multilevel-models
CommunityModel hierarchical data with valid uncertainty.
Education & Research#hierarchical data#multilevel modeling#mixed effects#ICC diagnostics#random intercepts#bayesian GLMM#social science research
AuthorYuuqq
Version1.0.0
Installs0
System Documentation
What problem does it solve?
Multilevel modeling fixes biased standard errors and pseudoreplication that happen when hierarchical social science data (e.g., students within schools, repeated measures within people) are analyzed with single-level regression.
Core Features & Use Cases
- ICC diagnostics & model selection: Estimate intraclass correlation to decide whether multilevel structure is warranted, then compare random-intercept and random-slope specifications.
- Hierarchical effects with centering: Build random intercepts/slopes, handle contextual effects via group-mean and grand-mean centering, and interpret coefficients correctly.
- Flexible outcomes & Bayesian estimation: Fit GLMMs for binary/count outcomes and use Bayesian multilevel workflows (e.g., via bambi/PyMC) for complex random-effect structures and better uncertainty reporting.
Quick Start
Use the multilevel-models skill to analyze a dataset where students are nested within schools by estimating ICC, fitting a random-intercept model, and extending to a random-slope model when the relationship plausibly varies across schools.
Dependency Matrix
Required Modules
numpypandasstatsmodelsscipymatplotlib
Components
scriptsreferencesassets
💻 Claude Code Installation
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Please help me install this Skill: Name: multilevel-models Download link: https://github.com/Yuuqq/claude-social-science-skills/archive/main.zip#multilevel-models Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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