Teaching Interests

Statistics and research methods, experiment design, causal inference, survey research, regression theory and application, maximum likelihood estimation, political psychology, political behavior, American politics, public opinion, ideology, parties, public choice, judgment and decision making, evolutionary psychology, personality and individual differences, econometrics, microeconomics, behavioral economics, game theory, agent-based models & computer simulations

Teaching Experience

All of the following were courses I taught at Stony Brook University.

As course instructor:

  • POL 201: Statistical Methods in Political Science, 2016 Fall
  • POL 201: Statistical Methods in Political Science, 2017 Spring
  • POL 201: Statistical Methods in Political Science, 2017 Fall
  • POL 201: Statistical Methods in Political Science, 2018 Spring
  • Math camp for incoming PhD students, 2018 Summer
  • POL 201: Statistical Methods in Political Science, 2019 Spring
  • Math camp for incoming PhD students, 2019 Summer
  • POL 348: Political Beliefs and Judgment, 2019 Fall
  • POL 348: Political Beliefs and Judgment, 2020 Spring

As teaching assistant:

  • POL 604: Applied Data Analysis 3 (doctoral-level course), 2018 Fall

Awards:

  • Recipient of the 2018 Departmental Teaching Award, Stony Brook University Department of Political Science.

Descriptions of courses I have taught

POL 201: Statistical Methods in Political Science
I was privileged to teach five semesters of the undergraduate introductory course in statistics and research methods for students of political science. In this course, my students learn that a political scientist is a scientist. They learn how to view human behavioral phenomena, including politics, from a scientific perspective. They learn how to think of any political phenomena in terms of quantitative variables and interactions between variables. We explore the concepts of theories and hypotheses and the importance of empirical evidence. We have lively discussions about the challenges of causal inference in which we debate many examples, and we discuss different types of research designs for overcoming those challenges. The students learn basic descriptive statistics, including how to produce and interpret various types of data visualizations. We then continue to inferential statistics, sampling error, confidence intervals, and hypothesis tests, including careful discussion of what a p-value is and what it is not. The course includes several types of statistical analyses along with the hypothesis tests associated with each, e.g. difference-of-means tests, correlation coefficients, OLS regression models, etc. Although many undergraduate students of politics are initially not happy to be taking a required course in quantitative methods, they quickly change their minds as they learn to see how an understanding of statistical concepts and an ability to evaluate empirical claims can be valuable in a broad range of careers, in their personal lives, and in their roles as participatory citizens.

POL 604: Applied Data Analysis 3
I served as teaching assistant for a doctoral-level course in quantitative research methods: “Applied Data Analysis 3”, which is the third course of the required three-course sequence of quantitative methods courses in the political-science PhD program at Stony Brook. This course covers Maximum Likelihood Estimation and many of the common types of non-linear statistical models that use MLE: binomial logit and probit models, ordered models, multinomial models, count models, censored and truncated models, and selection models. The course also covers strong research designs for causal inference and current debates in the methodology literature. As a teaching assistant, I was responsible for leading weekly recitation/discussion sections with the students, in which I reviewed concepts with the students or taught them new concepts as needed and helped the students find their way through problem sets. The professor asked me to write some of the questions for homework problem sets and exams, and I did all of the grading of homework exercises and exams. I occasionally did the primary course lectures when the professor was absent or whenever the lecture topic was a topic in which the professor believed my expertise would be particularly beneficial to the class (such as computer simulations or data visualizations in R).

POL 348: Political Beliefs and Judgment
Because this was a course that was not previously being offered by the department, the department gave me a lot of freedom to design the course the way I want it. They gave me the course title and allowed me to choose the topics to be included in the course. I decided to make it a course about irrationality. In this course we investigate the extent to which judgments and beliefs in the context of politics and public policy are rational and well-informed. We start by studying evidence of how a lack of political knowledge, policy knowledge, and economic understanding among the public produces public opinion and beliefs that deviate from those we would expect to find among a more informed public. We then study evidence of how the formation of political opinions, beliefs, and judgments are affected by subconscious cognitive processes. This includes discussions about how the processing of new information is influenced by prior beliefs and attitudes and especially influenced by a motivation to reach a desired conclusion. Finally, we review some of the research from the field known as “judgment and decision making.” This sub-field of research at the intersection of psychology and behavioral economics has identified many ways in which human judgments deviate from rational norms. We read about the research that has documented these various cognitive biases and we discuss how these biases can manifest in the context of reasoning about politics or public policy.

Math camp for PhD program
For two years I was the instructor of the math camp for incoming PhD students in the political-science department. This is a required course that all incoming PhD students take before their first semester. The class meets six hours per day for one week prior to the start of the fall semester. In this class I taught linear algebra, differential and integral calculus, and coding in R.

Comments from Students

  • "James is a very good teacher and will take the time to make sure every student understands the material."
  • "James has always been accessible whenever anyone needed help. He stayed after class to help us review the class material. This course was truly enjoyable and well-taught. He is very passionate about the material he teaches, and it motivated me to learn more."
  • "Even though statistics is a difficult subject to learn and understand, Professor Cragun made the course as interesting and informative as it could possibly be. The most valuable aspect of the course was his ability to grasp the students' attention and keep them involved."
  • "Prof was beyond helpful. You can tell he puts in a lot of effort into teaching unlike other professors."
  • "Interesting subject material, delivered in a way that makes it applicable to my life."
  • "Perhaps most relevant to the field of Political Science were the depictions of how statistics can be skewed to support an agenda. Professor Cragun was also approachable and friendly, which is an appreciable quality in an educator."
  • "The professor took extra care into choosing which topics of content we should learn and review and that makes the whole experience much more worthwhile.
  • "Perhaps the most significant thing I learn about this course was the difference between causation and correlation. It changed my perception of reality by leading me to question the perceived patterns which I had held as truths."
  • "He put a lot of effort in to teaching and kept everyone interested."
  • "Whenever I needed help he'd always give great one on one help in office hours."
  • "I loved the professor."
  • "This course did a very good job of applying logic to real world situations. It taught me to think of possible logical errors when trying to prove causal relations. It also taught me a fair amount of vocabulary that has allowed to me effectively talk about probability distributions, sample sets, etc."
  • "He’s a great instructor, you can really tell he cares about the topic and wants to make you care too. Discussions are never boring. A lot of reading, but I learned so much that I don’t mind it."

Teaching Resources I Have Created

Because I enjoy teaching quantitative methods, I have created some interactive simulations and written some explanations to demonstrate important statistical concepts: http://jamescragun.com/teaching/statistics_demonstrations.html
If any of these are helpful to you in any way as you are thinking of ways to explain these concepts to your students, you are welcome to use them. If you have ideas for improving or expanding any of these demonstrations, I would love to hear about that.