Quantitative Social Science
Chair: Michael Herron
Faculty: Professor M. C. Herron; Assistant Professors H. Chang and E. Voytas.
QSS steering committee members: Professors Y. Horiuchi (Government), B. Nyhan (Government), D. Rockmore (Mathematics, Computer Science), R. A. Shumsky (Operations at Tuck Business School); Associate Professors J. Ferwerda (Government), F. Fu (Mathematics); Assistant Professors M. Costa (Government), E. Moen (Biomedical Data Science); Adjunct Assistant Professor J. Chipman (Geography and Earth Sciences)
The Program in Quantitative Social Science (QSS) brings together Dartmouth faculty and students who are interested in applying statistical, computational, and mathematical tools to social science questions. QSS offers undergraduates a minor and a major, both of which combine quantitative training with one or more of the social sciences. Through QSS, Dartmouth undergraduates can integrate the power of modern quantitative and computational methods with the substance of a social science discipline.
To view Quantitative Social Science courses, click here.
The Major in Quantitative Social Science
The major consists of courses to be selected from the following areas.
Six prerequisites
• Programming: either COSC 1, ENGS 20, or another programming course approved by the QSS Chair
• Mathematics: MATH 3 and MATH 8
• Introductory statistics: either ECON 10, GOVT 10, MATH 10, QSS 15, PSYC 10, SOCY 10, or another introductory statistics course approved by the QSS Chair
• Intermediate statistics: either ECON 20, GOV 19.01, MATH 40, MATH 50, QSS 54, or another intermediate statistics course approved by the QSS Chair
• Mathematical modeling: either ECON 21, QSS 18, QSS 30.04, QSS 36, or another course approved by the QSS Chair
• Introductory social science: one of ANTH 1, ANTH 3, ANTH 6, ANTH 9, ECON 1, EDUC 1, ENVS 3, GEOG 1, GEOG 6, GOVT 3, GOVT 4, GOVT 5, GOVT 6, LING 1, PBPL 5, PSYC 1, SOCY 1, SOCY 2, or another course approved by the QSS Chair
Core curriculum
The core curriculum for the major in Quantitative Social Science consists of QSS 17 and QSS 20.
Methods requirements
One of the following courses:
MATH 11, Accelerated Multivariable Calculus
MATH 13, Calculus of Vector-Valued Functions
MATH 22, Linear Algebra with Applications or COSC 70, Foundations of Applied Computer Science
One course from the following:
COSC 74, Machine Learning and Statistical Data Analysis
GEOG 9.01, Geographical Information Systems
GEOG 54, Geovisualization
MATH 50, Introduction to Linear Models
MATH 76, Topics in Applied Mathematics (to be approved by the QSS Chair)
QSS 17, Data Visualization
QSS 19, Advanced Data Visualization
QSS 30, Special Topics in QSS
QSS 36, Mathematical Models in the Social Sciences
QSS 41, Analysis of Social Networks
QSS 45, AI and Machine Learning
The special topics course, QSS 30, may be taken more than once as long as different electives are selected. Moreover, with permission of the QSS Chair, students may substitute other courses offered at Dartmouth for the required courses listed above.
Social science requirements
Four non-introductory courses that focus on a social science area of the student's choosing. A student pursuing the major in QSS should consider the extent to which his or her social science courses are coherent, and the QSS Chair will be available to offer guidance on this.
Research project requirement
To graduate with a major in QSS, a student must complete an independent research project. Each QSS major must choose one of two project options: either an intensive, one quarter project or a three quarters honors thesis. The honors thesis option requires approval from the program, and QSS honors theses are governed by guidelines established by the College. Per these guidelines, a student completing a thesis judged to be of sufficiently high quality will graduate from Dartmouth with Honors or with High Honors in QSS. A QSS major who elects the intensive project track will work on his or her project during the student's fourth year on campus. Participating in the thesis track requires work and engagement during the fall, winter, and spring terms of a student’s fourth year. Students applying to write an honors thesis in QSS should have at time of application an overall grade point average of 3.5 or higher. In limited circumstances, a student pursuing a major in QSS will be permitted to change research project tracks during his or her fourth year. A student completing an intensive, one quarter research project will take QSS 82 in the student’s last year on campus. A student in the honors thesis track of the QSS major will take QSS 81 in the fall of the student’s last year on campus. For further details on the two research project tracks in QSS, consult the program chair.
The Minor in Quantitative Social Science
The QSS minor was designed based on the belief that quantitatively- and computationally-oriented students who have interests in social science should be taught a core set of skills. Such students need to know the basics of computer programming; they need a foundation in calculus; they need to know the basics of statistical inference; they need exposure to mathematical modeling; they need to be familiar with research design; and, they need hands-on exposure to the rewards and difficulties of research. The QSS minor embodies these objectives and empowers students to answer important empirical questions about the world.
The minor consists of courses to be selected from the following areas.
Five prerequisites
• Programming: either COSC 1, ENGS 20, or another programming course approved by the QSS Chair
• Mathematics: MATH 3 and MATH 8
• Introductory statistics: either ECON 10, ENVS 10, GOVT 10, MATH 10, QSS 15, PSYC 10, SOCY 10, or another introductory statistics course approved by the QSS Chair
• Intermediate statistics: either ECON 20, GOV 19.01, MATH 40, MATH 50, QSS 54, or another intermediate statistics course approved by the QSS Chair
• Mathematical modeling: either ECON 21, QSS 18, QSS 30.04, QSS 36, or another course approved by the QSS Chair
Core curriculum
The core curriculum for the major in Quantitative Social Science consists of QSS 17 and QSS 20.
Methods requirements
Two courses from the following:
COSC 74, Machine Learning and Statistical Data Analysis
GEOG 9.01, Geographical Information Systems
GEOG 54, Geovisualization
MATH 50, Introduction to Linear Models
MATH 76, Topics in Applied Mathematics (to be approved by the QSS Chair)
QSS 17, Data Visualization
QSS 19, Advanced Data Visualization
QSS 30, Special Topics in QSS
QSS 36, Mathematical Models in the Social Sciences
QSS 41, Analysis of Social Networks
QSS 45, AI and Machine Learning
The special topics course, QSS 30, may be taken more than once. Moreover, with permission of the QSS chair, students may substitute other courses offered at Dartmouth for any of the two required courses listed above.
Research project
QSS 82, One quarter research project