College of LS&A

Winter Academic Term 2004 Graduate Course Guide

Note: You must establish a session for Winter Academic Term 2004 on wolverineaccess.umich.edu in order to use the link "Check Times, Location, and Availability". Once your session is established, the links will function.

Courses in Survey Methodology


This page was created at 6:19 PM on Wed, Jan 21, 2004.

Winter Academic Term 2004 (January 6 - April 30)


SURVMETH 600. Fundamentals of Survey Methodology.

Section 001.

Instructor(s): Michael Couper (mcouper@umich.edu)

Prerequisites: (3). May not be repeated for credit.

Credits: (3).

Course Homepage: http://coursetools.ummu.umich.edu/2004/winter/survmeth/600/001.nsf

The course is intended as an introduction to the field, taught at a graduate level. It introduces a set of principles of survey design that are the basis of standard practices in the field. The course examines research literatures that use both observational and experimental methods to test key hypotheses about the nature of human behavior that affect the quality of survey data. It also presents statistical concepts and techniques in sample design, execution, and estimation, and models of behavior describing errors in responding to survey questions. The course uses techniques to sample design, execution, and estimation, and models of behavior describing errors in responding to survey questions. The course uses total survey error as a framework to discuss coverage properties of sampling frames, alternative sample designs, and their impact on standard errors of survey statistics, alternative modes of data collection, field administration operations, the role of the survey interviewer, impacts of non-response on survey static's, the effect of questions structure, wording and context on respondent behavior, models of measurement error, post-survey processing, and estimation in surveys.

Check Times, Location, and Availability Cost: No Data Given. Waitlist Code: 5, Permission of Instructor

SURVMETH 612 / SOC 612 / PSYCH 687. Methods of Survey Sampling.

Section 001.

Instructor(s): James M Lepkowski (jimlep@umich.edu)

Prerequisites: Two graduate-level courses in statistical methods. (3). May not be repeated for credit.

Credits: (3).

Course Homepage: No homepage submitted.

Methods of Survey Sampling/Applied Sampling is an applied statistical methods course, but differs from most statistics courses. It is concerned almost exclusively with the design of data collection. Little of the analysis of collected data will be discussed in the course. The course will concentrate on problems of applying sampling methods to human populations, since survey practices are more widely used in that area, and since sampling human populations poses a number particular problems not found in sampling of other types of units. The principles of sample selection, though, can be applied to many other types of populations.

The course is presented at a moderately advanced statistical level. While we will not develop the mathematical aspects of sampling theory, statistical notation and outlines of some algebraic proofs will be given. A sound background in applied statistics is necessary, since a few algebraic derivations will be presented. Little emphasis will be placed on the derivations. Nonetheless, a thorough understanding of the notation and results will be needed.

Check Times, Location, and Availability Cost: No Data Given. Waitlist Code: 4

SURVMETH 618. Inference From Complex Samples.

Section 001.

Instructor(s): Richard L Valliant

Prerequisites: BIOSTAT 602 or STAT 511, SURVMETH 612, and SURVMETH 617. (3). May not be repeated for credit.

Credits: (3).

Course Homepage: No homepage submitted.

This course 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 survey are also examined, and the results contrasted to the design-based type of inference used as the standard in the course. The course uses real survey data to illustrate the methods discussed in class. Students learn the use of computer software that takes account of the sample design in estimation. Students carry out a research and analysis project, using techniques and skills learned during the course. A paper describing the student's research is submitted at the end of the course, and each student gives a short presentation of his/her findings.

Check Times, Location, and Availability Cost: No Data Given. Waitlist Code: No Data Given.

SURVMETH 619. Topics in Survey Sampling.

Section 001.

Instructor(s): Trivellore E Raghunathan

Prerequisites: SURVMETH 612; and prior or concurrent enrollment in SURVMETH 617. (3). May not be repeated for credit.

Credits: (3).

Course Homepage: No homepage submitted.

This is an advanced course in selected topics in survey sampling. Topics to be covered include: estimation and imputation approaches; small area estimation; and sampling methods for rare populations. A selection of additional topics, chosen by the instructor, will also be covered. Examples of such additional topics are: sample designs for time and space; panel and rotating panel survey designs; maximizing overlap between samples; controlled selection and lattice sampling; adaptive cluster sampling; capture-recapture sampling; sampling for telephone surveys; sampling for establishment surveys; and measurement error models. Both applied and theoretical aspects of the topics will be examined.

Check Times, Location, and Availability Cost: No Data Given. Waitlist Code: No Data Given.

SURVMETH 627. Total Survey Error.

Section 001.

Instructor(s): Roger Tourangeau

Prerequisites: SURVMETH 612. (3). May not be repeated for credit.

Credits: (3).

Course Homepage: No homepage submitted.

Reviews the 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 errors, interviewer effects, questionnaire effects, and mode of data collection effects are reviewed. Statistical and social science approaches to the error sources are compared.

Check Times, Location, and Availability Cost: No Data Given. Waitlist Code: No Data Given.

SURVMETH 632. Cognition, Communication, and Survey Measurement.

Section 001.

Instructor(s): Frederick G Conrad

Prerequisites: Background in psychology is helpful, but not required. (3). May not be repeated for credit.

Credits: (3).

Course Homepage: No homepage submitted.

Survey data are only as meaningful as the answers that respondents provide. Hence, the processes that underlie respondents' answers are of crucial importance. This course draws on current theorizing in cognitive and social psychology pertaining to issues like language comprehension, information storage and retrieval, autobiographical memory, social judgment, and the communication dynamics of survey interviewing to understand how respondents deal with the questions asked and arrive at an answer.

Check Times, Location, and Availability Cost: No Data Given. Waitlist Code: No Data Given.

SURVMETH 660. Survey Management.

Section 001.

Instructor(s): Steven G Heeringa

Prerequisites: Degree-seeking student in Program in Survey Methodology. (3). May not be repeated for credit.

Credits: (3).

Course Homepage: No homepage submitted.

This course describes modern practices in the administration of large-scale surveys. It reviews alternative management structures for large field organization, supervisory and training regimes, handling of turnover, and multiple surveys with the same staff. Practical issues in budgeting of surveys are reviewed with examples from actual surveys. Scheduling of sequential activities in the design, data collection, and processing of data is described.

Check Times, Location, and Availability Cost: No Data Given. Waitlist Code: No Data Given.

SURVMETH 671. Survey Management.

Section 001.

Instructor(s): James M Lepkowski (jimlep@umich.edu)

Prerequisites: Degree-seeking student in Program in Survey Methodology. (1). May not be repeated for credit.

Credits: (1).

Course Homepage: No homepage submitted.

Continuation of SURVMETH 670 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.

Check Times, Location, and Availability Cost: No Data Given. Waitlist Code: No Data Given.

SURVMETH 672. Detroit Area Study Practicum: Data Collection.

Section 001 — Meets with SOC 512.001.

Instructor(s): Martha Scott Hill (hillm@umich.edu)

Prerequisites: (4). May not be repeated for credit.

Credits: (4).

Course Homepage: http://coursetools.ummu.umich.edu/2004/winter/soc/512/001.nsf

See SOC 512.001.

Check Times, Location, and Availability Cost: No Data Given. Waitlist Code: No Data Given.

SURVMETH 699. Directed Research.

Instructor(s):

Prerequisites: Graduate Standing and permission of instructor. (1-3). (INDEPENDENT). May not be repeated for credit.

Credits: (1-3).

Course Homepage: No homepage submitted.

Directed research on a topic of the student's choice. An individual instructor must agree to direct such research, and the requirements are specified when approval is granted.

Check Times, Location, and Availability Cost: No Data Given. Waitlist Code: 5, Permission of instructor/department

SURVMETH 891. Doctoral Seminar II.

Section 001.

Instructor(s): Robert M Groves

Prerequisites: SURVMETH 890. (3). May not be repeated for credit.

Credits: (3).

Course Homepage: No homepage submitted.

This is the second course in a two-term seminar designed to develop skills in the identification of research problems, specification of hypothesis / theorems to extend current understanding of the field, and planning for original research. A common set of readings in four to six advanced research activities of the faculty are studied, with the faculty engaged in research discussing areas of potential innovation.

Check Times, Location, and Availability Cost: No Data Given. Waitlist Code: No Data Given.

SURVMETH 899. Directed Research.

Instructor(s):

Prerequisites: Graduate Standing and permission of instructor. Graduate School authorization for admission as a doctoral Candidate. (1-3). (INDEPENDENT). May not be repeated for credit.

Credits: (1-3).

Course Homepage: No homepage submitted.

Directed research on a topic of the student's choice. An individual instructor must agree to direct such research, and the requirements are specified when approval is granted.

Check Times, Location, and Availability Cost: No Data Given. Waitlist Code: 5, Permission of instructor/department

SURVMETH 990. Dissertation Pre-Candidacy.

Instructor(s):

Prerequisites: Graduate Standing and permission of instructor. Election for dissertation work by doctoral student not yet admitted as a Candidate. (1-8). (INDEPENDENT). May be elected twice for credit. This course has a grading basis of "S" or "U."

Credits: (1-8).

Course Homepage: No homepage submitted.

No Description Provided. Contact the Department.

Check Times, Location, and Availability Cost: No Data Given. Waitlist Code: No Data Given.


Undergraduate Course Listings for SURVMETH.


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This page was created at 6:19 PM on Wed, Jan 21, 2004.


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