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Courses - Fall 2017
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 Methods 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.
SURV621
Fundamentals of Data Collection I
Credits: 3
Grad Meth: Reg, Aud
Prerequisite: Permission of Instructor required.
This course is the 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.
SURV623
Data Collection Methods in Survey Research
Credits: 3
Grad Meth: Reg, Aud
Prerequisite: SURV400; or students who have taken courses with comparable content may contact the department.
Review of alternative data collection methods used in surveys, such as current advances in computer-assisted telephone interviewing (CATI), computer-assisted personal interviewing (CAPI), and other methods such as touchtone data entry (TDE) and voice recognition (VRE).
SURV626
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.
SURV632
Social and Cognitive Foundations of 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.
SURV641
Survey Practicum II
Credits: 2
Grad Meth: Reg
Prerequisite: SURV620.
Restriction: Must be in one of the following programs (Survey Methodology (Doctoral); Survey Methodology (Master's)).
Credit only granted for: SURV621 or SURV641.
Formerly: SURV621.
Additional information: SURV640 and SURV641 must be taken in consecutive semesters.
Second part of applied workshop in sample survey design. Course focus on post data collection process of data processing, editing and anlysis.
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.
SURV662
An Introduction to Small Area Estimation Methods
Credits: 3
Grad Meth: Reg, Aud
Prerequisite: STAT400; and permission of instructor; and STAT401.
Restriction: Permission of instructor.
There is a growing demand to produce reliable estimates of various socio-economic and health characteristics at both national and sub-national levels. However, data availability at the sub-national (small area) level from a survey is often limited by cost and thus analysts must make the best possible use of all available information. The course will begin with a history of small-area estimation and different uses of small-area statistics in both public and private sectors. This course will provide an introduction to the main concepts and issues in small estimation and describes various approaches for estimating different small area parameters. Topics include standard design-based methods, various traditional indirect methods and the state-of-the-art small-area estimation methods that use both Bayesian and empirical best prediction methods. Monte Carlo simulation results and data analysis using available statistical software will be presented.
SURV699
(Perm Req)
Special Topics in Survey Methodology; Reading in Survey Methodology
Credits: 1 - 3
Grad Meth: Reg, Aud
SURV701
(Perm Req)
Analysis of Complex Sample Data
Credits: 3
Grad Meth: Reg, Aud
Prerequisite: SURV625.
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 and clustering, and imputed values in estimation and inference procedures for complex sample survey data; and generalized design effects and variance functions. Computer software that takes account of complex sample design in estimation.
It runs concurrently with the University of Michigan course.
SURV720
Total Survey 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.
SURV722
Research Design: Causal inference from randomized and observational data
Credits: 3
Grad Meth: Reg, Aud
Restriction: Must be in Survey Methodology (Doctoral) program; or must be in Survey Methodology (Master's) program; or must be in a major within the BSOS-Joint Program in Survey Methodology department; or permission of BSOS-Joint Program in Survey Methodology department.
Research designs from which causal inferences are sought. Classical experimental design will be contrasted with quasi-experiments, evaluation studies, and other observational study designs. Emphasis placed on how design features impact the nature of statistical estimation and inference from the designs. Issues of blocking, balancing, repeated measures, control strategies, etc.
SURV725
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.
SURV730
Measurement Error Models
Credits: 1
Grad Meth: Reg, Aud
Prerequisite: SURV623 or SURV630; or have equivalent survey research experience. And must have completed a basic statistics course in regression modeling.
Restriction: Permission of BSOS-Joint Program in Survey Methodology department.
Measurement error in survey data can significantly distort analyses of substantive interest. Means, totals, and proportions will be off if the average answer people give is inaccurate. However, measurement error distorts not only estimates of means but can also severely bias apparent relationships, conditional probabilities, means differences, and other regression-type analyses. To remove such biases it is therefore essential to estimate the extent of measurement error in survey variables. This can be done using a gold standard or, in the absence of such a standard, modeling the error. This course introduces the latter and trains students to perform regression analyses without the influence of measurement error.
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.
SURV746
(Perm Req)
Applications of Statistical Modeling
Credits: 3
Grad Meth: Reg, Aud
Prerequisite: SURV615 and SURV616; or permission of instructor.
Credit only granted for: SURV746 or SURV699R.
Formerly: SURV699R.
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.
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.