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Registration and payment are required a minimum of two weeks prior to the start of the course.\n\nFounded in 1948, the Summer Institute in Survey Research Techniques is designed specifically to meet the needs of professionals and graduate students seeking to deepen their expertise in survey methodology and data collection. Offered through the Michigan Program in Survey and Data Science within the Institute for Social Research at the University of Michigan, the program provides a rigorous and flexible curriculum that blends theoretical foundations with practical application \u2014 entirely online.\n\nSampling in Practice\n\nUnlocking the art and science of sampling with an applied, hands-on approach, the course Sampling in Practice is designed for applied practitioners who want to master real-world sampling techniques through active learning and practical programming. Students will learn about probability sampling methods, including simple random sampling, stratification, systematic selection, cluster sampling, probability proportional to size sampling, and multistage sampling. We will also cover sampling cost models, sampling error estimation techniques, non-sampling errors, missing data, and nonprobability samples. The course emphasizes practical implementation, featuring interactive coding exercises and in-class examples to reinforce each concept. A culminating project will give students the opportunity to integrate multiple techniques into a comprehensive sample design and demonstrate the profession in designing surveys, selecting subjects, analyzing sample data, and solving real sampling problems using modern statistical tools.\n\nWhy take this course? \n\nThe course is crafted for students and practitioners eager: \n\nTo build proficiency in modern sampling techniques through active engagement and practical coding experience\nTo understand the basic ideas, concepts and principles of probability sampling from an applied perspective\nTo be able to identify and appropriately apply sampling techniques to survey design problems\nTo understand and be able to assess the impact of the sample design on survey estimates\nTo be able to compute the sample size for a variety of sample designs\nTo learn how to design and select a probability sample involving complex sampling techniques in a survey project, and receive expert feedback on a sampling report. \n\nYajuan Si is a Research Associate Professor in the Michigan Program in Survey and Data Science, located within in the Institute for Social Research at the University of Michigan. She holds a Ph.D. in statistical science from Duke and received postdoctoral training at Columbia. Yajuan\u2019s research focuses on methodology development, from data analysis to study design, in streams of Bayesian statistics, linking design- and model-based approaches for survey inference, data integration, missing data analysis, confidentiality protection, and causal inference, with applications in the social and health sciences. 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It is important to be part of a culture that supports and embraces disability inclusion. This session will cover:\n\n-Background about the Americans with Disabilities Act (ADA)\n-Ways to raise disability awareness and inclusion\n-Best practices for creating and maintaining inclusive working and learning environments\n-Content to dispel ableist myths and misconceptions about disability\n-We will engage in discussions about increasing accessibility and disability inclusion. Discussions will include how to do this at the university, starting in our own units and departments. \n\nPart 2 of this program will take place on July 15 from 2:00 - 4:00. Register at https:\/\/umich.zoom.us\/webinar\/register\/WN_9fQYIs0pRpq0zGuRktpDhw. \n\nAmerican Sign Language (ASL) interpreting services and Communication Access Realtime Translation (CART) captioning services will be provided. 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West", "event_subtitle":"A noncredit course presented by Brady T. West", "event_type":"Class \/ Instruction", "event_type_id":"8", "description":"Founded in 1948, the Summer Institute in Survey Research Techniques is designed specifically to meet the needs of professionals and graduate students seeking to deepen their expertise in survey methodology and data collection. Offered through the Michigan Program in Survey and Data Science within the Institute for Social Research at the University of Michigan, the program provides a rigorous and flexible curriculum that blends theoretical foundations with practical application \u2014 entirely online. \n\nClasses are open for registration.\nYou do not have to be affiliated with the University to attend. \nRegistration and payment are required a minimum of two weeks prior to the start of the class. \n\nJuly 7 and 9, 2026 (T\/Th)\n9:00am-1:00pm\nInterventions in a Responsive Survey Design Framework\nPresented by Brady T. West\nCourse Fee: $600\n\nThis course focuses on the implementation of potential interventions within a responsive survey design (RSD) framework. Many of these interventions have been tested experimentally, and the course will examine evaluations of those experiments to highlight their effectiveness. Emphasis will be placed on the role of experimental evaluation in the early early stages of RSD. This course will also cover practical methods for implementing interventions, including the design and execution of experiments to assess new approaches. In addition, strategies for applying these interventions in both interviewer-mediated and self-administered surveys (e.g., web and mail) will be discussed.\n\nBrady T. West is a Research Professor in the Michigan Program in Survey and Data Science, located within the Survey Research Center at the Institute for Social Research on the University of Michigan-Ann Arbor (U-M) campus. He earned his PhD from the Michigan Program in Survey Methodology in 2011. Before that, he received an MA in Applied Statistics from the U-M Statistics Department in 2002, being recognized as an Outstanding First-year Applied Masters student, and a BS in Statistics with Highest Honors and Highest Distinction from the U-M Statistics Department in 2001. His current research interests include the implications of measurement error in auxiliary variables and survey paradata for survey estimation, selection bias in surveys, adaptive and responsive survey design, interviewer effects, and multilevel regression models for clustered and longitudinal data. He is the lead author of a book comparing different statistical software packages in terms of their mixed-effects modeling procedures (Linear Mixed Models: A Practical Guide using Statistical Software, Third Edition, Chapman Hall\/CRC Press, 2022), and he is a co-author of a second book entitled Applied Survey Data Analysis (with Steven Heeringa and Pat Berglund), the second edition of which was published by Chapman Hill in June 2017. 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