Welcome to a curated collection of quantitative methods courses designed for both undergraduate and graduate social science students. This repository hosts two complete, interactive curricula focused on real-world data analysis with reproducible code:
A hands-on, theory-in-context course introducing:
- Foundations of the scientific process (conceptualization → operationalization → measurement)
- Descriptive statistics and data cleaning with R
- Inferential statistics: probability, confidence intervals, hypothesis testing
- Multivariate analysis: simple and multiple regression, interactions
Materials include:
- 14 PowerPoint lectures introducing concepts and R-based examples
- 8 team-based applied exercises (Netflix simulation scenario)
- A master R script, survey data files, and synthetic team datasets
Ideal for instructors or students new to quantitative analysis who want a critical, applied approach.
🔗 Explore: undergrad-quant folder
An advanced, GLM-focused course designed for students with existing statistical background.
- OLS diagnostics and theory-based modeling
- Logit, probit, ordered logit/probit, multinomial logit, Poisson, and negative binomial models
- Postestimation: odds ratios, marginal effects (AME, MEM, MER), predicted probabilities
- Mediation, moderation, and introductory SEM
- Student-driven research project culminating in journal-style paper and presentation
Materials include:
- 11 lecture PowerPoints with Stata applications and
.dosyntax - 5 scaffolded assignments + final project and presentation guides
- Cleaned GSS dataset (
.dta) and master Stata.dofile
🔗 Explore: graduate-quant folder
1. Navigate to the subfolder that interests you:
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undergrad-quant for the introductory course
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graduate-quant for the advanced course
2. Each subfolder includes:
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PowerPoints for lecture support
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Data files and scripts for hands-on lessons
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Assignments with prompts and grading rubrics
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Integrate theory, measurement, and analysis
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Use real data from the General Social Survey and synthetic case studies
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Provide reproducible workflows in R and Stata
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Support student development through projects, not just problem sets
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Include scaffolded materials for both undergrad and graduate audiences
All materials are open-source. You are welcome to fork, adapt, and reuse under the repository’s license.
Feedback or suggestions? Use the Issues tab or open a pull request.
Thanks for visiting — and happy learning!
✏️ Tony Bardo