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Statistical Intuition without Coding (or Teachers)

Published online by Cambridge University Press:  19 September 2025

Natalie Ayers
Affiliation:
Institute for Quantitative Social Science, Harvard University, United States
Gary King
Affiliation:
Institute for Quantitative Social Science, Harvard University, United States
Zagreb Mukerjee
Affiliation:
Yale University, United States
Dominic Skinnion
Affiliation:
Institute for Quantitative Social Science, Harvard University, United States

Abstract

Teaching political methodology classes typically requires a set of technical, instructor-led lectures on sophisticated statistical concepts (such as probability modeling, inference, and proper interpretation), followed by chances for students to iteratively adjust methodological specifications, parameters, and data sets so they can understand how each combination affects the results. Iteration is essential to learning complicated concepts, but until now has required simultaneous mastery of a statistical programming language (such as R), which makes learning both harder. Teaching R the semester before would make methods classes easier but also delay research experiences and demotivate our students eager to begin substantive research. We address both problems through a new type of interactive teaching tool that lets students iterate while learning the big conceptual picture and all its separate parts, without having to simultaneously become programmers. We make this tool available for use in classes now (via one click in a web browser) and as an example of a new type of more friendly methods instruction for students and instructors alike.

Information

Type
Article
Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of American Political Science Association

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References

REFERENCES

Bailey, Michael A. (2019). “Teaching Statistics: Going from Scary, Boring, and Useless To, Well, Something Better.” IPS: Political Science & Politics 52 (2): 367–70.Google Scholar
King, Gary. (1998). Unifying Political Methodology: The Likelihood Theory of Statistical Inference. Ann Arbor: University of Michigan Press.10.3998/mpub.23784CrossRefGoogle Scholar
King, Gary, Tomz, Michael, and Wittenberg, Jason. (2000). “Making the Most of Statistical Analyses: Improving Interpretation and Presentation.” American Journal of Political Science 44 (2): 341–55.10.2307/2669316CrossRefGoogle Scholar
Metzger, Shawna K. (2022). “Teaching Econometrics Dynamically with R-Shiny.” PS: Political Science & Politics 55 (1); 225–29.Google Scholar
Schleutker, Elina. (2022). “Seven Suggestions for Teaching Quantitative Methods.” PS: Political Science & Politics 55 (2): 419–23.Google Scholar
Williams, Rob. (2022). “Teaching Programming Skills in Methods Courses Is an Opportunity, Not a Burden.” PS: Political Science & Politics 55 (1): 221–24.Google Scholar