Contact the Statistics Department for enrollment information.
The goal of the Ph.D. track is to provide education in a particular area of social science for Ph.D. students in Statistics. The courses listed below can satisfy the elective requirements for the Ph.D. in Statistics.
The core requirements of the track consist in:
At the present time, there are two areas of subject specialization: Statistical Demography and Econometrics.
Other subject area sequences may be added later by action of the Statistics faculty. Students who complete the 5 courses can waive one of the three core sequences currently required for the Ph.D. in Statistics.
Additionally, at least three consecutive quarters of participation in the CS&SS seminar (1 credit/quarter) will be required.
Track Requirements (see below for course descriptions):
SOC 513 Demography and Ecology (3)
Theories and research on human fertility, mortality, mobility, migration, and urbanization in social/economic context. Comparative and historical materials on Europe, the United States, and the Third World.
SOC 531 Fertility and Mortality (3)
Theories of fertility and mortality, demographic transitions, individual variations. Specific analytic approaches. Familiarity with basic fertility and mortality measures, and with the life table, is assumed.
SOC 533 Research Methods in Demography (3)
Basic measures and models used in demographic research. Sources and quality of demographic data. Rate construction, standardization, the life table, stable population models, migration models, population estimation and projection, measures of concentration and dispersion, measures of family formation and dissolution.
ECON 583 Econometric Theory I (3)
Estimation and testing in linear and nonlinear regression models. Asymptotic theory, bootstrapping. Theoretical developments are reinforced with a variety of empirical examples and applications.
ECON 584 Econometric Theory II (3)
Continuation of 583. Analysis of stationary and nonstationary, univariate, and multivariate time series models. Emphasis on empirical applications.
ECON 585 Applied Microeconometrics (3)
Econometric issues that arise in applied microeconomic research. Topics include the use of panel data and models with limited and qualitative dependent variables.
519 Time Series Analysis (3)
Descriptive techniques. Stationary and nonstationary processes, including ARIMA processes. Estimation of process mean and autocovariance function. Fitting ARIMA models to data. Statistical tests for white noise. Forecasting. State space models and the Kalman filter. Robust time series analysis. Regression analysis with correlated errors. Statistical properties of long memory processes. (This course is recommended for students specializing in Time-Series Econometrics.)
526 Structural Equation Models (3)
Structural equation models for the social sciences, including specification, estimation, and testing. Topics include path analysis, confirmatory factor analysis, linear models with latent variables, MIMIC models, non-recursive models, models for nested data. Emphasizes applications to substantive problems in the social sciences.
529 Sample Survey Techniques (3)
This is an applied statistical methods course that will cover the statistical design and analysis of complex surveys, with applications in the social and health sciences. In addition to traditional topics in survey analysis we will cover data visualization, regression modelling of data from complex surveys, and the design and analysis of two-phase samples from existing cohorts.
536 Log-Linear Modeling and Logistic Regression for the Social Sciences (3)
Log-linear modeling of multidimensional contingency tables. Logistic regression. Applications to social mobility, educational opportunity, and assortative marriage. Applied and computing focus.
544 Event History Analysis for the Social Sciences (5)
Examines life course research using event-history analysis with applications to the substantive areas of household dynamics, family formation and dissolution, marriage, cohabitation, and divorce, migration histories, residential mobility, and housing careers. Examines continuous- and discrete-time longitudinal models during practical laboratory sessions.
560 Hierarchical Modeling for the Social Sciences (4)
Explores ways in which data are hierarchically organized, such as voters nested within electoral districts that are in turn nested within states. Provides a basic theoretical understanding and practical knowledge of models for clustered data and a set of tools to help make accurate inferences.
564 Bayesian Statistics for the Social Sciences (4)
Statistical methods based on the idea of probability as a measure of uncertainty. Topics covered include subjective notion of probability, Bayes' Theorem, prior and posterior distributions, and data analysis techniques for statistical models.
565 Inequality: Current Trends and Explanations (3)
This course will cover the recent growth in economic inequality in the US, and the competing explanations for these new trends. The explanations take three forms: those that emphasize supply side shifts in the demographics of the labor market; those that emphasize the demand side shifts in industrial composition and firm-level restructuring; and those that emphasize the broader political context that affects policies like the minimum wage, the strength of unions, and the impact of foreign trade. We focus strongly on evaluating the data and methods used to make these arguments.
566 Causal Modeling (3)
Construction of causal hypotheses. Theories of causation, counterfactuals, intervention vs. passive observation. Contexts for causal inference: randomized experiments; sequential randomization; partial compliance; natural experiments, passive observation. Path diagrams, conditional independence and d-separation. Model equivalence and causal under-determination.
567 Statistical Analysis of Social Networks (4)
Statistical and mathematical descriptions of social networks. Topics include graphical and matrix representations of social networks, sampling methods, statistical analysis of network data, and applications.
568 Game Theory for Social Scientists (5)
Studies non-cooperative game-theory and provides tools to derive appropriate statistical models from game-theoretic models of behavior. Equilibrium concepts, learning, repeated games and experimental game theory.
569 Visualizing Data (4)
Explores techniques for visualizing social science data to complement graduate training methods. Emphasis on principles and perception of visualization, novel exploration and presentation of data and statistical models, and implementation of recommended techniques in statistics packages.