Measurement and DAGs
Content for Thursday, February 2, 2023
Readings
Measurement
- The witch trial scene from Monty Python and the Holy Grail
- Chapter 5 in Evaluation: A Systematic Approach (Rossi, Lipsey, and Henry 2019). This is available on iCollege.
- Chapter 5 in The Effect (Huntington-Klein 2021)
DAGs
- Julia M. Rohrer, “Thinking Clearly About Correlations and Causation: Graphical Causal Models for Observational Data” (Rohrer 2018) This will be posted on iCollege.
- Section 2 only (pp. 4–11) from Julian Schuessler and Peter Selb, “Graphical Causal Models for Survey Inference.” (Schuessler and Selb 2019) The PDF is available at SocArXiv.
- Chapters 6 and 7 in The Effect (Huntington-Klein 2021)
DAG example page
- The example page on DAGs shows how to draw and analyze DAGs with both dagitty.net and R + ggdag
Slides
The slides for today’s lesson are available online as an HTML file. Use the buttons below to open the slides either as an interactive website or as a static PDF (for printing or storing for later). You can also click in the slides below and navigate through them with your left and right arrow keys.
Fun fact: If you type ? (or shift + /) while going through the slides, you can see a list of special slide-specific commands.
Videos
Videos for each section of the lecture are available at this YouTube playlist.
You can also watch the playlist (and skip around to different sections) here:
In-class stuff
Here are all the materials we’ll use in class:
Bayesian statistics resources
In class I briefly mentioned the difference between frequentist and Bayesian statistics. You can see a bunch of additional resources and examples of these two approaches to statistics here. This huge blog post also shows how to do multilevel models with Bayesian models.