Courses - Spring 2019
SURV
Survey Methodology
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, MATH341, or MATH461; and (MATH241 or MATH340).
Also offered as: STAT410.
Credit only granted for: STAT410 or SURV410.
Probability and its properties. Random variables and distribution functions in one and several dimensions. Moments. Characteristic functions. Limit theorems.
SURV420
Introduction to Statistics
Credits: 3
Grad Meth: Reg, P-F, Aud
Prerequisite: SURV410 or STAT410.
Also offered as: STAT420.
Credit only granted for: STAT420 or SURV420.
Point estimation, sufficiency, completeness, Cramer-Rao inequality, maximum likelihood. Confidence intervals for parameters of normal distribution. Hypothesis testing, most powerful tests, likelihood ratio tests. Chi-square tests, analysis of variance, regression, correlation. Nonparametric methods.
SURV440
(Perm Req)
Sampling Theory
Credits: 3
Grad Meth: Reg, P-F, Aud
Prerequisite: STAT401 or STAT420.
Credit only granted for: STAT440 or SURV440.
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.
SURV616
(Perm Req)
Statistical Modeling II
Credits: 3
Grad Meth: Reg, Aud
Prerequisite: SURV615.
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.
SURV622
(Perm Req)
Fundamentals of Data Collection II
Credits: 3
Grad Meth: Reg, Aud
Prerequisite: Permission of Instructor required; or fundamentals of Data Collection I.
This is the second course in 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. The second semester builds on the discussion of survey mode during the first semester, considering the role played by interviewers in telephone and in-person surveys and their effects on the data collected. Students next are introduced to the methods for extracting and re purposing found data for social science research. Methods for the classification of text, with an emphasis on automated coding methods, are introduced and selected applications considered (e.g., coding of open-ended survey responses, classification of the sentiments expressed in social media posts). Issues in using survey data and administrative records to measure change over time (longitudinal comparisons) are explored. The term concludes with an examination of methods for evaluating the quality of both designed and found data.
SURV625
(Perm Req)
Applied Sampling
Credits: 3
Grad Meth: Reg, Aud
Prerequisite: Must have completed a course in statistics approved by department.
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.
SURV630
Questionnaire Design and Evaluation
Credits: 3
Grad Meth: Reg, Aud
Credit only granted for: SURV430 and SURV630.
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.
SURV631
(Perm Req)
Questionnaire Design
Credits: 2
Grad Meth: Reg
Restriction: Permission of BSOS-Joint Program in Survey Methodology department.
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.
SURV635
Usability Testing for Survey Research
Credits: 1
Grad Meth: Reg, Aud
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.
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
SURV667
(Perm Req)
Introduction to Record Linkage with Big Data Applications
Credits: 1
Grad Meth: Reg, Aud
Restriction: Permission of BSOS-Joint Program in Survey Methodology department.
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.
SURV699
(Perm Req)
Special Topics in Survey Methodology; Readings in Survey Methodology
Credits: 1 - 4
Grad Meth: Reg, Aud
SURV699C
Special Topics in Survey Methodology; Big Data in Immigration Research
Credits: 3
Grad Meth: Reg, Aud
Prerequisites: At least one statistics course, basic familiarity with R, Python or SAS.

Data from traditional sources (e.g., national population censuses, sample surveys, and administrative sources) on migration and immigration are limited in quantity and quality, and new alternatives have recently emerged. Some of these new types of "Big Data" are particularly promising for the study of migration-related phenomena. These include mobile phone call logs, Internet activity (e.g., Google searches, tracking of online media content use), geo-referenced social media activity, and other passively collected (mobile) data. This course is shared between the University of Maryland and University of Mannheim, and students will virtually attend the same class/lecture and then collaborate via online tools. Students from the two partnering universities will form international groups to collaboratively work on the collection and analysis of Big Data to answer immigration-related research questions.
SURV699J
(Perm Req)
Special Topics in Survey Methodology; General Linear Models (G.L.M)
Credits: 2
Grad Meth: Reg, Aud
Prerequisite: A sound understanding of the linear regression models (OL S), knowledge in linear algebra and calculus is useful. The main focus lies on the introduction to statistical models and estim ators beyond linear regression useful to a social and economic scientis ts. The first two units are dedicated to an introduction to a maximum likelihood estimation while the rest of the units will discuss generali zed linear models (GLS) for binary choice decisions (Logit, Probit), or dinal dependent variables, and count data (Poisson, Negative Binomial).
SURV699Q
Special Topics in Survey Methodology; Web Survey Methodology
Credits: 2
Grad Meth: Reg, Aud
Students must have access to a standalone web camera and a headset to participate in the weekly online meetings.
SURV701
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.
SURV702
Analysis of Complex Survey Data
Credits: 2
Grad Meth: Reg, Aud
Prerequisite: One or more graduate courses in statistics covering techniques through OLS and logistic regression, a course in applied sampling methods (e.g., SURV625), or permission of the instructor.
Restriction: Permission of BSOS-Joint Program in Survey Methodology department.
The development and handling of selection and other compensatory weights for survey data analysis; the effects of stratification and clustering on survey estimation and inference; alternative variance estimation procedures for estimated survey statistics; methods and computer software that take into account the effects of complex sample designs on survey estimation and inference; and methods for handling missing data, including weighting adjustment and imputation.
SURV704
(Perm Req)
Computer-Based Content Analysis II
Credits: 1
Grad Meth: Reg, Aud
Prerequisite: SURV703; and background knowledge in programming in Python and SQL structures.
Recommended: SURV736.
Investigates the foundations of Natural Language Processing (NLP) as tool for analyzing natural language texts in the social sciences, thus providing an alternative to traditional ways of data generation through surveys. The course introduces general use cases for NLP, provides a guide to standard operations on text as well as their implementation in the Python-based Natural Language Toolkit (NLTK) and introduces the text mining functionalities of the WEKA Machine Learning workbench. The theory part of the course worth one credit can be supplemented by an optional project part worth another credit point
SURV721
(Perm Req)
Total Survey Error and Data Quality II
Credits: 2
Grad Meth: Reg, Aud
Prerequisite: SURV720.
Restriction: Permission of instructor.
Credit only granted for: (SURV720 and SURV721) or SURV723.
Formerly: SURV723.
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.
SURV726
(Perm Req)
Multiple 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; 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.
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.
SURV735
(Perm Req)
Data Confidentiality and Statistical Disclosure Control
Credits: 2
Grad Meth: Reg, Aud
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.
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.
SURV736
Introduction to Web Scraping with R
Credits: 1
Grad Meth: Reg, Aud
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.
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.
SURV742
(Perm Req)
Inference from Complex Surveys
Credits: 3
Grad Meth: Reg, Aud
Prerequisite: SURV440.
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.
SURV747
(Perm Req)
Practical Tools for Sampling and Weighting Part I
Credits: 2
Grad Meth: Reg, Aud
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.
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.
SURV751
(Perm Req)
Introduction to Big Data and Machine Learning
Credits: 1
Grad Meth: Reg, Aud
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.
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.
SURV752
(Perm Req)
Introduction to Data Visualization
Credits: 1
Grad Meth: Reg, Aud
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.
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.
SURV772
Survey Design Seminar
Credits: 3
Grad Meth: Reg, Aud
Credit only granted for: (SURV770 and SURV771) or SURV772.
Formerly: SURV770 and SURV771.
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.
SURV798B
Advanced Topics in Survey Methodology; Small Area Estimation
Credits: 3
Grad Meth: Reg
Prerequisite: STAT420/SURV420 or equvialent; or permission of instructor. Model-based small-area estimation has portance oer the past two decades. Students will learn the state-of-the-art model-based small-area estimation methods (e.g., empirical best prediction, empirical Bayes andhierarchical Bayes, etc.) and the associated important issues regarding measures of uncertainty, model selection, model diagnostics, design-consistency, etc. The bootstrap, jackknife, and delta methods will be discussed in details in the context of measuring uncertainty of EB/EBP. In order to explain certain concepts, it will be necessary to gothrough a few derivations. Data analyses using several real life examples will be presented. Application of SAS and BUGS in certain small-area data analyses will be shown. The course includes practical exercises.
SURV829
(Perm Req)
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.