CSDE Affiliate Clara Berridge (Social Work) released a new article with co-authors in The Gerontologist, titled “Addressing the Black Box of AI – A Model and Research Agenda on the Co-Constitution of Aging and Artificial Intelligence“. Algorithmic technologies and large data infrastructures, often referred to as AI, have received increasing attention from gerontological research in the last decade. While there is much literature that dissects and explores the development, application, and evaluation of AI relevant for gerontology, this article makes a novel contribution by critically engaging with the theorizing in this growing field of research.
CSDE Computational Demography Working Group (CDWG) Hosts Aja Sutton on Incorporating spatial structure into multilevel regression and poststratification for subnational demographic and small area estimation (5/22/2024)
On Wednesday, May 22nd from 9:00 AM – 10:00 AM, CDWG will host Dr. Aja Sutton to introduce her research. Dr. Aja Sutton is a Postdoctoral Scholar in the Social Sciences Division at the Stanford Doerr School of Sustainability, Stanford University. She received her PhD from the Department of Geography at the University of Washington (UW). From 2020-2022 she was TADA-BSSR NIH T32 Fellow in Data Science and Demography Training at UW’s Center for Studies in Demography and Ecology, from which she also holds a Certificate in Demographic Methods. She received an MA in History from Western University, and an MSc in Palaeopathology from Durham University. Her work is focused on population health, computational social science, and epidemiology. The event will take place in 223 Raitt and on Zoom (register here).
Title: Incorporating spatial structure into multilevel regression and poststratification for subnational demographic and small area estimation
Abstract: Demography is a discipline dependent on the accurate enumeration of population-level processes, especially the count of individuals. When near-perfect census enumeration or representative survey data are unavailable for a particular outcome of interest, it is often difficult to establish the count of that outcome in a population. Instead, we may be able to make indirect estimates through small area estimation (SAE) methods; these use additional contextual information to produce statistically robust estimates of under- or unobserved subpopulations or geographic units. Multilevel regression and poststratification (MRP) is a model-based statistical method that uses national/high-level administrative survey or census data to adjust for non-representativeness in subnational surveys, and to provide small area estimation in areas where subnational survey data are sparse or nonexistent. MRP is computationally inexpensive and generally able to produce quick, consistent, and accurate estimates, and prevailing approaches borrow strength from other administrative areal data through partial (global) pooling. While this approach is effective, it does not account for any potential existing neighborhood spatial structure in the data. Including spatial specifications in MRP is a powerful way to better handle existing spatial relationships and generate more accurate area-level estimates. Join us as we learn more about MRP, Bayesian methods for exploring and handling spatial structure in data, while considering how to improve indirect estimates of 2021 COVID-19 vaccination in California using MRP with spatial structure.
Webinar on Structural Racism in WA State’s Tax Code: Strategies for Action & Improving Health
Goldhaber’s New Research Examines the College and Employment Pathways of Prospective Teachers
CSDE Affiliate Dan Goldhaber (Social Work) in Educational Researcher, titled “The Long and Winding Road: Mapping the College and Employment Pathways to Teacher Education Program Completion in Washington State“. Nationally, more than 75% of individuals who are credentialed to teach are prepared in traditional college- or university-based teacher education programs (TEPs). But the college and employment pathways that prospective teachers take to TEP enrollment and completion have not been comprehensively examined. A better understanding of how credentialed individuals find their way into TEPs helps us understand the sources of new teacher supply early in the prospective teacher pipeline. With that in mind, authors analyze pathways into and through TEPs using historical postsecondary and unemployment insurance data from Washington State.
New Research by McElroy Critically Examines Landlord Technology
CSDE Affiliate Erin McElroy (Geography) authored new research in Environment and Planning D: Society and Space, titled “The work of landlord technology: The fictions of frictionless property management“. Landlord technology—or the systems used by landlords to control and regulate tenant lives, spaces, and data—frequently promises “frictionless” building management and residential experiences. Yet services such as “digital doormen” and virtual property management platforms often create more work for tenants. With a proposition that new forms of material and affective labor are created by landlord technologies despite promises of frictionless living, this article focuses on the various struggles that workers and tenants face in using, maintaining, refusing, and, at times, organizing against property automation.
CSSS Seminar – The Promise and Peril of State Administrative Data: An Example from an Evaluation of Low-Barrier Buprenorphine Treatment for Opioid Use Disorder
Burt Discusses Use of Polygenic Indices in Social Science
CSDE Affiliate Callie Burt (Criminal Justice and Criminology, Georgia State University) released a new article in Sociological Methodology, titled “Polygenic Indices (aka Polygenic Scores) in Social Science: A Guide for Interpretation and Evaluation“. Polygenic indices (PGI)—the new recommended label for polygenic scores in social science applications—are genetic summary scales often used to represent an individual’s liability for a disease, trait, or behavior on the basis of the additive effects of measured genetic variants. Enthusiasm for linking genetic data with social outcomes and the inclusion of premade PGIs in social science data sets have facilitated increased uptake of PGIs in social science research, a trend that will likely continue. Yet most social scientists lack the expertise to interpret and evaluate PGIs in social science research. In this article, Burt provides a primer on PGIs for social scientists focusing on key concepts, unique statistical genetic considerations, and best practices in calculation, estimation, reporting, and interpretation.
*New* Issue of Journal of Demographic Economics
Read volume 90, issue 2 here!
CSDE Computational Demography Working Group (CDWG) Hosts Aja Sutton on Incorporating spatial structure into multilevel regression and poststratification for subnational demographic and small area estimation
*New* Registration is Now Open for the 2024 IAPHS Annual Conference
Registration is open on April 4, 2024 for the IAPHS Annual Conference from September 10-13 in St. Louis, MO. Numerous population health topics will be discussed related to the 2024 conference theme, Tackling Declining Life Expectancy in the U.S.: Investigating Social Drivers and Policy Solutions, are particularly encouraged. Register here before August 16th for the early registration rate.