scholarly journals Survey Research in Times of Big Data

2022 ◽  
Author(s):  
Pablo Cabrera-Álvarez
Keyword(s):  
Social Sciences ◽  
Big Data ◽  
Data Sources ◽  
Gps Data ◽  
Redes Sociales ◽  
Relevant Research ◽  
Data Ownership ◽  
Survey Costs ◽  
Research Questions ◽  
Accessibility Issues

La encuesta es la técnica de investigación predominante en la investigación en Ciencias Sociales. Sin embargo, la aparición de otras fuentes de datos como las publicaciones en redes sociales o los datos generados por GPS suponen nuevas oportunidades para la investigación. En este escenario, algunas voces han defendido la idea de que, debido a su menor coste y la velocidad a la que se generan, los big data irán sustituyendo progresivamente a los datos de encuesta. Sin embargo, este optimismo contrasta con los problemas de calidad y accesibilidad que presentan los big data como la fata de cobertura de algunos grupos de la población o el acceso restringido a alguna de estas fuentes. Este artículo, a partir de una revisión profunda de la literatura de los últimos años, explora como la cooperación entre los big data y las encuestas resulta en mejoras significativas de la calidad de los datos y una reducción de los costes. Nowadays, while surveys still dominate the research landscape in social sciences, alternative data sources such as social media posts or GPS data open a whole range of opportunities for researchers. In this scenario, some voices advocate for a progressive substitution of survey data. They anticipate that big data, which is cheaper and faster than surveys, will be enough to answer relevant research questions. However, this optimism contrasts with all the quality and accessibility issues associated with big data such as the lack of coverage or data ownership and restricted accessibility.  The aim of this paper is to explore how, nowadays, the combination of big data and surveys results in significant improvements in data quality and survey costs.

Big Data and the Challenge of Construct Validity

2015 ◽  
Vol 8 (4) ◽  
pp. 521-527 ◽  
Author(s):  
Michael T. Braun ◽  
Goran Kuljanin
Keyword(s):  
Social Sciences ◽  
Big Data ◽  
Construct Validity ◽  
Industrial Engineering ◽  
The Other ◽  
Critical Importance ◽  
Organizational Research ◽  
Research Questions ◽  
Organizational Sciences

One important issue not highlighted by Guzzo, Fink, King, Tonidandel, and Landis (2015) is that simply establishing construct validity will be significantly more challenging with big data than ever before. One needs to only look as far as the other social sciences analyzing big data (e.g., communications, economics, industrial engineering) to observe the difficulty of making valid claims as to what measured variables substantively “mean.” This presents a significant hurdle in the application of big data to organizational research questions because of the critical importance of demonstrating validity in the organizational sciences as highlighted by Guzzo et al.

Big Data Driven Clinical Informatics & Surveillance (BDD_CIS) – A Multimodal Database Focused Clinical, Community, and Multi-Omics Surveillance Plan for COVID-19: A study Protocol (Preprint)

2020 ◽  
Author(s):  
Bankole Olatosi ◽  
Jiajia Zhang ◽  
Sharon Weissman ◽  
Zhenlong Li ◽  
Jianjun Hu ◽  
...  
Keyword(s):  
Big Data ◽  
South Carolina ◽  
Data Science ◽  
Age Groups ◽  
The Elderly ◽  
The United States ◽  
Data Sources ◽  
Patient Registries ◽  
Multiple Partner ◽  
Multimodal Data

BACKGROUND The Coronavirus Disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus (SARS-CoV-2) remains a serious global pandemic. Currently, all age groups are at risk for infection but the elderly and persons with underlying health conditions are at higher risk of severe complications. In the United States (US), the pandemic curve is rapidly changing with over 6,786,352 cases and 199,024 deaths reported. South Carolina (SC) as of 9/21/2020 reported 138,624 cases and 3,212 deaths across the state. OBJECTIVE The growing availability of COVID-19 data provides a basis for deploying Big Data science to leverage multitudinal and multimodal data sources for incremental learning. Doing this requires the acquisition and collation of multiple data sources at the individual and county level. METHODS The population for the comprehensive database comes from statewide COVID-19 testing surveillance data (March 2020- till present) for all SC COVID-19 patients (N≈140,000). This project will 1) connect multiple partner data sources for prediction and intelligence gathering, 2) build a REDCap database that links de-identified multitudinal and multimodal data sources useful for machine learning and deep learning algorithms to enable further studies. Additional data will include hospital based COVID-19 patient registries, Health Sciences South Carolina (HSSC) data, data from the office of Revenue and Fiscal Affairs (RFA), and Area Health Resource Files (AHRF). RESULTS The project was funded as of June 2020 by the National Institutes for Health. CONCLUSIONS The development of such a linked and integrated database will allow for the identification of important predictors of short- and long-term clinical outcomes for SC COVID-19 patients using data science.

From Science as a Vocation to Science as a Game: and the Resulting Loss of Meaning

2017 ◽  
Author(s):  
Mats Alvesson ◽  
Yiannis Gabriel ◽  
Roland Paulsen
Keyword(s):  
Social Sciences ◽  
Technical Expertise ◽  
Academic Institutions ◽  
Relevant Research ◽  
World Science ◽  
Social Scientists ◽  
The Social ◽  
The Voice

Against a generalized loss of meaning in society, social scientists find it hard to undertake relevant research that addresses problems facing our world. Science has turned from a vocation aimed at improving the lot of humanity to a careerist game dominated by publishing hits in starred journals. Instrumental rewards replace the passion for discovery and the intrinsic quest for knowledge. Competition among academics and academic institutions, such as journals, universities, and professional bodies, is not intrinsically harmful. Competition in the social sciences, however, is currently resulting in large quantities of formulaic publications, increasing specialization, faddishness, opportunism, and a general ironing out of originality and relevance. Academic authorship and the voice of individual scholars is wiped out as most papers are co-authored by several researchers, each a specialist in his or her area. The result is a devaluation of scholarship and a privileging of technical expertise in narrow disciplinary areas.

scholarly journals Big Data: Some Ethical Concerns for the Social Sciences

2021 ◽  
Vol 10 (2) ◽  
pp. 36
Author(s):  
Michael Weinhardt
Keyword(s):  
Social Sciences ◽  
Big Data ◽  
Ethical Issues ◽  
Online Behavior ◽  
Ethical Concerns ◽  
Ethical Implications ◽  
The Social ◽  
And Behaviors ◽  
State Actors

While big data (BD) has been around for a while now, the social sciences have been comparatively cautious in its adoption for research purposes. This article briefly discusses the scope and variety of BD, and its research potential and ethical implications for the social sciences and sociology, which derive from these characteristics. For example, BD allows for the analysis of actual (online) behavior and the analysis of networks on a grand scale. The sheer volume and variety of data allow for the detection of rare patterns and behaviors that would otherwise go unnoticed. However, there are also a range of ethical issues of BD that need consideration. These entail, amongst others, the imperative for documentation and dissemination of methods, data, and results, the problems of anonymization and re-identification, and the questions surrounding the ability of stakeholders in big data research and institutionalized bodies to handle ethical issues. There are also grave risks involved in the (mis)use of BD, as it holds great value for companies, criminals, and state actors alike. The article concludes that BD holds great potential for the social sciences, but that there are still a range of practical and ethical issues that need addressing.

scholarly journals Introduction to the Special Issue on New Longitudinal Data for Retirement Analysis and Policy

2021 ◽  
pp. 1-5
Author(s):  
Marco Angrisani ◽  
Anya Samek ◽  
Arie Kapteyn
Keyword(s):  
Big Data ◽  
Academic Research ◽  
Cost Effective ◽  
Research Data ◽  
Data Sources ◽  
Special Issue ◽  
Administrative Records ◽  
The Past ◽  
Survey Questionnaires ◽  
Internet Panels

The number of data sources available for academic research on retirement economics and policy has increased rapidly in the past two decades. Data quality and comparability across studies have also improved considerably, with survey questionnaires progressively converging towards common ways of eliciting the same measurable concepts. Probability-based Internet panels have become a more accepted and recognized tool to obtain research data, allowing for fast, flexible, and cost-effective data collection compared to more traditional modes such as in-person and phone interviews. In an era of big data, academic research has also increasingly been able to access administrative records (e.g., Kostøl and Mogstad, 2014; Cesarini et al., 2016), private-sector financial records (e.g., Gelman et al., 2014), and administrative data married with surveys (Ameriks et al., 2020), to answer questions that could not be successfully tackled otherwise.

Mapping the United Nations Fundamental Principles of Official Statistics against new and big data sources

2021 ◽  
Vol 37 (1) ◽  
pp. 161-169
Author(s):  
Dominik Rozkrut ◽  
Olga Świerkot-Strużewska ◽  
Gemma Van Halderen
Keyword(s):  
Big Data ◽  
Public Information ◽  
Fundamental Principle ◽  
Data Sources ◽  
Official Statistics ◽  
Development Agenda ◽  
Data Gaps ◽  
Data Source ◽  
Exciting Time ◽  
Statistical Systems

Never has there been a more exciting time to be an official statistician. The data revolution is responding to the demands of the CoVID-19 pandemic and a complex sustainable development agenda to improve how data is produced and used, to close data gaps to prevent discrimination, to build capacity and data literacy, to modernize data collection systems and to liberate data to promote transparency and accountability. But can all data be liberated in the production and communication of official statistics? This paper explores the UN Fundamental Principles of Official Statistics in the context of eight new and big data sources. The paper concludes each data source can be used for the production of official statistics in adherence with the Fundamental Principles and argues these data sources should be used if National Statistical Systems are to adhere to the first Fundamental Principle of compiling and making available official statistics that honor citizen’s entitlement to public information.

Big data and portfolio optimization: A novel approach integrating DEA with multiple data sources

Omega ◽  
2021 ◽  
pp. 102479
Author(s):  
Zhongbao Zhou ◽  
Meng Gao ◽  
Helu Xiao ◽  
Rui Wang ◽  
Wenbin Liu
Keyword(s):  
Big Data ◽  
Portfolio Optimization ◽  
Data Sources ◽  
Multiple Data Sources ◽  
Multiple Data ◽  
Novel Approach
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