scholarly journals Automating Open Science for Big Data

2015 ◽  
Vol 659 (1) ◽  
pp. 260-273 ◽  
Author(s):  
Mercè Crosas ◽  
Gary King ◽  
James Honaker ◽  
Latanya Sweeney
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Social Science Research ◽  
Science Research ◽  
Open Science ◽  
Streaming Data ◽  
Sensitive Information ◽  
Increased Risk ◽  
The Social ◽  
New Challenges

The vast majority of social science research uses small (megabyte- or gigabyte-scale) datasets. These fixed-scale datasets are commonly downloaded to the researcher’s computer where the analysis is performed. The data can be shared, archived, and cited with well-established technologies, such as the Dataverse Project, to support the published results. The trend toward big data—including large-scale streaming data—is starting to transform research and has the potential to impact policymaking as well as our understanding of the social, economic, and political problems that affect human societies. However, big data research poses new challenges to the execution of the analysis, archiving and reuse of the data, and reproduction of the results. Downloading these datasets to a researcher’s computer is impractical, leading to analyses taking place in the cloud, and requiring unusual expertise, collaboration, and tool development. The increased amount of information in these large datasets is an advantage, but at the same time it poses an increased risk of revealing personally identifiable sensitive information. In this article, we discuss solutions to these new challenges so that the social sciences can realize the potential of big data.

scholarly journals Can we Trust Science and, if so, Why?

2020 ◽  
Author(s):  
Alexander Wuttke
Keyword(s):  
Social Sciences ◽  
Social Science ◽  
Social Science Research ◽  
Science Research ◽  
Open Science ◽  
New Wave ◽  
Research On Research ◽  
The Social ◽  
Public Debates ◽  
Knowledge Claims

The trustworthiness of scientific findings is at the center of current scholarly and public debates. The contestation of scientific knowledge claims is reason to take a break from our every-day tasks as scientists and to reflect upon our doing as professional truth-seekers. This essay reviews two recent books on foundational and practical questions on the scholarly generation of knowledge. 'Why Trust Science' (Oreskes) is an intellectual expedition into the epistemological foundations of science. 'Transparent and Reproducible Social Science Research' (Christensen, Freese, Miguel) is the first book-length primer on contemporary Open Science debates in the social sciences. Together, these books demonstrate the range of what we can learn from the new wave of ‘research on research’, both as curious citizens and as academic scholars.

scholarly journals Sýn Íslendinga á utanríkis- og öryggismál

2018 ◽  
Vol 14 (2) ◽  
pp. 1-18
Author(s):  
Silja Bára Ómarsdóttir
Keyword(s):  
Security Policy ◽  
Large Scale ◽  
Social Science Research ◽  
Science Research ◽  
Foreign Affairs ◽  
Military Conflict ◽  
The Public ◽  
The Social ◽  
The Cold War ◽  
The University

Icelanders’ views on security and foreign affairs since the end of the Cold War are an understudied issue. This article presents the findings of a large scale survey on the position and ideas about foreign affairs and security. The survey was conducted by the Social Science Research Institute of the University of Iceland in November and December 2016. The results of the survey are placed in the context of developments in security studies, with an emphasis on security sectors, ontological security, and securitization. The main findings are that the Icelandic public believes that its security is most threatened by economic and financial instability, as well as natural hazards, but thinks there is a very limited chance of military conflict or terrorist attacks directly affecting the country. These findings are incongruent with the main emphases of Icelandic authorities, as they appear in security policy and political discourse. It is therefore important that the authorities understand how to engage with the public about the criteria upon which risk assessments and security policies are based.

A Tale of Two Cultures

2012 ◽  
Author(s):  
Gary Goertz ◽  
James Mahoney
Keyword(s):  
Social Sciences ◽  
Quantitative Research ◽  
Social Science Research ◽  
Measurement Data ◽  
Science Research ◽  
Quantitative Research Methods ◽  
Qualitative And Quantitative ◽  
The Social ◽  
Scientific Worldview ◽  
Different Cultures

Some in the social sciences argue that the same logic applies to both qualitative and quantitative research methods. This book demonstrates that these two paradigms constitute different cultures, each internally coherent yet marked by contrasting norms, practices, and toolkits. The book identifies and discusses major differences between these two traditions that touch nearly every aspect of social science research, including design, goals, causal effects and models, concepts and measurement, data analysis, and case selection. Although focused on the differences between qualitative and quantitative research, the book also seeks to promote toleration, exchange, and learning by enabling scholars to think beyond their own culture and see an alternative scientific worldview. The book is written in an easily accessible style and features a host of real-world examples to illustrate methodological points.

Designing and Evaluating Survey Instruments for Research in Human Issues in Horticulture

HortScience ◽  
1998 ◽  
Vol 33 (3) ◽  
pp. 554c-554
Author(s):  
Sonja M. Skelly ◽  
Jennifer Campbell Bradley
Keyword(s):  
Survey Research ◽  
Survey Methodology ◽  
Human Subjects ◽  
Social Science Research ◽  
Reliability And Validity ◽  
Science Research ◽  
Survey Instrument ◽  
Measurement Tool ◽  
The Social ◽  
Survey Instruments

Survey research has a long precedence of use in the social sciences. With a growing interest in the area of social science research in horticulture, survey methodology needs to be explored. In order to conduct proper and accurate survey research, a valid and reliable instrument must be used. In many cases, however, an existing measurement tool that is designed for specific research variables is unavailable thus, an understanding of how to design and evaluate a survey instrument is necessary. Currently, there are no guidelines in horticulture research for developing survey instruments for use with human subjects. This presents a problem when attempting to compare and reference similar research. This workshop will explore the methodology involved in preparing a survey instrument; topics covered will include defining objectives for the survey, constructing questions, pilot testing the survey, and obtaining reliability and validity information. In addition to these topics some examples will be provided which will illustrate how to complete these steps. At the conclusion of this session a discussion will be initiated for others to share information and experiences dealing with creating survey instruments.

An Introduction to Statistical Models for Networks

2020 ◽  
pp. 218-233
Author(s):  
Valentina Kuskova ◽  
Stanley Wasserman
Keyword(s):  
Social System ◽  
Exponential Family ◽  
Behavioral Science ◽  
Social Science Research ◽  
Science Research ◽  
Uniform Distributions ◽  
The Social ◽  
Special Cases ◽  
Social And Behavioral Science ◽  
Network Methods

Network theoretical and analytic approaches have reached a new level of sophistication in this decade, accompanied by a rapid growth of interest in adopting these approaches in social science research generally. Of course, much social and behavioral science focuses on individuals, but there are often situations where the social environment—the social system—affects individual responses. In these circumstances, to treat individuals as isolated social atoms, a necessary assumption for the application of standard statistical analysis is simply incorrect. Network methods should be part of the theoretical and analytic arsenal available to sociologists. Our focus here will be on the exponential family of random graph distributions, p*, because of its inclusiveness. It includes conditional uniform distributions as special cases.

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