Lectures
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 tidyverseoriented 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 quickanddirty 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 postestimation 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.