Please visit www.testudo.umd.edu for up-to-date information and to register for courses.

The following classes are held in conjunction with the University of Michigan's Summer Institute in Survey Research Techniques. All classes are held at the University of Maryland, College Park campus. Each class can be taken for college credit or it can be audited. You may call 301-314-7911 for additional information.

SURV 623 Data Collections Methods in Survey Research
June 7 - July 2, 1-3 PM, M-F, Fred Conrad - Instructor
Room 1208 LeFrak Hall

Prerequisite: An introductory course in survey research methods or equivalent experience.

This course reviews alternative data collection methods used in surveys, focusing on interviewer-administered methods. It concentrates on the impact these techniques have on the quality of survey data, including measurement error properties, nonresponse, and coverage errors. The course reviews the literature on data collection methods, focusing on comparisons of major modes (face-to-face, telephone, and mail) and alternative methods of data collection (diaries, administrative records, direct observation, etc.) The implications of mode decisions for data quality and the data collection process are discussed. Special attention is paid to the statistical and social science literatures on interviewer effects and nonresponse. Current advances in computer-assisted survey information collection (including CATI, CAPI, TDE, and VRE) will be reviewed. This is not a how-to-do-it course on survey data collection, but rather focuses on the error properties of key aspects of the data collection process.

SURV 625 Applied Sampling
June 7 - July 30, 3-5 PM, TuTh, James Lepkowski - Instructor
Room 1208 LeFrak Hall

Prerequisite: Two graduate-level courses in statistical methods.

Practical aspects of sample design. Topics include: probability sampling (including simple random, systematic, stratified, clustered, multistage and two-phase sampling methods), sampling with probabilities proportional to size, area sampling, telephone sampling, ratio estimation, sampling error estimation, frame problems, nonresponse, and cost factors.

SURV 630 Questionnaire Design
June 1 - July 11, 10:30-12:30 PM, M-F, Frauke Kreuter - Instructor
Room 1208 LeFrak Hall

Prerequisite: An introductory course in survey research methods or equivalent experience.

The stages of questionnaire design; developmental interviewing, question writing, question evaluation, pretesting, and questionnaire ordering and formatting. Reviews of the literature on questionnaire construction, the experimental literature on question effects, and the psychological literature on information processing. Examination of the diverse challenges posed by self versus proxy reporting and special attention is paid to the relationship between mode of administration and questionnaire design.

SURV 699D Introduction to Survey Quality
July 12 - July 16, 9-12 PM, M-F, Paul Biemer - Instructor
Room 1218 LeFrak Hall

Prerequisite: This course is taught at an intermediate level, emphasizing both the theory and practice of the analysis of survey error. The course does not require rigorous training in mathematics; however, proficiency in basic mathematics is essential. Knowledge of calculus is useful but not required for the course. A first course in survey sampling methods and a basic understanding of sampling concepts such as stratification, clustering and weighting is required. Students should also have familiarity with basic statistical concepts, such as point estimates, sampling variance, confidence intervals, p-values and the maximum likelihood method of estimation. Familiarity with logistic regression models is useful but not required.

This course presents a statistical framework for modeling and estimating classification error in surveys. It begins by examining some of the early models for survey measurement error (Census Bureau models; Kish model; etc.) and demonstrating their similarities, strengths and weaknesses. Then these models are cast in a general latent class modeling (LCM) framework where the true values of a variable are assumed to be unobserved (latent) and the survey response constitutes a single indicator of this latent variable. The model parameters include the target population proportions for a categorical variable to be estimated in the survey and the misclassification probabilities (for e.g., false positive and false negative, for dichotomous response variables) for measuring the variable. Survey item reliability and construct validity as well as estimator bias are defined and interpreted within this general framework. Methods for estimating the model parameters and issues of model identifiability will be discussed

SURV 699F Psychology of Survey Response
July 12 - July 16, 1-4 PM, M-F, Roger Tourangeau - Instructor
Room 1218 LeFrak Hall

SURV 699G Envisioning the Survey Interview of the Future
July 19 - July 30, 1-4 PM, MWF, Fred Conrad - Instructor
Room 1218 LeFrak Hall

SURV 699K Multi-Level Analysis of Survey Data
June 7 - July 2, 10:30-12:30 PM, M-F, Bob Croninger - Instructor
Room 2208 LeFrak Hall

Prerequisite: At least one graduate-level course in statistics or quantitative methods, and experience with multivariate regression models, including both analysis of data and interpretation of results.

Although many surveys gather data on multiple units of analysis, most statistical procedures cannot make full use of these data and their nested structures: for example, individuals nested within groups, measures nested within individuals, and other nesting levels that may be of analytic interest. In this course, students are introduced to an increasingly common statistical technique used to address both the methodological and conceptual challenges posed by nested data structures -- hierarchical linear modeling (HLM). The course demonstrates multiple uses of the HLM software, including growth-curve modeling, but the major focus is on the basic logic of multi-level models and the investigation of organizational effects on individual-level outcomes. Although we use data drawn from a nationally representative sample of U.S. elementary schools, students, and teachers for instructional exercises, students should feel free to use their own data provided the data have a multi-level structure and are suitable for course goals (developing and interpreting a two-level model with a random intercept and a random slope). The multi-level analysis skills taught in this course are equally applicable in many social science fields: sociology, public health, psychology, demography, political science, and in the general field of organizational theory. Typically the course enrolls students from all these fields. Students will learn to conceptualize, conduct, interpret, and write up their own multi-level analyses, as well as to understand relevant statistical and practical issues.