conjoint-analysis
CommunityMeasure preferences across multiple attributes.
System Documentation
What problem does it solve?
Conjoint analysis helps you estimate how different attributes causally affect choice when respondents evaluate multiple features at the same time.
Core Features & Use Cases
- Forced-choice conjoint designs: Run pairwise (or multi-option) profile choice tasks to elicit multidimensional preferences.
- AMCE estimation: Compute Average Marginal Component Effects to quantify the causal impact of changing each attribute level relative to a baseline.
- Subgroup comparisons & diagnostics: Evaluate heterogeneous preferences (e.g., by party/demographics) and check randomization, order effects, fatigue/satisficing, and model assumptions.
Example use case: you want to understand which candidate or immigration-policy attributes drive voting intent by estimating AMCEs for education, language, and origin while accounting for respondent-level clustering.
Quick Start
Use the conjoint-analysis skill to design a forced-choice conjoint experiment, estimate AMCEs with clustered standard errors, and run the included diagnostic checks on your survey dataset.
Dependency Matrix
Required Modules
Components
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: conjoint-analysis Download link: https://github.com/Yuuqq/claude-social-science-skills/archive/main.zip#conjoint-analysis Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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