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.