Courses

Registration and Auditing of CSSS Courses for UW Students, UW Employees (Faculty, Staff, other) and Non-UW Individuals

Select a different quarter: Choose a quarter... Autumn 2000 Winter 2001 Spring 2001 Autumn 2001 Winter 2002 Spring 2002 Autumn 2002 Winter 2003 Spring 2003 Autumn 2003 Winter 2004 Spring 2004 Autumn 2004 Winter 2005 Spring 2005 Autumn 2005 Winter 2006 Spring 2006 Autumn 2006 Winter 2007 Spring 2007 Autumn 2007 Winter 2008 Spring 2008 Autumn 2008 Winter 2009 Spring 2009 Fall 2009 Winter 2010

Go to Winter 2010 Courses

CSSS Course List 2009-10

UW Course Descriptions and Time Schedule

CSSS Course Catalog

Fall Quarter 2009

CS&SS 221

Statistical Concepts and Methods for the Social Sciences (5)

Day: MTWTh

Time: 9:30-10:20am (MW), SAV 260

         8:30-9:20pm (TTh) (QZ AA), LOW 222

         9:30-10:20pm (TTh) (QZ AB), DEN 315

         8:30-9:20pm (TTh) (QZ AC), BLM 209

         9:30-10:20pm (TTh) (QZ AD), BLM 209

         8:30-9:20pm (TTh) (QZ AC), THO 135

         9:30-10:20pm (TTh) (QZ AD), THO 135

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; and estimation, confidence intervals, and significance tests. Emphasizes social science examples and cases. (Students may receive credit for only one of STAT 220, STAT 311, STAT/CS&SS/SOC 221, and ECON 311.)

Offered jointly with SOC 221/STAT 221; AWSp.

CS&SS 321

Case-Based Social Statistics I (5)

Day: MWF

Time: 10:30-11:20am (MWF)

         10:30-11:20pm (T)

         10:30-11:20pm (Th)

Room: LOW 101 (MTWF)

             CMU B027 (Th)

Instructor: Sibel Sirakaya

Introduction to statistical reasoning for social scientists. Built around cases representing in-depth investigations into the nature and content of statistical and social-science principles and practice. Hands-on approach: weekly data-analysis laboratory. Fundamental statistical topics: measurement, exploratory data analysis, probabilistic concepts, distributions, assessment of statistical evidence.

Offered: jointly with SOC/STAT 321; W.

CS&SS 481

Introduction to Mathematical Statistics (5)

Day: MWF

Time: 9:30-10:50am (MW)

           12:30-1:20pm (F) (QZ AA)

           1:30-2:20pm (F) (QZ AB)

Room: ARC 160 (MW)

             CMU 243 (F)

Instructor: Thomas Richardson

Prerequisites: STAT/ECON 311; either MATH 136 or MATH 126 with either MATH 308 or MATH 309.

Recommended: MATH 324.

Probability, generating functions; the d-method, Jacobians, Bayes theorem; maximum likelihoods, Neyman-Pearson, efficiency, decision theory, regression, correlation, bivariate normal. (Credit allowed for only one of 390, 481, and ECON 580.)

Offered jointly with ECON/STAT 481.

CS&SS 510

Maximum Likelihood Methods for the Social Sciences (5)

Day: TTh

Time: 1:30-3:20

Room: MGH 085

Instructor: Chris Adolph

Prerequisites: POL S/CS&SS 501; POL S/CS&SS 503.

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.

Offered jointly with Pol S 510.

CS&SS 526

Structural Equation Models for the Social Sciences (3)

Day: TTh

Time: 3:00-4:50pm

Room: MEB 245

Instructor: Ross Matsueda

Prerequisites: SOC 504, SOC 505, SOC 506 or equivalent.

Recommended: CS&SS 505 and CS&SS 506, or equivalent.

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.

Offered jointly with SOC 529.

CS&SS 536

Analysis of Categorical Data (3)

Day: TTh

Time: 1:30-2:50pm

Room: DEN 311

Instructor: Adrian Dobra

Prerequisites: SOC 424, SOC 425, SOC 426, or equivalent.

Recommended: CS&SS 505 and CS&SS 506.

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.

Offered jointly with SOC 536/STAT 536.

CS&SS 568

Statistical Analysis of Game-Theoretic Data (5)

Day: MW

Time: 1:30-12:50

Room: SMI 304

Instructor: Sibel Sirakaya

Prerequisites: MATH 112, MATH 124, or MATH 134; STAT/ECON 311 or equivalent. Intermediate knowledge of multivariate calculus and probability theory.

Recommended: Basic knowledge of intermediate microeconomics.

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.

Offered jointly with ECON 568; W.

CS&SS 590

CSSS Seminar (1)

Day: W

Time: 12:30-1:20pm

Room: SAV 409

Instructor: Peter Hoff

This course offers a stimulating intellectual interaction among faculty and students by running a dynamic seminar series featuring presentations of ongoing social science research that involves cutting edge statistical methods.

CS&SS 594

CSSS Special Topics

Day: To be arrange

Time: To be arrange

Room: To be arrange

Instructor: Elena Erosheva

Statistical Modeling with Latent Variables

University of Washington