diff --git a/hot-dog-survey-data/README.md b/hot-dog-survey-data/README.md index 13e8e5e5..4c68160a 100644 --- a/hot-dog-survey-data/README.md +++ b/hot-dog-survey-data/README.md @@ -1,13 +1,13 @@ # Hot Dog Survey Data -We collected data from students by having them filling out a survey. The raw data are uploaded in the file hot_dog_survey_spring25.xlsx, and the data dictionary describing the data is in the file data_dictionary_sp25. +We collected data from students by having them filling out a survey. The raw data are uploaded in the Hotdog Survey (Responses) - Form Responses 1.csv, and the data dictionary describing the data is in the file data_dictionary_sp26. * Raw Data: - * Hotdog Form and YikYak - * https://myuva-my.sharepoint.com/:x:/g/personal/tbh7cm_virginia_edu/EU5yFl_KW5FGhbHH7qBTUhgBp9r1UPJ_iS4GKGTcA2PJ-A?e=HxAxGm + * Hotdog Form + * https://docs.google.com/forms/d/e/1FAIpQLSezZpF928-VT6ni4q9eiV3ARgxC1ETJo7THdXbLGDpmauzfoQ/viewform * Data Dictionary: - * Included within the HotdogSurvey.csv -* Raw Data: -* Established Data: Combined Yik Yak, Survey, and Srat/Frat Data + * Included within the data_dictionary_sp26 +* Raw Data: Hotdog Survey (Responses) - Form Responses 1.csv +* Established Data: Survey Data diff --git a/hot-dog-survey-data/sp26/DS Project 0 Analysis Plan.pdf b/hot-dog-survey-data/sp26/DS Project 0 Analysis Plan.pdf new file mode 100644 index 00000000..7dc0c57b Binary files /dev/null and b/hot-dog-survey-data/sp26/DS Project 0 Analysis Plan.pdf differ diff --git a/hot-dog-survey-data/sp26/DS-4002-sp26-survey-results.csv b/hot-dog-survey-data/sp26/DS-4002-sp26-survey-results.csv new file mode 100644 index 00000000..db3add09 --- /dev/null +++ b/hot-dog-survey-data/sp26/DS-4002-sp26-survey-results.csv @@ -0,0 +1,80 @@ +Timestamp,Are you a full-time undergraduate UVA student?,What year are you?,Do you believe a hotdog is a sandwich? +1/14/2026 14:54:44,Yes,3rd,No +1/14/2026 14:55:14,Yes,4th,No +1/14/2026 14:56:49,Yes,4th,No +1/14/2026 14:58:15,Yes,4th,Yes +1/14/2026 14:58:25,Yes,4th,Yes +1/14/2026 14:58:27,Yes,4th,Yes +1/14/2026 14:58:31,Yes,3rd,Yes +1/14/2026 15:02:11,Yes,3rd,No +1/14/2026 15:02:11,Yes,3rd,No +1/14/2026 15:02:19,Yes,4th,Yes +1/14/2026 15:02:21,Yes,4th,No +1/14/2026 15:02:28,Yes,2nd,No +1/14/2026 15:02:38,No,4th,No +1/14/2026 15:03:19,Yes,2nd,No +1/14/2026 15:03:34,Yes,2nd,No +1/14/2026 15:03:36,Yes,4th,No +1/14/2026 15:04:39,Yes,3rd,No +1/14/2026 15:05:29,Yes,3rd,No +1/14/2026 15:10:35,Yes,4th,No +1/14/2026 15:12:15,Yes,4th,No +1/14/2026 15:14:01,Yes,2nd,No +1/14/2026 15:17:17,Yes,2nd,No +1/14/2026 15:17:34,Yes,4th,Yes +1/14/2026 15:20:57,Yes,4th,No +1/14/2026 15:21:43,Yes,4th,No +1/14/2026 15:28:13,Yes,4th,No +1/14/2026 15:28:25,Yes,4th,No +1/14/2026 15:34:58,Yes,4th,Yes +1/14/2026 15:39:52,Yes,3rd,No +1/14/2026 15:44:11,Yes,4th,Yes +1/14/2026 15:50:26,Yes,2nd,Yes +1/14/2026 15:50:44,Yes,4th,No +1/14/2026 15:53:59,Yes,4th,Yes +1/14/2026 15:58:58,Yes,2nd,No +1/14/2026 16:06:04,Yes,4th,Yes +1/14/2026 16:09:44,Yes,2nd,No +1/14/2026 16:09:51,No,4th,No +1/14/2026 16:10:18,Yes,4th,No +1/14/2026 16:14:01,Yes,4th,No +1/14/2026 16:21:13,Yes,3rd,Yes +1/14/2026 16:26:22,Yes,2nd,No +1/14/2026 16:27:56,Yes,4th,No +1/14/2026 16:35:33,Yes,4th,No +1/14/2026 16:51:46,Yes,4th,No +1/14/2026 17:00:12,Yes,4th,No +1/14/2026 17:06:45,Yes,4th,Yes +1/14/2026 17:08:01,Yes,2nd,No +1/14/2026 17:09:07,Yes,4th,No +1/14/2026 17:14:08,Yes,4th,No +1/14/2026 17:26:46,Yes,4th,No +1/14/2026 17:27:54,Yes,4th,Yes +1/14/2026 17:28:19,Yes,4th,No +1/14/2026 17:35:55,Yes,4th,No +1/14/2026 17:43:56,Yes,4th,No +1/14/2026 18:10:12,Yes,4th,No +1/14/2026 18:13:24,Yes,4th,No +1/14/2026 18:23:24,Yes,4th,No +1/14/2026 18:35:44,Yes,3rd,No +1/14/2026 18:39:22,Yes,4th,No +1/14/2026 19:39:40,Yes,2nd,No +1/14/2026 21:38:33,Yes,4th,No +1/14/2026 21:38:58,No,3rd,Yes +1/14/2026 22:16:42,Yes,4th,Yes +1/14/2026 22:27:14,Yes,4th,No +1/14/2026 23:47:52,Yes,4th,No +1/14/2026 23:48:02,Yes,4th,No +1/14/2026 23:48:09,Yes,4th,Yes +1/14/2026 23:48:17,Yes,4th,No +1/15/2026 10:32:16,Yes,4th,Yes +1/15/2026 10:48:47,Yes,4th,No +1/15/2026 10:54:45,Yes,4th,No +1/15/2026 12:06:44,Yes,4th,No +1/15/2026 15:14:34,Yes,4th,Yes +1/15/2026 20:25:00,Yes,3rd,No +1/16/2026 0:10:16,Yes,4th,No +1/16/2026 0:10:44,Yes,4th,No +1/16/2026 1:05:27,Yes,4th,No +1/16/2026 10:40:10,Yes,4th,No +1/16/2026 10:59:34,Yes,4th,No \ No newline at end of file diff --git a/hot-dog-survey-data/sp26/data_dictionary_sp26.md b/hot-dog-survey-data/sp26/data_dictionary_sp26.md new file mode 100644 index 00000000..eda58633 --- /dev/null +++ b/hot-dog-survey-data/sp26/data_dictionary_sp26.md @@ -0,0 +1,13 @@ +## Dictionary Data File - Hot Dog Survey Data + +*Field: Are you a full-time undergraduate UVA student? +- Description: This field validates that the respondent is a full time undergraduate UVA student +- Potential Response: The responder will respond to this field with "Yes" or "No". + +*Field: What year are you? +- Description: This field collects the school year that the responder is in. +- Potential Response: The respondent will respond to this question with "1st", "2nd", "3rd", or "4th". + +*Field: Do you believe a hot dog is a sandwich? +- Description: This field collects the respondent's opinion on whether or not a hot dog is a sandwich +- Potential Response: The respondent will respond to this question with "Yes" or "No". \ No newline at end of file diff --git a/hot-dog-survey-data/sp26/eda.ipynb b/hot-dog-survey-data/sp26/eda.ipynb new file mode 100644 index 00000000..fb6437f7 --- /dev/null +++ b/hot-dog-survey-data/sp26/eda.ipynb @@ -0,0 +1,319 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 18, + "id": "37066013", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from scipy.stats import chi2_contingency" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "687cb6d3", + "metadata": {}, + "outputs": [], + "source": [ + "data = pd.read_csv('DS-4002-sp26-survey-results.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "38516a13", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['Timestamp', 'Are you a full-time undergraduate UVA student?',\n", + " 'What year are you?', 'Do you believe a hotdog is a sandwich?'],\n", + " dtype='object')" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "e8b00cab", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Timestamp 0\n", + "Are you a full-time undergraduate UVA student? 0\n", + "What year are you? 0\n", + "Do you believe a hotdog is a sandwich? 0\n", + "dtype: int64" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.isna().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f1b9ab34", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Timestamp Are you a full-time undergraduate UVA student? \\\n", + "0 1/14/2026 14:54:44 Yes \n", + "1 1/14/2026 14:55:14 Yes \n", + "2 1/14/2026 14:56:49 Yes \n", + "3 1/14/2026 14:58:15 Yes \n", + "4 1/14/2026 14:58:25 Yes \n", + "\n", + " What year are you? Do you believe a hotdog is a sandwich? \n", + "0 3rd No \n", + "1 4th No \n", + "2 4th No \n", + "3 4th Yes \n", + "4 4th Yes \n", + "\n", + "Data shape: (79, 4)\n", + "\n", + "Data types:\n", + "Timestamp object\n", + "Are you a full-time undergraduate UVA student? object\n", + "What year are you? object\n", + "Do you believe a hotdog is a sandwich? object\n", + "dtype: object\n" + ] + } + ], + "source": [ + "print(data.head())\n", + "print(\"\\nData shape:\", data.shape)\n", + "print(\"\\nData types:\")\n", + "print(data.dtypes)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "670497e2", + "metadata": {}, + "outputs": [], + "source": [ + "data.drop(data[data['Are you a full-time undergraduate UVA student?'] == 'No'].index, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "30dee00a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(76, 4)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.shape" + ] + }, + { + "cell_type": "markdown", + "id": "53c6d304", + "metadata": {}, + "source": [ + "## Potential Bias Analysis\n", + "\n", + "### 1. **Selection Bias**\n", + "- Survey conducted on UVA students only - may not represent broader population\n", + "- Self-selection: people with strong opinions about hot dogs more likely to respond\n", + "\n", + "### 2. **Response Bias**\n", + "- Leading questions could bias responses toward particular answers\n", + "- Social desirability bias: respondents may answer based on perceived \"correct\" opinion\n", + "- Potential for joke/satirical responses given the seemingly trivial topic\n", + "\n", + "### 3. **Coverage Bias**\n", + "- Limited to UVA community (academic, affluent, younger demographic)\n", + "- Geographic bias: Virginia/Southeast preferences not representative nationally\n", + "- Socioeconomic status likely skewed toward college-educated, middle+ income" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "6d9ede69", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "============================================================\n", + "CHI-SQUARED TESTS FOR INDEPENDENCE\n", + "============================================================\n", + "\n", + "1. Class Year vs. Hot Dog Sandwich Opinion\n", + "------------------------------------------------------------\n", + "Chi-squared statistic: 1.7630\n", + "P-value: 0.4142\n", + "Degrees of freedom: 2\n", + "Result: NO significant association (p >= 0.05)\n", + "\n", + "2. Full-time Student Status vs. Hot Dog Sandwich Opinion\n", + "------------------------------------------------------------\n", + "Chi-squared statistic: 0.0000\n", + "P-value: 1.0000\n", + "Degrees of freedom: 0\n", + "Result: NO significant association (p >= 0.05)\n", + "\n", + "============================================================\n", + "SURVEY SUMMARY STATISTICS\n", + "============================================================\n", + "\n", + "Total Responses: 76\n", + "\n", + "--- Hot Dog is a Sandwich? ---\n", + "Yes: 18 (23.7%)\n", + "No: 58 (76.3%)\n", + "\n", + "--- Breakdown by Year ---\n", + "2nd Year: 11 responses (1 think it's a sandwich - 9.1%)\n", + "3rd Year: 10 responses (2 think it's a sandwich - 20.0%)\n", + "4th Year: 55 responses (15 think it's a sandwich - 27.3%)\n" + ] + } + ], + "source": [ + "# Survey Results Analysis\n", + "\n", + "# 1. Main Question: Is a hot dog a sandwich?\n", + "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", + "\n", + "# Plot 1: Overall distribution - Hot dog is sandwich\n", + "sandwich_counts = data['Do you believe a hotdog is a sandwich?'].value_counts()\n", + "colors_sandwich = ['#ff6b6b', '#4ecdc4']\n", + "axes[0].pie(sandwich_counts.values, labels=sandwich_counts.index, autopct='%1.1f%%', \n", + " colors=colors_sandwich, startangle=90, textprops={'fontsize': 12, 'weight': 'bold'})\n", + "axes[0].set_title('Is a Hot Dog a Sandwich?\\n(Overall Distribution)', fontsize=13, weight='bold')\n", + "\n", + "# Plot 2: Hot dog opinion by year\n", + "year_sandwich = pd.crosstab(data['What year are you?'], data['Do you believe a hotdog is a sandwich?'])\n", + "year_sandwich.plot(kind='bar', ax=axes[1], color=['#ff6b6b', '#4ecdc4'], edgecolor='black')\n", + "axes[1].set_title('Hot Dog Sandwich Opinion by Year', fontsize=13, weight='bold')\n", + "axes[1].set_xlabel('Year')\n", + "axes[1].set_ylabel('Number of Responses')\n", + "axes[1].legend(title='Is it a Sandwich?', labels=['No', 'Yes'])\n", + "axes[1].set_xticklabels(axes[1].get_xticklabels(), rotation=0)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Chi-Squared Tests\n", + "print(\"\\n\" + \"=\"*60)\n", + "print(\"CHI-SQUARED TESTS FOR INDEPENDENCE\")\n", + "print(\"=\"*60)\n", + "\n", + "# Test 1: Year vs. Hot dog sandwich opinion\n", + "print(\"\\n1. Class Year vs. Hot Dog Sandwich Opinion\")\n", + "print(\"-\" * 60)\n", + "chi2_year, p_year, dof_year, expected_year = chi2_contingency(year_sandwich)\n", + "print(f\"Chi-squared statistic: {chi2_year:.4f}\")\n", + "print(f\"P-value: {p_year:.4f}\")\n", + "print(f\"Degrees of freedom: {dof_year}\")\n", + "if p_year < 0.05:\n", + " print(\"Result: SIGNIFICANT association (p < 0.05)\")\n", + "else:\n", + " print(\"Result: NO significant association (p >= 0.05)\")\n", + "\n", + "# Test 2: Student status vs. Hot dog sandwich opinion\n", + "print(\"\\n2. Full-time Student Status vs. Hot Dog Sandwich Opinion\")\n", + "print(\"-\" * 60)\n", + "student_sandwich = pd.crosstab(data['Are you a full-time undergraduate UVA student?'], \n", + " data['Do you believe a hotdog is a sandwich?'])\n", + "chi2_student, p_student, dof_student, expected_student = chi2_contingency(student_sandwich)\n", + "print(f\"Chi-squared statistic: {chi2_student:.4f}\")\n", + "print(f\"P-value: {p_student:.4f}\")\n", + "print(f\"Degrees of freedom: {dof_student}\")\n", + "if p_student < 0.05:\n", + " print(\"Result: SIGNIFICANT association (p < 0.05)\")\n", + "else:\n", + " print(\"Result: NO significant association (p >= 0.05)\")\n", + "\n", + "# Summary statistics\n", + "print(\"\\n\" + \"=\"*60)\n", + "print(\"SURVEY SUMMARY STATISTICS\")\n", + "print(\"=\"*60)\n", + "print(f\"\\nTotal Responses: {len(data)}\")\n", + "\n", + "print(f\"\\n--- Hot Dog is a Sandwich? ---\")\n", + "print(f\"Yes: {(data['Do you believe a hotdog is a sandwich?'] == 'Yes').sum()} ({(data['Do you believe a hotdog is a sandwich?'] == 'Yes').sum()/len(data)*100:.1f}%)\")\n", + "print(f\"No: {(data['Do you believe a hotdog is a sandwich?'] == 'No').sum()} ({(data['Do you believe a hotdog is a sandwich?'] == 'No').sum()/len(data)*100:.1f}%)\")\n", + "\n", + "print(f\"\\n--- Breakdown by Year ---\")\n", + "for year in sorted(data['What year are you?'].unique()):\n", + " count = len(data[data['What year are you?'] == year])\n", + " yes_count = len(data[(data['What year are you?'] == year) & (data['Do you believe a hotdog is a sandwich?'] == 'Yes')])\n", + " print(f\"{year} Year: {count} responses ({yes_count} think it's a sandwich - {yes_count/count*100:.1f}%)\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "ds4003", + "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": "3.8.18" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/hot-dog-survey-data/sp26/project 0.pptx.pdf b/hot-dog-survey-data/sp26/project 0.pptx.pdf new file mode 100644 index 00000000..51ef4af8 Binary files /dev/null and b/hot-dog-survey-data/sp26/project 0.pptx.pdf differ