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CSSS Course List 2015-16

UW Course Descriptions and Time Schedule

CSSS Course Catalog

Fall Quarter 2015

CS&SS 221
Statistical Concepts and Methods for the Social Sciences (5) NW, QSR
Day: MWF
Time: 9:30-10:20am (MWF)
Room: GWN 301
         8:30-9:20pm (TTh) (QZ AA), SAV 136
         9:30-10:20pm (TTh) (QZ AB), ART 317
         8:30-9:20pm (TTh) (QZ AC), SMI 313
         9:30-10:20pm (TTH) (QZ AD), PAR 108
         8:30-9:20pm (TTh) (QZ AE), LOW 201
                9:30-10:20pm (TTH) (QZ AF), LOW 206
Instructor: June Morita
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 221, STAT 311, STAT 221/CS&SS 221/SOC 221, and ECON 311.)
Offered jointly with SOC 221/STAT 221.

CS&SS 321
Case-based Social Statistics 1 (5) I&S, QSR
Day: TTh
Time: 10:30-11:50am
Room: HSB BB1602
         10:30-11:50am (W), HSE E216
         10:30-11:50am (T), HST T478
Instructor: Katherine Stovel
TA: Erin Carll

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 321/STAT 321.
CS&SS 509
Introduction to Mathematical Statistics: Econometrics I (5) NW
Day: TTh
Time: 1:30-2:50pm
Room: SAV 264
         12:30-1:20pm (F) (AA QZ), CMU 228
         1:30-2:20pm (F) (AB QZ), CMU 326
Instructor: Thomas Richardson
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. (Credit allowed for only one of STAT 390, STAT 481, and ECON 580.)
Prerequisite: STAT 311/ECON 311; either MATH 136 or MATH 126 with either MATH 308 or MATH 309; recommended: MATH 324.
Offered jointly with ECON 580/STAT 509.

CS&SS 510
Maximum Likelihood Methods for the Social Sciences (5)
Day: TTh
Time: 4:30-5:50pm
Room: SAV 264
Instructor: Christopher Adolph
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.

CS&SS 536
Analysis of Categorical Data (3)
Day: WF
Time: 2:30-3:50pm
Room: LOW 113
Instructor: Adrian Dobra
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: SOC 504, SOC 505, SOC 506, or equivalent; recommended: CS&SS 505 and CS&SS 506, or equivalent. Offered jointly with SOC 536/STAT 536.

CS&SS 590
CSSS Seminar (1, max 20)
Day: W
Time: 12:30-1:20pm
Room: SAV 409
Instructor: Jeff Arnold
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
Time Series and Panel Data for the Social Sciences
Day: TTh
Time: 10:30-11:50pm
Room: SAV 166
           2:00-2:50 (W) SAV 117 (Lab)
Instructor: Elena Erosheva
TA: Rebecca Ferrell
Applied Longitudinal Data Analysis
Prerequisites: SOC 504-505-506 or equivalent
Solid knowledge of linear regression