{"id":20554061,"url":"https://github.com/vasilescur/decsci-group-9","last_synced_at":"2026-04-17T22:31:45.773Z","repository":{"id":115938489,"uuid":"156143027","full_name":"vasilescur/DecSci-Group-9","owner":"vasilescur","description":"DECSCI 101 - Final Project (Group 9)","archived":false,"fork":false,"pushed_at":"2018-12-03T01:19:00.000Z","size":49,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-03-06T06:24:32.719Z","etag":null,"topics":["data-analysis","decision-science","qualtrics","statistics","visualization"],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/vasilescur.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2018-11-05T01:22:42.000Z","updated_at":"2018-12-03T01:19:01.000Z","dependencies_parsed_at":"2023-04-04T12:17:10.196Z","dependency_job_id":null,"html_url":"https://github.com/vasilescur/DecSci-Group-9","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/vasilescur/DecSci-Group-9","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vasilescur%2FDecSci-Group-9","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vasilescur%2FDecSci-Group-9/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vasilescur%2FDecSci-Group-9/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vasilescur%2FDecSci-Group-9/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/vasilescur","download_url":"https://codeload.github.com/vasilescur/DecSci-Group-9/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vasilescur%2FDecSci-Group-9/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31948402,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-17T17:29:20.459Z","status":"ssl_error","status_checked_at":"2026-04-17T17:28:47.801Z","response_time":62,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.6:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-analysis","decision-science","qualtrics","statistics","visualization"],"created_at":"2024-11-16T02:46:12.644Z","updated_at":"2026-04-17T22:31:45.755Z","avatar_url":"https://github.com/vasilescur.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Decision Science 101 Final Project - Group 9\n\nAnalysis of the effect of multi/single-attribute decisions on the choice overload effect.\n\nThis repository contains the code used to analyze data collected from a Qualtrics survey.\n\n## Data Processing\n\nThe data were exported from Qualtrics as a CSV format and then processed in Excel in order to remove irrelevant information such as IP address, date/time, and so on.\n\nThen, the records were split into four different groups based on which columns were non-null:\n\n```python\n# Split into confidence values for each type of question\nmeal_short = data['meal_short'].dropna()\nmeal_long = data['meal_long'].dropna()\n\nclass_short = data['class_short'].dropna()\nclass_long = data['class_long'].dropna()\n```\n\n## Statistical Analysis\n\n### 2x2 ANOVA\n\nThe `statsmodels` package was used to perform a 2-way ANOVA. After the satisfaction values\nwere aggregated into lists by category, the following code was used for the ANOVA:\n\n```python\nformula = 'Satisfaction ~ C(Type) + C(Length) + C(Type):C(Length)'\nmodel = ols(formula, aggregate).fit()\naov_table = statsmodels.stats.anova.anova_lm(model, typ=1)\n```\n\nThe results of the ANOVA were:\n\n```\n                      df      sum_sq    mean_sq         F    PR(\u003eF)\nC(Type)              1.0    3.582745   3.582745  0.875582  0.350383\nC(Length)            1.0   17.122664  17.122664  4.184584  0.041916\nC(Type):C(Length)    1.0    0.300051   0.300051  0.073329  0.786790\nResidual           233.0  953.399603   4.091844       NaN       NaN\n```\n\n## Results Visualization\n\n### Box \u0026 Whiskers Plot\n\n[Exploratory.io](https://exploratory.io) was used to create a data visualization. First, we used Excel to coalesce the data into the following format:\n\n| Type | Length | Response |\n| ---- | ------ | -------- |\n| class | short | 7 |\n| meal | short | 7 |\n| class | short | 6 |\n| class | long | 10 |\n| ... | \n\nThen, the following box-and-whiskers plots were created from the newly formatted data:\n\n\u003cimg alt=\"box-plot\" height=\"500\" src=\"https://user-images.githubusercontent.com/10100323/49200394-91066b00-f36a-11e8-9bdd-b3f2b9e91c96.png\"\u003e\n\n### Histogram Summary\n\nIn addition, the `pyplot` package from `matplotlib` was used to create a full visualization of the data consisting of a series of histograms outlining the preference reported by users for each category of question.\n\nGraphs were creates as follows:\n\n```python\nplt.subplot(4, 2, 1)    # 1st plot\nplt.title('Meals (short)')\nplt.xlabel('Satisfaction Level')\nplt.ylabel('Responses')\nplt.hist(\n    meal_short,\n    bins=range(1, 11), # 1, 2, ..., 10\n    rwidth=0.75,\n    label='Meals (short)'\n)\n\n...\n\n# Fix overlapping in layout, and then display the plots\nplt.tight_layout()\nplt.gcf().canvas.set_window_title('Results')    # Window title\nplt.show()\n```\n\nThis resulted in the following visualization:\n\n![results-all](https://user-images.githubusercontent.com/10100323/48680965-7879a200-eb6d-11e8-83e7-04011268514a.png)\n\n\n## Website\n\nA website view of this readme is hosted at [decsci.raduvasilescu.com](http://decsci.raduvasilescu.com).\n\nThis host is specified in the `CNAME` file in this repository, and the main HTML page template has been adjusted\nfrom the default Jekyll theme and can be found in `_layouts/default.html`.\n\n## Project Contributors\n\n- Radu Vasilescu\n- Zoe Tang\n- Claire Hutchinson\n- Prateek Khandelwal\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvasilescur%2Fdecsci-group-9","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fvasilescur%2Fdecsci-group-9","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvasilescur%2Fdecsci-group-9/lists"}