Fall
https://jpsm.umd.edu/
enSampling FALL semester Nishimura, Raphael
https://jpsm.umd.edu/node/4758
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Sampling
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SURV626
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Online
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Fall
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<span class="inline-left"> <strong class="field-label-above">Instructor</strong>
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<a href="https://jpsm.umd.edu/facultyprofile/nishimura/raphael" hreflang="en">Nishimura, Raphael</a>
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<div class="field field--name-body field--type-text-with-summary field--label-hidden field--item"><p>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.</p>
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Thu, 05 Aug 2021 06:35:11 +0000lsureshk4758 at https://jpsm.umd.eduSynthetic Population FALL semester Lahiri, Partha
https://jpsm.umd.edu/node/4616
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Synthetic Population
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SURV699W
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Onsite
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Fall
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<span class="inline-left"> <strong class="field-label-above">Instructor</strong>
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<a href="https://jpsm.umd.edu/facultyprofile/lahiri/partha" hreflang="en">Lahiri, Partha</a>
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<div class="field--label">Syllabus</div>
<div class="field--item"><span class="file file--mime-application-vnd-openxmlformats-officedocument-wordprocessingml-document file--x-office-document icon-before"><span class="file-icon"><span class="icon glyphicon glyphicon-file text-primary" aria-hidden="true"></span></span><span class="file-link"><a href="https://jpsm.umd.edu/sites/jpsm.umd.edu/files/syllabi/Syllabus_2019_SyntheticPopulation.docx" type="application/vnd.openxmlformats-officedocument.wordprocessingml.document; length=21893" title="Open file in new window" target="_blank" data-toggle="tooltip" data-placement="bottom">Syllabus_2019_SyntheticPopulation.docx</a></span><span class="file-size">21.38 KB</span></span></div>
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<p><a class="btn btn-primary btn-lg" href="https://umdsurvey.umd.edu/jfe/form/SV_9FEKHITvIJm9hv7" target="_blank"><span class="text">Request a Seat!</span></a></p>
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<p> </p>
<p class="MsoNormal"><strong><span>Prerequisites:</span></strong></p>
<p class="MsoNormal"><span>The course should be accessible to both masters and PhD students in survey methodology, statistics, biostatistics and related areas. A prerequisite for this course is a mathematical statistics course at the master’s level (e.g., UMD STAT/SURV 420 or equivalent). If you are unsure about your qualifications for the course, please contact the instructor.</span></p>
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Thu, 30 May 2019 21:30:30 +0000amehta174616 at https://jpsm.umd.eduFundamentals of Inference Fall
https://jpsm.umd.edu/node/4603
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Fundamentals of Inference
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SURV740
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Onsite
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Fall
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<p><strong>Prerequisite:</strong> SURV410 and SURV420; or (SURV615 and SURV616); or permission of Instructor required.</p>
<p><strong>Description: </strong>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.</p>
<p>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.</p>
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Mon, 11 Mar 2019 18:33:29 +0000rmdalal4603 at https://jpsm.umd.eduFundamentals of Computing and Data Display Fall
https://jpsm.umd.edu/node/4602
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Fundamentals of Computing and Data Display
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SURV727
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Onsite
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Fall
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<p><strong>Restriction:</strong> Must be in a major within the BSOS-Joint Program in Survey Methodology department; or permission of BSOS-Joint Program in Survey Methodology department.</p>
<p><strong>Additional information:</strong> 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.</p>
<p>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.</p>
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Mon, 11 Mar 2019 18:31:22 +0000rmdalal4602 at https://jpsm.umd.eduSpecial Topics in Survey Methodology; Machine Learning for Social Science FALL semester Kern, Christoph
https://jpsm.umd.edu/node/4601
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Special Topics in Survey Methodology; Machine Learning for Social Science
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SURV699U
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Onsite
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Fall
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<span class="inline-left"> <strong class="field-label-above">Instructor</strong>
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<a href="https://jpsm.umd.edu/node/4419" hreflang="en">Kern, Christoph</a>
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<div class="field field--name-body field--type-text-with-summary field--label-hidden field--item"><div class="well text-align-center"><a class="btn btn-primary btn-lg" href="https://umdsurvey.umd.edu/jfe/form/SV_9FEKHITvIJm9hv7" target="_blank"><span class="text">Request a Seat!</span></a></div>
<p><strong>Prerequisite: </strong>Students should work through one or more R tutorials prior or during the first weeks of class due to the short introduction to R presented in the class. Some resources can be found at the following: <a href="https://www.rstudio.com/online-learning/#R">https://www.rstudio.com/online-learning/#R</a>, <a href="https://cran.r-project.org/manuals.html">https://cran.r-project.org/manuals.html</a>, or <a href="http://www.statmethods.net">http://www.statmethods.net</a>.</p>
<p><strong>Description:</strong><br />
Provides an 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 data-driven 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.</p>
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Mon, 11 Mar 2019 18:18:26 +0000rmdalal4601 at https://jpsm.umd.eduSpecial Topics in Survey Methodology; Statistical Data Integration FALL semester Lahiri, Partha
https://jpsm.umd.edu/node/4600
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Special Topics in Survey Methodology; Statistical Data Integration
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SURV699L
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Onsite
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Fall
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<span class="inline-left"> <strong class="field-label-above">Instructor</strong>
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<a href="https://jpsm.umd.edu/facultyprofile/lahiri/partha" hreflang="en">Lahiri, Partha</a>
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<div class="field field--name-body field--type-text-with-summary field--label-hidden field--item"><div class="well text-align-center"><a class="btn btn-primary btn-lg" href="https://umdsurvey.umd.edu/jfe/form/SV_9FEKHITvIJm9hv7" target="_blank"><span class="text">Request a Seat!</span></a></div>
<p><strong>Prerequisite:</strong> SURV/STAT410 and SURV/STAT420 or equivalent; Permisson of instructor required<strong>.</strong></p>
<p><strong>Description:</strong><br />
A single available data source is often not sufficient in order to carry out required statistical data analyses to make certain decisions. To avoid high costs of collecting new data in such cases, there is a growing need to combine multiple survey and/or administrative existing data sources using appropriate statistical techniques. In the first two-third of the course, we shall discuss various issues and methods in statistical data integration. In particular, we shall cover various methods available in statistical matching, a body of statistical techniques that use a few common variables in combining multiple data sources with no or negligible overlapping units.</p>
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Mon, 11 Mar 2019 18:12:39 +0000rmdalal4600 at https://jpsm.umd.eduFundamentals of Data Collection I Fall
https://jpsm.umd.edu/node/4599
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Fundamentals of Data Collection I
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SURV621
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Onsite
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Fall
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<p><strong>Description:</strong></p>
<p>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.</p>
<p>It runs concurrently with the University of Michigan course.</p>
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Mon, 11 Mar 2019 18:03:08 +0000rmdalal4599 at https://jpsm.umd.eduApplications of Statistical Modeling Fall
https://jpsm.umd.edu/node/4596
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Applications of Statistical Modeling
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SURV617
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Onsite
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Fall
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<div class="field field--name-body field--type-text-with-summary field--label-hidden field--item"><div class="well text-align-center"><strong>Prerequisite:</strong><span> </span></div>
<p>SURV615 and SURV616; or permission of instructor.</p>
<p><strong>Description:</strong></p>
<p>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.</p>
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Mon, 04 Mar 2019 19:15:48 +0000rmdalal4596 at https://jpsm.umd.eduDoctoral Research Seminar in Survey Methodology FALL semester Abraham, Katharine West, Brady
https://jpsm.umd.edu/node/4595
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Doctoral Research Seminar in Survey Methodology
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SURV829
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Onsite
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Fall
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<a href="https://jpsm.umd.edu/facultyprofile/abraham/katharine" hreflang="en">Abraham, Katharine</a>
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<a href="https://jpsm.umd.edu/facultyprofile/west/brady" hreflang="en">West, Brady</a>
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<div class="field field--name-body field--type-text-with-summary field--label-hidden field--item"><div class="well text-align-center"><a class="btn btn-primary btn-lg" href="https://umdsurvey.umd.edu/jfe/form/SV_9FEKHITvIJm9hv7" target="_blank"><span class="text">Request a Seat!</span></a></div>
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Fri, 01 Mar 2019 20:21:42 +0000rmdalal4595 at https://jpsm.umd.eduBayesian Modeling and Inference FALL semester
https://jpsm.umd.edu/node/4593
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Bayesian Modeling and Inference
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SURV789Z
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Onsite
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Fall
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<div class="field field--name-body field--type-text-with-summary field--label-hidden field--item"><p> </p>
<div class="well text-align-center"><a class="btn btn-primary btn-lg" href="https://umdsurvey.umd.edu/jfe/form/SV_9FEKHITvIJm9hv7" target="_blank"><span class="text">Request a Seat!</span></a></div>
<p><strong>Prerequisites: </strong></p>
<p>STAT 410 and 420 or equivalent.</p>
<p><strong>Description: </strong></p>
<p>The purpose of the course is to provide a blend of theory, methods, and applications. The course will begin with a review of relevant concepts of classical statistical inference, which is needed to compare different paradigms. Following this, elements of the Bayesian inference and decision theory will be introduced in order to emphasize the advantages and challenges of the Bayesian methods. The course will cover a wide range of topics in Bayesian analysis, including objective priors, model selection, Bayesian computations, high-dimensional problems, Bayesian analysis with missing data and finite population sampling. The course emphasizes data analysis via modern computer methods and R freeware packages that are introduced and used throughout the course.</p>
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Fri, 01 Mar 2019 20:20:49 +0000rmdalal4593 at https://jpsm.umd.edu