2.1 Good practices in data analysis (X)

2.1.1 Why reproducability?

  • Terminology: Replication, replication (King 1995)
  • Errors
    • A crisis… (e.g. Open Science Collaboration 2015) that should be avoided (e.g. Psychoticism)
    • Manual steps (e.g. manual copy/paste) introduces errors
    • Reproducible documents allow for automatization (counter argument?)
  • Access:
    • Taxpayers (= researchers) pay for research → should have access
    • Better all humans → human progress! (Sci-hub controversy)
    • Implies relying on open-source software
    • Access in 100 years.. will STATA still exist?
  • Memory
    • You will forget what you did.. think of others..
    • Reproducable document helps you trace your steps
    • Ideally all stages of workflow
  • Efficiency
    • Automatization → paper revisions much faster

2.1.2 Reproducability: My current approach


Acharya, Avidit, Matthew Blackwell, and Maya Sen. 2016. “Explaining Causal Findings Without Bias: Detecting and Assessing Direct Effects.” Am. Polit. Sci. Rev. 110 (3): 512–29.
Bauer, Paul. 2018. “Writing a Reproducible Paper in R Markdown,” May.
Gill, Jeff. 1999. “The Insignificance of Null Hypothesis Significance Testing.” Polit. Res. Q. 52 (3): 647–74.
King, Gary. 1995. “Replication, Replication.” PS, Political Science & Politics 28 (3): 444–52.
———. 2010. “A Hard Unsolved Problem? Post-Treatment Bias in Big Social Science Questions.” In Hard Problems in Social Science” Symposium, Harvard University. scholar.harvard.edu.
Open Science Collaboration. 2015. “Estimating the Reproducibility of Psychological Science.” Science 349 (6251): aac4716.

  1. See for instance the discussion surrounding the use of p-values/statistical significance(e.g. Gill 1999) and current discussion about post-treatment bias (e.g. King 2010; Acharya, Blackwell, and Sen 2016).↩︎