Complete Course Catalog

Develops statistical literacy. Examines objectives and pitfalls of statistical studies; study designs, data analysis, inference; graphical and numerical summaries of numerical and categorical data; correlation and regression; estimation, confidence intervals, and significance tests. Emphasizes social science examples and cases. May only receive credit for one of STAT 220, STAT 221/CSSS 221/SOC 221, or STAT 290. Course overlaps with: STMATH 341. Offered: jointly with SOC 221/STAT 221; AWSp.

A critical introduction to the methods used to collect data in social science: surveys, archival research, experiments, and participant observation. Evaluates "facts and findings" by understanding the strengths and weaknesses of the methods that produce them. Case based. Offered: jointly with SOC 320/STAT 320.

Introduction to applied data analysis for social scientists. Focuses on using programming to prepare, explore, analyze, and present data that arise in social science research. Data science topics include loading, cleaning, and exploring data, basic visualization, reproducible research practices. Statistical topics include measurement, probability, modeling, assessment of statistical evidence. Lectures intermixed with programming and lab sessions. Course overlaps with: B BUS 301. Offered: jointly with SOC 321/STAT 321; W.

Continuation of CSSS 321/SOC 321/STAT 321. Progresses to more sophisticated models including regression methods. Builds literacy in responsibly consuming and producing quantitative social science, including through situating statistical methods within critical epistemological perspectives on social scientific research. Assignments structured around a quarter-long project on a topic chosen by the student. Prerequisite: CSSS 321/SOC 321/STAT 321. Offered: jointly with SOC 322/STAT 322; Sp.

Considers topics relevant to modern voting and elections through statistical and social choice lenses. Topics include the purpose and limits of democratic decision-making; social choice theory and the associated theorems; judgement aggregation; voting procedures; election case studies; election polling and forecasting; electoral redistricting and gerrymandering; fairness aspects in voting; voting in contexts other than elections. Prerequisite: either STAT 311, STAT 390, STAT 391, or CSE 312; recommended: familiarity with reading and writing proofs; at least one introductory statistics course; and beginner ability with data programming at the level of either CSE 121, CSE 160, or STAT 302. Offered: jointly with PHIL 452/STAT 452; A, even years.

How data science integrates with various domains, especially the arts, humanities, and social sciences. Reflects on the opportunities of data science and its potential negative effects on society. Covers various subject areas, allowing students to see data science skills and studies in a variety of disciplinary settings. Credit/no-credit only.

Testing theories with empirical evidence. Examines current topics in research methods and statistical analysis in political science. Content varies according to recent developments in the field and with interests of instructor. Offered: jointly with POL S 501.

Theory and practice of likelihood inference. Includes probability modeling, maximum likelihood estimation, models for binary responses, count models, sample selection, and basis time series analysis. Offered: jointly with POL S 503.

Least squares estimation. Hypothesis testing. Interpretation of regression coefficients. Categorical independent variables. Interactions. Assumption violations: outliers, residuals, robust regression; nonlinearity, transformations, ACE, CART; nonconstant variance. Variable selection and model averaging. Prerequisite: either STAT 342, STAT 390, STAT 421, STAT 509/CSSS 509/ECON 580, or SOC 505. Offered: jointly with STAT 504.

Reviews basic mathematical skills needed for a meaningful understanding of elementary statistics, data analysis, and social science methodology. Overview of core knowledge required for graduate courses in quantitative methods in social sciences. Topics include discrete mathematics, differential and integral calculus, review of matrix algebra, and basic probabilistic and statistical concepts. Credit/no-credit only. Offered: jointly with SOC 512.

Familiarizes graduate students in the social sciences with modern environments for statistical computing. Provides an overview of available resources and a description of fundamental tools used in quantitative courses and doctoral research. Topics include interfaces to web-based resources, UNIX-based computing, and major statistical packages.

Applied regression analysis with emphasis on interactive computer graphics techniques and interpretation. Application to typical sociological problems. Offered: jointly with SOC 506.

Familiarizes students with the R environment for statistical computing. Students gain experience organizing, managing, cleaning, and merging complex social science data; writing basic R functions; and producing clear, replicable analyses and visualizations. Emphasizes workflows that support transparency and efficiency in empirical research and prepares students to integrate R with other statistical tools used in social science inquiry. Credit/no-credit only.

Examines methods, tools, and theory of mathematical statistics. Covers probability densities, transformations, moment generating functions, conditional expectation. Bayesian analysis with conjugate priors, hypothesis tests, the Neyman-Pearson Lemma. Likelihood ratio tests, confidence intervals, maximum likelihood estimation, Central limit theorem, Slutsky Theorems, and the delta-method. Prerequisite: STAT 311; either MATH 126 or MATH 136; and either MATH 208 or MATH 209. Offered: jointly with ECON 580/STAT 509.

Introduces maximum likelihood, a more general method for modeling social phenomena than linear regression. Topics include discrete, time series, and spatial data, model interpretation, and fitting. Prerequisite: POL S 501/CSSS 501 and POL S 503/CSSS 503. Offered: jointly with POL S 510.

Extends the linear model to account for temporal dynamics and cross-sectional variation. Focuses on model selection and real-world interpretation of model results. Topics include autoregressive processes, trends, seasonality, stationarity, lagged dependent variables, ARIMA models, fixed effects, random effects, cointegration and error correction models, panel heteroskedasticity, missing data in panel models, causal inference with panel data. Recommended: graduate level coursework in linear regression and social science research design; and basic familiarity with or willingness to learn the R statistical language. Offered: jointly with POL S 512.

Examines the many ways that social interactions positively and negatively influence our health, and vice versa. Considers why such influences are important to understand, how one measures them, what recent research has shown, and explores how they relate to other health determinants, both biological and cultural. Offered: jointly with BIO A 523.

Provides students with skills in questionnaire development and survey methods. Students develop a questionnaire and design a survey research proposal on a health-related or social topic. Prerequisite: either HSERV 511 and HSERV 513 (which may be taken concurrently), BIOST 517 and BIOST 518 (which may be taken concurrently), EPI 512 and EPI 513 (which may be taken concurrently), or permission of instructor. Offered: jointly with G H 533/HSERV 527.

Design and implementation of selection and estimation procedures. Emphasis on human populations. Simple, stratified, and cluster sampling; multistage and two-phase procedures; optimal allocation of resources; estimation theory; replicated designs; variance estimation; national samples and census materials. Prerequisite: either STAT 421, STAT 423, STAT 504/CSSS 504, QMETH 500, BIOST 511, BIOST 517, or equivalent, or permission of instructor. Offered: jointly with BIOST 529/STAT 529.

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. Offered: jointly with CSDE 533/SOC 533; W.

Analysis of categorical data in the social sciences. Binary, ordered, and multinomial outcomes, event counts, and contingency tables. Focuses on maximum likelihood estimations and interpretations of results. Prerequisite: either SOC 504, SOC 505, SOC 506/CSSS 507, STAT 423, or STAT 504/CSSS 504. Offered: jointly with SOC 536/STAT 536.

Statistical analysis of times to life-course events with applications for the social sciences, demography, and biodemography. Covers parametric, semi-parametric, and non-parametric models for both continuous- and discrete-time longitudinal observations. Advanced topics include mixture and immunity models, multi-state models, and social contagion processes. Includes hands-on computing lab sessions.

Considers topics relevant to modern voting and elections through statistical and social choice lenses. Topics include the purpose and limits of democratic decision-making; social choice theory and the associated theorems; judgement aggregation; voting procedures; election case studies; election polling and forecasting; electoral redistricting and gerrymandering; fairness aspects in voting; voting in contexts other than elections. Prerequisite: introductory applied statistics from a graduate course sequence at the level of SOC 504; SOC 505; and SOC 506/CSSS 507 (or equivalent); recommended: familiarity with reading and writing proofs; and beginner ability with data programming. Offered: A, even years.

Addresses the need for, and describes methods for, the analysis of spatial data. Topics include clustering, cluster detection, spatial regression, modeling neighborhood effects, and geographical information systems. Considers point and aggregated data and data from complex surveys. Course overlaps with: BIOST 555/EPI 555/G H 534. Prerequisite: either BIOST 513, BIOST 518, BIOST 522, SOC 506/CSSS 507, or STAT 512. Offered: jointly with SOC 534/STAT 554; W.

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. Prerequisite: SOC 504; SOC 505; and SOC 506/CSSS 507 (or equivalent). Offered: jointly with SOC 560/STAT 560.

Covers statistical methods and models for estimating and forecasting population quantities. Topics include: demographic rates; population projection; Leslie matrix; modeling age-specific patterns; probabilistic population projections and Bayesian hierarchical models; estimating past and present fertility, mortality, migration, and population; and big data in demography. Prerequisite: either STAT 509/CSSS 509/ECON 580, STAT 513, or permission from the instructor. Offered: jointly with SOC 563/STAT 563; Sp.

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. Prerequisite: two introductory applied statistics courses at the level of SOC 504 and SOC 505 (or equivalent). Offered: jointly with STAT 564.

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. Prerequisite: two introductory applied statistics courses at the level of SOC 504 and SOC 505 (or equivalent). Offered: jointly with STAT 566.

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. Prerequisite: two introductory applied statistics courses at the level of SOC 504 and SOC 505 (or equivalent). Offered: jointly with STAT 567.

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. Prerequisite: either MATH 112, MATH 124, or MATH 134; and STAT 311 or equivalent. Offered: jointly with ECON 568.

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. Prerequisite: introductory applied statistics (either SOC 504, SOC 505, or equivalent).

Multivariate analysis aims to summarize and describe patterns among multiple observed characteristics. Explores theoretical introduction and practical skills to carry out multivariate analysis methods such as cluster analysis, principal components, factor analysis, and latent class analysis. Prerequisite: SOC 504; SOC 505; and SOC 506/CSSS 507. Offered: jointly with SOC WL 589; Sp.

Features local and visiting scholars presenting current research at the intersection of statistics and the social sciences. Credit/no-credit only.

Explores assumptions and applications of Item Response Theory (IRT) measurement models, testing and evaluating differential item functioning, and procedures for equating and linking. Prerequisite: EDPSY 539. Offered: jointly with EDPSY 591.

Addresses statistical methodology for using longitudinal data to answer research questions about changes over time including exploratory analysis tools, and random coefficient, growth curve, multi-level and hierarchical models, and their extensions. Course overlaps with: BIOST 540. Prerequisite: SOC 504; SOC 505; SOC 506/CSSS 507; and a solid knowledge of linear regression. Offered: jointly with SOC WL 592; W.

Topics vary. Prerequisite: permission of instructor. Offered: AWSp.

Methods, procedures, and assumptions involved in simulating univariate and multivariate normal and non-normal data, applying models to simulated data, saving simulation results, and assessing properties of simulated results. Prerequisite: EDPSY 575. Offered: jointly with EDPSY 595.