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ICPSR Working Paper 4

This paper provides an overview of a methodology used to identify and organize health questions and measures related to Alzheimer’s and other cognitive impairments using data maintained or supported by NACDA. This project specifically used the National Social Life, Health and Aging Project (NSHAP) and the National Health and Aging Trends Study (NHATS) as our comparison proof of concept. The methodology used in this process identifies variables that measure Alzheimer’s disease (A.D.) and other cognitive impairments within NSHAP and NHATS as well as sociodemographic, and comorbidity data commonly associated with increased risk of A.D. and other cognitive impairments. As both NSHAP and NHATS represent multiple waves of longitudinal follow-up information we created longitudinal metadata files that allow for the comparison of A.D. and other cognitive impairments risk across time using these two studies. The project generated enhanced metadata using DDI Lifecycle software to make the discovery of A.D. and other cognitive impairments variables more straightforward and increase the user-friendly elements of these studies. Finally, the proposed supplement included the creation of a customized bibliography (see Appendix) of the use of NSHAP and NHATS data in the analysis of A.D. and other cognitive impairments research, allowing researchers to more easily review the existing body of literature using these data resources. This report describes NACDA’s effort to increase the availability, usability, and discoverability of A.D. and other cognitive impairments information in these studies, encouraging use of NSHAP and NHATS for Alzheimer’s related research and adding to our understanding of how cognitive issues change across time.National Institute on Aging (NIA)http://deepblue.lib.umich.edu/bitstream/2027.42/156403/4/NACDA_cross-series_nshap-nhats_ICPSR_working_paper4_aug2020v2.pdfSEL

A Digital Library for Research Data and Related Information in the Social Sciences

In the social sciences, researchers search for information on the Web, but this is most often distributed on different websites, search portals, digital libraries, data archives, and databases. In this work, we present an integrated search system for social science information that allows finding information around research data in a single digital library. Users can search for research data sets, publications, survey variables, questions from questionnaires, survey instruments, and tools. Information items are linked to each other so that users can see, for example, which publications contain data citations to research data. The integration and linking of different kinds of information increase their visibility so that it is easier for researchers to find information for re-use. In a log-based usage study, we found that users search across different information types, that search sessions contain a high rate of positive signals and that link information is often explored

DataChat: Prototyping a Conversational Agent for Dataset Search and Visualization

Data users need relevant context and research expertise to effectively search for and identify relevant datasets. Leading data providers, such as the Inter-university Consortium for Political and Social Research (ICPSR), offer standardized metadata and search tools to support data search. Metadata standards emphasize the machine-readability of data and its documentation. There are opportunities to enhance dataset search by improving users' ability to learn about, and make sense of, information about data. Prior research has shown that context and expertise are two main barriers users face in effectively searching for, evaluating, and deciding whether to reuse data. In this paper, we propose a novel chatbot-based search system, DataChat, that leverages a graph database and a large language model to provide novel ways for users to interact with and search for research data. DataChat complements data archives' and institutional repositories' ongoing efforts to curate, preserve, and share research data for reuse by making it easier for users to explore and learn about available research data.Comment: 6 pages, 2 figures, and 1 table. Accepted to the 86th Annual Meeting of the Association for Information Science & Technolog

Challenges and Opportunities in Social Science Research Data Management

Pre-print of paper to be published in the proceedings (http://dgd.de/pub_onlinetagung.aspx) of the conference "Semantic Web & Linked Data - Elemente zukünftiger Informationsinfrastrukturen" (1. DGI-Konferenz, 62. DGI Jahrestagung), held in Frankfurt, Germany from Oct. 7-9, 2010 (http://www.dgi-konferenz.de).With the necessity for developing better methods for the discovery and access of available research data, mounting pressure from funding agencies to make and keep research data accessible for the long term, and the complexity of the relationships of different types and formats of files involved in many studies, social science research data management has emerged as an area of multiple challenges and opportunities for information professionals

ICPSR Working Paper 2

This report reviews best practices for using data resources from ICPSR, its projects, and its collaborating partners for measuring the impact of epidemics. The report summarizes resources to identify measures of well-being, social connectedness, and other constructs to measure the social and behavioral effects of the COVID-19 epidemic on population health outcomes. The report suggests data resources to identify pre-crisis measures of social distancing, social networks, consumer confidence, unemployment, and the use of social media.https://deepblue.lib.umich.edu/bitstream/2027.42/154682/1/Best Practices Measuring Impact of Epidemics Version April 3, 2020.pdfDescription of Best Practices Measuring Impact of Epidemics Version April 3, 2020.pdf : White pape

Health Insurance Literacy Impacts on Enrollment and Satisfaction with Health Insurance

Health insurance literacy (HIL) contributes to the lack of understanding basic health insurance (HI) terms, subsidies eligibility, health plan selection, and HI usage. The study is one of few to address the existing gap in the literature regarding the exploration of the relationship between HIL, individuals\u27 HI enrollment, and individuals\u27 satisfaction with their HI. The theoretical framework selected for this study was the prospect theory, which describes the behavior of individuals who make decisions. In this cross-sectional correlational study, secondary data set from the third Quarter 2015 Health Reform Monitoring Survey was used. Binary logistic regression models were used to test hypotheses of four predictive relationships between (a) HI enrollment and HIL with HI terms; (b) marketplace enrollment and HIL with HI terms; (c) satisfaction with HI and HIL with HI access to care; and (d) satisfaction with HI and HIL with HI cost of care. Results indicated that participants with high HIL with HI terms had 4.2 times higher odds that those with low HIL to be enrolled in HI and 81% higher odds than those with low HIL to be enrolled in marketplace HI. The most significant relationship indicated that participants with high HIL with HI activities had 12.8 times higher odds than those with low HIL to have high satisfaction with access to care and 8.8 times higher odds than those with low HIL participants to have high satisfaction with cost of care. The finding that low HIL is associated with lower enrollment and lower satisfaction with HI has implications for social change. Policymakers may have the opportunity to utilize this study to promote policies that promote higher HIL, which may lead to increased HI enrollment and improved satisfaction with HI selection

Guide to Social Science Data Preparation and Archiving: Best Practice Throughout the Data Life Cycle

http://deepblue.lib.umich.edu/bitstream/2027.42/134032/1/dataprep.pdfDescription of dataprep.pdf : Boo