SURV - Survey Methodology

SURV400 Fundamentals of Survey and Data Science (3 Credits)

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.

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.

SURV410 Introduction to Probability Theory (3 Credits)

Probability and its properties. Random variables and distribution functions in one and several dimensions. Moments, characteristic functions, and limit theorems.

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.

SURV420 Introduction to Statistics (3 Credits)

Mathematical statistics, presenting point estimation, sufficiency, completeness, Cramer-Rao inequality, maximum likelihood, confidence intervals for parameters of normal distributions, chi-square tests, analysis of variance, regression, correlation, and nonparametric methods.

Prerequisite: SURV410 or STAT410. Also offered as: STAT420.

Credit Only Granted for: STAT420 or SURV420.

SURV430 Fundamentals of Questionnaire Design (3 Credits)

Introduction to the scientific literature on the design, testing and evaluation of survey questionnaires, together with hands-on application of the methods discussed in class.

Restriction: Permission of BSOS-Joint Program in Survey Methodology department.

Credit Only Granted for: SURV430 or SURV630.

SURV440 Sampling Theory (3 Credits)

Simple random sampling, sampling for proportions, estimation of sample size, sampling with varying probabilities of selection, stratification, systematic selection, cluster sampling, double sampling, and sequential sampling.

Prerequisite: STAT401 or STAT420.

Credit Only Granted for: STAT440 or SURV440.

SURV600 Fundamentals of Survey and Data Science (3 Credits)

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.

Prerequisite: Any University of Maryland approved college-level statistics course.

Restriction: Permission of BSOS-Joint Program in Survey Methodology department; and must have graduate student status.

Credit Only Granted for: SURV699M, SURV400 or SURV600.

SURV615 Statistical Modeling I (3 Credits)

First course in a two term sequence in applied statistical methods covering topics such as regression, analysis of variance, categorical data, and survival analysis.

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.

SURV616 Statistical Modeling II (3 Credits)

Builds on the introduction to linear models and data analysis provided in Statistical Methods I. Topics include analysis of longitudinal data and time series, categorical data analysis and contingency tables, logistic regression, log-linear models for counts, statistical methods in epidemiology, and introductory life testing.

Prerequisite: SURV615.

SURV617 Applications of Statistical Modeling (3 Credits)

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.

Prerequisite: SURV615 and SURV616; or permission of instructor.

Credit Only Granted for: SURV617, SURV746, or SURV699R.

Formerly: SURV699R and SURV746.

SURV621 Fundamentals of Data Collection I (3 Credits)

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.

Restriction: Permission of BSOS-Joint Program in Survey Methodology department.

SURV623 Data Collection Methods in Survey Research (3 Credits)

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).

Prerequisite: SURV400; or students who have taken courses with comparable content may contact the department.

SURV624 Privacy Law (1 Credit)

To acquaint the students with the origins and basic principles of privacy law mainly in Europe. Furthermore, it will contrast the European privacy foundations with the U.S. approach. At the core of this course stands the new European General Data Protection Regulation (GDPR) and its applicability to specific cases and basic principles. Moreover, the course will cover current challenges to the existing privacy paradigms by big data and big data analytics.

Restriction: Permission of BSOS-Joint Program in Survey Methodology department.

SURV625 Applied Sampling (3 Credits)

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.

Prerequisite: Must have completed a course in statistics approved by department.

SURV626 Sampling (2 Credits)

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.

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.

SURV627 Experimental Design for Surveys (2 Credits)

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.

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.

SURV630 Questionnaire Design and Evaluation (3 Credits)

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.

Credit Only Granted for: SURV430 and SURV630.

SURV631 Questionnaire Design (2 Credits)

This course introduces students to the stages of questionnaire development. The course reviews the scientific literature on questionnaire construction, the experimental literature on question effects, and the psychological literature on information processing. It will also discuss the diverse challenges posed by self- versus proxy-reporting and special attention is paid to the relationship between mode of administration and questionnaire design. Students will also get hands-on experience in developing their own questionnaire.

Restriction: Permission of BSOS-Joint Program in Survey Methodology department.

SURV632 Cognition, Communication and Survey Measurement (3 Credits)

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.

SURV635 Usability Testing for Survey Research (1 Credit)

Introduces the concepts of usability and usability testing and why they are needed for survey research. The course provides a theoretical model for understanding the respondent-survey interaction and then provides practical methods for incorporating iterative user-centered design and testing into the survey development process. The course provides techniques and examples for designing, planning, conducting and analyzing usability studies on web or mobile surveys

Recommended: Students should be familiar with the basics of questionnaire design. Experience with cognitive testing is a plus, but not a requirement.

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.

SURV640 Survey Practicum I (2 Credits)

First part of an applied workshop in sample survey design, implementation, and analysis. Problems of moving from substantive concepts to questions on a survey questionnaire, designing a sample, pretesting and adminstering the survey.

Restriction: Must be in one of the following programs (Survey Methodology (Doctoral); Survey Methodology (Master's)) ; or permission of instructor.

Credit Only Granted for: SURV620 or SURV640.

Formerly: SURV620.

Additional Information: SURV640 and SURV641 must be taken in consecutive semesters.

SURV641 Survey Practicum II (2 Credits)

Second part of applied workshop in sample survey design. Course focus on post data collection process of data processing, editing and anlysis.

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.

SURV642 Project Consulting (6 Credits)

Students will apply the core skills that they learned in the IPSDS program to address real-world problems. The course will provide experience with the steps involved in carrying out a data consulting project, such as discussing the problems to solve with a client, data handling, and communicating work in both written and oral forms. The project is completed in teams (3-4 students per team).

Prerequisite: SURV703; and background knowledge in programming in Python and SQL structures.

Recommended: SURV736.

SURV650 Economic Measurement (3 Credits)

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.

Prerequisite: Must have completed a course in intermediate microeconomics.

Credit Only Granted for: SURV650 or SURV699L.

Formerly: SURV699L.

SURV656 Web Survey Methodology (1 Credit)

Fundamental concepts of web surveys and web survey design. The course is organized in 3 main sections which follow the way a proper web survey is organized: prefielding, fielding and post fielding.

Restriction: Must have completed SURV400; or must have completed SURV623; or permission of instructor. And permission of BSOS-Joint Program in Survey Methodology department.

SURV665 Introduction to Real World Data Management (2 Credits)

Data is omnipresent in the contemporary world coming in different shapes and sized: from survey data to found data. In order to make use of such data through analysis it is necessary first to import and clean it. This is often one of the most time consuming and difficult parts of data analysis. In this course you will learn both the conceptual steps needed in preparing data for analysis as well as the practical skills to do this. The course will cover all the essential skills needed to prepare data be it survey data, administrative data or found data.

Restriction: Permission of BSOS-Joint Program in Survey Methodology department.

SURV667 Introduction to Record Linkage with Big Data Applications (1 Credit)

Methods to combine data on given entities (people, households, firms etc.) that are stored in different data sources. By showing the strengths of these methods and by providing numerous practical examples the course will demonstrate the various benefits of record linkage. The participants will also learn about potential pitfalls record linkage projects may face.

Restriction: Permission of BSOS-Joint Program in Survey Methodology department.

SURV672 Introduction to the Federal Statistical System and the Survey Research Profession (1 Credit)

The U.S. statistical system and its goals are reviewed. The federal statistical agencies are described, and their primary missions and data collections are examined. The statistical systems of other countries are compared with the U.S. system. Organizational and budgetary aspects are presented. Students will learn about organizations and groups outside of the Federal Statistical System that affect the actions of the System. These include other governmental units, professional associations, and advisory groups created by the agencies themselves. Students will review current laws regarding privacy and confidentiality affecting government agency work and consider a variety of ethical issues confronting government statisticians.

Credit Only Granted for: (SURV670 and SURV671) or SURV672.

Formerly: SURV670 and SURV671.

SURV699 Special Topics in Survey Methodology (1-6 Credits)

Credit according to time scheduled and organization of the course. Organized as a lecture series on specialized advanced topics in survey methodology.

Prerequisite: Must have completed a graduate-level course in statistics or quantitive methods; and must have familiarity with survey research methods.

SURV701 Analysis of Complex Sample Data (3 Credits)

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.

Prerequisite: SURV625.

SURV720 Total Survey Error and Data Quality I (2 Credits)

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.

Prerequisite: SURV625.

Restriction: Permission of instructor.

Credit Only Granted for: (SURV720 and SURV721) or SURV723.

Formerly: SURV723.

SURV721 Total Survey Error and Data Quality II (2 Credits)

Second part of a review of total survey error structure of sample survey data. Reviewing current research findings on the magnitudes of different error sources. Students will continue work on an independent research project which provides empirical investigation of one or more error source. An analysis paper presenting findings of the project will be submitted at the end of the course.

Prerequisite: SURV720.

Restriction: Permission of instructor.

Credit Only Granted for: (SURV720 and SURV721) or SURV723.

Formerly: SURV723.

SURV722 Research Design: Causal inference from randomized and observational data (3 Credits)

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.

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.

SURV725 Item Nonresponse and Imputation (1 Credit)

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.

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.

SURV726 Multiple Imputation (1 Credit)

This course will provide a detailed introduction to multiple imputation, a convenient strategy for dealing with (item) nonresponse in surveys. We will motivate the concept and illustrate why multiple imputation should generally be preferred over single imputation methods. The main focus of the course will be on strategies to generate (multiple) imputations and how to deal with common problems when applying the methods for large scale surveys. We will also discuss various options for assessing the quality of the imputations. All concepts will be demonstrated using software illustrations in R.

Prerequisite: Be comfortable with generalized linear models and basic probability theory through coursework or work experience; and familiarity with the statistical software R; and must have completed Surv 725 Item Nonresponse and Imputation or be familiar with the content through relevant work experience.

Restriction: Permission of BSOS-Joint Program in Survey Methodology department.

SURV727 Fundamentals of Computing and Data Display (3 Credits)

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.

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.

SURV730 Measurement Error Models (1 Credit)

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.

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.

SURV735 Data Confidentiality and Statistical Disclosure Control (2 Credits)

This course will provide a gentle introduction to statistical disclosure control with a focus on generating synthetic data for maintaining the confidentiality of the survey respondents. The first part of the course will introduce several traditional approaches for data protection that are widely used at statistical agencies. Some limitations of these approaches will also be discussed. The second part of the course will introduce synthetic data as a possible alternative. This part of the course will discuss different approaches to generating synthetic datasets in detail. Possible modeling strategies and analytical validity evaluations will be assessed and potential measures to quantify the remaining risk of disclosure will be presented. To provide the participants with hands on experience, all steps will be illustrated using simulated and real data examples in R.

Prerequisite: Must have familiarity with the statistical software R; and must have completed a basic statistics course in regression modeling.

Restriction: Permission of BSOS-Joint Program in Survey Methodology department.

SURV736 Introduction to Web Scraping with R (1 Credit)

Provides a condensed overview of web technologies and techniques to collect data from the web in an automated way. To this end, students will use the statistical software R. The course introduces fundamental parts of web architecture and data transmission on the web. Furthermore, students will learn how to scrape content from static and dynamic web pages and connect to APIs from popular web services. Finally, practical and ethical issues of web data collection are discussed.

Prerequisite: Students are expected to be familiar with the statistical software R.

Recommended: Knowledge about the ?tidyverse? packages, in particular, dplyr, plyr, magrittr, and stringr.

Restriction: Permission of BSOS-Joint Program in Survey Methodology department.

SURV740 Fundamentals of Inference (3 Credits)

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.

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.

SURV742 Inference from Complex Surveys (3 Credits)

Inference from complex sample survey data covering the theoretical and empirical properties of various variance estimation strategies (e.g., Taylor series approximation, replicated methods, and bootstrap methods for complex sample designs). Incorporation of those methods into inference for complex sample survey data. Variance estimation procedures applied to descriptive estimators and to analysis of categorical data. Generalized variances and design effects presented. Methods of model-based inference for complex sample surveys examined, and results contrasted to the design-based type of inference used as the standard in the course. Real survey data illustrating the methods discussed. Students will learn the use of computer software that takes account of the sample design in estimation.

Prerequisite: SURV440.

SURV744 Topics in Survey Methodology (3 Credits)

Advanced course in survey sampling theory.

Prerequisite: SURV440.

SURV745 Practical Tools for Sampling and Weighting (3 Credits)

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.

Prerequisite: SURV615, SURV616, and SURV625; or permission of instructor.

Credit Only Granted for: SURV745 or SURV699E.

Formerly: SURV699E.

SURV747 Practical Tools for Sampling and Weighting Part I (2 Credits)

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. Base weights are discussed in context of the sample designs chosen.Note: Part II of the course will provide a more in-depth discussion on weighting.

Prerequisite: Sampling theory (e.g., SURV440 or equivalent) and Applied sampling (e.g., SURV626 or equivalent).

Recommended: Experience in the use of statistical software package R.

Restriction: Permission of BSOS-Joint Program in Survey Methodology department.

SURV751 Introduction to Big Data and Machine Learning (1 Credit)

This is an introduction to the uses and methods of working with Big data. Students explore how Big Data concepts, processes and methods can be used within the context of Survey Research.

Prerequisite: Familiarity with the statistical software R.

Restriction: Permission of BSOS-Joint Program in Survey Methodology department.

Additional Information: Familiarity with model building and model selection as well as the R program is not required but could also be helpful. Students without prior knowledge in R should plan on using free online resources to make themselves familiar with the basics of this statistical programming language.

SURV752 Introduction to Data Visualization (1 Credit)

Data visualization is one of the most powerful tools to explore, understand and communicate patterns in quantitative information. At the same time, good data visualization is a surprisingly difficult task and demands three quite different skills: substantive knowledge, statistical skill, and artistic sense. The course is intended to introduce participants to a) key principles of analytic design and useful visualization techniques for the exploration and presentation of univariate and multivariate data. This course is highly applied in nature and emphasizes the practical aspects of data visualization in the social sciences. Students will learn how to evaluate data visualizations based on principles of analytic design, how to construct compelling visualizations using the free statistics software R, and how to explore and present their data with visual methods.

Prerequisite: Basic statistics understanding and bivariate linear regression.

Recommended: Experience in the use of statistical software package R.

Restriction: Permission of BSOS-Joint Program in Survey Methodology department.

SURV760 Survey Management (3 Credits)

Modern practices in the administration of large scale surveys. Alternative management structures for large field organizations, supervisory and training regimens, 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.

SURV772 Survey Design Seminar (3 Credits)

Students present solutions to design issues presented to the seminar. Readings are selected from literatures not treated in other classes and practical consulting problems are addressed.

Credit Only Granted for: (SURV770 and SURV771) or SURV772.

Formerly: SURV770 and SURV771.

SURV798 Advanced Topics in Survey Methodology (3 Credits)

Individual instruction.

Repeatable to: 12 credits if content differs. Also offered as: STAT798.

Credit Only Granted for: STAT798 or SURV798.

SURV819 Doctoral Research Seminar in Survey Methodology (1-6 Credits)

This is the first, two term seminar introducing the doctoral student to areas of integration of social science and statistical science approaches in the design, collection, and analysis of surveys.

Restriction: Permission of instructor.

SURV829 Doctoral Research Seminar in Survey Methodology (3-6 Credits)

An advanced research seminar for students preparing to do research or take doctoral comprehensive examinations.

Restriction: Permission of instructor.

Repeatable to: 6 credits if content differs.

SURV898 Pre-Candidacy Research (1-8 Credits)

SURV899 Doctoral Dissertation Research (1-8 Credits)