quantitative-methods

# 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