Key concepts

• Formulating research hypothesis (lecture)
• Unit of analysis, level of analysis, and observation (lecture)
• Tidy vs. messy data (Wickham, 2014)
• Good data management practices (Gentzkow & Shapiro, 2014; Wilson et al., 2017)

Before class

Required readings

1. Wickham, H. (2014). Tidy Data. The Journal of Statistical Software, 59(10).
2. Gentzkow, M., & Shapiro, J. M. (2014). Code and Data for the Social Sciences: A Practitioner’s Guide.
3. Wilson, G., Bryan, J., Cranston, K., Kitzes, J., Nederbragt, L., & Teal, T. K. (2017). Good Enough Practices in Scientific Computing. PLOS Computational Biology, 13(6), e1005510. https://doi.org/10.1371/journal.pcbi.1005510

In class

• Lecture on key concepts and frameworks.
• In class practice:
  • Paper practice
  • In-class practice
• Review progress of assignments:
  • Learning plan.
  • Group qualitative project.

After class

• Learning plan proposal (draft; n.g.) due next week.