# Measurement and DAGs

Content for Thursday, February 2, 2023

### DAGs

• Julia M. Rohrer, “Thinking Clearly About Correlations and Causation: Graphical Causal Models for Observational Data” This will be posted on iCollege.
• Section 2 only (pp. 4–11) from Julian Schuessler and Peter Selb, “Graphical Causal Models for Survey Inference.” The PDF is available at SocArXiv.
• Chapters 6 and 7 in The Effect

## 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.

Tip

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.

## References

Huntington-Klein, Nick. 2021. The Effect: An Introduction to Research Design and Causality. Boca Raton, Florida: Chapman and Hall / CRC. https://theeffectbook.net/.
Rohrer, Julia M. 2018. “Thinking Clearly about Correlations and Causation: Graphical Causal Models for Observational Data.” Advances in Methods and Practices in Psychological Science 1 (1): 27–42. https://doi.org/10.1177/2515245917745629.
Rossi, Peter H., Mark W. Lipsey, and Gary T. Henry. 2019. Evaluation: A Systematic Approach. 8th ed. Los Angeles: Sage.
Schuessler, Julian, and Peter Selb. 2019. “Graphical Causal Models for Survey Inference.” Working Paper. SocArXiv. https://doi.org/10.31235/osf.io/hbg3m.