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Political Methodology

Political Methodology is a basic component of modern Political Science. The OSU field in Political Methodology includes a wide variety of courses and related programs.

Graduate students can take Political Methodology as their major field, along with American Politics, Comparative Politics, International Relations, or Political Theory as their minor. Graduate students taking a minor in Political Methodology either can focus exclusively on statistical modeling or can take a combination of courses in statistical modeling, research design, and/or a special topics area. Additionally, students can take a joint minor in Political Methodology and Formal Theory. Political Methodology can also be taken jointly with Formal Theory as a major.

Political Methodology Faculty

Jan Box-Steffensmeier, Skyler Cranmer, Marcus Kurtz, Chris Gelpi, William Minozzi, Tom Nelson, Jan Pierskalla, Amanda Robinson, Herb Weisberg (emeritus).

Political Methodology Courses

Statistical Methodology courses that are offered on a yearly basis:

  • Math workshop for political science
  • Basic statistics
  • Linear and generalized linear models (regression analysis by OLS and MLE)
  • Causal inference
  • Machine learning

Qualitative Methods courses:

  • Foundations of political analysis 
  • Introduction to Qualitative Methodology

We also offer, on a non-yearly basis and depending on instructor availability, more advanced courses in the following areas:

  • Research Design
  • Questions in Survey Design
  • Experimental Methods
  • Survey Research Practicum

Advanced Statistical Courses:

  • Time Series Analysis
  • Event History
  • Scaling and Dimensional Analysis
  • Bayesian Analysis
  • Computational Modeling
  • Cross-level Inference
  • Measurement
  • Panel Data Analysis
  • Descriptive Network Analysis
  • Inferential Network Analysis
  • Text as Data

Additional Statistical Methods Courses:

Related programs at OSU

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Minor/Major Programs

Minor: 4 classes for a minor (typically 1 per semester for the first 2 years and then students take the minor exam): 685/7551, 686/7552, 786/MLE/7553 and then either an advanced course or 684/7780.

Major: 5 classes, where at least two are advanced beyond 786/MLE/7553 (so if students also take 684/7780, then 6 classes). These additional courses can and likely will be taken beyond the second year.

685/7551: This course is designed for Political Science graduate students intending to do empirical research. It introduces students to methods for constructing simple empirical representations of social science theories and for rigorously testing those theories with data. We focus on four topics, beginning with the logics of empirical analysis; descriptive statistics and the basic linear model; probability and statistics; and statistical inference. The course will emphasize fundamental statistical concepts as well as their practical application and will draw examples from a range of substantive subfields in Political Science. Topics include random variables, basic hypothesis testing, BLUE, regression and assumptions. This course is designed for students with little or no formal training in statistics or in the analysis of social science data. There are no prerequisites, though it is assumed that students have the equivalent of college algebra and will have taken our department’s summer math camp. Upon completion of this course, students will be able to read and critically evaluate empirical political science research; will have sufficient background and experience to formulate and test simple empirical representations of social science theories; and will have a solid statistical background needed for more advanced methodological training. The course also provides work with statistical software, such as Stata or R.

686/7550: The course covers all core elements of OLS regression: bivariate and multivariate regression analysis, interaction effects, hypothesis testing, and violations of OLS assumptions. The aim of the course is to explore the statistical background of OLS in combination with its empirical application. Topics include regression, hypothesis testing, functional form, diagnostics, heteroskedasticity, autocorrelation, endogeneity, introduction to dichotomous dependent variables and other MLE topics. The course also provides work with statistical software, such as Stata or R.

The Ph.D. Candidacy Examination

To demonstrate mastery of the field, students are required to pass a Candidacy Examination. There is both a written and an oral component to the Candidacy Examination. There is an 8 hour written exam for the both the major and minor. A paper is required for the major after the 3rd year.