Last summer, the OPOSSEM project distributed a call for participants to bring together a group of instructors to build some initial content to distribute on the stie. Two workshops were held--one full at the University of Iowa in July and a half-day at the Tower Center in Washington, D.C. in late August. Workshop participants drafted a list of core modules that correspond to topics covered in typical (primarily) quantitative methodology courses offered for undergraduates in political science. Participants then committed to developing a set of lecture slides/notes and problem sets for each of the modules. The results of the workshop are being contributed to OPOSSEM and are linked to below.


  1. The logic of scientific reasoning/inquiry including how its done in political science (linking theory to hypotheses generally and hypothesis testing)
  2. Philosophy of science
  3. Research ethics (discussion questions)
  4. The elements of research design
  5. Components of a research paper & presenting research
  6. Causality
  7. Measurement (slides & notes)
  8. Sampling (notes & problems)
  9. Quantitative Methods (overview) and Survey Research
  10. Qualitative Political Science (overview)
  11. Quantitative Data Analysis-Univariate descriptive statistics (mean, median, mode, variance, standard deviation) and problems
  12. Data (univariate) visualization including displaying data (graphing, interpretation of graphs) and statistical distributions of univariate data (including outliers)
  13. Quantitative Data Analysis-Bivariate descriptive statistics (crosstabs and the mechanics of doing cross tabs, including adding a 3rd variable and problems)
  14. Quantitative Data Analysis-Probability theory, inference
  15. Quantitative Data Analysis-Statistical distributions (and central limit theorem , z, t, and F).
  16. Quantitative Data Analysis-Hypothesis testing (alpha, power, etc.)
  17. Confidence intervals and single sample z/t-tests
  18. Quantitative Data Analysis-Bivariate inferential (parametric) measures of differences (z-tests, t-tests, and F-tests)
  19. Quantitative Data Analysis-Bivariate inferential (nonparametric) measures of differences (chi-square, etc.)
  20. Quantitative Data Analysis-Bivariate (parametric) measures of relationships: Pearson’s R
  21. Quantitative Data Analysis-Bivariate (nonparametric) measures of relationships
  22. Quantitative Data Analysis-ANOVA
  23. Quantitative Data Analysis-Linear (bivariate) regression, (correlation and regression)
  24. Quantitative Data Analysis-Linear (multivariate) regression, including diagnostics/outliers (dummies as predictors)
  25. Debates around R squared
  26. Logistic regression
  27. Codex to provide the equations used by modules to make compatible with various textbooks

Workshop participants