Course Descriptions

Methods and Theory of Sample Design is concerned with the theory underlying the methods of survey sampling widely used in practice. It covers the basic techniques of simple random sampling, stratification, systematic sampling, cluster and multi-stage sampling, and probability proportional to size sampling. It also examines methods of variance estimation for complex sample designs, including the Taylor series expansion method, balanced repeated replications, and jackknife methods.

This course is crosslisted with the Biostatistics Department.

This is the second course in a two-semester sequence that provides a broad overview of the processes that generate data for use in social science research. Students will gain an understanding of different types of data and how they are created, as well as their relative strengths and weaknesses. A key distinction is drawn between data that are designed, primarily survey data, and those that are found, such as administrative records, remnants of online transactions, and social media content. The course combines lectures, supplemented with assigned readings, and practical exercises. The second semester builds on the discussion of survey mode during the first semester, considering the role played by interviewers in telephone and in-person surveys and their effects on the data collected. Students next are introduced to the methods for extracting and repurposing found data for social science research.  Methods for the classification of text, with an emphasis on automated coding methods, are introduced and selected applications considered (e.g., coding of open-ended survey responses, classification of the sentiments expressed in social media posts).  Issues in using survey data and administrative records to measure change over time (longitudinal comparisons) are explored.  The term concludes with an examination of methods for evaluating the quality of both designed and found data.

This course focuses on the development of the survey instrument, the questionnaire. Topics include wording of questions (strategies for factual and non-factual questions), cognitive aspects, order of response alternatives, open versus closed questions, handling sensitive topics, combining individual questions into a meaningful questionnaire, issues related to question order and context, and aspects of a questionnaire other than questions. Questionnaire design is shown as a function of the mode of data collection such as face-to-face interviewing, telephone interviewing, mail surveys, diary surveys, and computer-assisted interviewing.

This is a wide-ranging graduate seminar in which several program faculty members join with the students in attempting to solve design issues presented to the seminar by clients from the private, government, or academic sectors of research. Readings are selected from literatures not treated in other classes, and practical consulting problems are addressed.

This builds on the introduction to linear models and data analysis provided in Statistical Methods I. Topics include: Multivariate analysis techniques (Hotelling's T-square, Principal Components, Factor Analysis, Profile Analysis, MANOVA); Categorical Data Analysis (contingency tables, measurement of association, log-linear models for counts, logistics and polytomous regression, GEE); and lifetime Data Analysis (Kaplan-Meier plots, logrank test, Cox regression).

Each course will cover a different area of survey methodology. Titles and descriptions will be listed each term in the University Time Schedule.

This introductory course on the analysis of data from complex sample designs covers the development and handling of selection and other compensatory weights; methods for handling missing data; the effect of stratification and clustering on estimation and inference; alternative variance estimation procedures; methods for incorporating weights, stratification, clustering, and imputed values in estimation and inference procedures for complex sample survey data; and generalized design effects and variance functions.

Empirical social scientists are often confronted with a variety of data sources and formats that extend beyond structured and handleable survey data. With the emergence of BigData, especially data from web sources play an increasingly important role in scientificresearch.  However, the potential of new data sources comes with the need for comprehensive computational skills in order to deal with loads of potentially unstructured information.  Against this background, the first part of this course provides an introduction to web scraping and APIs for gathering data from the weband then discusses how to store and manage (big) data from diverse sources efficiently.  The second part of the course demonstrates techniques for exploring an dfinding patterns in (non-standard) data, with a focus on data visualization.  Tools for reproducible research will be introduced to facilitate transparent and collaborative programming.  The course focuses on R as the primary computing environment, with excursus into SQL and Big Data processing tools.

Inference from complex sample survey data covers the theoretical and empirical properties of various variance estimation strategies (e.g., Taylor series approximation, replicated methods, and bootstrap methods for complex sample designs) and how to incorporate those methods into inference for complex sample survey data. Variance estimation procedures are applied to descriptive estimators and to analysis techniques such as regression, analysis of variance, and analysis of categorical data. Generalized variances and design effects are presented. Methods of model-based inference for complex sample surveys are also examined, and the results are contrasted with the design-based type of inference used as the standard in the course. The course will use real survey data to illustrate the methods discussed in class. Students will learn the use of computer software that takes account of the sample design in estimation. Students will carry out a research and analysis project, using techniques and skills learned during the course. A paper describing the student's research will be submitted at the end of the course, and each student will give a short presentation of his/her findings.

This course is a statistical methods class appropriate for second year Master’s students and PhD students. The course will be a combination of hands-on applications and general review of the theory behind different approaches to sampling and weighting. Topics covered include:

- Sample size calculations using estimation targets based on relative standard error, margin of error, and power requirements;

- Use of mathematical programming to determine sample sizes needed to achieve estimation goals for a series of subgroups and analysis variables;

- Resources for designing area probability samples;

- Methods of sample allocation for multistage samples;

- Steps in weighting, including computation of base weights, nonresponse adjustments, and uses of auxiliary data;

- Nonresponse adjustment alternatives, including weighting cell adjustments, formation of cells using regression trees, and propensity score adjustments;

- Weighting via poststratification, raking, general regression estimation, and other types of calibration.

This is the first course in a two-term introduction to the integration of social science and statistical science approaches to the design, collection, and analysis of surveys. The seminar will focus on six to eight areas of statistical and methodological literature that have benefited from alternative approaches. Students demonstrate mastery of those literatures through critical review papers, ideas for extensions of the literature, and empirical projects related to research reviewed.

These special seminar courses address specific research problems currently under study by faculty members. The topics will be announced in the University Time Schedule each academic term.

Election for dissertation work by doctoral student not yet admitted as a candidate. Students doing dissertation work prior to achieving candidacy should register for SurvMeth 990 for that portion of their schedule spent on dissertation work.