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BEGIN:VEVENT
DTSTAMP:20250604T104048
DTSTART;TZID=America/Detroit:20250610T120000
DTEND;TZID=America/Detroit:20250610T160000
SUMMARY:Class / Instruction:Summer Institute Course - Designing and Writing Questions for Surveys: Guidelines and Recommendations
DESCRIPTION:Designing and Writing Questions for Surveys: Guidelines and Recommendations\nJune 9-13\, 2025\n1:00pm-4:00pm EDT\nLive Online via Zoom\n\nCourse Objectives\n• Introduce a structural analysis of parts of a survey question\n• Introduce cognitive interviewing as a method for testing survey questions\n• Describe guidelines for diagnosing problems in survey questions and writing new survey questions\n• Focus on the structure and wording of survey questions\, whether for interviewer-administered or self- administered instruments\n• Provide an opportunity to apply the guidelines and principles during in-class exercises\n• Focus on improving individual questions and sets of questions.\n• Summarize research that underlies key decisions in writing survey questions.\n\nDescription\nThis workshop distills research about survey questions to principles that can be applied to write survey questions that are clear and obtain reliable answers. The workshop provides students with tools to use in diagnosing problems in survey questions and in writing their own survey questions. Sessions combine lecture with group exercises and discussion. The lecture provides guidelines for writing and revising survey questions and illustrates how to revise troubled questions. Assignments require that students analyze problematic questions\, revise them\, and administer them to fellow students. Sessions consider both questions about events and behaviors and questions about subjective phenomena (such as attitudes\, evaluations\, and internal\nstates).\n\nWho Should attend\nIndividuals who will be writing or reviewing survey questions or survey instruments or analyzing survey data. This course gives practical guidance to those who have written survey questions but who are not familiar with research on question design\, those who are just beginning to design survey instruments\, and those who use survey data but do not themselves design survey instruments.\n\nThe Summer Institute in Survey Research Techniques provides rigorous and high quality graduate level training in all phases of survey research. The noncredit courses are open to all. The courses are live online via Zoom. Registration and payment are required. Course fees are based on the total number of hours assigned to each course\, the hours are listed on the course description. The 2025 schedule lists additional courses. If you have any questions regarding the application process\, please use the online contact form or email the Summer Institute at isr-summer@umich.edu .\n\nThe program teaches state-of-the-art practice and theory in the design\, implementation\, and analysis of surveys. The Summer Institute in Survey Research Techniques has presented courses on the sample survey since the summer of 1948\, and has offered such courses every summer since. The Summer Institute uses the sample survey as the basic instrument for the scientific measurement of human activity. It presents sample survey methods in courses designed to meet the educational needs of those
UID:135993-21877627@events.umich.edu
URL:https://events.umich.edu/event/135993
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Data,Data Analysis,Data Collection,Data Curation,Data Linkage,Data Management,Data Science,Survey Methodology,Survey Methods,Survey Research
LOCATION:Off Campus Location
CONTACT:
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BEGIN:VEVENT
DTSTAMP:20241004T130515
DTSTART;TZID=America/Detroit:20250610T120000
DTEND;TZID=America/Detroit:20250610T130000
SUMMARY:Workshop / Seminar:Understanding and Managing ADHD: Free Parent & Guardian Workshop
DESCRIPTION:This free virtual workshop is designed to help parents or guardians. Your child may have received a formal diagnosis already\, or you may suspect they have ADHD or a similar learning challenge. Participants learn more about what ADHD is\, what it “looks” like in children\, how it differs from what you might expect/how it is presented in media\, and where to go from here.\n\nParticipants can expect to learn:\n+ What causes ADHD.\n+ What ADHD looks like in children.\n+ How to support a child with ADHD at home\, school\, and with friends.\n\nThis workshop includes interactive components and a Question-and-Answer session at the end. To help us better prepare and tailor the content of each workshop\, we ask participants to provide their most pressing questions in writing when they register.
UID:127422-21859038@events.umich.edu
URL:https://events.umich.edu/event/127422
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:adhd,Children,Free,parenting,Workshop
LOCATION:Off Campus Location
CONTACT:
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20250527T170539
DTSTART;TZID=America/Detroit:20250610T130000
DTEND;TZID=America/Detroit:20250610T150000
SUMMARY:Lecture / Discussion:Mechanistic Modeling of Complex Health Problems with Deep Learning
DESCRIPTION:Though they show impressive empirical accuracy\, machine learning methodologies have been criticized for not producing interpretable\, scientific theories. In both clinical medicine and public health\, the researchers aim not just to predict health outcomes\, but to improve them. Hence\, causal\, human-interpretable models of nature hold particular value in these fields. In this dissertation\, I investigate how deep learning\, when integrated into scientifically-informed models and principled statistical frameworks\, can be used to advance mechanistic modeling in the health sciences.\n\nSince the widespread adoption of electronic health records (EHRs)\, there has been growing interest in evaluating medical interventions through large-scale observational studies of diverse patient populations. In the first chapter\, I examine the opportunities and challenges that arise from applying deep neural networks to EHR data. Despite the vast scale of EHR datasets\, black box predictive modeling has limited value for informing clinical care\, where human judgment is indispensable. Medical researchers are often interested in estimating counterfactual treatment eff ects on patients’ time-to-event outcomes. In the second chapter\, I propose the Dynamic Survival Transformer (DynST)\, a deep survival model that flexibly estimates hazards from both static and time-varying features typical of EHR data\, and demonstrate how DynST supports robust\, semiparametric inference for causal survival analysis.\n\nStochastic infectious disease models capture uncertainty in public health outcomes and off er mechanistic explanations of transmission patterns. However\, they are often nonlinear dynamical systems with massive latent state spaces\, making likelihood-based inference of model parameters difficult. In the third chapter\, I develop a methodology for efficiently calibrating large-scale stochastic epidemic simulation models to observed data using Neural Posterior Estimation. In NPE\, a neural network trained on simulated data learns to “invert” a stochastic simulator and returns a parametric approximation of the posterior distribution. I use NPE to calibrate a stochastic Susceptible-Infected model to a study of a healthcare-associated infection in a long-term acute care hospital and find evidence of spatially heterogeneous patient-to-patient transmission risk.
UID:135846-21877321@events.umich.edu
URL:https://events.umich.edu/event/135846
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Dissertation
LOCATION:West Hall - 438
CONTACT:
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BEGIN:VEVENT
DTSTAMP:20250527T143544
DTSTART;TZID=America/Detroit:20250610T131500
DTEND;TZID=America/Detroit:20250610T151500
SUMMARY:Presentation:Reducibility and Anosov Representations
DESCRIPTION:Abstract:\n\nIn this thesis\, we explore the framework of Anosov representations for reducible representations of a non-elementary word hyperbolic group. We give characterizations of the Anosov condition for these reducible representations in terms of the eigenvalues of the irreducible block factors of its semisimplification\, or more generally\, of the block factors of its block diagonalization. In the character variety\, these Anosov representations comprise a collection of bounded convex domains in certain finite-dimensional vector spaces\, and this perspective allows us to conclude for many non-elementary hyperbolic groups that connected components of the character variety which consist entirely of Anosov representations do not contain reducible representations. Applying these results to reducible suspensions\, we obtain explicit examples of non-Anosov limits of reducible Anosov representations.
UID:135842-21877318@events.umich.edu
URL:https://events.umich.edu/event/135842
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Dissertation,Graduate,Graduate Students,Mathematics
LOCATION:East Hall - 3096
CONTACT:
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