{"id":16525631,"url":"https://github.com/camdavidsonpilon/lifestyles","last_synced_at":"2025-03-21T09:30:45.198Z","repository":{"id":66073637,"uuid":"101595293","full_name":"CamDavidsonPilon/lifestyles","owner":"CamDavidsonPilon","description":"Work-In-Progress: conjoint analysis in Python","archived":false,"fork":false,"pushed_at":"2018-04-29T21:31:55.000Z","size":17,"stargazers_count":52,"open_issues_count":3,"forks_count":11,"subscribers_count":9,"default_branch":"master","last_synced_at":"2025-03-17T23:38:39.348Z","etag":null,"topics":["conjoint-analysis","pymc3","statistics","survey"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/CamDavidsonPilon.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2017-08-28T02:15:05.000Z","updated_at":"2023-10-25T14:19:02.000Z","dependencies_parsed_at":null,"dependency_job_id":"e56f6001-a272-4b31-b9f8-d9e6cef0c763","html_url":"https://github.com/CamDavidsonPilon/lifestyles","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CamDavidsonPilon%2Flifestyles","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CamDavidsonPilon%2Flifestyles/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CamDavidsonPilon%2Flifestyles/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CamDavidsonPilon%2Flifestyles/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/CamDavidsonPilon","download_url":"https://codeload.github.com/CamDavidsonPilon/lifestyles/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":244771367,"owners_count":20507795,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","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":["conjoint-analysis","pymc3","statistics","survey"],"created_at":"2024-10-11T17:04:22.575Z","updated_at":"2025-03-21T09:30:44.924Z","avatar_url":"https://github.com/CamDavidsonPilon.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"![lifestyles_logo](https://imgur.com/SjZBq1V.png) \n\n_lifestyles_ is a Python package for performing conjoint analysis. What is conjoint analysis? I'm glad you asked! Conjoint analysis is an alternative survey analysis technique. Instead of asking survey partcipants about how they feel about specific characteristics, instead the particpants are asked to evaluate holistically. For example, suppose you are interested in creating a new lemonade beverage, and you want to better understand what your potential customers' preferences are. We _could_ design a survey like:\n\n\n\u003e Q1. How much sugar do you prefer in your lemonade?\n\u003e  - [ ] No sugar\n\u003e  - [ ] 1 sugar\n\u003e  - [ ] 2 sugar\n\u003e \n\u003e Q2. How much lemon do you prefer in your lemonade?\n\u003e  - [ ] None\n\u003e  - [ ] Some\n\u003e \n\u003e Q3. How much ....\n \nThere are some drawbacks to this survey design. We have isolated the attributes of the lemonade, so participants must also compare in isolation. This isn't how consumers make choices. Instead they compare products holistically. Compare the above survey to this instead:\n\n\u003e Q1. Which lemonade would you prefer to purchase?\n\u003e - [ ] Some sugar, ice cold and strong lemon flavour\n\u003e - [ ] No sugar, ice cold and mild lemon and mild mint flavour\n\nOr, something like:\n\n\u003e Q2. On a scale of 1 to 10, how likely are you to purchase the following lemonade? \n\u003e \n\u003e Warm, honey-sweetened, with strong lemon flavour. \n\u003e \n\u003e 1 ♢ ♢ ♢ ♢ ♢ ♢ ♢ ♢ ♢ ♢ 10\n\n\nThe latter surveys asks us to look at beverages, and not attributes. This is the much more common consumer task. Indeed, walking into a convience store for a lemonade implies the consumer will have to make these decisions. \n\nHow can we analyze surveys like this? That's where conjoint analysis comes in. The statistical methods will decompose the consumers' choices into what attributes strongly correlate with purchase or selection.\n\n### Work in Progress\n\nThis library is a work-in-progress, and alpha-stage development. \n\n### References \n - [Hierarchical Bayes Conjoint Analysis: Recovery of Partworth Heterogeneity from Reduced Experimental Designs. Peter J. Lenk; Wayne S](http://webuser.bus.umich.edu/plenk/HB%20Conjoint%20Lenk%20DeSarbo%20Green%20Young%20MS%201996.pdf).\n - [Software for Hierarchical Bayes\nEstimation for CBC Data](https://www.sawtoothsoftware.com/download/ssiweb/CBCHB_Manual.pdf).\n - [Case study into university pricing](https://conjoint.online/2017/04/20/pricing-case-study/)\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcamdavidsonpilon%2Flifestyles","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcamdavidsonpilon%2Flifestyles","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcamdavidsonpilon%2Flifestyles/lists"}