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 hypothetico-deductivism 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 Theory-Writing
  tl;dr: A discussion of some techniques to theory-writing, 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 to--consistently--capture 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 interval-level 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.      
• Post-estimation Simulation
  tl;dr: Simulated estimated quantities of interest from a multivariate normal distribution using the regression model's parameters.      
• 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.