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. 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 innovate methodologically, who understand how to use the theory of statistical inference to tackle really hard problems in social, behavioral, and health science. We look to theory-based models for populations and societies, examining biological, behavioral, and environmental factors and the way they interact. We are primarily interested in (a) theoretical controversies, questions, and hypotheses that arise in scientific discourse and the formal models that can make these precise, (b) systematic measurement of key theoretical constructs with known and consistent psychometric properties, (c) the design of research to test these models, (d) the specification of assumptions that are required for linking analytic results to theoretical claims, and (e) the validity of statistical inferences.
The QMSA Concentration
This concentration is for students who seek rigorous training and critical exposure to the latest techniques of quantitative social science. Admitted candidates may participate on research teams or conduct independent projects applying quantitative thinking and analysis to important research questions. 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.
Students who declare an interest in QMSA admission must have a minimum quantitative GRE of 75%. They must also furnish a statement of purpose outlining their intended research and the two QMSA faculty members they most hope to work with.
If accepted, students will select a minimum of 5 courses in theoretical modeling, research design, causal inference, and statistical analysis, and write their MA thesis with a member of the QMSA faculty.
One of those 5 courses will be Design and Analysis in Social, Behavioral, and Health Sciences, taught in the Fall quarter.
In addition, students will take Perspectives in Social Science Analysis and up to 3 electives in their social science field.
Finally, QMSA students must attend the biweekly Workshop on Quantitative Methods in Education, Health, and Social Sciences (QMeHSS). That 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.
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.
For a complete list of current courses, please click here.
CHDV 30101. Applied Statistics in Human Development Research. This course provides an introduction to quantitative methods of inquiry and a foundation for more advanced courses in applied statistics for students in social sciences with a focus on human development research. The course covers univariate and bivariate descriptive statistics, and introduction to statistical inference, t test, two-way contingency table, analysis of variance, and regression. All statistical concepts and methods will be illustrated with application studies in which we will consider the research questions, study design, analytical choices, validity of inferences, and reports of findings. The examples include (1) examining the relationship between home environment and child development, (2) evaluating the effectiveness of welfare-to-work programs on maternal and child well-being, and (3) assessing the academic growth of English language learners in comparison with their English-speaking peers. At the end of the course, students should be able to define and use the descriptive and inferential statistics taught in this course to analyze data and to interpret the analytical results. Students will learn to use the SPSS software. No prior knowledge in statistics is assumed.
CHDV 30102. Introduction to Causal Inference. This course is designed for graduate students and advanced undergraduate students from the social sciences, education public health science, public policy, social service administration, and statistics who are involved in quantitative research and are interested in studying causality. The goal of this course is to equip students with basic knowledge of and analytic skills in causal inference. Topics for this course will include the potential outcomes framework for causal inference; experimental and observational studies; identification assumptions for causal parameters; potential pitfalls of using ANCOVA to estimate a causal effect; propensity score based methods including matching, stratification, inverse-probability-of-treatment-weighting (IPTW). marginal mean weighting through stratification (MMWS), and doubly robust estimation; the instrumental variable (IV) method; regression discontinuity design (RDD) including sharp RDD and fuzzy RDD; difference in difference (DID) and generalized DID methods for cross-sectional and panel data, and fixed effects model.
CHDV 32411. Mediation, Moderation and Spillover Effects. This course is designed for graduate students and advanced undergraduate students from social sciences, statistics, public health science, public policy, and social services administration who will be or are currently involved in quantitative research. Questions about why a treatment works, for whom, under what conditions, and whether on individual's treatment could affect other individuals' outcomes are often key to the advancement of scientific knowledge. We will clarify the theoretical concepts of mediated effects, moderated effects, and spillover effects under the potential outcomes framework. The course introduces cutting-edge methodological approaches and contrasts them with conventional strategies including multiple regression, path analysis, and structural equation modeling. The course content is organized around application examples. The textbook "Causality in a Social World: Moderation, Mediation and Spill-Over" (Hong, 2015) will be supplemented with other reading reflecting latest developments and controversies.
ECON 31100. Empirical Analysis II. This course develops methods of analyzing Markov specifications of dynamic economic models. Models with stochastic growth are accommodated and their properties analyzed. Methods for identifying macroeconomic shocks and their transmission mechanisms are developed. Related filtering methods for models with hidden states are studied. The properties estimation and inference methods based on maximum likelihood and generalized method of moments are derived. These econometric methods are applied to models from macroeconomics and financial economics. Note: MAPSS students must secure Victor Lima’s permission to take this course.
ECON 31200. Empirical Analysis III. The course will review some of the classical methods you were introduced to in previous quarters and give examples of their use in applied microeconomic research. Our focus will be on exploring and understanding data sets, evaluating predictions of economic models, and identifying and estimating the parameters of economic models. The methods we will build on include regression techniques, maximum likelihood, method of moments estimators, as well as some non-parametric methods. Lectures and homework assignments will seek to build proficiency in the correct application of these methods to economic research questions. Note: MAPSS students must secure Victor Lima’s permission to take this course.
ECON 34930. Inequality, Theory, Methods & Evidence. This is a graduate seminar designed to investigate basic issues in the study of inequality and social mobility. It is more than a readings course. It is designed to go into topics in depth. Students (individuals or groups off students) will discuss a topic in depth for the entire group. The instructors will work with the students in advance of their presentations, reviewing and participating in the formal presentations. Theory, methods, and evidence will be synthesized. Discussions of topics may straddle class meetings as required. Students and faculty will choose topics of mutual interest. Some outside visitors will coordinate discussions. Note: MAPSS students must secure Victor Lima’s permission to take this course.
MEDC/ISTP 42000. Topics in Data Analysis in Biomedical Research: Big Data. The technological advances in biomedical research that allow high-throughput methods mean vast data sets are rapidly becoming the norm in the field. This course is intended to introduce medical students in their M4 year to the challenges and opportunities of big data in clinical and translational contexts. Topics will include: the extent of natural genetic variability in genetics; human genetics and the association with disease; non-genetics databases (such as Medicare) and how they are used for research; mapping complex disease loci using large datasets; causal inference; the future of bioinformatics.
PBHS 33300. Applied Longitudinal Analysis. Longitudinal data consist of multiple measures over time on a sample of individuals. These types of data occur extensively in both observational and experimental biomedical and public health studies, as well as in studies in sociology and applied economics. This course will provide an introduction to the principles and methods for the analysis of longitudinal data. Whereas some supporting statistical theory will be given, emphasis will be on data and interpretation of models for longitudinal data. Problems will be motivated by applications primarily in mental health, public health, prevention research, and health services research.
PBHS 33500. Statistical Applications. This course provides a transition between statistical applications in medicine, mental health, environmental science, analytical chemistry, and public policy. Lectures are oriented around specific examples from a variety of content areas. Opportunities for the class to work on interesting applied problems presented by U of C faculty will be provided. Although an overview of relevant statistical theory will be presented, emphasis is on the development of statistical solutions to interesting applied problems.
PLSC 43100. Maximum Likelihood. The purpose of this course is to familiarize students with the estimation and interpretation of maximum likelihood, a statistical method which permits a close linkage of deductive theory and empirical estimation. Among the problems considered in this course include: models of dichotomous choice, such as turnout and vote choice; models of limited categorical data, such as those for multi-party elections and survey responses; models for counts of uncorrelated events, such as executive orders and book burnings; models for duration, such as the length of parliamentary coalitions or the tenure of bureaucracies; models for compositional data, such as allocation of time by bureaucrats to task and district vote shares; and models for latent variables, such as for predispositions. The emphasis in this course will be on the extraction of information about political and social phenomena, not upon properties of estimators.
PPHA 30525. Next Generation Data: Sources, Access, Analytics. For decades, sample surveys have produced the data that provide the basis for decisions of policy makers and decision makers in both public and the private sectors. Traditional surveys are however coming under a dual threat: decreasing response rates and increasing costs. At the same time a wide array of new sources of data is emerging. Although survey researchers and methodologists are actively seeking to adapt to an ever-changing social and technological environment, it is increasingly difficult to maintain the desired relevance, accuracy, and the timeliness of survey-based statistics. At the same time, there are many potentially valuable non-survey data sources, such as federal, state, and local government administrative records, credit card, and store transactions, sensor data, and a wide and growing variety of web-based data, such as social media, price data, etc. This class will discuss the new forms of data that are being collected to conduct social, economic, behavioral, and policy research, while at the same time addressing innovations in traditional methods, such as survey research. Issues of access, quality, ethics/privacy, analysis, and storage will be discussed. A range of policy domains will be addressed, including education, finance, transportation, welfare programs, and healthcare. We hope to invite guest speakers to present the perspective of data generators, data providers, and data users. This course counts toward the Survey Research Certificate.
PPHA 31202. Advanced Statistics for Data Analysis I. This course focuses on the statistical concepts and tools used to study the association between variables and causal inference. This course will introduce students to regression analysis and explore its uses in policy analyses. This course will assume a greater statistical sophistication on the part of students than is assumed in PPHA 31002.
PPHA 34600. Program Evaluation Section I. To introduce students to program evaluation and provide an overview of current issues and methods for estimating treatment impacts.
PPHA 34600. Program Evaluation Section II. To introduce students to program evaluation and provide an overview of current issues and methods for estimating treatment impacts.
PPHA 41400. Applied Regression Analysis: Analysis of Microeconomic Data. This course is based on the theory and practice of econometrics. Its intention is to provide hands-on experience with econometric analysis, without neglecting sound knowledge of econometric theory. It is designed to help students acquire skills that make them effective consumers and producers of empirical research in public policy, economics, and related fields. Throughout the course, concepts will be illustrated with application in economics. Various aspects will be covered in the course, in particular: I) Development of testable econometric models; II) Use of appropriate data, and; III) specification and estimation of econometric models.
PPHA 42000. Applied Econometrics I. This class covers basic Gauss-Markov theory and some extensions. Think of it as a theoretical course for applied researchers.
PPHA 44900. Social Experiments: Design and Generalization. The pressure in many fields (notably medicine, health research, and education) for evidence-based results has increased the importance of the design and analysis of social investigations. This course will address three broad issues: the design and analysis of social experiments and quasi-experiments; the design and analysis of sample surveys; and how the interrelationships between the two approaches can inform generalization from experiments. There are two parallel streams in the course. First, the course will tackle the issues of generalization from three different perspectives: (i) the classic statistical design of experiments; (ii) the design of experiments and quasi-experiments in the social sciences; (iii) the design and analysis of sample surveys. Second, using a set of readings on research design in a variety of settings, we will consider how evidence from research is gathered and used. Randomized clinical trials in medicine, tests of interventions in education and manpower planning, and the use of scientific evidence in policy formulation will be among the examples.
PPHA 58002. Data Analytics II: Intro to Program Evaluation (Evening MA). The purpose of the course is to introduce students to program evaluation and provide an overview of current issues and methods. This is a course directed not at people who seek to carry out evaluations themselves, but rather who might need to use the results from program evaluations, select contractors to carry out evaluations, select contractors to carry out evaluations, or supervise their work.
PSYC 43360. Computational Models of Cognition and Development. Computational Models are powerful tools for integrating empirical research, and for making novel predictions about cognition and development. This course will survey computational models of attention. Learning, Decision Making, and Language Processing, aiming to develop students' understanding of what models are for broadly, as well as what kinds of models are used and useful in their individual research areas.
SOCI 30004. Statistical Methods of Research I. This course provides an introduction to quantitative methods and a foundation for other methods courses in the social sciences. The course considers standard topics: graphical and tabular displays of univariate and bivariate distributions, an introduction to statistical inference, and commonly arising applications such as the t-test, the two-way contingency table, analysis of variance, and regression. However, all statistical ideas are embedded in case studies including a national survey of adult literacy and an experimental test of alternative methods of writing instruction. For each case study, we will consider the issues motivating the research, the key research questions, and reports of findings. We will then re-analyze the data using the techniques described above, and based on the reanalysis, we will critically evaluate the validity of inferences previously drawn. Thus, the course will consider all statistical choices and inferences in the context of the broader logic of inquiry with the aim of strengthening our understanding of that logic as well as the statistical methods.
SOCI 30005. Statistical Methods of Research II. This course aims to prepare students to read, critically evaluate, and conduct research that relies on the most common methods of analysis use in sociology and related social sciences. Date of interest include surveys, large-scale experiments, and non-experiments in which the primary interest is focused on describing the association between one or more explanatory variables and an outcome. The outcomes of interest will include continuous outcomes, including retention in school, graduation from high school, employment, and criminal victimization; and count data, including family size, crime events and absenteeism. The most popular methods of analysis for such outcomes include multiple regression and its generalization to discrete outcomes.
SOCI 30125. Rational Foundations of Social Theory. This course is concerned with the introduction to the rational foundation of sociological theory, and covers the following topics: (1) the conceptualizations of social mechanism by Peter Hedstrom and Richard Swedberg, (2) social exchange theory by Peter Blau and by George C. Hoans (3) theory of network exchange and dependence by Robert Emerson and Karen Cook, and by Kazuo Yamaguchi, (4) theory and model of collective action by James S. Coleman, by Mark Granovettor, and by Thomas Schelling, (5) theory/model of relative deprivation by Raymond Boudon and by Kazuo Yamaguchi, (6) social capital theory by James S. Coleman, by Robert Putnum, and by Robert Frank, (7) rational theory of emotions by Robert Frank, (8) rational choice theory/model of the family by Stephen Coate and Glenn Loury, (10) rational characterizations of concepts related to Robert K. Merton's theory by Kazuo Yamaguchi, including self-fulfilling prophecy and anticipatory socialization, (11) theories of relative status by Guillermina Jasso and by Robert Frank and Cass Sunstein, and (12) rational choice theories/models of trust and cooperation especially about prisoner's dilemma situations and social network.
SOCI 30157. Mathematical Models. Mathematical models in sociology include (1) models of social processes, (2) models of social relations, and (3) models of social choice. The probability theory is especially relevant for models of social processes, matrix algebra is especially relevant for models of social relations, and multivariate differential calculus is especially relevant for models of social choices. More substantively, the course intends to cover some aspects of the following topics. (1) processes of social influence and social contagion, (2) social network, (3) balance theory, (4) exchange and power, (5) trust, and (6) collective action. The major mathematical tools employed in mathematical sociology are (1) probability and stochastic processes, (2) matrix algebra, and (3) multivariate differential calculus. Cognizant of the fact that many students do not know them well, the course covers many reviews of them with simple applications. You will see in the first two handouts a review of probability theory and matrix algebra. There will be more such reviews of probability theory and matrix algebra in later sessions as well as reviews of multivariate differential calculus.
SOCI 30253. Introduction to Spatial Data Science. Spatial Data Science is an evolving field that can be thought of as a collection of concepts and methods drawn from both statistics and computer science. These techniques deal with accessing, transforming manipulating, visualizing, exploring and reasoning about data where the locational component is important (spatial data). The course introduces the types of spatial data relevant in social science inquiry and reviews a range of methods to explore these data.
SOCI 40217. Spatial Regression Analysis. This course covers statistical and econometric methods specifically geared to deal with the problems of spatial dependence and spatial heterogeneity in cross-sectional and panel (space-time) data. The main objective of the course is to gain insight into the scope of spatial regression methods, to be able to apply them in an empirical setting, and to properly interpret the results of spatial regression analysis. While the focus is on spatial aspects, the types of methods covered have general validity in statistical practice. The course covers specification of spatial regression models in order to incorporate spatial dependence and spatial heterogeneity, as well as different estimation methods and specification tests to detect the presence of spatial autocorrelation and spatial heterogeneity.
STAT 22400. Applied Regression Analysis. This course is an introduction to the methods and applications of fitting and interpreting multiple regression models. The main emphasis is on the method of least squares. Topics include the examination of residuals the transformation of data, strategies, and criteria for the selection of a regression equation, the use of dummy variables, tests of it. Stata computer package will be used extensively, but previous familiarity with Stata is not assumed. The techniques discussed will be illustrated by real examples involving biological and social science data.
STAT 24400-1. Statistical Theory and Methods I. This is the first quarter of a two-quarter sequence. Enrollment in the first quarter alone is permitted, although not recommended. The first quarter will cover the basic tools from probability and the elements of statistical theory. Topics will include the definitions of probability and random variables, binomial and other discrete probability distributions, normal and other continuous probability distribution, 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. Some large sample theory will be included. The emphasis will be upon statistical theory, specifically methodology.
STAT 26700/36700. History of Statistics. This course covers topics in the history of statistics, from the eleventh century to the middle of the twentieth century. We focus on the period from 1650 to 1950, with an emphasis on the mathematical developments in the theory of probability and how they came to be used in the sciences. Our goals are both to quantify uncertainty in observational data and to develop a conceptual framework for scientific theories. This course includes broad views of the development of the subject closer looks at specific people and investigations, including reanalysis of historical data.
PLSC 30700. Linear Models. The purpose of this course is to bring graduate students up to speed with the dominant method in statistical social scientific research, namely, regression analysis. Regression analysis so ubiquitous in political science (and other social science) research that one will see some variation on the method in just about any volume of the top journals. There are variants of the general idea, of course, but the notion of trying to abstract the "effect" of a change in one variable upon some outcome is the cornerstone of statistical work in our field.
PPHA 31200. Mathematical Statistics for Public Policy I. This is an introductory course in mathematical statistics. Knowledge of calculus assumed. Linear algebra is helpful but not required.
PPHA 31300. Regression Analysis for Public Policy II. This class covers basic Gauss-Markov theory and other topics in regression analysis.
PPHA 42100. Applied Econometrics II. The goal of this course is for students to learn a set of statistical tools and research designs that are useful in conducting high-quality empirical research on topics applied microeconomics and related fields. Since most applied economic research examines questions with direct policy implications this course will focus on methods for estimating causal effects. This course differs from many other econometrics courses in that it is oriented toward applied practitioners rather than future econometricians. It therefore emphasizes research design (relative to statistical technique) and applications (relative to theoretical proofs), though it covers some of each.
STAT 24500. Statistics Theory and Methods II. This course is the second of a two-quarter introduction to the principles and techniques of statistics: the first quarter covers tools from probability and the elements of statistical theory, while the second quarter focuses on 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. Although theoretical concepts will be discussed, computers will also be used to practice their application. Most of the material covered in this course will be from but not be limited to Chapters 6, 8, 19, 12, 13, and 14 of Rice.
STAT 34700. Generalized Linear Models. This applied course covers factors, variates, contrasts, and interactions; exponential-family models (i.e. variance function); definition of a generalized linear model (i.e. link functions); specific examples of GLMS; logistic and probit regression; cumulative logistic models; log-linear models and contingency tables; inverse linear models; Quasi-likelihood and least squares; estimating functions; and partially linear models.