Quantitative Social Science Curriculum

Course Requirements for Joint Degree

Three courses in applied statistics

• Course Examples: Advanced Statistics for Psychology, Econometrics, Quantitative Political Analysis II, many Statistics classes with a prerequisite (including but not limited to: Dealing with Data II, Introduction to Categorical Data Analysis, Statistical Learning). QSS considers a “Level II” class as a quantitative analysis or applied statistics course with a statistical or quantitative prerequisite.

Two classes in computation

• Examples: Research Methods in Psychology, Research Design Workshop in Political Science, Research Methods in Sociology, Method and Theory in Archaeology

Calculus and Math Tools for the Social Sciences

• Alternately, students may take Calculus I and Linear Algebra to satisfy this requirement

Two additional quantitative electives

• Recommended Electives: GIS, Visualization, Social Network Analysis, Statistical/Machine/Deep Learning, Probability, Linear Algebra, Calculus, Mathematical/Statistical/Computational Modeling, Algorithms, R Programming Tutorial

Total Course Requirements: 9 courses. Up to 3 courses can double count toward a student’s other joint AOC.

Additional Requirements

Language: R and Python must be utilized as the dominant language in at least one course each. 

• This requirement can be supplemented with additional tutorials if necessary. 

Distribution: Primarily quantitative coursework must be undertaken in at least two different social science fields: Psychology, Economics, Political Science, Sociology, Geography, Anthropology, and History. Primarily substantive courses in those fields cannot count for this distribution requirement.
Practice: The student must complete a quantitatively oriented independent research project. Typically this is met through one of the following paths:

• Quantitative ISP project
• Quantitative chapter in a thesis (or a quantitative thesis)
• Quantitative tutorial/IRP

Requirements for a Secondary Field Degree

Identical to joint AOC, but with only one elective and without the second computation course. Up to 2 courses can double count toward student’s primary AOC. Total course requirements: 7 courses.

Student Learning Outcomes

Quantitative & Statistical Methods: Students will have an understanding of quantitative analyses conducted in social science research and be able to conduct data analysis using descriptive, inferential, and visual statistical methods using appropriate tools.

Programming & Computational Skills: Students will learn basic programming skills and be able to utilize appropriate tools for programming tasks, data management, and statistical methods.

Quantitative Integration & Research Design: Students will demonstrate the ability to understand social science research methodology and integrate quantitative analysis with substantive social science theory and data across multiple fields.

Mathematics: Students will demonstrate the ability to understand the mathematical underpinnings of statistical models.

Data Communication & Practice: Students will learn how to describe the implications of their research to a non-expert audience and will demonstrate strong written and oral communication skills when presenting the results of their research.