| name | quantitative-methods |
| 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"] |
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
