Courses - Fall 2024
SURV400
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: 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.
SURV612
Ethical Considerations for Data Science Research
Credits:
1
Grad Meth:
Reg, Aud
Credit only granted for: SURV699A or SURV612.
Formerly: SURV699A.
The goal of research ethics is to protect human subjects from harm when they participate in a study. In the digital age, however, what constitutes "participation" has become blurry, especially with the rise of social media platforms and other online apps and services. Furthermore, new applications of big data raise important questions about how to protect consumers from harms, and what kinds of notice and consent should be obtained. This course provides an introduction and overview of research ethics in the 21st century and evaluates the many challenges to conducting ethical research.
SURV615
Statistical Modeling and Machine Learning I
Credits:
3
Grad Meth:
Reg, Aud
Prerequisite: Must have basic R Programming skills; and must have completed a two course sequence in probability and statistics; or students who have comparable content may contact the department for permission.
Restriction: Must be in Survey Methodology (Master's) program; or permission of instructor.
This is the first course in a two-term sequence in applied statistical methods and machine learning that are the basis in handling complex datasets. The topics covered include: overview on the quantitative research, linear regression, analysis of variance, inference, prediction, model diagnostics and selection and resampling methods. The emphasis will be to understand and apply the methods.
Runs concurrently with the University of Michigan course.
SURV617
Applications of Statistical Modeling
Credits:
3
Grad Meth:
Reg, Aud
Prerequisite: SURV615 and SURV616; or permission of instructor.
Credit only granted for: SURV617, SURV746, or SURV699R.
Formerly: SURV699R and SURV746.
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.
SURV621
Fundamentals of Data Collection I
Credits:
3
Grad Meth:
Reg, Aud
Restriction: Permission of BSOS-Joint Program in Survey Methodology department.
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.
It runs concurrently with the University of Michigan course.
SURV626
Sampling
Credits:
2
Grad Meth:
Reg, Aud
Prerequisite: Permission of BSOS-Joint Program in Survey Methodology department; and must have taken at least a graduate level statistics course or an undergraduate level advanced statistics course.
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.
SURV627
Experimental Design and Causal Inference
Credits:
2
Grad Meth:
Reg, Aud
Prerequisite: Basic knowledge of data analysis. Familiarity with the R programming language and the RStudio IDE.
Recommended: Experience in the use of SAS or STATA statistical analysis software.
Restriction: Permission of BSOS-Joint Program in Survey Methodology department.
Many of the questions we are interested in as researchers and practitioners are of a causal nature. We act upon the world; how can we tell if our actions have impact? How can we decide if an intervention would get us closer to our goals? In this course, we introduce the basic concepts from causal inference and econometrics, and show what makes a valid causal claim, and what would undo it. We then demonstrate how experiments can be used to evaluate causal hypotheses, and what options are available to conduct experiments in practice. Having discussed experimental data collection, we turn to the analysis of experiments, show how this, again, is linked to the logic of causal inference, and how to work with experimental data. We discuss how to design studies so that statistical inferences are informative and reliable. Next, we cover situations in which experiments might not be possible, and show how these can be addressed through study design ex ante and ex post through analysis.
SURV632
Cognition, Communication and Survey Measurement
Credits:
3
Grad Meth:
Reg, Aud
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.
Restricted to SURV majors only.
SURV665
Introduction to Real World Data Management
Credits:
2
Grad Meth:
Reg, Aud
Restriction: Permission of BSOS-Joint Program in Survey Methodology department.
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.
Basic knowledge of R and prior experience working with data is required for success in this course.
SURV667
Introduction to Record Linkage with Big Data Applications
Credits:
2
Grad Meth:
Reg, Aud
Prerequisite: Basic statistical concepts; and intermediate knowledge of R.
Recommended: Familiarity with regular expressions, the R packages ggplot2 and tidyverse.
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 showing how each of them are performed in practice using R, the course will demonstrate the various benefits of record linkage. Participants will also learn about potential challenges that record linkage projects may face.
SURV699
Special Topics in Survey Methodology; Reading in Survey Methodology
Credits:
1 - 3
Grad Meth:
Reg, Aud
SURV720
Total Survey Error and Data Quality I
Credits:
2
Grad Meth:
Reg, Aud
Prerequisite: SURV625.
Restriction: Permission of instructor.
Credit only granted for: (SURV720 and SURV721) or SURV723.
Formerly: SURV723.
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.
It runs concurrently with the University of Michigan course.
SURV726
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.
SURV727
Fundamentals of Computing and Data Display
Credits:
3
Grad Meth:
Reg, Aud
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.
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.
Runs concurrently with the University of Michigan course.
SURV740
Fundamentals of Inference
Credits:
3
Grad Meth:
Reg, Aud
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.
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.
Runs concurrently with the University of Michigan course.
SURV753
Machine Learning II
Credits:
2
Grad Meth:
Reg, Aud
Prerequisite: SURV751; or comparable knowledge or experience.
Recommended: Familiarity with the statistical programming language R.
Social scientists and survey researchers are confronted with an increasing number of new data sources such as apps and sensors that often result in (para)data structures that are difficult to handle with traditional modeling methods. At the same time, advances in the field of machine learning (ML) have created an array of flexible methods and tools that can be used to tackle a variety of modeling problems. Against this background, this course discusses advanced ML concepts such as cross validation, class imbalance, Boosting and Stacking as well as key approaches for facilitating model tuning and performing feature selection. In this course we also introduce additional machine learning methods including Support Vector Machines, Extra-Trees and LASSO among others. The course aims to illustrate these concepts, methods and approaches from a social science perspective. Furthermore, the course covers techniques for extracting patterns from unstructured data as well as interpreting and presenting results from machine learning algorithms. Code examples will be provided using the statistical programming language R.
SURV829
Doctoral Research Seminar in Survey Methodology
Credits:
3 - 6
Grad Meth:
S-F
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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