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Courses - Fall 2018
SURV
Survey Methodology Department Site
SURV400
(Perm Req)
Fundamentals of Survey and Data Science
Credits: 3
Grad Meth: Reg
Prerequisite: STAT100; or permission of BSOS-Joint Program in Survey Methodology department.
Restriction: Course open to SURV certificate students, SURV Advanced Special Students, and SURV undergraduate minors. Graduate students from other departments may enroll with permission from the department.
Credit only granted for: SURV699M or SURV400.
Formerly: SURV699M.
The course introduces the student to a set of principles of survey and data science that are the basis of standard practices in these fields. The course exposes the student to key terminology and concepts of collecting and analyzing data from surveys and other data sources to gain insights and to test hypotheses about the nature of human and social behavior and interaction. It will also present a framework that will allow the student to evaluate the influence of different error sources on the quality of data.
SURV410
Introduction to Probability Theory
Credits: 3
Grad Meth: Reg, P-F, Aud
Prerequisite: MATH240 and MATH241; or permission of BSOS-Joint Program in Survey Methodology department.
Also offered as: STAT410.
Credit only granted for: SURV410 or STAT410.
Probability and its properties. Random variables and distribution functions in one and several dimensions. Moments, characteristic functions, and limit theorems.
SURV615
(Perm Req)
Statistical Modeling I
Credits: 3
Grad Meth: Reg, Aud
Prerequisite: Must have completed a two course sequence in probability and statistics; or students who have taken courses with comparable content may contact the department.
Restriction: Must be in Survey Methodology (Master's) program; or permission of instructor.
First course in a two term sequence in applied statistical methods covering topics such as regression, analysis of variance, categorical data, and survival analysis.
It runs concurrently with the University of Michigan course.
SURV617
(Perm Req)
Applications of Statistical Modeling
Credits: 3
Grad Meth: Reg, Aud
Prerequisite: SURV615 and SURV616; or permission of instructor.
Credit only granted for: SURV617, SURV746, or SURV699R.
Formerly: SURV699R and SURV746.
Designed for students on both the social science and statistical tracks for the two programs in survey methodology, will provide students with exposure to applications of more advanced statistical modeling tools for both substantive and methodological investigations that are not fully covered in other MPSM or JPSM courses. Modeling techniques to be covered include multilevel modeling (with an application to methodological studies of interviewer effects), structural equation modeling (with an application of latent class models to methodological studies of measurement error), classification trees (with an application to prediction of response propensity), and alternative models for longitudinal data (with an application to panel survey data from the Health and Retirement Study). Discussions and examples of each modeling technique will be supplemented with methods for appropriately handling complex sample designs when fitting the models. The class will focus on practical applications and software rather than extensive theoretical discussions.
SURV621
(Perm Req)
Fundamentals of Data Collection I
Credits: 3
Grad Meth: Reg, Aud
Restriction: Permission of BSOS-Joint Program in Survey Methodology department.
First semester of 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. In the first semester, the focus will be on the error that is inherent in data, specifically errors of representation and errors of measurement, whether the data are designed or found. The psychological origins of survey responses are examined as a way to understand the measurement error that is inherent in answers. The effects of the mode of data collection (e.g., mobile web versus telephone interview) on survey responses also are examined.
SURV626
(Perm Req)
Credits: 2
Grad Meth: Reg, Aud
Prerequisite: Permission of BSOS-Joint Program in Survey Methodology department; and must have completed an introductory graduate level statistics course covering material through OLS and logistic regression.
Practical aspects of sample design. The course will cover the main techniques used in sampling practice: simple random sampling, stratification, systematic selection, cluster sampling, multistage sampling, and probability proportional to size sampling. The course will also cover sampling frames, cost models, and sampling error (variance) estimation techniques.
SURV627
Experimental Design for Surveys
Credits: 2
Grad Meth: Reg, Aud
Prerequisite: At least one prior course in data analysis.
Recommended: Experience in the use of SAS or STATA statistical analysis software.
Restriction: Permission of BSOS-Joint Program in Survey Methodology department.
A key tool of methodological research is the split-ballot experiment, in which randomly selected subgroups of a sample receive different questions, different response formats, or different modes of data collection. In theory, such experiments can combine the clarity of experimental designs with the inferential power of representative samples. All too often, though, such experiments use flawed designs that leave serious doubts about the meaning or generalization of the findings. The purpose of this course is to consider the issues involved in the design and analysis of data from experiments embedded in surveys. It covers the purposes of experiments in surveys, examines several classic survey experiments in detail, and takes a close look at some of the pitfalls and issues in the design of such studies. These pitfalls include problem, such as the confounding of the experimental variables, that jeopardize the comparability of the experimental groups, problems, such as non response, that cast doubts on the generality of the results, and problems in determining the reliability of the results. The course will also consider some of the design decisions that almost always arise in planning experiments; issues such as identifying the appropriate error term for significance tests and including necessary comparison groups.
SURV632
Cognition, Communication and Survey Measurement
Credits: 3
Grad Meth: Reg, Aud
Major sources of survey error-such as reporting errors and nonresponse bias-from the perspective of social and cognitive psychology and related disciplines. Topics: psychology of memory and its bearing on classical survey issues (e.g., underreporting and telescoping); models of language use and their implications for the interpretation and misinterpretation of survey questions; and studies of attitudes, attitude change, and their possible application to increasing response rates and improving the measurement of opinions. Theories and findings from the social and behavioral sciences will be explored.
Restricted to SURV majors only.
SURV650
Economic Measurement
Credits: 3
Grad Meth: Reg, Aud
Prerequisite: Must have completed a course in intermediate microeconomics.
Credit only granted for: SURV650 or SURV699L.
Formerly: SURV699L.
An introduction to the field of economic measurement. Sound economic data are of critical importance to policymakers, the business community, and others. Emphasis is placed on the economic concepts that underlie key economic statistics and the translation of those concepts into operational measures. Topics addressed include business survey sampling; the creation of business survey sampling frames; the collection of data from businesses; employment and earnings statistics; price statistics; output and productivity measures; the national accounts; and the statistical uses of administrative data. Lectures and course readings assume prior exposure to the tools of economic analysis.
SURV699
(Perm Req)
Special Topics in Survey Methodology; Reading in Survey Methodology
Credits: 1 - 3
Grad Meth: Reg, Aud
SURV699C
Special Topics in Survey Methodology; Introduction to Python and SQL
Credits: 1
Grad Meth: Reg, Aud
SURV699L
(Perm Req)
Special Topics in Survey Methodology; Statistical Data Integration
Credits: 3
Grad Meth: Reg, Aud
Prerequisite: SURV/STAT410 and SURV/STAT420 or equivalent; Permisson of instructor required.

A single available data source is often not sufficient in order to carry out required statistical data analyses to make certain decisions. To avoid high costs of collecting new data in such cases, there is a growing need to combine multiple survey and/or administrative existing data sources using appropriate statistical techniques. In the first two-third of the course, we shall discuss various issues and methods in statistical data integration. In particular, we shall cover various methods available in statistical matching, a body of statistical techniques that use a few common variables in combining multiple data sources with no or negligible overlapping units.
SURV699U
Special Topics in Survey Methodology; Machine Learning for Social Science
Credits: 3
Grad Meth: Reg, Aud
Prerequisite: Students should work through one or more R tutorials prior or during the first weeks of class due to the short introduction to R presented in the class. Some resources can be found at the following: https://www.rstudio.com/online-learning/#R, https://cran.r-project.org/manuals.html, or http://www.statmethods.net.

Provides an introduction to supervised statistical learning techniques such as decision trees, random forests and boosting and discusses their potential application in the social sciences. These methods focus on predicting an outcome Y based on some data-driven function f(X) and therefore facilitate new research perspectives in comparison with traditional regression models, which primarily focus on causation. Predictive methods also provide a valuable extension to the empirical social scientists' toolkit as new data sources become more prominent. In addition to introducing supervised learning methods, the course will include practical sessions to exemplify how to tune and evaluate prediction models using the statistical programming language R.
SURV699Z
(Perm Req)
Special Topics in Survey Methodology; Applied Data Analytics
Credits: 6
Grad Meth: Reg, Aud
Innovative forms of data never seen before, combined with traditional survey and administrative data, provide a rich resource for data-driven decisions in public policy and the public sector. The program's design offers hands-on training in the context of real challenges facing the sector, by providing direct collaboration among government agencies and students in the program. Skills learned as part of the project work are SQL, Python, Record Linkage, Database Management, use of APIs, Web Scraping, Basic Machine Learning, Text Analysis, Analysis of Network and Geospatial data, Inference, Privacy and Ethics.
SURV720
(Perm Req)
Total Survey Error and Data Quality I
Credits: 2
Grad Meth: Reg, Aud
Prerequisite: SURV625.
Restriction: Permission of instructor.
Credit only granted for: (SURV720 and SURV721) or SURV723.
Formerly: SURV723.
Total error structure of sample survey data, reviewing current research findings on the magnitudes of different error sources, design features that affect their magnitudes, and interrelationships among the errors. Coverage, nonresponse, sampling, measurement, and postsurvey processing errors. For each error source reviewed, social science theories about its causes and statistical models estimating the error source are described. Empirical studies from the survey methodological literature are reviewed to illustrate the relative magnitudes of error in different designs. Emphasis on aspects of the survey design necessary to estimate different error sources. Relationships to show how attempts to control one error source may increase another source. Attempts to model total survey error will be presented.
It runs concurrently with the University of Michigan course.
SURV725
(Perm Req)
Item Nonresponse and Imputation
Credits: 1
Grad Meth: Reg, Aud
Prerequisite: Be comfortable with generalized linear models and basic probability theory through coursework or work experience; and familiarity with the statistical software R.
Restriction: Permission of BSOS-Joint Program in Survey Methodology department.
Missing data are a common problem which can lead to biased results if the missingness is not taken into account at the analysis stage. Imputation is often suggested as a strategy to deal with item nonresponse allowing the analyst to use standard complete data methods after the imputation. However, several misconceptions about the aims and goals of imputation make some users skeptical about the approach. In this course we will illustrate why thinking about the missing data is important and clarify which goals a useful imputation method should try to achieve.
SURV727
Fundamentals of Computing and Data Display
Credits: 3
Grad Meth: Reg, Aud
Restriction: Must be in a major within the BSOS-Joint Program in Survey Methodology department; or permission of BSOS-Joint Program in Survey Methodology department.
Additional information: Students without any R knowledge are encouraged to work through one or more R web tutorials prior or during the first weeks of the course.
The first part of this course provides an introduction to web scraping and APIs for gathering data from the web and then discusses how to store and manage (big) data from diverse sources efficiently. The second part of the course demonstrates techniques for exploring and finding patterns in (non-standard) data, with a focus on data visualization. The course focuses on R as the primary computing environment, with excursus into SQL and Big Data processing tools.
The first part of this course provides an introduction to web scraping and APIs for gathering data from the web and then discusses how to store and manage (big) data from diverse sources efficiently. The second part of the course demonstrates techniques for exploring and finding patterns in (non-standard) data, with a focus on data visualization. The course focuses on R as the primary computing environment, with excursus into SQL and Big Data processing tools.
SURV740
Fundamentals of Inference
Credits: 3
Grad Meth: Reg, Aud
Prerequisite: SURV410 and SURV420; or (SURV615 and SURV616); or permission of Instructor required.
Restriction: Must be in a major within the BSOS-Joint Program in Survey Methodology department; or permission of BSOS-Joint Program in Survey Methodology department.
Focuses on the fundamentals of statistical inference in the finite population setting. Overview and review fundamental ideas of making inferences about populations. Basic principles of probability sampling; focus on differences between making predictions and making inferences; explore the differences between randomized study designs and observational studies; consider model-based vs. design-based analytic approaches; review techniques designed to improve efficiency using auxiliary information; and consider non-probability sampling and related inferential techniques.
Focuses on the fundamentals of statistical inference in the finite population setting. Overview and review fundamental ideas of making inferences about populations. Basic principles of probability sampling; focus on differences between making predictions and making inferences; explore the differences between randomized study designs and observational studies; consider model-based vs. design-based analytic approaches; review techniques designed to improve efficiency using auxiliary information; and consider non-probability sampling and related inferential techniques.
SURV745
Practical Tools for Sampling and Weighting
Credits: 3
Grad Meth: Reg, Aud
Prerequisite: SURV615, SURV616, and SURV625; or permission of instructor.
Credit only granted for: SURV745 or SURV699E.
Formerly: SURV699E.
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 behinddifferent 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, non response adjustments, and uses of auxiliary data. Non response adjustment alternatives, including weighting cell adjustments, formation of cells using regression trees, and propensity score adjustments. Weighting via post stratification, raking, general regression estimation, and other types of calibration.
SURV829
Doctoral Research Seminar in Survey Methodology
Credits: 3 - 6
Grad Meth: Reg
SURV898
Pre-Candidacy Research
Credits: 1 - 8
Grad Meth: Reg, S-F
Contact department for information to register for this course.
SURV899
(Perm Req)
Doctoral Dissertation Research
Credits: 6
Grad Meth: Reg, S-F
Contact department for information to register for this course.