UW Bothell Course Descriptions
UW Tacoma Course Descriptions
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CS&SS 221 Statistical Concepts and Methods for the Social Sciences (5) NSc, RSN
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/CS&SS 221/SOC 221, or STAT 290. Course overlaps with: STMATH 341. Offered: jointly with SOC 221/STAT 221; AWSp.
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CS&SS 320 Evaluating Social Science Evidence (5) SSc, RSN
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.
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CS&SS 321 Data Science and Statistics for Social Sciences I (5) SSc, RSN
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.
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CS&SS 322 Case-Based Social Statistics II (5) SSc, RSN
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.
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CS&SS 452 Statistics and Philosophy of Voting (3)
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.
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CS&SS 490 Data Science Community Seminar (1)
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.
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CS&SS 501 Advanced Political Research Design and Analysis (5)
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.
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CS&SS 503 Advanced Quantitative Political Methodology (5)
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.
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CS&SS 504 Applied Regression (4)
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.
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CS&SS 505 Review of Mathematics for Social Scientists (1)
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.
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CS&SS 506 Computer Environments for the Social Sciences (1)
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).
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CS&SS 507 Methodology: Quantitative Techniques in Sociology (3)
Applied regression analysis with emphasis on interactive computer graphics techniques and interpretation. Application to typical sociological problems. Offered: jointly with SOC 506.
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CS&SS 508 Introduction to R for Social Scientists (1)
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.
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CS&SS 509 Econometrics I: Introduction to Mathematical Statistics (4)
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.
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CS&SS 510 Maximum Likelihood Methods for the Social Sciences (5)
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.
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CS&SS 512 Time Series and Panel Data for the Social Sciences (5)
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.
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CS&SS 523 Social Networks and Health: Biocultural Perspectives (5)
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.
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CS&SS 526 Structural Equation Models for the Social Sciences (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. Prerequisite: SOC 504, SOC 505, SOC 506 or equivalent. Offered: jointly with SOC 529.
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CS&SS 527 Survey Research Methods (4)
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.
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CS&SS 529 Sample Survey Techniques (3)
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.
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CS&SS 533 Research Methods in Demography (3/5)
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.
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CS&SS 536 Analysis of Categorical and Count Data (3)
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.
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CS&SS 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.
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CS&SS 552 Statistics and Philosophy of Voting (3)
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/CS&SS 507 (or equivalent); recommended: familiarity with reading and writing proofs; and beginner ability with data programming.
Offered: A, even years.
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CS&SS 554 Statistical Methods for Spatial Data (3)
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/CS&SS 507, or STAT 512. Offered: jointly with SOC 534/STAT 554; W.
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CS&SS 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. Prerequisite: SOC 504, SOC 505, SOC 506 or equivalent. Offered: jointly with SOC 560/STAT 560.
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CS&SS 563 Statistical Demography (4)
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.
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CS&SS 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. Prerequisite: SOC 504, SOC 505, SOC 506 or equivalent. Offered: jointly with STAT 564.
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CS&SS 565 Inequality: Current Trends and Explanations (3)
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.
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CS&SS 566 Causal Modeling (4)
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.
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CS&SS 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. Prerequisite: SOC 504, SOC 505, SOC 506, or equivalent. Offered: jointly with STAT 567.
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CS&SS 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. Prerequisite: MATH 112, MATH 124, or MATH 134; STAT 311/ECON 311 or equivalent. Offered: jointly with ECON 568.
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CS&SS 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. Prerequisite: SOC 504, SOC 505, and SOC 506.
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CS&SS 589 Multivariate Data Analysis for the Social Sciences (4, max. 8)
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/CS&SS 507. Offered: jointly with SOC WL 589; Sp.
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CS&SS 590 CSSS Seminar (1, max. 20)
Credit/no-credit only.
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CS&SS 592 Applied Longitudinal Data Analysis For Social Sciences (4)
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/CS&SS 507; and a solid knowledge of linear regression. Offered: jointly with SOC WL 592; W.
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CS&SS 594 Special Topics in Social Science and Statistics (1-5, max. 30)
Topics vary. Prerequisite: permission of instructor. Offered: AWSp.
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