This page contains link to the lectures I give throughout the semester. Clicking the title of the week’s lecture will go to a PDF, embedded in the user’s browser, by default. The bottom right icons link to the Github directory for the lecture (), the R Markdown document for the lecture (), and a PDF, embedded on Github, for the lecture ().

  • What Is Scientific Research?
    tl;dr: A brief introduction to thinking about and discussing scientific research.      
  • LAB: Intro to R and Rstudio
    tl;dr: a tidyverse-oriented lab for introducing students to R and Rstudio.      
  • Descriptive Inference
    tl;dr: A brief discussion on moving from interpretation to inference, at least thinking of 'inference' statistically.      
  • LAB: Some Basics of Descriptive Inference
    tl;dr: Central tendency, dispersion, levels of measurement, and more, all in R.      
  • Causality
    tl;dr: On association and causality: the tools to evaluate the former and the framework to think about the latter.      
  • Random Assignment and Experiments
    tl;dr: Random assignment and experiments constitute the 'gold standard' for causal identification.      
  • Measurement Error
    tl;dr: Measurement error is unwelcome. Different forms of it can make our inferences unreliable or, worse yet, bias them.      
  • OLS Regression
    tl;dr: A quick-and-dirty discussion of ordinary least squares regression.      
  • Instrumental Variables
    tl;dr: On instrumental variables, what they do, their 'weirdness', and when to use them.      
  • Regression Discontinuity Design
    tl;dr: A brief discussion on RDDs, both sharp and fuzzy.      
  • Logistic Regression
    tl;dr: OLS may be the workhorse, but logistic regression might be more common. Here's a walkthrough of logistic regression.      
  • Ordinal Logistic Regression
    tl;dr: A primer on ordinal logistic regression (i.e. what to do when your DV is ordered, but still has finite observations).      
  • Making the Most of Regression
    tl;dr: You can't just run a regression and call it a day. Better explain your model by scaling inputs and doing post-estimation simulation.      
  • The Basics of Bayesian Inference
    tl;dr: Conditional probability through Bayes is uncontroversial. Inference from it is, but it's novel and worth discussion.      
  • Ethics and Replication
    tl;dr: A discussion of emerging ethical issues in social science research and the importance of replication.