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 ().

Syllabus Day (i.e. Welcome)
tl;dr: This lecture discusses the syllabus and outlines course expectations for the rest of the semester. 
Scientific Study of Politics
tl;dr: This is a gentle lecture on how to think about the study of politics scientifically. 
EITM and Science as Simplification
tl;dr: Another gentle lecture, this one on hypotheticodeductivism and the EITM framework. 
How to Do a Literature Review
tl;dr: Cribbed from a blog post and 2014 presentation, here's how I recommend students approach a literature review. 
On TheoryWriting
tl;dr: A discussion of some techniques to theorywriting, the hardest part of the research process (I think). 
Defining Concepts
tl;dr: A discussion of politics is ultimately conceptual. Political *science* entails operationalizing those concepts. 
Reliability and Validity
tl;dr: Ideally our measures are supposed toconsistentlycapture the true concept without measuring anything else. 
Defining and Measuring Variables
tl;dr: Measurement is the heart of science. Let's talk about how we can categorize measurement. 
Central Tendency and Dispersion
tl;dr: We can describe variables by reference to what is typical and how typical is "typical." 
Framing Hypotheses
tl;dr: A quick primer on hypotheses, how to do them, and how not to do them. 
Making Comparisons
tl;dr: A discussions of types of relationships between variables and how to make comparisons between them. 
Probability and Counting for Political Science
tl;dr: A gentle introduction to some rules for probability and counting, with political science applications. 
Probability Distributions and Functions
tl;dr: A discussion of two types of distributions you'll see in political science applications. 
Controlled Comparisons and Controlled Relationships
tl;dr: A discussion of controlled comparisons and how to make them with or without experiments. 
Applied Controlled Comparisons
tl;dr: A primer on making controlled comparisons and estimating partial effects in simple cases. 
Random Sampling and Variation
tl;dr: A pivot into the world of inference, starting with a discussion of random sampling. 
Central Limit Theorem, Normal Distribution, and Inference
tl;dr: Inferring from sample to population is the core of what we do in applied statistics. Here's what the process resembles. 
Correlation and Linear Regression
tl;dr: A gentle introduction to correlation and linear regression as a means to describe a relationship between two intervallevel variables. 
Extending OLS: Fixed Effects, Controls, and Interactions
tl;dr: Once you understand OLS in a simple bivariate context, its extensions are akin to adding more ingredients. 
Logistic Regression
tl;dr: If your DV is a dummy variable, you're going to want a logistic regression instead of OLS. It's really not daunting. 
Making the Most of Regression ("Divide By 4", Scaling)
tl;dr: You, the regression modeler, are also a storyteller. Here are two parlor tricks to help you tell your story. 
The Basics of Bayesian Inference
tl;dr: Conditional probability is uncontroversial, but inference by way of it is. Let's discuss it first, emphasizing benefits and limitations. 
What Explains Union Density? A Replication with Updated Bayesian Approaches
tl;dr: Western and Jackman's (1994) article is an accessible applied introduction to Bayesian inference for students. 
Ethics in Social/Political Science Research
tl;dr: A discussion of emerging ethical issues in social science research and the importance of replication. 
Growth in a Time of Debt (or: a Workflow Gone Bad)
tl;dr: Does debt decrease GDP growth? If you have a bad research design and institutional prestige to burn, it does.