Develops statistical literacy. Examines objectives & pitfalls of statistical studies; study designs, data analysis, inference; graphical & numerical summaries of numerical &categorical data; correlation and regression; estimation, confidence intervals, & significance tests. Emphasizes social science examples and cases. May only receive credit for one of STAT 220, STAT 221/CS&SS 221/SOC 221, or STAT 290. Offered: jointly with SOC 221/STAT 221; AWSp.
Statistical Concepts and Methods for the Social Sciences
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.
Evaluating Social Science Evidence
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. Offered: jointly with SOC 321/STAT 321; W.
Data Science and Statistics for Social Sciences I
Continuation of CS&SS 321/SOC 321/STAT 321. Progresses to questions of assessing the weight of evidence and more sophisticated models including regression-based methods. Built around cases investigating the nature and content of statistical principles and practice. Hands-on approach: weekly data analysis laboratory. Prerequisite: CS&SS 321/SOC 321/STAT 321, or permission of instructor. Offered: jointly with SOC 322/STAT 322.
Case-Based Social Statistics II
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.
Data Science Community Seminar
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.
Advanced Political Research Design and Analysis
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.
Advanced Quantitative Political Methodology
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/MATH 390, STAT 421, STAT 509/CS&SS 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.
Review of Mathematics for Social Scientists
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 (R, SPLUS, and SAS).
Computer Environments for the Social Sciences
Applied regression analysis with emphasis on interactive computer graphics techniques and interpretation. Application to typical sociological problems. Offered: jointly with SOC 506.
Methodology: Quantitative Techniques in Sociology
Familiarizes students with the R environment for statistical computing (http://www.r-project.org). R is a freely available, multi-platform, and powerful program for analysis and graphics similar to S-PLUS. Covers the basics of organizing, managing, and manipulating social science data; basic applications; introduction to programming; links to other major statistical packages. Credit/no-credit only.
Introduction to R for Social Scientists
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/ECON 311; either MATH 136 or MATH 126 with either MATH 308 or MATH 309. (Credit allowed for only one of STAT 390, STAT 481, and ECON 580.) Offered: jointly with ECON 580/STAT 509.
Econometrics I: Introduction to Mathematical Statistics
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/CS&SS 501; POL S 503/CS&SS 503. Offered: jointly with POL S 510.
Maximum Likelihood Methods for the Social Sciences
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. Basic familiarity with or willingness to learn the R statistical language. Offered: jointly with POL S 512.
Time Series and Panel Data for the Social Sciences
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.
Social Networks and Health: Biocultural Perspectives
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. Prerequisite: SOC 504, SOC 505, SOC 506 or equivalent. Offered: jointly with SOC 529.
Structural Equation Models for the Social Sciences
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/HSERV 513; BIOST 517/BIOST 518; or EPI 512/EPI 513, which may be taken concurrently, or permission of instructor. Students should have a survey project in mind. Offered: jointly with G H 533/HSERV 527.
Survey Research Methods
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, QMETH 500, BIOST 511, or BIOST 517, or equivalent; or permission of instructor. Offered: jointly with BIOST 529/STAT 529.
Sample Survey Techniques
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.
Research Methods in Demography
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/CS&SS 507, STAT 423, or STAT 504/CS&SS 504. Offered: jointly with SOC 536/STAT 536.
Analysis of Categorical and Count Data
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.
Event History Analysis for the Social Sciences
Motivates the need for, and describes methods for, the analysis of spatial data. Topics: Clustering, cluster detection, spatial regression, modeling neighborhood effects, geographical information systems. Point and aggregated data considered and data from complex surveys. Offered: jointly with SOC 534/STAT 554; W.
Statistical Methods for Spatial Data
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, SOC 506 or equivalent. Offered: jointly with SOC 560/STAT 560.
Hierarchical Modeling for the Social Sciences
Statistical methods and models for estimating and forecasting population quantities. Topic: 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; big data in demography. Prerequisite: Either STAT 509/CS&SS 509/ECON 509, 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: SOC 504, SOC 505, SOC 506 or equivalent. Offered: jointly with STAT 564.
Bayesian Statistics for the Social Sciences
Discussion of recent growth in economic inequality in the United States and competing explanations for these new trends through examination of labor market demographics, industrial composition and restructuring, and the broader political context that impacts policies like minimum wage, strength of unions, and foreign trade. Prerequisite: SOC 504, SOC 505, SOC 506, or equivalent. Offered: jointly with SOC 565.
Inequality: Current Trends and Explanations
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: course in statistics, SOC 504, SOC 505, SOC 506, 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: SOC 504, SOC 505, SOC 506, or equivalent. Offered: jointly with STAT 567.
Statistical Analysis of Social Networks
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: MATH 112, MATH 124, or MATH 134; STAT 311/ECON 311 or equivalent. Offered: jointly with ECON 568.
Game Theory for Social Scientists
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: SOC 504, SOC 505, and SOC 506.
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, or SOC 506. Offered: jointly with SOC WL 589; A.
Multivariate Data Analysis for the Social Sciences
Addresses statistical methodology for using longitudinal data to answer research questions about changes over time including exploratory analysis tools, and random coefficient, growth curve, multilevel and hierarchical models, and their extensions. Prerequisite: Successful completion of SOC 504, SOC 505, and SOC 506; and a solid knowledge of linear regression. Offered: jointly with SOC WL 592; A, odd years.
Applied Longitudinal Data Analysis For Social Sciences
Topics vary. Prerequisite: permission of instructor. Offered: AWSp.
Special Topics in Social Science and Statistics