Political Science 3302 (PLSC 3302): Statistics for Political Science is a required course for political science majors. It is the second of a two-course methods sequence for those receiving a Bachelor of Arts in Political Science. It has the following prerequisites: successful completion of PLSC 2305, 2306 and 3301 or instructor permission. Additionally, some of the concepts covered in this course should have been covered in a scientific research methods course. The University of Texas of the Permian Basin’s undergraduate catalog describes the course as,
“Focus on conducting political analyses. The course includes basic components of correlation and linear regression, the basic components of multiple regression, and instruction in writing empirical research.”
Also, students should have an openness to basic mathematical concepts. Additionally, students should expect to spend roughly 10 hours a week on the course. This is just an estimate as some students may complete the work in less or more time; it depends on the student.
Course Credits: 3
Prerequisites: It has the following prerequisites: successful completion of PLSC 2305, 2306 and 3301 or instructor permission. Additionally, some of the concepts covered in this course should have been covered in a scientific research methods course.
Student Learning Outcomes
At the completion of this course, students will be able to:
- Explain and indicate the basic data structures prevalent in political science
- Recognize both simple and multiple linear regression models and their components.
- Interpret the findings of Ordinary Least Squares (OLS) regression models and their applicability in political science.
- Estimate parameters for OLS regressions and explain the significance of these estimated parameters.
- Understand the importance of classical hypothesis testing in political science research
- Calculate, explain and understand the tests of statistical significance and confidence intervals in political science
- Understand and explain the limitations of OLS regressions in political science.
- Explain why political scientists often Maximum Likelihood Estimation rather than OLS.