Workshops

Proposed workshop duration, format, activities, and schedule The half-day workshop will overview the IPUMS data collections, with special emphasis on those with restricted-use versions available (IPUMS NHIS, IPUMS MEPS, and IPUMS USA) and provide a demonstration of the web interface to create custom data files for analysis. Second, the workshop will review restricted use data available, with presentations focused on access via both an FSRDC and the NCHS Research Data Center. Third, the workshop will describe the new Standard Application Process for restricted data from federal statistical agencies, and share considerations for using restricted data. We will include time for questions and set up opportunities for participants to connect with workshop leaders for individual consultations during or immediately following the workshop.

This workshop is designed for both researchers who are interested in learning about using The Future of Families and Child Wellbeing Study data for the first time and current FFCWS data users who would like a deeper dive into navigating our data. This workshop will introduce researchers to 1) a brief overview of the history and data collection of the FFCWS, including the variety of survey and activity components included in our available data; 2) an overview of our latest data collection of the Year 22 study and some preliminary analysis results; 3) a guide to the file structure, contents, and data conventions used in FFCWS including tips and advice from experts in FFCWS data support; 4) an interactive tutorial on the contents and use of our documentation and metadata web interface; and 5) an exploration of how the FFCWS data can be used for multi-disciplinary research topics and for teaching. Our goals for this workshop are to provide a solid background for working with FFCWS data and to support participants as they begin variable exploration and selection relevant to their own research questions.

The US Panel Study of Income Dynamics (PSID) is the world’s longest-running longitudinal household panel study. It is used in the fields of demography, economics, sociology, public health, and public policy to investigate individual and family socioeconomic status, health, and well-being in longitudinal and intergenerational contexts. This workshop familiarizes new and prospective users with the design, content, and applications of the main PSID interview and two youth-centered supplements: The Child Development Supplement (CDS) and Transition into Adulthood Supplement (TAS). One segment of the workshop will introduce the available genomic data collected from CDS children aged 0-17 years and their primary caregivers, as well as additional biomarker collections. The workshop also provides a hands-on introduction to data access, key data files, and user education resources.

The purpose of the workshop is to introduce attendees to two extraordinary new data resources for studying the long-term effects of education and other early-life factors on later-life morbidity, mortality, and cognition. Both long-running cohort studies began as large, nationally representative, and diverse longitudinal studies of education. Both have been repurposed as studies of aging, health, mortality, and cognition by reviving them at midlife and following sample members forward. The Education Studies for Healthy Aging Research (EdSHARe; www.edshareproject.org) project is conducting these two cohort studies.

The U.S. Census Bureau implemented a new Disclosure Avoidance System (DAS) based on differential privacy (DP) to protect respondent information in the published 2020 Census data products. The 2020 Census DAS protects confidentiality by infusing small amounts of "noise" (error) into published tabulations. While the magnitude of this privacy-preserving error is generally small, it can impact fitness-for-use of the resulting data for particular use cases, especially for analyses focusing on very small geographic areas or demographic subgroups.  

This workshop will provide census data users with new guidance and resources they can use to assess the fitness-for-use of 2020 Census data for their specific, individual applications and use cases, and to be able to adapt their analyses accordingly. This new guidance and accompanying resources include best practices for calculating averages and ratios, measures of the comparative impact of different degrees of aggregation to reduce disclosure avoidance-related error, and resources for calculating confidence intervals (margins of DP-related error) for published 2020 Census statistics.

Presenters:

• Sallie Ann Keller, U.S. Census Bureau
• Robert Ashmead, U.S. Census Bureau
• Matthew Spence, U.S. Census Bureau
• Alexandra Krause, U.S. Census Bureau
• Beth Jarosz, Population Reference Bureau

Extreme climate events are increasingly recognized as associated with adverse health outcomes as well as broad range social and economic impacts. While impacts on health is a major area of investigation within the scientific community, demographic repercussions of climate events remain elusive. A growing number of studies reported that the health risks associated with these extreme events are exacerbated by socioeconomic and demographic factors, where children, elderly, racial and ethnic groups and low-income individuals tend to be impacted disproportionately. Studies also suggested differences in effect of these on mortality and morbidity due to the timing, duration, and intensity of the events. Meanwhile, robust definitions of exposure to extreme climate events is not available. Current studies often use ad-hoc methods to access and define climate exposure data (e.g., extreme temperature events), such as directly linking closest temperature data to different geographic units (e.g., cities, census tracts), to pursue research questions. To facilitate evaluating the spatiotemporal patterns and assess disparities of extreme climate impacts, we have developed a geoinformatics pipeline and curated a longitudinal dataset of precise extreme heat and cold events (EHEs/ECEs) in the United States, at a very fine spatial and temporal resolution (500×500-meter grids). We will present this data source, which is publicly available to the scientific community, and provide tutorials on how to access and address different demographic and epidemiological research questions.

This workshop will provide hands-on demonstrations for accessing data from the IPUMS Global Health data collections and discuss newly released data and data analysis tools. The workshop will also discuss upcoming improvements planned for the near future, including the addition of features to improve interoperability between IPUMS DHS, PMA, and MICS.

In this workshop, we discuss methods for drawing causal inferences when analyzing observational rather than experimental data. We present a variety of estimators for average treatment effects (ATEs) and average treatment effects on the treated (ATETs) and discuss when each estimator is useful. Throughout the workshop, we cover the conceptual and theoretical underpinnings of treatment effects and demonstrate the methods with many practical examples worked using Stata software. After a discussion of the potential outcome framework and an overview of the parameters estimated, the workshop introduces the following treatment-effect estimators:

• regression-adjustment estimator
• inverse-probability-weighted (IPW) estimator
• augmented IPW estimator
• IPW regression-adjustment estimator
• nearest-neighbor matching estimator
• propensity-score matching estimator
• difference-in-differences (DID)
• Heterogeneous DID

The course also discusses

• Standard errors and diagnostics for DID estimation
• When traditional DID analysis is inadequate
• double-robustness property of the augmented IPW and IPW regression adjustment
• estimators using different functional forms for outcome model and treatment

All topics are discussed using a combination of theory and Stata examples.

Instructors of undergraduate demography courses are moving away from traditional lectures and textbooks in favor of skills-based learning and student immersion in real-world population data. The number and availability of on-line data analysis and visualization tools has increased dramatically in recent years. Some examples of government agencies and organizations that provide interactive data analysis tools include: The American Community Survey, The Opportunity Atlas, America’s Health Rankings, Kids Count, The United Nations, and even The New York Times. This type of training has become vitally important for navigating an increasingly data-driven society, and employers urgently need workers able to competently access, analyze, and interpret data. Meanwhile, student learning has been transformed to incorporate active learning, collaboration, interaction with technology (e.g., virtual reality), familiarity with new forms of digital media, as well as new teaching contexts such as on-line and hybrid course delivery. The goal of this workshop is to provide a space for instructors to share with one another their ideas, methods, and techniques for teaching population studies and demography to undergraduates.

Presenter: Susan D. Stewart, Iowa State University

In this third edition of the workshop, we aim to introduce two types of big data, i.e., Google Trends and Twitter data. This workshop will be the great opportunity for the researchers in the study of migration and demography in general to get familiar with two popular sources of data. We plan to begin the session by introducing the data format, related literature, empirical findings, advantages and critical challenges of such data. We then plan to have more interactive sessions on how to retrieve these data and write and search inquiries.