


Introduction
This course will cover the statistical design and analysis of complex surveys, with applications in the social sciences and health sciences. This is an applied statistical methods course, focusing on the conceptual aspects of sampling rather than the mathematical. Implementation will rely on software. This is not a course in how to implement a survey (e.g., instrument construction) but one in the sampling theory that underlies complex surveys. Both designbased and modelbased inference will be considered. In addition to traditional topics in survey analysis we will cover data visualization, regression modeling of data from complex surveys, the design and analysis of twophase samples from existing cohorts. We will not cover itemresponse theory. Prerequisites
Students must have taken an graduatelevel introductory course in applied statistics, and a regression modeling course is recommended. Knowledge of R would be very helpful. Learning Objectives
After successfully completing this course, students should ordinarily expect to be able to:
This course is part
of the curriculum of the Center
for Statistics and the Social Sciences (CSSS), with funding from the
University Initiatives Fund. The CSSS is includes faculty members from the
Department of Statistics and a broadrange of social science disciplines
including Anthropology, Economics, Geography, Political Science, and
Sociology. This curriculum is been developed to complement and strengthen the
quantitative methods course offerings for social science students at both the
undergraduate and graduate levels. Structure of the CourseThere
will be a two lectures per week. The lecture on Thursday may occasionally be a
laboratory session. Textbooks[CS] Mailing list and
Discussion Forum
I will be using a mailing list to provide discussion of issues in class and related questions. For questions that might be of interest to other students, please use the mailing list rather than solely emailing me. Example of questions are about interesting articles you have seen in the media, problems with access to resources, homework or computer questions. Enjoy! Please regularly consult this class home page and archive of the mailing list. It will contain lecture notes, homework, solutions and course information. Computer Usage and SoftwareThe computer is the scientific
laboratory of the applied researcher in quantitative fields. As such this
course requires the student to develop a degree of comfort and competence
“in the lab”. If you want more background consult the lecture
notes in CSSS
505. Course Requirements and GradesThere will be weekly homeworks
and exercises both the theory and real data analysis. Students will be graded
on a scale of 1 to 10 for each homework. This will
be 40% of the grade. Discussion of homework
problems is encouraged. However, each student is required to prepare
and submit solutions (including computer work) to the assignments and project
on their own; solutions prepared “in committee” are not
acceptable. Duplication of homework solutions and computer output prepared in
whole or in part by someone else is not acceptable and is considered
plagiarism. If you receive assistance from anyone, you must give due
credit in your report. (Example: “Since the data are all
positive, and skewed to the right, a logarithmic transformation is clearly
appropriate as a next step. I thank David Cox for pointing this out to
me.”) There will be a midterm exam worth 30% of the grade. There will be a
term project
worth 40% of the grade. I welcome comments or suggestions about the course at any time, either in person, by letter, or by anonymous email. Please feel free to use these ways make comments to me about any aspect of the course. Use the menu on the topleft of this page to find out more about the course. STUDENTS WITH DISABILITIES If you have a disability that requires special testing accommodations or other classroom modifications you need to notify the instructor and the Office of Disabled Student Services as soon as possible. You may contact the DSS office at 5438925. 

