About the Committee

Our new concentration in Quantitative Methods and Social Analysis (QMSA) draws from the interdisciplinary faculty of the University-wide Committee on Quantitative Methods in Social, Behavioral, and Health Sciences. Those faculty are focused on the development and application of innovative research methods – including geospatial modeling, intensive longitudinal data analysis, multilevel modeling, network analysis, causal inference, econometrics, demographic techniques, survey methods, machine learning, and content analysis – to improve our understanding of populations, societies, and behavior.

This concentration is for students who seek rigorous training and critical exposure to the latest techniques of quantitative social science. Dramatic advances in statistical modeling, experimental design, and statistical analysis have created unprecedented opportunities for advancing knowledge across a wide range of disciplines. There is an ever-greater demand for scholars who can apply sophisticated theories of statistical inference to tackle challenging problems in areas like poverty, crime, health disparity, public opinion, political participation, human development, cognition and emotions, genes and environment, and knowledge diffusion.

The QMSA Concentration

QMSA is a one-year program with 9 graduate-level courses over three academic quarters, culminating in an article-length MA thesis. Students receive rigorous education in statistical theory, advanced quantitative methods, and social scientific research. Each student is mentored directly by a faculty member on the Committee for the MA thesis.

International students who graduate from our MAPSS/QMSA Concentration are eligible for three years of work authorization in the US, as a STEM-approved field of study.

Our goal is to prepare students for PhD study in quantitative social science, and for professional positions at research institutions and government or nongovernment agencies. Applicants who declare an interest in QMSA admission must have a minimum quantitative GRE score at the 75th percentile. They must list up to five prior courses in the social sciences, and up to five prior courses in mathematics, statistics, or quantitative methods. They should have prior exposure to calculus and linear algebra. They must furnish a statement of purpose indicating a domain of substantive interest in the social or behavioral sciences, outlining their intended research, and naming two QMSA faculty members they most hope to work with.

QMSA Program Requirements

1.  Summer Math Camp and Placement Exam

QMSA students are required to attend an intensive math camp in September – MACS 33000: Computational Math and Statistics – for a review of linear algebra, differential/integral calculus, and probability/statistical theory that constitute the mathematical foundations of quantitative research methods. QMSA students must take a math/statistics placement exam during orientation week. 

The result of this exam and a student’s prior coursework will jointly determine the sequence of courses that the student will take to meet the curricular requirement in statistical theory.

Exceptions are made for QMSA students who receive special permission to attend the Econ doctoral math camp. That camp is exceptionally advanced, and requires prior exposure to real analysis, econometrics, advanced statistics/probability, linear algebra, and multivariable calculus. Interested QMSA students should contact Chad Cyrenne if they believe they may be qualified.

2.  Core Sequence in Statistical Theory

Students who have not taken probability and statistics in their prior coursework, or who need to develop a better command of the mathematical foundations of statistical theory, will take SOSC 36006: Foundations for Statistical Theory in the Fall Quarter and STAT 24400: Statistical Theory and Methods I in the Winter Quarter. 

Other QMSA students will take STAT 24400: Statistical Theory and Methods I in the Fall and STAT 24500: Statistical Theory and Methods II in the Winter.

Those with particularly strong mathematics and statistics backgrounds may, in consultation with their faculty mentor or QMSA Senior Lecturer, take BUSN 41901: Probability and Statistics and BUSN 41902: Inference in Econometrics and Statistics to satisfy this course requirement. This sequence of PhD-level courses provides a thorough introduction to classical and Bayesian statistical theory, and offers the necessary tools for many of the advanced courses in the Chicago Booth curriculum.

3.  Core Sequence in Perspectives and Quantitative Methods

QMSA students must take MAPS 30000: Perspectives in Social Science Analysis in the Fall and SOSC 36007: Overview of Quantitative Methods in the Social and Behavioral Sciences in the Winter.

The former provides an overview of major theoretical perspectives that have influenced multiple disciplines in the social and behavioral sciences and serves as a foundation for the social science field that a student may choose in developing a thesis project. The latter offers an overview of a wide range of quantitative methods, reveals their common logic and inherent connections, and provides a gateway to more advanced coursework across the University.

4.  Elective Courses in Advanced Quantitative Methods

In consultation with their faculty mentors and the QMSA Senior Lecturer, students select at least two advanced methods courses from the wide range of courses listed on the QMSA webpage and updated quarterly.

Eligible courses must not be at the introductory level or exclusively about learning a software package. Students who hope to take a course not on the QMSA course list must petition the QMSA Senior Lecturer for permission before the quarter begins.

5.  Elective Courses in the Social and Behavioral Sciences

In consultation with the QMSA faculty and their MAPSS preceptor, students select up to 3 social science electives that equip them with the theoretical understanding and the empirical knowledge necessary for their MA projects. Eligible courses must introduce students to the literatures surrounding a significant research problem, and they must have a writing component. 

6.  Academic Performance

A minimal “B” average is required to earn the QMSA concentration. QMSA students who take SOSC 36006: Foundations for Statistical Theory in the Fall must obtain at least a B+ to ensure that they will succeed in STAT 24400: Statistical Theory and Methods I. Up to two elective courses may be taken pass/fail.

7.  Master’s Thesis

Under the supervision of a faculty mentor and the preceptor, every QMSA student is required to complete a research article by the end of the academic year that demonstrates the student’s ability to conduct and present rigorous investigations of a significant research question.

8.  Quantitative Methods Workshop

QMSA students must attend the biweekly Workshop on Quantitative Methods that meets every other Friday from 10:30-noon. The Workshop invites leading methodologists to present their work and offers an ideal venue for students to get up to speed with the latest developments in quantitative research. In addition, QMSA students are required to attend a pre-workshop session designed to provide scaffolding and prepare them for active participation in the intellectual discourse around the topic of each workshop.

QMSA Course Offerings


 Statistics Core - Beginning


SOSC 36006: Foundations for Statistical Theory. This course is designed for graduate and advanced undergraduate students who aim to develop conceptual understanding of the fundamentals of statistical theory underlying a wide array of quantitative research methods. The course introduces students to probability and statistical theory and emphasizes the connection between statistical theory and the routine practice of statistical applications in quantitative research. Students will gain basic understanding of the concepts of joint, marginal, and conditional probability, Bayes rule, probability distributions of random variables, principles of statistical inference, sampling distributions, and estimation strategies. The course can serve as a preparation for mathematical statistics courses such as STAT 244 (Statistical Theory and Methods 1) and as a theoretical foundation for various advanced quantitative methods courses in the social, behavioral, and health sciences. Prerequisite: Basic knowledge of linear algebra and calculus, specifically differentiation and integration, is necessary to understand the material on continuous distributions, multivariate distributions, and functions of random variables.

STAT 24400: Statistical Theory and Methods I. This course is the first quarter of a two-quarter systematic introduction to the principles and techniques of statistics, as well as to practical considerations in the analysis of data, with emphasis on the analysis of experimental data. This course covers tools from probability and the elements of statistical theory. Topics include the definitions of probability and random variables, binomial and other discrete probability distributions, normal and other continuous probability distributions, joint probability distributions and the transformation of random variables, principles of inference (including Bayesian inference), maximum likelihood estimation, hypothesis testing and confidence intervals, likelihood ratio tests, multinomial distributions, and chi-square tests. Examples are drawn from the social, physical, and biological sciences. The coverage of topics in probability is limited and brief, so students who have taken a course in probability find reinforcement rather than redundancy. 


Statistics Core – Intermediate


STAT 24400: Statistical Theory and Methods I. This course is the first quarter of a two-quarter systematic introduction to the principles and techniques of statistics, as well as to practical considerations in the analysis of data, with emphasis on the analysis of experimental data. This course covers tools from probability and the elements of statistical theory. Topics include the definitions of probability and random variables, binomial and other discrete probability distributions, normal and other continuous probability distributions, joint probability distributions and the transformation of random variables, principles of inference (including Bayesian inference), maximum likelihood estimation, hypothesis testing and confidence intervals, likelihood ratio tests, multinomial distributions, and chi-square tests. Examples are drawn from the social, physical, and biological sciences. The coverage of topics in probability is limited and brief, so students who have taken a course in probability find reinforcement rather than redundancy.

STAT 24500: Statistical Theory and Methods II. This course is the second quarter of a two-quarter systematic introduction to the principles and techniques of statistics, as well as to practical considerations in the analysis of data, with emphasis on the analysis of experimental data. This course continues from STAT 24400 and covers statistical methodology, including the analysis of variance, regression, correlation, and some multivariate analysis. Some principles of data analysis are introduced, and an attempt is made to present the analysis of variance and regression in a unified framework. Statistical software is used.


Statistics Core – Advanced


BUSN 41901: Probability and Statistics. The central topics of BUSN 41901 are probability, martingales and stochastic processes. Basic concepts in probability are also covered. Prerequisites: One year of calculus. The text for the course is DeGroot and Schervish, Probability and Statistics. BUSN 41901 is cross-listed as STAT 32400.

BUSN 41902: Inference in Econometrics and Statistics. The focus of this course is methods to draw inferences in econometric models. The course covers linear regression models, generalized methods of moments, nonlinear models, and time series models. The majority of the discussion covers frequentist methods focusing on the use of approximations to finite-sample sampling distributions as a means for obtaining inference. It covers methods that are appropriate for independent data as well as dependent data. We will discuss intuition for how and when to use the econometric tools developed in the class in addition to deriving some of the relevant theoretical properties. Three recommended texts are Econometrics by Hayashi, Econometric Analysis of Cross Section and Panel Databy Wooldridge, and Time Series Analysis by Hamilton. Asymptotic Theory for Econometricians (revised edition) by White provides a useful and concise reference on asymptotic results. BUSN 41901 is cross-listed as STAT 32900.


Perspectives and Quantitative Methods Core


MAPS 30000: Perspectives in Social Science Analysis. Perspectives are stances from which social thinkers see the world and explain the world; they are not just ways of “looking” but also starting points for “acting” in doing research. Different perspectives may complement or contradict one another. This course presents some of the main traditions of theoretical argument in the social sciences today about the nature of social life and individual behavior. The course readings draw upon foundational works representing diverse theoretical perspectives or deemed to be exemplary of the perspectives as applied in empirical research. It will show that some of the most important work in the social sciences derives from scholars who were willing to think beyond the confines of a single perspective.

SOSC 36007: Overview of Quantitative Methods in the Social and Behavioral Sciences. The course is designed to offer an overview of and present the common logic underlying a wide range of methods developed for rigorous quantitative inquiry in the social and behavioral sciences. Students will become familiar with various research designs, measurement, and advanced analytic strategies broadly applicable to theory-driven and data-informed quantitative research in many disciplines. Moreover, they will understand the inherent connections between different statistical methods, and will become aware of the strengths and limitations of each. In addition, this course will provide a gateway to the numerous offerings of advanced quantitative methods courses. It is suitable for undergraduate and graduate students at any stage of their respective programs. Prerequisite: Introductory level statistics


Sample Electives in Advanced Quantitative Methods


See https://voices.uchicago.edu/qrmeth/mapss-qmsa/ for the most updated list of available courses. Final course selections must be approved by the QMSA Senior Lecturer.


Sample Electives in Social and Behavioral Sciences


See https://coursesearch.uchicago.edu/ for the available alternatives. Final course selections must be approved by the QMSA Senior Lecturer.


Workshops

Quantitative Methods in Education, Health, and Social Sciences

Computational Social Science

Centers and Institutes   

Center for Data Science and Public Policy

Center for Decision Research

Center for Global Health

Center for Health and the Social Sciences

Center for Health Statistics

Center for Research Informatics

Center for Robust Decision Making on Climate and Energy Policy

Center for Spatial Data Science

Center for the Economics of Human Development

Center on Demography and Economics of Aging

Chapin Hall

Computation Institute

Crime Lab

FLASH Center for Computational Science

Grossman Institute for Neuroscience, Quantitative Biology and Human Behavior

Joint Center for Education Research

Knowledge Lab

Population Research Center

Research Computing Center

Spatial Intelligence and Learning Center

Survey Lab

UChicago Urban

Urban Center for Computation and Data

Related Departments of Interest to MAPSS Students

Political ScienceSociologyEconomicsPsychology,  Harris School of Public Policy