Quantitative Social Science
Overview
Quantitative Social Science (QSS) is a burgeoning interdisciplinary field that combines quantitative analysis skills with a variety of substantive social science disciplines. The Quantitative Social Science program provides students across the social sciences with the core skills and tools required to conduct sophisticated quantitatively-oriented social science research. Designed to accompany the substantive core courses of another social science area of concentration (AOC), this program of study emphasizes the mathematical foundations as well as practical methodological components of quantitative data analysis, including research design, applied statistics, and programming.
Quantitative Social Science is not just the study of how to do data analysis but also the study of why data analysis should be done, what its possibilities are and its limitations. Students who pursue a joint concentration or secondary field in QSS will emerge with a better understanding of how decisions with data get made, how to interpret the profusion of data in our increasingly digital society, how different social science fields utilize data to understand their areas of study, and how to communicate effectively and clearly with data.
The Quantitative Social Science program will be of particular interest to students hoping to pursue careers in state or federal government, media and communications, consulting, marketing and finance, and health and environmental organizations—all of which increasingly require data analysis skills. Additionally, this AOC may be helpful for those interested in graduate school in the social sciences, where sophisticated quantitative skills are increasingly expected as prerequisites for admission.
Faculty in Quantitative Social Science
Catherine Cottrell, Associate Professor of Psychology (On Leave)
Tarron Khemraj, Professor of Economics and International Economics/William and Marie Selby Chair
Jack Reilly, Associate Professor of Political Science (On Leave)
Sherry Yu, Associate Professor of Economics and Finance
Requirements for the Joint AOC in Quantitative Social Science
A minimum of nine (9) academic units.
| Code | Title |
|---|---|
| Level I Applied Statistics Course 1 | |
| Select one of the following: | |
| Introduction to Applied Statistics* | |
| Quantitative Political Analysis I* | |
| Dealing with Data I* | |
| Statistics for Economics and the Social Sciences* | |
| Level II Statistics or Applied Statistics Courses 2 | |
| Select two from the following examples: | |
| Advanced Statistics for Psychology | |
| Econometrics | |
| Quantitative Political Analysis II | |
| Dealing with Data II | |
| Introduction to Categorical Data Analysis | |
| Statistical Learning | |
| Introduction to Programming Course | |
| Introduction to Programming in Python* | |
or CSCI 2550 | Introduction to Programming in C |
| Computation Course | |
| Databases for Data Science | |
| Social Science Research Design Course | |
| Select one from the following examples: | |
| Research Methods in Psychology | |
| Research Design Workshop in Political Science | |
| Sociological Research Methods | |
| Method and Theory in Archaeology* | |
| Mathematics Course | |
| Mathematics for the Social Sciences 3 | |
or MATH 2311 | Calculus I* |
| Additonal Quantitative Electives | |
| Select two from the following examples: | |
| Introduction to GIS | |
| Introductory Econometrics and Big Data Applications | |
| GIS II | |
| GIS and Remote Sensing | |
| R for GIS and Political Geography | |
| Data Visualization and Communication | |
| Statistical Learning | |
| Probability I and Probability II* | |
| Calculus I* | |
| Calculus II* | |
| Calculus III | |
| Linear Algebra | |
| Mathematical Modeling | |
| Algorithms for Data Science | |
| Quantitatively-Oriented Thesis or Independent Project | |
| Typically this requirement is met through the completion of a quantitative-based thesis; a quantitative chapter in a thesis; a quantitative Independent Study Project (ISP); a quantitative Independent Research Project (IRP); or a quantitative-focused tutorial. | |
| 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. | |
- 1
Level I refers to quantitative analysis or applied statistics courses with no prerequisites.
- 2
Level II refers to quantitative analysis or applied statistics courses with statistical or quantitative prerequisites.
- 3
Students may also take Linear Algebra (MATH 3105) to satisfy this requirement.
Requirements for a Secondary Field in Quantitative Social Science
A minimum of seven (7) academic units.
| Code | Title |
|---|---|
| Level I Applied Statistics Course 1 | |
| Select one of the following: | |
| Introduction to Applied Statistics* | |
| Quantitative Political Analysis I* | |
| Dealing with Data I* | |
| Statistics for Economics and the Social Sciences* | |
| Level II Statistics or Applied Statistics Courses 2 | |
| Select two from the following examples: | |
| Advanced Statistics for Psychology | |
| Econometrics | |
| Quantitative Political Analysis II | |
| Dealing with Data II | |
| Introduction to Categorical Data Analysis | |
| Statistical Learning | |
| Introduction to Programming Course | |
| Introduction to Programming in Python* | |
or CSCI 2550 | Introduction to Programming in C |
| Social Science Research Design Course | |
| Select one from the following examples: | |
| Research Methods in Psychology | |
| Research Design Workshop in Political Science | |
| Sociological Research Methods | |
| Method and Theory in Archaeology* | |
| Mathematics Course | |
| Mathematics for the Social Sciences 3 | |
or MATH 2311 | Calculus I* |
| Additional Quantitative Elective | |
| Select one from the following examples: | |
| Introduction to GIS | |
| Introductory Econometrics and Big Data Applications | |
| GIS II | |
| GIS and Remote Sensing | |
| R for GIS and Political Geography | |
| Data Visualization and Communication | |
| Statistical Learning | |
| Probability I and Probability II* | |
| Linear Algebra | |
| Mathematical Modeling | |
| Algorithms for Data Science | |
| 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, but up to two courses can double count toward a student's primary AOC. | |
- 1
Level I refers to quantitative analysis or applied statistics courses with no prerequisites.
- 2
Level II refers to quantitative analysis or applied statistics courses with statistical or quantitative prerequisites.
- 3
Students may also take Linear Algebra (MATH 3105) to satisfy this requirement.