------------------------------
+tpnw_atts_always_cheat
+--------------------------------------------------------------------------------
+
+Outcome question
+
+Some countries will always cheat and disobey nuclear treaties (reverse-coded
+in cleaned experimental data).						
+
+2  - Strongly Agree
+1  - Agree
+-1 - Disagree
+-2 - Strongly disagree
+
+--------------------------------------------------------------------------------
+tpnw_atts_cannot_elim
+--------------------------------------------------------------------------------
+
+Outcome question
+
+Now that nuclear weapons exist, they can never be eliminated (reverse-coded
+in cleaned experimental data).	
+
+2  - Strongly Agree
+1  - Agree
+-1 - Disagree
+-2 - Strongly disagree
+
+--------------------------------------------------------------------------------
+tpnw_atts_slow_reduc
+--------------------------------------------------------------------------------
+
+Outcome question
+
+Reducing the number of nuclear weapons over time is safer than immediate nuclear 
+disarmament.						
+
+2  - Strongly Agree
+1  - Agree
+-1 - Disagree
+-2 - Strongly disagree
+
+================================================================================
+YOUGOV DATA (tpnw_aware.csv)
+================================================================================
+
+--------------------------------------------------------------------------------
+caseid
+--------------------------------------------------------------------------------
+
+YouGov embedded data field
+
+Numeric variable indicating case ID.
+
+--------------------------------------------------------------------------------
+starttime
+--------------------------------------------------------------------------------
+
+YouGov embedded data field
+
+Date that respondent started the survey
+
+--------------------------------------------------------------------------------
+endtime
+--------------------------------------------------------------------------------
+
+YouGov embedded data field
+
+Date that respondent finished the survey
+
+--------------------------------------------------------------------------------
+weight
+--------------------------------------------------------------------------------
+
+YouGov weighting variable
+
+Numeric variable containing post-stratification weights.
+
+--------------------------------------------------------------------------------
+birthyr
+--------------------------------------------------------------------------------
+
+YouGov demographic question
+
+Respondent's birth year (numeric).
+
+--------------------------------------------------------------------------------
+gender
+--------------------------------------------------------------------------------
+
+YouGov demographic question
+
+Respondent's self-reported gender.
+
+1 - Male
+2 - Female
+
+--------------------------------------------------------------------------------
+race
+--------------------------------------------------------------------------------
+
+YouGov demographic question
+
+Respondent's self-reported race.
+
+1 - White
+2 - Black
+3 - Hispanic
+4 - Asian
+5 - Native American
+6 - Mixed
+7 - Other
+8 - Middle Eastern
+
+--------------------------------------------------------------------------------
+educ
+--------------------------------------------------------------------------------
+
+YouGov demographic question
+
+Respondent's self-reported education level.
+
+1 - No high school
+2 - High school graduate
+3 - Some college
+4 - 2-year college degree
+5 - 4-year college degree
+6 - Post-graduate degree
+
+--------------------------------------------------------------------------------
+marstat
+--------------------------------------------------------------------------------
+
+YouGov demographic question
+
+Respondent's self-reported marital status.
+
+1 - Married
+2 - Separated
+3 - Divorced
+4 - Widowed
+5 - Never married
+6 - Domestic / civil partnership
+
+--------------------------------------------------------------------------------
+employ
+--------------------------------------------------------------------------------
+
+YouGov demographic question
+
+Respondent's self-reported employment status.
+
+1 - Full-time
+2 - Part-time
+3 - Temporarily laid off
+4 - Unemployed
+5 - Retired
+6 - Permanently disabled
+7 - Homemaker
+8 - Student
+9 - Other
+
+--------------------------------------------------------------------------------
+faminc_new
+--------------------------------------------------------------------------------
+
+YouGov demographic question
+
+Respondent's self-reported family income.
+
+1  - Less than $10,000
+2  - $10,000 - $19,999
+3  - $20,000 - $29,999
+4  - $30,000 - $39,999
+5  - $40,000 - $49,999
+6  - $50,000 - $59,999
+7  - $60,000 - $69,999
+8  - $70,000 - $79,999
+9  - $80,000 - $99,999
+10 - $100,000 - $119,999
+11 - $120,000 - $149,999
+12 - $150,000 - $199,999
+13 - $200,000 - $249,999
+14 - $250,000 - $349,999
+15 - $350,000 - $499,999
+16 - $500,000 or more
+97 - Prefer not to say
+
+--------------------------------------------------------------------------------
+pid3
+--------------------------------------------------------------------------------
+
+YouGov demographic question
+
+Respondent's self-reported three-point partisan identification.
+
+1 - Democrat
+2 - Republican
+3 - Independent
+4 - Other
+5 - Not sure
+
+--------------------------------------------------------------------------------
+pid7
+--------------------------------------------------------------------------------
+
+YouGov demographic question
+
+Respondent's self-reported seven-point partisan identification.
+
+1 - Strong Democrat
+2 - Not very strong Democrat
+3 - Lean Democrat
+4 - Independent
+5 - Lean Republican
+6 - Not very strong Republican
+7 - Strong Republican
+8 - Not sure
+9 - Don't know
+
+--------------------------------------------------------------------------------
+presvote2016post
+--------------------------------------------------------------------------------
+
+YouGov demographic question
+
+Respondent's self-reported 2016 Presidential Election vote choice.
+
+1 - Hillary Clinton
+2 - Donald Trump
+3 - Gary Johnson
+4 - Jill Stein
+5 - Evan McMullin
+6 - Other
+7 - Did not vote for President
+
+--------------------------------------------------------------------------------
+inputstate
+--------------------------------------------------------------------------------
+
+YouGov demographic question
+
+Respondent's state of residence.
+
+1  - Alabama
+2  - Alaska
+4  - Arizona
+5  - Arkansas
+6  - California
+8  - Colorado
+9  - Connecticut
+10 - Delaware
+11 - District of Columbia
+12 - Florida
+13 - Georgia
+15 - Hawaii
+16 - Idaho
+17 - Illinois
+18 - Indiana
+19 - Iowa
+20 - Kansas
+21 - Kentucky
+22 - Louisiana
+23 - Maine
+24 - Maryland
+25 - Massachusetts
+26 - Michigan
+27 - Minnesota
+28 - Mississippi
+29 - Missouri
+30 - Montana
+31 - Nebraska
+32 - Nevada
+33 - New Hampshire
+34 - New Jersey
+35 - New Mexico
+36 - New York
+37 - North Carolina
+38 - North Dakota
+39 - Ohio
+40 - Oklahoma
+41 - Oregon
+42 - Pennsylvania
+44 - Rhode Island
+45 - South Carolina
+46 - South Dakota
+47 - Tennessee
+48 - Texas
+49 - Utah
+50 - Vermont
+51 - Virginia
+53 - Washington
+54 - West Virginia
+55 - Wisconsin
+56 - Wyoming
+
+--------------------------------------------------------------------------------
+votereg
+--------------------------------------------------------------------------------
+
+YouGov demographic question
+
+Respondent's self-reported voter registration status.
+
+1 - Yes
+2 - No
+3 - Don't know
+
+--------------------------------------------------------------------------------
+ideo5
+--------------------------------------------------------------------------------
+
+YouGov demographic question
+
+Respondent's self-reported, five-point political ideology.
+
+1 - Very liberal
+2 - Liberal
+3 - Moderate
+4 - Conservative
+5 - Very conservative
+6 - Not sure
+
+--------------------------------------------------------------------------------
+newsint
+--------------------------------------------------------------------------------
+
+YouGov demographic question
+
+Respondent's self-reported political interest.
+
+1 - Most of the time
+2 - Some of the time
+3 - Only now and then
+4 - Hardly at all
+7 - Don't know
+
+--------------------------------------------------------------------------------
+religpew
+--------------------------------------------------------------------------------
+
+YouGov demographic question
+
+Pew religion
+
+1  - Protestant
+2  - Roman Catholic
+3  - Mormon
+4  - Eastern or Greek Orthodox
+5  - Jewish
+6  - Muslim
+7  - Buddhist
+8  - Hindu
+9  - Atheist
+10 - Agnostic
+11 - Nothing in particular
+12 - Something else
+
+--------------------------------------------------------------------------------
+awareness
+--------------------------------------------------------------------------------
+
+Outcome question
+
+Has respondent heard of international treaty to ban nuclear weapons
+
+1 - Yes, and I support it 
+2 - Yes, and I oppose it
+3 - No, but it sounds like I would support it
+4 - No, but it sounds like I would oppose it
+8 - Skipped
+
+================================================================================
+ORIGINAL INCOME DATA (tpnw_orig_income.csv)
+================================================================================
+
+--------------------------------------------------------------------------------
+income
+--------------------------------------------------------------------------------
+
+Demographic question
+
+Numeric text-entry variable indicating respondent's self-reported income; 
+converted to categorical variable to match with income from tpnw_raw.csv,
+described above.
+
+--------------------------------------------------------------------------------
+consent
+--------------------------------------------------------------------------------
+
+Custom embedded data field
+
+Indicator of whether a respondent consented to participate in the survey.
+
+0 - Respondent did not consent
+1 - Respondent consented
+
+--------------------------------------------------------------------------------
+pid
+--------------------------------------------------------------------------------
+
+Dynata embedded data field
+
+Numeric variable uniquely identifying a panelist (panelist ID).
+
+================================================================================
+CLEANED EXPERIMENTAL DATA (tpnw_data.csv)
+
+Only newly instantiated variables are described below; any recodings of 
+variables described above are documented in the replication code cleaning script
+(hbg_cleaning.R) available in ../scripts
+================================================================================
+
+--------------------------------------------------------------------------------
+female
+--------------------------------------------------------------------------------
+
+Demographic question
+
+Indicator of whether respondent self-reported female gender.
+
+0  - No
+1  - Yes
+NA - Other/skipped
+
+--------------------------------------------------------------------------------
+age
+--------------------------------------------------------------------------------
+
+Demographic question
+
+Numeric variable indicating respondent's age, subtracting self-reported birth
+year from 2019, the year in which the survey was conducted (2019 - birthyr).
+
+--------------------------------------------------------------------------------
+northeast
+--------------------------------------------------------------------------------
+
+Demographic question
+
+Indicator of whether respondent's state is in the Northeast region defined by
+the U.S. Census Bureau.
+
+0  - No
+1  - Yes
+NA - Skipped
+
+--------------------------------------------------------------------------------
+midwest
+--------------------------------------------------------------------------------
+
+Demographic question
+
+Indicator of whether respondent's state is in the Midwest region defined by the 
+U.S. Census Bureau.
+
+0  - No
+1  - Yes
+NA - Skipped
+
+--------------------------------------------------------------------------------
+south
+--------------------------------------------------------------------------------
+
+Demographic question
+
+Indicator of whether respondent's state is in the South region defined by the 
+U.S. Census Bureau.
+
+0  - No
+1  - Yes
+NA - Skipped
+
+--------------------------------------------------------------------------------
+west
+--------------------------------------------------------------------------------
+
+Demographic question
+
+Indicator of whether respondent's state is in the West region defined by the 
+U.S. Census Bureau.
+
+0  - No
+1  - Yes
+NA - Skipped
+
+--------------------------------------------------------------------------------
+caseid
+--------------------------------------------------------------------------------
+
+Weighting variable
+
+Unique identifier for each respondent for the purposes of computing raked 
+post-stratification weights with anesrake.
+
+--------------------------------------------------------------------------------
+age_wtng
+--------------------------------------------------------------------------------
+
+Weighting variable
+
+Coarsened and factorized age variable for the purposes of computing raked 
+post-stratification weights with anesrake.
+
+age1824 - Respondent is in the 18-24-year-old age group
+age2534 - Respondent is in the 25-34-year-old age group
+age3544 - Respondent is in the 35-44-year-old age group    
+age4554 - Respondent is in the 45-54-year-old age group
+age5564 - Respondent is in the 55-64-year-old age group
+age6599 - Respondent is in the 65-99-year-old age group
+
+--------------------------------------------------------------------------------
+female_wtng
+--------------------------------------------------------------------------------
+
+Weighting variable
+
+Factorized female variable for the purposes of computing raked 
+post-stratification weights with anesrake.
+
+female - Respondent is female
+na     - Skipped/Other
+male   - Respondent is male
+
+--------------------------------------------------------------------------------
+northeast_wtng
+--------------------------------------------------------------------------------
+
+Weighting variable
+
+Factorized northeast variable for the purposes of computing raked 
+post-stratification weights with anesrake.
+
+northeast - Respondent is from the Northeast
+other     - Respondent is from another region
+
+--------------------------------------------------------------------------------
+midwest_wtng
+--------------------------------------------------------------------------------
+
+Weighting variable
+
+Factorized midwest variable for the purposes of computing raked 
+post-stratification weights with anesrake.
+
+midwest - Respondent is from the Midwest
+other   - Respondent is from another region
+
+--------------------------------------------------------------------------------
+south_wtng
+--------------------------------------------------------------------------------
+
+Weighting variable
+
+Factorized south variable for the purposes of computing raked 
+post-stratification weights with anesrake.
+
+south - Respondent is from the South
+other - Respondent is from another region
+
+--------------------------------------------------------------------------------
+west_wtng
+--------------------------------------------------------------------------------
+
+Weighting variable
+
+Factorized west variable for the purposes of computing raked 
+post-stratification weights with anesrake.
+
+west  - Respondent is from the West
+other - Respondent is from another region
+
+--------------------------------------------------------------------------------
+anesrake_weight
+--------------------------------------------------------------------------------
+
+Custom weighting variable
+
+Raked post-stratification weights computed with anesrake.

1/replication-package/scripts/hbg_analysis.R

@@ -0,0 +1,1033 @@
+### Initialize workspace.
+## Clear workspace.
+rm(list = ls(all = TRUE))
+
+## Confirm working directory.
+setwd("~/Downloads/hbg_replication")
+
+## Set seed.
+set.seed(123)
+
+## Set number of iterations for bootstrap replication.
+n_iter <- 10000
+
+## Load relevant packages.
+library(sandwich)
+library(car)
+
+## Load relevant helper functions.
+source("scripts/helper_functions.R")
+
+## Load data.
+# Load experimental data.
+tpnw <- read.csv("data/tpnw_data.csv", row.names = 1, 
+				 stringsAsFactors = FALSE)
+
+# Load YouGov data.
+aware <- read.csv("data/tpnw_aware.csv", row.names = 1,
+				  stringsAsFactors = FALSE)
+
+### Define relevant objects.
+## Define objects specifying outcomes.
+# Specify join_tpnw object, representing main outcome.
+join_tpnw <- "join_tpnw"
+
+# Specify tpnw_atts object, representing attitudinal outcomes.
+tpnw_atts <- names(tpnw)[startsWith(names(tpnw), "tpnw_atts")]
+
+# Specify all_outs object, concatenating main and attitudinal outcomes.
+all_outs <- c(join_tpnw, tpnw_atts)
+
+## Define objects specifying predictors.
+# Define object specifying main treatments.
+treats <- c("group_cue", "security_cue", "norms_cue", "institutions_cue")
+
+# Define object specifying general demographics.
+demos <- c("age", "female", "midwest", "west", "south", "income", "educ")
+
+# Define object specifying politically relevant demographics.
+pol_demos <- c("ideo", "pid3")
+
+# Define list of conditioning sets (NULL corresponds to Model 1, whereas the use
+# of demographic and political covariates corresponds to Model 2).
+covars <- list(NULL, c(demos, pol_demos))
+
+### Produce analysis.
+## Produce balance table.
+# Specify covariates to be used for balance table.
+bal_covars <- c("age", "female", "northeast", "midwest", "west", 
+				"south", "income", "educ", "ideo", "pid3")
+
+# Produce balance table matrix output, looping over treatment group.
+bal_mat <- lapply(0:4, function (i) {
+				# For each treatment value ...
+		   		apply(tpnw[bal_covars][tpnw$treatment == i,], 2, function (x) {
+		   			
+		   			# Calculate the mean of each covariate.
+		   			mean_x <- mean(x)
+
+		   			# Calculate SE estimates using 10,000 bootstrap replicates.
+		   			sd_x <- sd(replicate(10000, {
+		   						samp <- x[sample(length(x), replace = TRUE)]
+		   						return(mean(samp))
+		   				}))
+
+		   			# Return a list containing both point estimates.
+		   			return(list(mean = mean_x, sd = sd_x))
+		   			})
+		   		})
+
+# Bind point estimates for each treatment group.
+bal_mat <- lapply(bal_mat, function (treat) {
+				  do.call("rbind", unlist(treat, recursive = FALSE))
+		   })
+
+# Convert list into a matrix, with columns representing treatment group.
+bal_mat <- do.call("cbind", bal_mat)
+
+# Round all estimates to within three decimal points and convert to character
+# for the purposes of producing tabular output.
+bal_tab <- apply(bal_mat, 2, function (x) format(round(x, 3), digits = 3))
+
+# Specify rows containing mean point estimates.
+mean_rows <- endsWith(rownames(bal_tab), ".mean")
+
+# Specify rows containing SE point estimates.
+se_rows <- endsWith(rownames(bal_tab), ".sd")
+
+# Reformat SE estimates to be within parentheses.
+bal_tab[se_rows,] <- apply(bal_tab[se_rows,], 2, function (x) {
+						   paste0("(", x, ")")
+					 })
+
+# Remove row names for rows with SE estimates.
+rownames(bal_tab)[se_rows] <- ""
+
+# Remove ".mean" string in row names for rows with mean estimates.
+rownames(bal_tab)[mean_rows] <- gsub(".mean", "", rownames(bal_tab)[mean_rows])
+
+# Concatenate data to comport with LaTeX tabular markup.
+bal_tab <- paste(paste(paste(
+		capwords(rownames(bal_tab)), apply(bal_tab, 1, function (x) {
+										   paste(x, collapse = " & ")
+									 }), 
+	   sep = " & "), collapse = " \\\\\n"), "\\\\\n")
+bal_tab <- gsub("\\( ", "\\(", bal_tab)
+
+# Produce tabular output.
+sink("output/balance_tab.tex")
+cat("\\begin{table}\n",
+	  "\\caption{Covariate Balance Across Treatment Arms}\n",
+	  "\\centering\\small\n",
+	  "\\sisetup{\n",
+      "\tdetect-all,\n",
+      "\ttable-number-alignment = center,\n",
+      "\ttable-figures-integer = 1,\n",
+      "\ttable-figures-decimal = 3,\n",
+      "\tinput-symbols = {()}\n",
+  	  "}\n",
+	  paste0("\\begin{tabular}{@{\\extracolsep{5pt}}L{2.75cm}*{5}",
+	  		 "{S[table-number-alignment = center, table-column-width = 1.75cm]}}\n"),
+	  "\\toprule\n",
+	  "& \\multicolumn{5}{c}{Arm}\\\\\\cmidrule{2-6}\n",
+	  "& {Control} & {Group} & {Security} & {Norms} & {Institutions} \\\\\\midrule\n",
+	  bal_tab,
+	  "\\bottomrule\n",
+	  "\\end{tabular}\n",
+	  "\\end{table}\n")
+sink()
+
+## Produce main results.
+# Compute main results, looping over conditioning sets.
+main_results <- lapply(covars, function (covar) {
+					# For each conditioning set ...
+					# Specify the relevant regression formula.
+					form <- as.formula(paste(join_tpnw, paste(c(treats, covar), 
+									   collapse = " + "), sep = " ~ "))
+
+					# Fit the OLS model per the specification.
+					fit <- lm(form, data = tpnw)
+
+					# Compute HC2 robust standard errors.
+					ses <- sqrt(diag(vcovHC(fit, type = "HC2")))
+
+					# Bind coefficient and SE output.
+					reg_out <- cbind(fit$coef[2:5], ses[2:5])
+
+					# Name output matrix columns and rows.
+					colnames(reg_out) <- c("coef", "se")
+					rownames(reg_out) <- treats
+
+					# Return output
+					return(as.data.frame(reg_out))
+				})
+
+# Name results to distinguish between Model 1 and Model 2 estimates.
+names(main_results) <- c("model_1", "model_2")
+
+## Assess significance of effect estimates and differences.
+# Estimate Bonferroni-Holm-adjusted p-values.
+bf_ps <- lapply(main_results, function (x) {
+				round(p.adjust(pnorm(x[, 1] / x[, 2], lower.tail = TRUE), 
+							   method = "holm"), 3)
+		 })
+
+# Estimate FDR-adjusted p-values, as an added robustness check.
+fdr_ps <- lapply(main_results, function (x) {
+				 round(p.adjust(pnorm(x[, 1] / x[, 2], lower.tail = TRUE), 
+				 	   method = "fdr"), 3)
+		  })
+
+# Redefine the main model (Model 2), and store full VCOV matrix.
+main_model <- lm(join_tpnw ~ group_cue + security_cue + norms_cue + 
+				 institutions_cue + age + female + midwest + 
+				 west + south + income + educ + ideo + pid3, tpnw)
+main_vcov <- vcovHC(main_model, "HC2")
+
+# Specify diff_sig function for assessing significance between two effect
+# estimates (defined here for the sake of clarity).
+diff_sig <- function (eff_1, eff_2) {
+				diff <- main_model$coef[eff_1] - main_model$coef[eff_2]
+				se <- sqrt(main_vcov[eff_1, eff_1] + main_vcov[eff_2, eff_2] -
+						   2 * main_vcov[eff_1, eff_2])
+				p <- 2 * (1 - pnorm(abs(diff) / se))
+				return (p)
+}
+
+# Assess the significance of the difference between institution and security cue
+# effect estimates .
+inst_sec_diff_p <- diff_sig("institutions_cue", "security_cue")
+
+# Assess the significance of the difference between institution and group cue
+# effect estimates
+inst_grp_diff_p <- diff_sig("institutions_cue", "group_cue")
+
+# Assess the significance of the difference between security and group cue
+# effect estimates
+sec_grp_diff_p <- diff_sig("security_cue", "group_cue")
+
+# Assess the significance of the difference between security and norms cue
+# effect estimates
+sec_norms_diff_p <- diff_sig("security_cue", "norms_cue")
+
+# Assess the significance of the difference between institution and group cue
+# effect estimates
+inst_norms_diff_p <- diff_sig("institutions_cue", "norms_cue")
+
+# Assess the significance of the difference between institution and group cue
+# effect estimates
+grp_norms_diff_p <- diff_sig("group_cue", "norms_cue")
+
+# The significance of differences between effect estimates was also assessed
+# using 10,000 bootstrap replicates and two-tailed p-values; relevant code is 
+# included below with the institutions and security cues, for posterity, but is 
+# not run.
+
+# Compute SE estimates.
+# diffs <- replicate(10000, {
+# 	samp <- tpnw[sample(nrow(tpnw), replace = TRUE),]
+# 	model <- lm(join_tpnw ~ group_cue + security_cue + norms_cue + 
+# 				institutions_cue + age + female + midwest + 
+# 				west + south + income + educ + ideo + pid3, samp)
+# 	model$coef[5] - model$coef[3]
+# })
+# diffs_se <- sd(diffs)
+# 
+# # Fit model.
+# model <- lm(join_tpnw ~ group_cue + security_cue + norms_cue + 
+# 				institutions_cue + age + female + midwest + 
+# 				west + south + income + educ + ideo + pid3, tpnw)
+# 
+# # Compute two-tailed p-value.
+# 2 * (1 - pnorm(abs((model$coef[5] - model$coef[3])/diffs_se)))
+
+## Assess YouGov results.
+# Tabulate responses.
+aware_table <- table(aware$awareness, useNA = "ifany")
+names(aware_table) <-  c("Yes, support", "Yes, oppose", 
+						 "No, support", "No, oppose", "Skipped")
+
+# Compute both weighted and unweighted means.
+aware_results <- lapply(1:4, function (resp) {
+				 	# Calculate weighted mean.
+				 	wt_mean <- with(aware, weighted.mean(awareness == resp, 
+				 										 w = weight, na.rm = TRUE))
+				 	
+				 	# Calculate raw mean.
+				 	rw_mean <- with(aware, mean(awareness == resp, na.rm = TRUE))
+				 	
+				 	# Concatenate means and rename vector.
+				 	means <- c(wt_mean, rw_mean)
+				 	names(means) <- c("weighted_mean", "raw_mean")
+
+				 	# Calculate SE estimates with 10,000 bootstrap replicates.
+				 	ses <- replicate(10000, {
+		   					      samp <- aware[sample(nrow(aware), 
+		   					      				replace = TRUE),]
+		   					      wt_mean <- with(samp, weighted.mean(awareness == resp, 
+		   					      									  w = weight, na.rm = TRUE))
+		   					      rw_mean <- with(samp, mean(awareness == resp, 
+		   					      							 na.rm = TRUE))
+		   					      return(c(wt_mean, rw_mean))
+		   				   })
+					ses <- apply(ses, 1, sd)
+					names(ses) <- c("weighted_mean", "raw_mean")
+		   			
+		   			# Bind mean and SE estimates.
+		   			outs <- rbind(means, ses)
+		   			rownames(outs) <- paste(names(aware_table)[resp], 
+		   									c("mean", "se"), sep = "_")
+		   			return(outs)
+				 })
+
+# Name results to distinguish between responses.
+names(aware_results) <- c("Yes, support", "Yes, oppose", 
+						  "No, support", "No, oppose")
+
+## Assess covariate means for experimental and YouGov data (used in Table A1).
+# Indicate the list of covariates to be assessed.
+demo_tab_vars <- c("age", "female", "northeast", "midwest", "west", "south")
+
+# Compute covariate averages for experimental data.
+tpnw_means <- apply(tpnw[demo_tab_vars], 2, mean, na.rm = TRUE)
+
+# Compute covariate averages for YouGov data.
+aware_means <- apply(aware[demo_tab_vars], 2, function (x) {
+					 	weighted.mean(x, na.rm = TRUE, w = aware$weight)
+					})
+
+# Compute bootstrap standard errors for demographic means.
+demo_ses <- replicate(10000, {
+	# Sample the experimental data.
+	samp_tpnw <- tpnw[sample(nrow(tpnw), replace = TRUE), demo_tab_vars]
+	
+	# Sample the YouGov data.
+	samp_aware <- aware[sample(nrow(aware), replace = TRUE), 
+						c(demo_tab_vars, "weight")]
+	
+	# Compute bootstrap means for experimental data.
+	tpnw_means <- apply(samp_tpnw[demo_tab_vars], 2, mean, na.rm = TRUE)
+
+	# Compute bootstrap means for YouGov data.
+	aware_means <- apply(samp_aware[demo_tab_vars], 2, function (x) {
+					 	weighted.mean(x, na.rm = TRUE, w = samp_aware$weight)
+					})
+
+	# Return the results as a list, and ensure that replicate() also returns a 
+	# list.
+	return(list(tpnw = tpnw_means, aware = aware_means))
+	}, simplify = FALSE)
+
+# Compute SE estimates for each set of demographics.
+demo_ses <- lapply(c("tpnw", "aware"), function (dataset) {
+	# Group all estimates from each dataset.
+	sep_res <- lapply(demo_ses, function (iteration) {
+					  	return(iteration[[dataset]])
+			   })
+
+	# Bind estimates.
+	sep_res <- do.call("rbind", sep_res)
+
+	# Compute SE estimates.
+	sep_ses <- apply(sep_res, 2, sd)
+
+	# Return SE estimates.
+	return(sep_ses)
+	})
+
+## Assess responses to the attitudinal battery.
+# Assess responses to the attitudinal battery, looping over treatment group. For
+# each treatment value ...
+att_results <- lapply(0:4, function (i) {
+	# Calculate the average response to each attitudinal battery question.
+	atts_mean <- apply(tpnw[tpnw$treatment == i, tpnw_atts], 2, function (x) {
+					   mean(x, na.rm = TRUE)
+				 })
+
+	# Calculate SE estimates using 10,000 bootstrap replicates.
+	bl_atts_boot <- replicate(10000, {
+		dat <- tpnw[tpnw$treatment == i, tpnw_atts]
+		samp <- dat[sample(nrow(dat), replace = TRUE),]
+		apply(samp, 2, function (x) mean(x, na.rm = TRUE))
+	})
+	bl_atts_ses <- apply(bl_atts_boot, 1, sd)
+	
+	# Combine mean and SE estimates and return results.
+	return(cbind(atts_mean, bl_atts_ses))
+})
+
+# Compute treatment effects on responses to the attitudinal battery, looping 
+# over conditioning sets.
+att_effs <- lapply(covars, function (covar) {
+			# For each conditioning set ...
+			model_res <- lapply(tpnw_atts, function (out) {
+								# Specify the relevant regression formula.
+								form <- as.formula(paste(out, 
+												   paste(c(treats, covar), 
+												   collapse = " + "), 
+												   sep = " ~ "))
+								
+								# Fit the OLS model per the specification.
+								fit <- lm(form, data = tpnw)
+
+								# Compute HC2 robust standard errors.
+								ses <- sqrt(diag(vcovHC(fit, type = "HC2")))
+								
+								# Bind coefficient and SE output.
+								reg_out <- cbind(fit$coef[2:5], ses[2:5])
+								
+								# Name output matrix columns and rows.
+								colnames(reg_out) <- c("coef", "se")
+								rownames(reg_out) <- treats
+
+								# Return output.
+								return(as.data.frame(reg_out))
+						 })
+			# Name results to distinguish between each attitudinal battery 
+			# outcome and return results.
+			names(model_res) <- tpnw_atts
+			return(model_res)
+	})
+
+# Name results to distinguish between Model 1 and Model 2 estimates.
+names(att_effs) <- c("model_1", "model_2")
+
+## Perform subgroup analysis.
+# Compute mean support by political party, looping over treatment group.
+pid_results <- lapply(0:4, function (treat) {
+					  # For each partisan group ...
+					  out <- lapply(-1:1, function (i) {
+					      			# Calculate average support.
+					      			pid_mean <- with(tpnw, 
+					      							 mean(join_tpnw[pid3 == i & 
+					      							 	  treatment == treat], 
+					      							 	  na.rm = TRUE))
+
+					      			# Calculate SE estimates with 10,000 
+					      			# bootstrap replicates.
+					      			pid_boot <- replicate(10000, {
+					      								 dat <- tpnw$join_tpnw[tpnw$pid3 == i & 
+					      							   			tpnw$treatment == treat]
+					      								 samp <- dat[sample(length(dat), 
+					      											 		replace = TRUE)]
+					      								 mean(samp, na.rm = TRUE)
+					  		 					})
+
+					      			# Concatenate and return mean and SE 
+					      			# estimates.
+					  	  	 		return(c(mean = pid_mean, se = sd(pid_boot)))
+			   		 })
+
+					 # Name results to distinguish estimates by political party,
+					 # and return output.
+					 names(out) <- c("dem", "ind", "rep")
+					 return(as.data.frame(out))
+			   })
+
+# Name results to distinguish between treatment groups.
+names(pid_results) <- c("Control", paste(c("Group", "Security", "Norms", 
+										   "Institutions"), "Cue"))
+
+# Assess significance between control-group means; for 10,000 bootstrap
+# replicates ...
+pid_diff_ses <- replicate(10000, {
+						  # Sample with replacement.
+		  				  samp <- tpnw[sample(nrow(tpnw), replace = TRUE),]
+		  				  
+		  				  # Compute the difference between Democrats' and
+		  				  # Independents' support.
+		  				  dem_ind_diff <- with(samp[samp$treatment == 0,], 
+						  			 		   mean(join_tpnw[pid3 == -1], 
+						  			 		   		na.rm = TRUE) - 
+						  			 		   mean(join_tpnw[pid3 == 0], 
+						  			 		   		na.rm = TRUE))
+						  # Compute the difference between Democrats' and
+		  				  # Republicans' support.
+		  				  dem_rep_diff <- with(samp[samp$treatment == 0,], 
+						  			 		   mean(join_tpnw[pid3 == -1], 
+						  			 		   		na.rm = TRUE) - 
+						  			 		   mean(join_tpnw[pid3 == 1], 
+						  			 		   		na.rm = TRUE))
+						  # Compute the difference between Independents' and
+		  				  # Republicans' support.
+		  				  ind_rep_diff <- with(samp[samp$treatment == 0,], 
+						  			 		   mean(join_tpnw[pid3 == 1], 
+						  			 		   		na.rm = TRUE) - 
+						  			 		   mean(join_tpnw[pid3 == 0], 
+						  			 		   		na.rm = TRUE))
+
+		  				  # Concatenate and name results.
+		  				  out <- c(dem_ind_diff, dem_rep_diff, ind_rep_diff)
+		  				  names(out) <- c("dem_ind", "dem_rep", "ind_rep")
+		  				  return(out)
+				})
+
+# Compute SE estimates for each difference.
+pid_diff_ses <- apply(pid_diff_ses, 1, sd)
+
+# Assess significance for each difference.
+dem_ind_p <- 2 * (1 - pnorm(abs(pid_results$Control["mean", "dem"] - 
+				pid_results$Control["mean", "ind"]) / pid_diff_ses["dem_ind"]))
+dem_rep_p <- 2 * (1 - pnorm(abs(pid_results$Control["mean", "dem"] - 
+				pid_results$Control["mean", "rep"]) / pid_diff_ses["dem_rep"]))
+ind_rep_p <- 2 * (1 - pnorm(abs(pid_results$Control["mean", "ind"] - 
+				pid_results$Control["mean", "rep"]) / pid_diff_ses["ind_rep"]))
+
+# Compute mean support by political ideology, looping over treatment group.
+tpnw$ideo <- recode(tpnw$ideo, "c(-2, -1) = 'liberal'; 
+								0 = 'moderate'; 
+								c(1, 2) = 'conservative'")
+ideo_results <- lapply(0:4, function (treat) {
+					  # For each ideological group ...
+					  out <- lapply(c("liberal", "moderate", "conservative"), function (i) {
+					      			# Calculate average support.
+					      			pid_mean <- with(tpnw, 
+					      							 mean(join_tpnw[ideo == i & 
+					      							 	  treatment == treat], 
+					      							 	  na.rm = TRUE))
+
+					      			# Calculate SE estimates with 10,000 
+					      			# bootstrap replicates.
+					      			pid_boot <- replicate(10000, {
+					      								 dat <- tpnw$join_tpnw[tpnw$ideo == i & 
+					      							   			tpnw$treatment == treat]
+					      								 samp <- dat[sample(length(dat), 
+					      											 		replace = TRUE)]
+					      								 mean(samp, na.rm = TRUE)
+					  		 					})
+
+					      			# Concatenate and return mean and SE 
+					      			# estimates.
+					  	  	 		return(c(mean = pid_mean, se = sd(pid_boot)))
+			   		 })
+
+					 # Name results to distinguish estimates by political ideology,
+					 # and return output.
+					 names(out) <- c("liberal", "moderate", "conservative")
+					 return(as.data.frame(out))
+			   })
+
+# Name results to distinguish between treatment groups.
+names(ideo_results) <- c("Control", paste(c("Group", "Security", "Norms", 
+										   "Institutions"), "Cue"))
+
+## Produce weighted main results.
+# Compute weighted main results, looping over conditioning sets.
+w_main_results <- lapply(covars, function (covar) {
+				  	# For each conditioning set ...
+				  	# Specify the relevant regression formula.
+				  	form <- as.formula(paste(join_tpnw, paste(c(treats, covar), 
+				  					   collapse = " + "), sep = " ~ "))
+
+					# Fit the OLS model per the specification.
+					fit <- lm(form, data = tpnw, weights = anesrake_weight)
+
+					# Compute HC2 robust standard errors.
+					ses <- sqrt(diag(vcovHC(fit, type = "HC2")))
+
+					# Bind coefficient and SE output.
+					reg_out <- cbind(fit$coef[2:5], ses[2:5])
+
+					# Name output matrix columns and rows.
+					colnames(reg_out) <- c("coef", "se")
+					rownames(reg_out) <- treats
+
+					# Return output
+					return(as.data.frame(reg_out))
+				})
+
+# Name results to distinguish between Model 1 and Model 2 estimates.
+names(w_main_results) <- c("model_1", "model_2")
+
+### Produce plots and tables.
+## Produce main results plot.
+# Produce main results matrix for plotting.
+main_mat <- do.call("rbind", lapply(1:2, function (model) {
+									cbind(main_results[[model]], model)
+							 }))
+
+# Store values for constructing 90- and 95-percent CIs.
+z_90 <- qnorm(.95)
+z_95 <- qnorm(.975)
+
+# Open new pdf device.
+setEPS()
+postscript("output/fg1.eps", width = 8, height = 5.5)
+
+# Define custom graphical parameters.
+par(mar = c(8, 7, 2, 2))
+
+# Open new, empty plot.
+plot(0, type = "n", axes = FALSE, ann = FALSE, 
+	 xlim = c(-.3, .05), ylim = c(.8, 4))
+
+# Produce guidelines to go behind point estimates and error bars.
+abline(v = seq(-.3, .05, .05)[-7], col = "lightgrey", lty = 3)
+
+# Add Model 1 point estimates.
+par(new = TRUE)
+plot(x = main_mat$coef[main_mat$model == 1], y = 1:4 + .05, 
+	 xlim = c(-.3, .05), ylim = c(.8, 4), pch = 16, col = "steelblue2", 
+	 xlab = "", ylab = "", axes = FALSE)
+
+# Add Model 2 point estimates.
+par(new = TRUE)
+plot(x = main_mat$coef[main_mat$model == 2], y = 1:4 - .05, 
+	 xlim = c(-.3, .05), ylim = c(.8, 4), pch = 16, col = "#FF8F37", main = "", 
+	 xlab = "", ylab = "", axes = FALSE)
+
+# Add horizontal axis indicating effect estimate size.
+axis(side = 1, at = round(seq(-.3, 0, .05), 2), labels = FALSE)
+mtext(side = 1, at = seq(-.3, .1, .1), text = c("-30", "-20", "-10", "0"), 
+	  cex = .9, line = .75)
+axis(side = 1, at = round(seq(-.25, .05, .05), 2), tck = -.01, labels = FALSE)
+
+# Add vertical axis specifying treatment names corresponding to point estimates.
+axis(side = 2, at = 1:4, labels = FALSE)
+mtext(side = 2, line = .75, at = 1:4, 
+	  text = paste(c("Group", "Security", "Norms", "Institutions"), "Cue"), 
+	  las = 1, padj = .35, cex = .9)
+
+# Add axis labels.
+mtext(side = 2, line = 2.3, at = 4.2, text = "Treatment", 
+	  font = 2, las = 1, xpd = TRUE)
+mtext(side = 1, text = "Estimated Effect Size", line = 2.5, at = -.15, font = 2)
+
+# Add a dashed line at zero.
+abline(v = 0.00, lty = 2)
+
+# Add two-sided, 90-percent CIs.
+with(main_mat[main_mat$model == 1,], 
+	 segments(x0 = coef - z_90 * se, y0 = 1:4 + .05, x1 = coef + z_90 * se, 
+	 	      y1 = 1:4 + .05, col = "steelblue2", lwd = 3))
+with(main_mat[main_mat$model == 2,], 
+	 segments(x0 = coef - z_90 * se, y0 = 1:4 - .05, x1 = coef + z_90 * se, 
+	 		  y1 = 1:4 - .05, col = "#FF8F37", lwd = 3))
+
+# Add two-sided 95-percent CIs.
+with(main_mat[main_mat$model == 1,], 
+	 segments(x0 = coef - z_95 *se, y0 = 1:4 + .05, x1 = coef + z_95 *se, 
+	 		  y1 = 1:4 + .05, col = "steelblue2", lwd = 1))
+with(main_mat[main_mat$model == 2,], 
+	 segments(x0 = coef - z_95 *se, y0 = 1:4 - .05, x1 = coef + z_95 *se, 
+	 		  y1 = 1:4 - .05, col = "#FF8F37", lwd = 1))
+
+# Add legend.
+legend(legend = paste("Model", 1:2), x = -.15, y = -.275, horiz = TRUE, 
+	   pch = 16, col = c("steelblue2", "#FF8F37"), xjust = .5, xpd = TRUE, 
+	   text.width = .05, cex = .9)
+
+# Draw a box around the plot.
+box()
+
+# Close the grpahical device.
+dev.off()
+
+## Create tabular output for main results.
+# Define matrix object of main results.
+tab_dat <- do.call("cbind", main_results)
+
+# Compute control-group means, with SE estimates; define OLS formula.
+ctrl_form <- as.formula(paste(join_tpnw, paste(treats, 
+							  collapse = " + "), sep = " ~ "))
+
+# Fit the OLS model per the specification and recover the control mean.
+ctrl_fit <- lm(ctrl_form, data = tpnw)
+
+# Recover the control-group mean.
+ctrl_mean <- ctrl_fit$coef["(Intercept)"]
+
+# Compute control SE.
+ctrl_se <- sqrt(diag(vcovHC(ctrl_fit, "HC2")))["(Intercept)"]
+
+# Concatenate mean and SE output with blank values for Model 2.
+ctrl_results <- c(format(round(c(ctrl_mean, ctrl_se), 3) * 100, digits = 2), 
+			  	  "|", "|")
+
+# Reformat data to include a decimal point.
+tab_dat <- apply(tab_dat, 2, function (y) format(round(y, 3) * 100, digits = 2))
+
+# Bind control-group means with main results data.
+tab <- rbind(ctrl_results, tab_dat)
+
+# Rename row containing control-group means.
+rownames(tab)[which(rownames(tab) == "1")] <- "control_mean"
+
+# Relabel coefficient columns.
+coef_cols <- grep("coef$", colnames(tab))
+
+# Relabel SE columns.
+se_cols <- grep("se$", colnames(tab))
+
+# Reformat SE estimates to be within parentheses.
+tab[,se_cols] <- apply(tab[, se_cols], 2, function (y) paste0("(", y, ")"))
+
+# Concatenate data to comport with LaTeX tabular markup.
+tab <- paste(paste(paste(capwords(gsub("_", " ", rownames(tab))), 
+			 			 apply(tab, 1, function (x) {
+			 			 	   paste(x, collapse = " & ")
+				 		 }), sep = " & "), collapse = " \\\\\n"), "\\\\\n")
+
+# Produce tabular output.
+sink("output/main_results_tab.tex")
+cat("\\begin{table}\n",
+	  "\\caption{Estimated Treatment Effects on Support for TPNW}\n",
+	  "\\begin{adjustbox}{width = \\textwidth, center}\n",
+	  "\\sisetup{\n",
+      "\tdetect-all,\n",
+      "\ttable-number-alignment = center,\n",
+      "\ttable-figures-integer = 1,\n",
+      "\ttable-figures-decimal = 3,\n",
+      "\ttable-space-text-post = *,\n",
+      "\tinput-symbols = {()}\n",
+  	  "}\n",
+	  paste0("\\begin{tabular}{@{\\extracolsep{5pt}}L{3.5cm}*{4}",
+	  		 "{S[table-number-alignment = right, table-column-width=1.25cm]}}\n"),
+	  "\\toprule\n",
+	  "& \\multicolumn{4}{c}{Model}\\\\\\cmidrule{2-5}\n",
+	  "& \\multicolumn{2}{c}{{(1)}} & \\multicolumn{2}{c}{{(2)}} \\\\\\midrule\n",
+	  tab,
+	  "\\bottomrule\n",
+	  "\\end{tabular}\n",
+	  "\\end{adjustbox}\n",
+	  "\\end{table}\n")
+sink()
+
+## Create tabular output for YouGov results.
+# Restructure data as a matrix.
+aware_tab <- rbind(do.call("rbind", aware_results))
+
+# Reformat data to include three decimal points.
+aware_tab <- apply(aware_tab, 2, function (y) format(round(y, 3) * 100, 
+				   digits = 3))
+
+# Relabel mean rows.
+mean_rows <- endsWith(rownames(aware_tab), "mean")
+
+# Relabel SE rows.
+se_rows <- endsWith(rownames(aware_tab), "se")
+
+# Reformat SE estimates to be within parentheses.
+aware_tab[se_rows,] <- paste0("(", aware_tab[se_rows,], ")")
+
+# Remove row names for rows with SE estimates.
+rownames(aware_tab)[se_rows] <- ""
+
+# Remove "_mean" indication in mean_rows.
+rownames(aware_tab)[mean_rows] <- gsub("_mean", "", 
+										rownames(aware_tab)[mean_rows])
+
+# Add an empty row, where excluded calculations of responses among skips are
+# noted in the table, and rename the relevant row.
+aware_tab <- rbind(aware_tab, c("|", "|"))
+rownames(aware_tab)[nrow(aware_tab)] <- "Skipped"
+
+# Add an empty column to the table, and insert the count column at the relevant
+# indices.
+aware_tab[which(rownames(aware_tab) %in% names(aware_table)),]
+aware_tab <- cbind(aware_tab, "")
+colnames(aware_tab)[ncol(aware_tab)] <- "N"
+aware_tab[which(rownames(aware_tab) %in% names(aware_table)), "N"] <- aware_table
+
+# Concatenate data to comport with LaTeX tabular markup.
+aware_tab <- paste(paste(paste(capwords(gsub("_", " ", rownames(aware_tab))), 
+				 apply(aware_tab, 1, function (x) {
+				 	   paste(x, collapse = " & ")
+				 }), 
+	   			 sep = " & "), collapse = " \\\\\n"), "\\\\\n")
+
+# Produce tabular output.
+sink("output/yougov_tab.tex")
+cat("\\begin{table}\n",
+	  "\\caption{YouGov Survey Responses}\n",
+	  "\\centering\\small\n",
+	  "\\sisetup{\n",
+      "\tdetect-all,\n",
+      "\ttable-number-alignment = center,\n",
+      "\ttable-figures-integer = 1,\n",
+      "\ttable-figures-decimal = 3,\n",      
+      "\tinput-symbols = {()}\n",
+  	  "}\n",
+	  paste0("\\begin{tabular}{@{\\extracolsep{5pt}}L{3.5cm}*{5}", 
+	  		 "{S[table-number-alignment = right, table-column-width=1.25cm]}}\n"),
+	  "\\toprule\n",
+	  "& \\multicolumn{5}{c}{Arm}\\\\\\cmidrule{2-6}\n",
+	  "& {Control} & {Group} & {Security} & {Norms} & {Institutions} \\\\\\midrule\n",
+	  aware_tab,
+	  "\\bottomrule\n",
+	  "\\end{tabular}\n",
+	  "\\end{table}\n")
+sink()
+
+## Create tabular output for attitudinal results.
+# Define matrix object of main results.
+tab_dat <- do.call("cbind", att_results)
+
+# Reformat matrix to alternate mean and SE estimates.
+tab <- sapply(seq(0, 8, 2), function (i) {
+	matrix(c(t(tab_dat[,1:2 + i])), 14, 1)
+})
+
+# Reformat data to include three decimal points.
+tab <- apply(tab, 2, function (y) format(round(y, 3), digits = 3))
+
+# Rename rows to indicate mean and SE estimates.
+rownames(tab) <- paste(rep(rownames(tab_dat), each = 2), 
+					   c("mean", "se"), sep = "_")
+
+# Relabel mean rows.
+mean_rows <- grep("_mean", rownames(tab))
+
+# Relabel SE rows
+se_rows <- grep("_se", rownames(tab))
+
+# Reformat SE estimates to be within parentheses.
+tab[se_rows,] <- apply(tab[se_rows,], 1, function (y) {
+					   paste0("(", gsub(" ", "", y), ")")
+				 })
+
+# Rename rows to improve tabular labels; remove "tpnw_atts, "mean," and "se" row
+# name strings.
+rownames(tab) <- gsub("tpnw_atts|mean$|se$", "", rownames(tab))
+
+# Remove leading and tailing underscores.
+rownames(tab) <- gsub("^_|_$", "", rownames(tab))
+
+# Remove row names for rows with SE estimates.
+rownames(tab)[se_rows] <- ""
+
+# Concatenate data to comport with LaTeX tabular markup.
+tab <- paste(paste(paste(capwords(gsub("_", " ", rownames(tab))), 
+						 apply(tab, 1, function (x) {
+						 	   paste(x, collapse = " & ")
+						 }), 
+	   sep = " & "), collapse = " \\\\\n"), "\\\\\n")
+
+# Produce tabular output.
+sink("output/atts_tab.tex")
+cat("\\begin{table}\n",
+	  "\\caption{Attitudes Toward Nuclear Weapons by Arm}\n",
+	  "\\centering\\small\n",
+	  "\\sisetup{\n",
+      "\tdetect-all,\n",
+      "\ttable-number-alignment = center,\n",
+      "\ttable-figures-integer = 1,\n",
+      "\ttable-figures-decimal = 3,\n",
+      "\ttable-space-text-post = *,\n",
+      "\tinput-symbols = {()}\n",
+  	  "}\n",
+	  paste0("\\begin{tabular}{@{\\extracolsep{5pt}}L{3.5cm}*{5}", 
+	  		 "{S[table-number-alignment = center, table-column-width=1.25cm]}}\n"),
+	  "\\toprule\n",
+	  "& \\multicolumn{5}{c}{Arm}\\\\\\cmidrule{2-6}\n",
+	  "& {Control} & {Group} & {Security} & {Norms} & {Institutions} \\\\\\midrule\n",
+	  tab,
+	  "\\bottomrule\n",
+	  "\\end{tabular}\n",
+	  "\\end{table}\n")
+sink()
+
+## Create tabular output for results by political party.
+# Restructure data such that mean and SE estimates are alternating rows in a
+# 1 x 6 matrix, in each of five list elements, corresponding to each treatment
+# group; and bind the results for each treatment group.
+pid_tab <- lapply(pid_results, function (x) {
+				  matrix(unlist(x), nrow = 6, ncol = 1)
+		   })
+pid_tab <- do.call("cbind", pid_tab)
+
+# Assign row names to distinguish results for each partisan group, and mean and
+# SE estimates.
+rownames(pid_tab) <- paste(rep(c("democrat", "independent", "republican"), 
+							   each = 2), c("mean", "se"))
+
+# Relabel mean rows.
+mean_rows <- endsWith(rownames(pid_tab), "mean")
+
+# Relabel SE rows.
+se_rows <- endsWith(rownames(pid_tab), "se")
+
+# Label columns per treatment, for the computation of ATEs.
+colnames(pid_tab) <- c("control", treats)
+
+# Compute ATEs, with control as baseline, and update tabular data.
+pid_tab[mean_rows, treats] <- pid_tab[mean_rows, treats] - 
+							  pid_tab[mean_rows, "control"]
+
+# Reformat data to include three decimal points.
+pid_tab <- apply(pid_tab, 2, function (y) format(round(y, 3) * 100, digits = 3))
+
+# Remove extraneous spacing.
+pid_tab <- gsub(" ", "", pid_tab)
+
+# Reformat SE estimates to be within parentheses.
+pid_tab[se_rows,] <- paste0("(", pid_tab[se_rows,], ")")
+
+# Remove row names for rows with SE estimates.
+rownames(pid_tab)[se_rows] <- ""
+
+# Concatenate data to comport with LaTeX tabular markup.
+pid_tab <- paste(paste(paste(capwords(gsub("_", " ", rownames(pid_tab))), 
+				 apply(pid_tab, 1, function (x) {
+				 	   paste(x, collapse = " & ")
+				 }), 
+	   			 sep = " & "), collapse = " \\\\\n"), "\\\\\n")
+
+# Produce tabular output.
+sink("output/pid_support.tex")
+cat("\\begin{table}\n",
+	  "\\caption{Support for Joining TPNW by Party ID}\n",
+	  "\\centering\\small\n",
+	  "\\sisetup{\n",
+      "\tdetect-all,\n",
+      "\ttable-number-alignment = center,\n",
+      "\ttable-figures-integer = 1,\n",
+      "\ttable-figures-decimal = 3,\n",      
+      "\tinput-symbols = {()}\n",
+  	  "}\n",
+	  paste0("\\begin{tabular}{@{\\extracolsep{5pt}}L{3.5cm}*{5}", 
+	  		 "{S[table-number-alignment = right, table-column-width=1.25cm]}}\n"),
+	  "\\toprule\n",
+	  "& \\multicolumn{5}{c}{Arm}\\\\\\cmidrule{2-6}\n",
+	  "& {Control} & {Group} & {Security} & {Norms} & {Institutions} \\\\\\midrule\n",
+	  pid_tab,
+	  "\\bottomrule\n",
+	  "\\end{tabular}\n",
+	  "\\end{table}\n")
+sink()
+
+## Create tabular output for results by political ideology.
+# Restructure data such that mean and SE estimates are alternating rows in a
+# 1 x 6 matrix, in each of five list elements, corresponding to each treatment
+# group; and bind the results for each treatment group.
+ideo_tab <- lapply(ideo_results, function (x) {
+				   matrix(unlist(x), nrow = 6, ncol = 1)
+			})
+ideo_tab <- do.call("cbind", ideo_tab)
+
+# Assign row names to distinguish results for each idelogical group, and mean 
+# and SE estimates.
+rownames(ideo_tab) <- paste(rep(c("liberal", "moderate", "conservative"), 
+							   each = 2), c("mean", "se"))
+
+# Reformat data to include three decimal points.
+ideo_tab <- apply(ideo_tab, 2, function (y) format(round(y, 3) * 100,
+				  digits = 3))
+
+# Relabel mean rows.
+mean_rows <- endsWith(rownames(ideo_tab), "mean")
+
+# Relabel SE rows.
+se_rows <- endsWith(rownames(ideo_tab), "se")
+
+# Reformat SE estimates to be within parentheses.
+ideo_tab[se_rows,] <- paste0("(", ideo_tab[se_rows,], ")")
+
+# Remove row names for rows with SE estimates.
+rownames(ideo_tab)[se_rows] <- ""
+
+# Concatenate data to comport with LaTeX tabular markup.
+ideo_tab <- paste(paste(paste(capwords(gsub("_", " ", rownames(ideo_tab))), 
+				 apply(ideo_tab, 1, function (x) {
+				 	   paste(x, collapse = " & ")
+				 }), 
+	   			 sep = " & "), collapse = " \\\\\n"), "\\\\\n")
+
+# Produce tabular output.
+sink("output/ideo_support_tab.tex")
+cat("\\begin{table}\n",
+	  "\\caption{Support for Joining TPNW by Ideology}\n",
+	  "\\centering\\small\n",
+	  "\\sisetup{\n",
+      "\tdetect-all,\n",
+      "\ttable-number-alignment = center,\n",
+      "\ttable-figures-integer = 1,\n",
+      "\ttable-figures-decimal = 3,\n",      
+      "\tinput-symbols = {()}\n",
+  	  "}\n",
+	  paste0("\\begin{tabular}{@{\\extracolsep{5pt}}L{3.5cm}*{5}",
+	  		 "{S[table-number-alignment = right, table-column-width=1.25cm]}}\n"),
+	  "\\toprule\n",
+	  "& \\multicolumn{5}{c}{Arm}\\\\\\cmidrule{2-6}\n",
+	  "& {Control} & {Group} & {Security} & {Norms} & {Institutions} \\\\\\midrule\n",
+	  ideo_tab,
+	  "\\bottomrule\n",
+	  "\\end{tabular}\n",
+	  "\\end{table}\n")
+sink()
+
+## Create tabular output for weighted main results.
+# Define matrix object of weighted main results.
+w_tab_dat <- do.call("cbind", w_main_results)
+
+# Compute weighted control-group means, with SE estimates; define OLS formula.
+w_ctrl_form <- as.formula(paste(join_tpnw, paste(treats, 
+							  collapse = " + "), sep = " ~ "))
+
+# Fit the OLS model per the specification and recover the control mean.
+w_ctrl_fit <- lm(w_ctrl_form, data = tpnw, 
+				  weights = anesrake_weight)
+
+# Recover the control-group mean.
+w_ctrl_mean <- w_ctrl_fit$coef["(Intercept)"]
+
+# Compute control SE.
+w_ctrl_se <- sqrt(diag(vcovHC(w_ctrl_fit, "HC2")))["(Intercept)"]
+
+
+# Concatenate mean and SE output with blank values for Model 2.
+w_ctrl_results <- c(format(round(c(w_ctrl_mean, w_ctrl_se), 3) * 100, 
+						   digits = 2), "|", "|")
+
+# Reformat data to include a decimal point.
+w_tab_dat <- apply(w_tab_dat, 2, function (y) format(round(y, 3) * 100, 
+				   digits = 2))
+
+# Bind control-group means with main results data.
+w_tab <- rbind(w_ctrl_results, w_tab_dat)
+
+# Rename row containing control-group means.
+rownames(w_tab)[which(rownames(w_tab) == "1")] <- "control_mean"
+
+# Relabel coefficient columns.
+coef_cols <- grep("coef$", colnames(w_tab))
+
+# Relabel SE columns.
+se_cols <- grep("se$", colnames(w_tab))
+
+# Reformat SE estimates to be within parentheses.
+w_tab[,se_cols] <- apply(w_tab[, se_cols], 2, function (y) paste0("(", y, ")"))
+
+# Concatenate data to comport with LaTeX tabular markup.
+w_tab <- paste(paste(paste(capwords(gsub("_", " ", rownames(w_tab))), 
+			 			 apply(w_tab, 1, function (x) {
+			 			 	   paste(x, collapse = " & ")
+				 		 }), sep = " & "), collapse = " \\\\\n"), "\\\\\n")
+
+# Produce tabular output.
+sink("output/weighted_main_results_tab.tex")
+cat("\\begin{table}\n",
+	  "\\caption{Estimated Treatment Effects on Support for TPNW (Weighted)}\n",
+	  "\\begin{adjustbox}{width = \\textwidth, center}\n",
+	  "\\sisetup{\n",
+      "\tdetect-all,\n",
+      "\ttable-number-alignment = center,\n",
+      "\ttable-figures-integer = 1,\n",
+      "\ttable-figures-decimal = 3,\n",
+      "\ttable-space-text-post = *,\n",
+      "\tinput-symbols = {()}\n",
+  	  "}\n",
+	  paste0("\\begin{tabular}{@{\\extracolsep{5pt}}L{3.5cm}*{4}", 
+	  		 "{S[table-number-alignment = right, table-column-width=1.25cm]}}\n"),
+	  "\\toprule\n",
+	  "& \\multicolumn{4}{c}{Model}\\\\\\cmidrule{2-5}\n",
+	  "& \\multicolumn{2}{c}{{(1)}} & \\multicolumn{2}{c}{{(2)}} \\\\\\midrule\n",
+	  w_tab,
+	  "\\bottomrule\n",
+	  "\\end{tabular}\n",
+	  "\\end{adjustbox}\n",
+	  "\\end{table}\n")
+sink()
+
+### Save image containing all objects.
+save.image(file = "output/hbg_replication_out.RData")

1/replication-package/scripts/hbg_cleaning.R

@@ -0,0 +1,406 @@
+### Initialize workspace.
+rm(list = ls(all = TRUE))
+setwd("~/Downloads/hbg_replication")
+
+# Load required packages
+library(plyr)
+library(car)
+library(anesrake)
+
+# Load relevant functions.
+source("scripts/helper_functions.R")
+
+## Load data.
+# Load TPNW experimental data.
+tpnw <- read.csv("data/tpnw_raw.csv", stringsAsFactors = FALSE, row.names = 1)
+
+# Load original income question data.
+orig_inc <- read.csv("data/tpnw_orig_income.csv", stringsAsFactors = FALSE, 
+                     row.names = 1)
+
+# Load YouGov data (including covariates and awareness question).
+aware <- read.csv("data/tpnw_aware_raw.csv", stringsAsFactors = FALSE, 
+                  row.names = 1)
+
+### Clean TPNW data.
+## Clean data.
+# Remove first two (extraneous) rows.
+tpnw <- tpnw[-c(1, 2),]
+orig_inc <- orig_inc[-c(1, 2),]
+
+# Remove respondents who did not consent.
+tpnw <- tpnw[tpnw$consent == "1",]
+orig_inc <- orig_inc[orig_inc$consent == "1",]
+
+# Coalesce income variables.
+orig_inc <- within(orig_inc, {
+	income <- as.numeric(income)
+	income <- ifelse(income < 1000, NA, income)
+	income <- ifelse(income < 15000, 1, income)
+	income <- ifelse(income >= 15000 & income < 25000, 2, income)
+	income <- ifelse(income >= 25000 & income < 50000, 3, income)
+	income <- ifelse(income >= 50000 & income < 75000, 4, income)
+	income <- ifelse(income >= 75000 & income < 100000, 5, income)
+	income <- ifelse(income >= 100000 & income < 150000, 6, income)
+	income <- ifelse(income >= 150000 & income < 200000, 7, income)
+	income <- ifelse(income >= 200000 & income < 250000, 8, income)
+	income <- ifelse(income >= 250000 & income < 500000, 9, income)
+	income <- ifelse(income >= 500000 & income < 1000000, 10, income)
+	income <- ifelse(income >= 1000000, 11, income)
+})
+orig_inc <- data.frame(pid = orig_inc$pid, income_old = orig_inc$income)
+tpnw <- plyr::join(tpnw, orig_inc, by = "pid", type = "left")
+tpnw <- within(tpnw, {
+			income <- coalesce(as.numeric(income), as.numeric(income_old))
+		})
+
+# Note meta variables.
+meta <- c("consent", "confirmation_code", "new_income_q")
+
+# Note Qualtrics variables.
+qualtrics_vars <- c("StartDate", "EndDate", "Status", "Progress", 
+                    "Duration..in.seconds.", "Finished", "RecordedDate", 
+                    "DistributionChannel", "UserLanguage")
+
+# Note Dynata variables.
+dynata_vars <- c("pid", "psid")
+
+# Note non-numeric variables.
+char_vars <- c(qualtrics_vars, dynata_vars,
+			   c("ResponseId"), names(tpnw)[grep("text", tolower(names(tpnw)))])
+char_cols <- which(names(tpnw) %in% char_vars)
+
+# Numericize other variables
+tpnw <- data.frame(apply(tpnw[, -char_cols], 2, as.numeric), tpnw[char_cols])
+
+tpnw_atts <- which(names(tpnw) %in% c("danger", "peace", "safe", "use_unaccept", 
+									  "always_cheat", "cannot_elim", "slow_reduc"))
+names(tpnw)[tpnw_atts] <- paste("tpnw_atts", names(tpnw)[tpnw_atts], sep = "_")
+
+# Coalesce relevant variables.
+tpnw <- within(tpnw, {
+	# Clean gender variable.
+	female <- ifelse(gender == 95, NA, gender)
+
+	# Transform birthyr variable to age.
+	age <- 2019 - birthyr
+
+	# Transform income variable.
+	income <- car::recode(income, "95 = NA")
+	
+	# Combine pid and pid_forc variables.
+	pid3 <- ifelse(pid3 == 0, pid_forc, pid3)
+	
+	# Recode ideology variable.
+	ideo <- car::recode(ideo, "3 = NA")
+
+	# Recode education variable.
+	educ <- car::recode(educ, "95 = NA")
+
+	# Recode state variable.
+	state <- recode(state, "1 = 'Alabama';
+                          2 = 'Alaska';
+                          4 = 'Arizona';
+                          5 = 'Arkansas';
+                          6 = 'California';
+                          8 = 'Colorado';
+                          9 = 'Connecticut';
+                          10 = 'Delaware';
+                          11 = 'Washington DC';
+                          12 = 'Florida';
+                          13 = 'Georgia';
+                          15 = 'Hawaii';
+                          16 = 'Idaho';
+                          17 = 'Illinois';
+                          18 = 'Indiana';
+                          19 = 'Iowa';
+                          20 = 'Kansas';
+                          21 = 'Kentucky';
+                          22 = 'Louisiana';
+                          23 = 'Maine';
+                          24 = 'Maryland';
+                          25 = 'Massachusetts';
+                          26 = 'Michigan';
+                          27 = 'Minnesota';
+                          28 = 'Mississippi';
+                          29 = 'Missouri';
+                          30 = 'Montana';
+                          31 = 'Nebraska';
+                          32 = 'Nevada';
+                          33 = 'New Hampshire';
+                          34 = 'New Jersey';
+                          35 = 'New Mexico';
+                          36 = 'New York';
+                          37 = 'North Carolina';
+                          38 = 'North Dakota';
+                          39 = 'Ohio';
+                          40 = 'Oklahoma';
+                          41 = 'Oregon';
+                          42 = 'Pennsylvania';
+                          44 = 'Rhode Island';
+                          45 = 'South Carolina';
+                          46 = 'South Dakota';
+                          47 = 'Tennessee';
+                          48 = 'Texas';
+                          49 = 'Utah';
+                          50 = 'Vermont';
+                          51 = 'Virginia';
+                          53 = 'Washington';
+                          54 = 'West Virginia';
+                          55 = 'Wisconsin';
+                          56 = 'Wyoming'")
+
+  # Create regional indicators.
+  northeast <- state %in% c("Connecticut", "Maine", "Massachusetts", 
+                            "New Hampshire", "Rhode Island", "Vermont", 
+                            "New Jersey", "New York", "Pennsylvania")
+  midwest <- state %in% c("Illinois", "Indiana", "Michigan", "Ohio", 
+                          "Wisconsin", "Iowa", "Kansas", "Minnesota", 
+                          "Missouri", "Nebraska", "North Dakota", 
+                          "South Dakota")
+  south <- state %in% c("Delaware", "Florida", "Georgia", "Maryland", 
+                        "North Carolina", "South Carolina", "Virginia", 
+                        "Washington DC", "West Virginia", "Alabama", 
+                        "Kentucky", "Mississippi", "Tennessee", "Arkansas", 
+                        "Louisiana", "Oklahoma", "Texas")
+  west <- state %in% c("Arizona", "Colorado", "Idaho", "Montana", "Nevada", 
+                        "New Mexico", "Utah", "Wyoming", "Alaska", 
+                        "California", "Hawaii", "Oregon", "Washington")
+
+  # Recode join_tpnw outcome.
+  join_tpnw <- car::recode(join_tpnw, "2 = 0")
+
+  # Create indicator variables for each treatment arm.
+  control <- treatment == 0
+  group_cue <- treatment == 1
+  security_cue <- treatment == 2
+  norms_cue <- treatment == 3
+  institutions_cue <- treatment == 4
+
+  # Recode attitudinal outcomes.
+  tpnw_atts_danger <- recode(tpnw_atts_danger, "-2 = 2; -1 = 1; 1 = -1; 2 = -2")
+  tpnw_atts_use_unaccept <- recode(tpnw_atts_use_unaccept, "-2 = 2; -1 = 1; 
+                                                            1 = -1; 2 = -2")
+  tpnw_atts_always_cheat <- recode(tpnw_atts_always_cheat, "-2 = 2; -1 = 1; 
+                                                            1 = -1; 2 = -2")
+  tpnw_atts_cannot_elim <- recode(tpnw_atts_cannot_elim, "-2 = 2; -1 = 1; 
+                                                            1 = -1; 2 = -2")
+})
+
+# Use mean imputation for missingness.
+# Redefine char_cols object.
+char_cols <- which(names(tpnw) %in% c(char_vars, meta, "state", "pid_forc", 
+									  "income_old", "gender"))
+
+# Define out_vars object.
+out_vars <- which(names(tpnw) %in% c("join_tpnw", "n_nukes", "n_tests") |
+				  startsWith(names(tpnw), "tpnw_atts") | 
+				  startsWith(names(tpnw), "physical_eff") |
+				  startsWith(names(tpnw), "testing_matrix"))
+
+# Mean impute.
+tpnw[,-c(char_cols, out_vars)] <- 
+      data.frame(apply(tpnw[, -c(char_cols, out_vars)], 2, function (x) {
+ 								     replace(x, is.na(x), mean(x, na.rm = TRUE))
+							 	 }))
+
+### Clean YouGov data.
+## Indicate all non-numeric variables.
+# Indicate YouGov metadata variables (e.g., start/end time, respondent ID) that
+# may contain characters.
+yougov_vars <- c("starttime", "endtime")
+
+# Numericize all numeric variables
+aware <- data.frame(apply(aware[, -which(names(aware) %in% yougov_vars)], 2, 
+             as.numeric), aware[which(names(aware) %in% yougov_vars)])
+
+# Coalesce relevant variables.
+aware <- within(aware, {
+  # Clean gender variable to an indicator of female gender (renamed below).
+  gender <- recode(gender, "8 = NA") - 1
+
+  # Transform birthyr variable to age (renamed below).
+  birthyr <- 2020 - birthyr
+
+  # Recode pid3 variable.
+  pid3 <- recode(pid3, "1 = -1; 2 = 1; 3 = 0; c(5, 8, 9) = NA")
+
+  # Recode pid7
+  pid7 <- recode(pid7, "1 = -3; 2 = -2; 3 = -1; 4 = 0; 5 = 1; 6 = 2; 7 = 3;
+              c(8, 98) = NA")
+
+  # Code pid variable from pid7.
+  party <- recode(pid7, "c(-3, -2, -1) = -1; c(1, 2, 3) = 1")
+
+  # Recode ideology variable.
+  ideo5 <- recode(ideo5, "c(6, 8, 9) = NA") - 3
+
+  # Recode education variable.
+  educ <- recode(educ, "c(8, 9) = NA")
+
+  # Recode state variable.
+  state <- recode(inputstate, "1 = 'Alabama';
+                               2 = 'Alaska';
+                               4 = 'Arizona';
+                               5 = 'Arkansas';
+                               6 = 'California';
+                               8 = 'Colorado';
+                               9 = 'Connecticut';
+                               10 = 'Delaware';
+                               11 = 'Washington DC';
+                               12 = 'Florida';
+                               13 = 'Georgia';
+                               15 = 'Hawaii';
+                               16 = 'Idaho';
+                               17 = 'Illinois';
+                               18 = 'Indiana';
+                               19 = 'Iowa';
+                               20 = 'Kansas';
+                               21 = 'Kentucky';
+                               22 = 'Louisiana';
+                               23 = 'Maine';
+                               24 = 'Maryland';
+                               25 = 'Massachusetts';
+                               26 = 'Michigan';
+                               27 = 'Minnesota';
+                               28 = 'Mississippi';
+                               29 = 'Missouri';
+                               30 = 'Montana';
+                               31 = 'Nebraska';
+                               32 = 'Nevada';
+                               33 = 'New Hampshire';
+                               34 = 'New Jersey';
+                               35 = 'New Mexico';
+                               36 = 'New York';
+                               37 = 'North Carolina';
+                               38 = 'North Dakota';
+                               39 = 'Ohio';
+                               40 = 'Oklahoma';
+                               41 = 'Oregon';
+                               42 = 'Pennsylvania';
+                               44 = 'Rhode Island';
+                               45 = 'South Carolina';
+                               46 = 'South Dakota';
+                               47 = 'Tennessee';
+                               48 = 'Texas';
+                               49 = 'Utah';
+                               50 = 'Vermont';
+                               51 = 'Virginia';
+                               53 = 'Washington';
+                               54 = 'West Virginia';
+                               55 = 'Wisconsin';
+                               56 = 'Wyoming'")
+
+  # Define US Census geographic regions.
+  northeast <- inputstate %in% c(9, 23, 25, 33, 44, 50, 34, 36, 42)
+  midwest <- inputstate %in% c(18, 17, 26, 39, 55, 19, 20, 27, 29, 31, 38, 46)
+  south <- inputstate %in% c(10, 11, 12, 13, 24, 37, 45, 51, 
+              54, 1, 21, 28, 47, 5, 22, 40, 48)
+  west <- inputstate %in% c(4, 8, 16, 35, 30, 49, 32, 56, 2, 6, 15, 41, 53)
+
+  # Recode employment.
+  employ <- recode(employ, "c(9, 98, 99) = NA")
+
+  # Recode outcome.
+  awareness <- recode(awareness, "8 = NA")
+
+  # Normalize weights.
+  weight <- weight / sum(weight)
+})
+
+# Rename demographic questions.
+aware <- rename(aware, c("gender" = "female", "birthyr" = "age", 
+             "faminc_new" = "income", "ideo5" = "ideo"))
+
+## Impute missing values.
+# Specify non-covariate numerical variables (other is exempted since over 10% of 
+# responses are missing; state is exempted since the variable is categorical).
+non_covars <- names(aware)[names(aware) %in% c("caseid", "starttime", "endtime", 
+                                               "awareness", "state", "weight")]
+
+# Use mean imputation for missingness in covariates.
+aware[, -which(names(aware) %in% non_covars)] <- 
+  data.frame(apply(aware[, -which(names(aware) %in% 
+               non_covars)], 2, function (x) {
+                     replace(x, is.na(x), mean(x, na.rm = TRUE))
+                 }))
+
+### Produce weights for TPNW experimental data using anesrake.
+## Create unique identifier variable for assigning weights.
+tpnw$caseid <- 1:nrow(tpnw)
+
+## Recode relevant covariates for reweighting: coarsen age; recode female; and 
+## recode geographic covariates.
+# Coarsen age into a categorical variable for age groups.
+tpnw$age_wtng <- cut(tpnw$age, c(0, 25, 35, 45, 55, 65, 99))
+levels(tpnw$age_wtng) <- c("age1824", "age2534", "age3544",
+                      "age4554", "age5564", "age6599")
+
+# Recode female as a factor to account for NA values.
+tpnw$female_wtng <- as.factor(tpnw$female)
+levels(tpnw$female_wtng) <- c("male", "na", "female")
+
+# Recode northeast as a factor.
+tpnw$northeast_wtng <- as.factor(tpnw$northeast)
+levels(tpnw$northeast_wtng) <- c("other", "northeast")
+
+# Recode midwest as a factor.
+tpnw$midwest_wtng <- as.factor(tpnw$midwest)
+levels(tpnw$midwest_wtng) <- c("other", "midwest")
+
+# Recode south as a factor.
+tpnw$south_wtng <- as.factor(tpnw$south)
+levels(tpnw$south_wtng) <- c("other", "south")
+
+# Recode west as a factor.
+tpnw$west_wtng <- as.factor(tpnw$west)
+levels(tpnw$west_wtng) <- c("other", "west")
+
+## Specify population targets for balancing (from US Census 2018 data).
+# Specify gender proportion targets and assign names to comport with factors.
+femaletarg <- c(.508, 0, .492)
+names(femaletarg) <- c("female", "na", "male")
+
+# Specify age-group proportion targets and assign names to comport with factors.
+agetarg <- c(29363, 44854, 40659, 41537, 41700, 51080)/249193
+names(agetarg) <- c("age1824", "age2534", "age3544",
+               "age4554", "age5564", "age6599")
+
+# Specify northeast proportion targets and assign names to comport with factors.
+northeasttarg <- c(1 - .173, .173)
+names(northeasttarg) <- c("other", "northeast")
+
+# Specify midwest proportion targets and assign names to comport with factors.
+midwesttarg <- c(1 - .209, .209)
+names(midwesttarg) <- c("other", "midwest")
+
+# Specify south proportion targets and assign names to comport with factors.
+southtarg <- c(1 - .380, .380)
+names(southtarg) <- c("other", "south")
+
+# Specify west proportion targets and assign names to comport with factors.
+westtarg <- c(1 - .238, .238)
+names(westtarg) <- c("other", "west")
+
+# Create a list of all targets, with names to comport with relevant variables.
+targets <- list(femaletarg, agetarg, northeasttarg, 
+                midwesttarg, southtarg, westtarg)
+names(targets) <- c("female_wtng", "age_wtng", "northeast_wtng", 
+                    "midwest_wtng", "south_wtng", "west_wtng")
+
+# Produce anesrake weights.
+anesrake_out <- anesrake(targets, tpnw, caseid = tpnw$caseid,
+                             verbose = TRUE)
+
+# Append anesrake weights to TPNW experimental data.
+tpnw$anesrake_weight <- anesrake_out$weightvec
+
+# Remove variables used for weighting.
+tpnw <- tpnw[-grep("wtng$", names(tpnw))]
+
+## Write data.
+# Write full experimental dataset.
+write.csv(tpnw, "data/tpnw_data.csv")
+
+# write full YouGov dataset.
+write.csv(aware, "data/tpnw_aware.csv")

1/replication-package/scripts/hbg_group_cue.R

@@ -0,0 +1,53 @@
+## Initialize workspace.
+# Remove objects.
+rm(list = ls(all = TRUE))
+
+## Generate data.
+# Create count object storing count data.
+count <- as.matrix(c(1547, 54, 2346))
+
+# Convert count object to an object storing percentages.
+perc <- sapply(count, function (x) x/sum(count))
+
+# Create a cumulative percentage object.
+cum_perc <- cumsum(perc)
+
+# Create separate objects for the plotting of each proportion.
+power_x <- c(0, rep(.74, 2), 0)
+both_x <- c(.74, rep(.96, 2), .74)
+weap_x <- c(.96, rep(1, 2), .96)
+
+# Create an object representing the y-axis plotting points for each polygon.
+plot_y <- c(2.25, 2.25, 3, 3)
+
+# Open new .pdf file.
+setEPS()
+postscript("fgc1.eps", width = 10, height = 3)
+
+# Modify graphical parameters (margins).
+par(mar = c(0, 6, 6, 1))
+
+# Create an empty plot.
+plot(1, type = "n", xlab = "", ylab = "", xlim = c(0, 1), ylim = c(1.5, 3), axes = FALSE)
+
+# Create polygons representing each proportion.
+polygon(power_x, plot_y, col = "#FF8F37", border = "white")
+polygon(both_x, plot_y, col = "steelblue3", border = "white")
+polygon(weap_x, plot_y, col = "gray", border = "white")
+
+# Create an axis and tick and axis labels.
+axis(side = 3, at = seq(0, 1, .1), labels = FALSE)
+text(x = seq(0, 1, .2), y = par("usr")[4] + .2, labels = c("0%", "20%", "40%", "60%", "80%", "100%"), xpd = TRUE)
+mtext(text = "Proportion of Responses", side = 3, line = 2.5, cex = 1.25, font = 2)
+
+# Add text denoting the percentage number associated of each proportion.
+text(x = .74/2, y = 2.2, pos = 1, cex = 2, labels = "74%", col = "#FF8F37", font = 2)
+text(x = .85, y = 2.2, pos = 1, cex = 2, labels = "22%", col = "steelblue3", font = 2)
+text(x = .98, y = 2.2, labels = "4%", pos = 1, cex = 2, col = "grey", font = 2, xpd = TRUE)
+
+# Add a legend.
+leg = legend(x = -.16,, y = 2.625, legend = c("Oppose", "Support", "Prefer not\nto answer"), xpd = TRUE, 
+	pch = 16, col = c("#FF8F37", "steelblue3", "grey"), box.lty = 0, cex = .9, y.intersp = 1.5, yjust = .5)
+
+# Close the device.
+dev.off()

1/replication-package/scripts/helper_functions.R

@@ -0,0 +1,16 @@
+# Define coalesce function for recoding of post-election thermometers.
+coalesce <- function (...) {
+  Reduce(function(x, y) {
+    i <- which(is.na(x))
+    x[i] <- y[i]
+    x},
+    list(...))
+}
+
+# Define capwords() function from the toupper() documentation.
+capwords <- function(s, strict = FALSE) {
+         cap <- function(s) paste(toupper(substring(s, 1, 1)),
+                       {s <- substring(s, 2); if(strict) tolower(s) else s},
+                                  sep = "", collapse = " " )
+         sapply(strsplit(s, split = " "), cap, USE.NAMES = !is.null(names(s)))
+     }

1/replication-package/scripts/run_hbg_replication.R

@@ -0,0 +1,36 @@
+## Initialize workspace.
+# Clear workspace.
+rm(list = ls(all = TRUE))
+
+# Set working directory to abp_replication directory.
+setwd("~/Downloads/hbg_replication")
+
+## Prepare output directory and main output files.
+# If an output directory does not exist, create the directory.
+if (!file.exists("output")) {
+	dir.create("output")
+}
+
+# Create a log file for console output.
+hbg_log <- file("output/hbg_log.txt", open = "wt")
+
+# Echo and sink console log to psv_log file.
+sink(hbg_log, append = TRUE)
+sink(hbg_log, append = TRUE, type = "message")
+
+## Replicate files and produce main output.
+# Run abp_replication_code.R script, storing run-time statistics.
+run_time <- system.time({source("scripts/hbg_cleaning.R", echo = TRUE, 
+								max.deparse.length = 10000)
+						 source("scripts/hbg_analysis.R", echo = TRUE,
+						 	    max.deparse.length = 10000)})
+
+# Close main output sink.
+sink()
+sink(type = "message")
+
+## Sink run-time statistics to a run_time output file.
+run_time_file <- file("output/run_time", open = "wt")
+sink(run_time_file, append = TRUE)
+print(run_time)
+sink()

1/should_reproduce.txt

@@ -0,0 +1,2 @@
+Figure 1
+Table 1