Courses - Spring 2025
SURV400
Fundamentals of Survey and Data Science
Credits:
3
Grad Meth:
Reg, P-F
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: 1 course with a minimum grade of C- from (MATH240, MATH461, MATH341); and 1 course with a minimum grade of C- from (MATH340, MATH241).
Cross-listed with: 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
Theory and Methods of Statistics
Credits:
3
Grad Meth:
Reg, P-F, Aud
Prerequisite: 1 course with a minimum grade of C- from (SURV410, STAT410).
Cross-listed with: 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
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.
It runs concurrently with the University of Michigan course.
SURV613
Machine Learning for Social Science
Credits:
3
Grad Meth:
Reg, Aud
Recommended: Students are encouraged to work through one or more R tutorials prior or during the first weeks of the course. Some resources are listed on the syllabus.
Credit only granted for: SURV613 or SURV699U.
Formerly: SURV699U.
Introduction to supervised statistical learning techniques such as decision trees, random forests and boosting and discusses their potential application in the social sciences. These methods focus on predicting an outcome Y based on some learned function f(X) and therefore facilitate new research perspectives in comparison with traditional regression models, which primarily focus on causation. Predictive methods also provide a valuable extension to the empirical social scientists' toolkit as new data sources become more prominent. In addition to introducing supervised learning methods, the course will include practical sessions to exemplify how to tune and evaluate prediction models using the statistical programming language R.
SURV616
Statistical Modeling and Machine Learning II
Credits:
3
Grad Meth:
Reg, Aud
Prerequisite: SURV615.
Build on material presented in Statistical Methods and Machine Learning I. Topics include: categorical data analysis, logistic regression, model selection for inference and prediction, classification using K-means and neural networks, survival analysis, principal components, and factor analysis.
It runs concurrently with the University of Michigan course.
SURV622
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.
It runs concurrently with the University of Michigan course.
SURV625
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.
It runs concurrently with the University of Michigan course.
SURV630
Questionnaire Design and Evaluation
Credits:
3
Grad Meth:
Reg, Aud
Restriction: Must be in the Survey and Data Science Doctoral or Master's Program, or the Applied Political Analytics Master's Program; or permission of the Joint Program in Survey Methodology.
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
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
SURV636
Sampling II
Credits:
1
Grad Meth:
Reg, Aud
Prerequisite: SURV626 or equivalent.
Recommended: Some experience with the R statistical computing software. Repeatable to 1 credit.
Different applications of the methods and techniques covered in the Sampling I course. This is also an applied statistics methods course concerned almost exclusively with the design of data collection rather than data analysis. The course will concentrate on sampling applications to human populations, since this poses a number of 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.
SURV675
Modern Workflows in Data Science
Credits:
2
Grad Meth:
Reg, Aud
Prerequisite: SURV665.
Recommended: R or a good knowledge of R base and tidyverse.
Restriction: Permission of BSOS-Joint Program in Survey Methodology department.
Credit only granted for: SURV699Y or SURV675.
Formerly: SURV699Y.
Large data, fast pace of production, and collaboration are hallmarks of the new data environment. In this context, researchers must have a good understanding of data workflows and they must ensure consistent and reproducible practices in order to collaborate and consistently produce insights. This course deals with some of these essential topics. We will discuss the main types of workflows in data and survey sciences and how tools such as GitHub can enhance collaboration and insure reproducibility. We will also discuss the use of reproducible documents such as Rmarkdown or Jupyter Notebooks before covering how to work with distributed data using Spark. We will finish the course by discussing the use of dashboards and how to develop such tools using R Shiny.
SURV699
Special Topics in Survey Methodology; Readings in Survey Methodology
Credits:
1 - 4
Grad Meth:
Reg, Aud
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.
It runs concurrently with the University of Michigan course.
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.
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.
SURV706
General Linear Models
Credits:
2
Grad Meth:
Reg, Aud
Recommended: Sound understanding of Linear Regression Models, Calculus and Linear Algebra.
Restriction: Must have permission of BSOS-Joint Program in Survey Methodology.
Credit only granted for: SURV706 or SURV699J.
Formerly: SURV699J.
The main focus of this course lies on the introduction to statistical models and estimators beyond linear regression useful to social and economic scientists. It provides an overview of generalized linear models (GLM) that encompass non-normal response distributions to model functions of the mean. GLMs thus relate the expected mean E(Y) of the dependent variable to the predictor variables via a specific link function. This link function permits the expected mean to be non-linearly related to the predictor variables. Examples for GLMs are the logistic regression, regressions for ordinal data, or regression models for count data. GLMs are generally estimated by use of maximum likelihood estimation. The course thus not only introduces GLMs but starts with an introduction to the principle of maximum likelihood estimation.
SURV721
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.
It runs concurrently with the University of Michigan course.
SURV735
Data Privacy and Data Confidentiality
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.
SURV742
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.
It runs concurrently with the University of Michigan course.
SURV745
Practical Tools for Study Design and Inference
Credits:
3
Grad Meth:
Reg, Aud
Prerequisite: SURV615, SURV616, and SURV625; or permission of instructor.
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.
SURV751
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.
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.
It runs concurrently with the University of Michigan course.
SURV829
Doctoral Research Seminar in Survey Methodology
Credits:
3 - 6
Grad Meth:
S-F
It runs concurrently with the University of Michigan course.
SURV898
Pre-Candidacy Research
Credits:
1 - 8
Grad Meth:
Reg
SURV899
Doctoral Dissertation Research
Credits:
6
Grad Meth:
S-F
Flagship Institution of the University System of Maryland
University of Maryland, College Park, MD 20742, USA
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