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Quantitative Methods Skill

Design and execute statistical analyses including regression modeling, hypothesis testing, power analysis, and robustness checks using R, Stata, SPSS, or Python

Installable skillMITBy a5c-aiUpdated Oct 28, 2026
INTENDED FORClaude CodeCodexOpenClawCursorInstall in a listed agent.

instructions.md

---
name: quantitative-methods-2ed1dca27381
description: Design and execute statistical analyses including regression modeling, hypothesis testing, power analysis, and robustness checks using R, Stata, SPSS, or Python
allowed-tools:
  - Read
  - Write
  - Edit
  - Grep
  - Glob
  - Bash
graph:
  domains: [domain:social-sciences]
  skillAreas: [skill-area:statistical-analysis, skill-area:data-analysis, skill-area:machine-learning-frameworks]
  workflows: [workflow:experiment-design, workflow:peer-review-cycle]
  roles: [role:data-scientist, role:research-scientist]
---

# Quantitative Methods Skill

Design and execute rigorous statistical analyses for social science research using modern analytical tools.

## Overview

The Quantitative Methods skill enables design and execution of statistical analyses including regression modeling, hypothesis testing, power analysis, and robustness checks using R, Stata, SPSS, or Python for rigorous quantitative social science research.

## Capabilities

### Regression Analysis
- Linear regression modeling
- Logistic and multinomial regression
- Panel data methods
- Time series analysis
- Hierarchical/multilevel modeling

### Hypothesis Testing
- Parametric tests
- Non-parametric alternatives
- Multiple comparison correction
- Effect size estimation
- Confidence interval construction

### Power Analysis
- Sample size determination
- Effect size specification
- Power calculation
- Design optimization
- Sensitivity analysis

### Robustness Checking
- Specification testing
- Outlier analysis
- Assumption verification
- Alternative estimators
- Sensitivity analysis

### Tool Proficiency
- R/RStudio workflows
- Stata programming
- SPSS procedures
- Python (statsmodels, scipy)
- Output visualization

## Usage Guidelines

### When to Use
- Designing quantitative studies
- Analyzing survey data
- Testing hypotheses
- Building predictive models
- Validating findings

### Best Practices
- Pre-register analyses
- Check assumptions
- Report fully
- Conduct robustness checks
- Document code

### Integration Points
- Causal Inference Methods skill
- Survey Design and Administration skill
- Psychometric Assessment skill
- Mixed Methods Integration skill

## References

- Statistical Analysis Pipeline process
- Experimental Design process
- Multilevel/Hierarchical Modeling process
- Quantitative Research Methodologist agent

Platform guidance

Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.