Using Big Data and Quantitative Methods to Estimate and Fight Modern Day Slavery

2014 ◽  
Vol 34 (1) ◽  
pp. 21-33 ◽  
Author(s):  
Monti Narayan Datta
Keyword(s):  
Big Data ◽  
Quantitative Methods ◽  
Modern Day Slavery

scholarly journals The limits of computation: A philosophical critique of contemporary Big Data research

2018 ◽  
Vol 5 (2) ◽  
pp. 205395171881184 ◽  
Author(s):  
Petter Törnberg ◽  
Anton Törnberg
Keyword(s):  
Big Data ◽  
Social Life ◽  
Quantitative Methods ◽  
Qualitative Change ◽  
Philosophical Discussion ◽  
Philosophical Critique ◽  
Social Phenomena ◽  
Social Patterns ◽  
Digital Era

This paper reviews the contemporary discussion on the epistemological and ontological effects of Big Data within social science, observing an increased focus on relationality and complexity, and a tendency to naturalize social phenomena. The epistemic limits of this emerging computational paradigm are outlined through a comparison with the discussions in the early days of digitalization, when digital technology was primarily seen through the lens of dematerialization, and as part of the larger processes of “postmodernity”. Since then, the online landscape has become increasingly centralized, and the “liquidity” of dematerialized technology has come to empower online platforms in shaping the conditions for human behavior. This contrast between the contemporary epistemological currents and the previous philosophical discussions brings to the fore contradictions within the study of digital social life: While qualitative change has become increasingly dominant, the focus has gone towards quantitative methods; while the platforms have become empowered to shape social behavior, the focus has gone from social context to naturalizing social patterns; while meaning is increasingly contested and fragmented, the role of hermeneutics has diminished; while platforms have become power hubs pursuing their interests through sophisticated data manipulation, the data they provide is increasingly trusted to hold the keys to understanding social life. These contradictions, we argue, are partially the result of a lack of philosophical discussion on the nature of social reality in the digital era; only from a firm metatheoretical perspective can we avoid forgetting the reality of the system under study as we are affected by the powerful social life of Big Data.

Business Analytics and Big Data

2015 ◽  
pp. 1-11 ◽  
Author(s):  
Dennis T. Kennedy ◽  
Dennis M. Crossen ◽  
Kathryn A. Szabat
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Quantitative Methods ◽  
Business Processes ◽  
Predictive Analytics ◽  
Big Data Analytics ◽  
Database Systems ◽  
Three Dimensions ◽  
Business Analytics ◽  
The People

Big Data Analytics has changed the way organizations make decisions, manage business processes, and create new products and services. Business analytics is the use of data, information technology, statistical analysis, and quantitative methods and models to support organizational decision making and problem solving. The main categories of business analytics are descriptive analytics, predictive analytics, and prescriptive analytics. Big Data is data that exceeds the processing capacity of conventional database systems and is typically defined by three dimensions known as the Three V's: Volume, Variety, and Velocity. Big Data brings big challenges. Big Data not only has influenced the analytics that are utilized but also has affected technologies and the people who use them. At the same time Big Data brings challenges, it presents opportunities. Those who embrace Big Data and effective Big Data Analytics as a business imperative can gain competitive advantage.

scholarly journals Affective economy of national-populist images: Investigating national and transnational online networks through visual big data

2019 ◽  
Vol 22 (5) ◽  
pp. 770-792
Author(s):  
Jenni Hokka ◽  
Matti Nelimarkka
Keyword(s):  
Social Media ◽  
Big Data ◽  
Quantitative Methods ◽  
Social Science Research ◽  
Historical Analysis ◽  
Science Research ◽  
Online Networks ◽  
Qualitative And Quantitative Methods ◽  
Computational Data

In our article, we investigate the affective economy of national-populist image circulation on Facebook. This is highly relevant, since social media has been an essential area for the spread of national-populist ideology. In our research, we analyse image circulation as affective practice, combining qualitative and quantitative methods. We use computational data analysis methods to examine visual big data: image fingerprints and reverse image search engines to track down the routes of thousands of circulated images as well as make discourse-historical analysis on the images that have gained most attention among supporters. Our research demonstrates that these existing tools allow social science research to make theory-solid approaches to understand the role of image circulation in creating and sustaining national and transnational networks on social media, and show how national-populist thinking is spread through images that catalyse and mobilise affects – fear, anger and resentment – thus creating an effective affective economy.

scholarly journals Empowering Social Science Research in the Big-Data Era: Addressing Quantyphobia in IR Researches

2019 ◽  
Vol 2 (2) ◽  
pp. 259
Author(s):  
Farizal Mohd Razalli
Keyword(s):  
Big Data ◽  
Quantitative Methods ◽  
International Politics ◽  
Research Output ◽  
Social Science Research ◽  
Science Research ◽  
Global Changes ◽  
Intellectual Community ◽  
Mechanical Field ◽  
Research Outcome

This paper tries to explore the employment of quantitative approach in political researches focusing on international relations (IR) or international politics. A debate emerged in the90s on whether IR or the field of international politics should be driven by quantitative(positivistic) approach at the expense of qualitative (interpretivist) approach. The debate then expanded to explicitly argue for an increased use of formal methods that are mathematically-based to study IR phenomena. It triggered then a quick reaction fromhardcore IR specialists who warned against mathematizing IR for fear of turning the field into a mechanical field that crunches numbers. Such a fear is further substantiated by theobservation that many quantitative works in IR have moved farther away from developing theory to testing hypotheses. Some scholars have even suggested that it is epistemologicallyrealism vs. instrumentalism; something that is unsurprising given the dominance of realism inIR for many years. This paper does not suggest that heavy emphasis on qualitative approach leads to a inferior research output. However, it does suggest an transformative incapability among IR scholars to accommodate to contemporary global changes. The big-data analyticshave affected the intellectual community of late with the influx of data. These data are bothqualitative and quantitative. Nonetheless, analyzing them requires one to be familiar with quantitative methods lest one risks not being able to offer a research outcome that is not only sound in its argumentation but also robust in its analytical logic. Furthermore, with so much data on the social media, it is almost unthinkable for meaningful interpretation tobe made without even the simplest descriptive statistical methods. The key findings revealthat in ensuring its relevance, international political researches have to start adapting to the contemporary changes by building new capability apart from upscaling existing capacity.

scholarly journals DATAFICATION AS A NECESSARY STEP IN THE PROCESSING OF BIG DATA IN DECISION-MAKING TASKS OF BUSINESS

2021 ◽  
Vol 2 ◽  
pp. 75-80
Author(s):  
Martin Misut ◽  
Pavol Jurik
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Quantitative Methods ◽  
Digital Data ◽  
Classification Model ◽  
Decision Making Process ◽  
Digital Form ◽  
Suitable Alternative ◽  
The Given ◽  
The Relationship

The digital transformation of business in the light of opportunities and focusing on the challenges posed by the introduction of Big Data in enterprises allows for a more accurate reflection of the internal and external environmental stimuli. Intuition ceases to be present in the decision-making process, and decision-making becomes strictly data-based. Thus, the precondition for data-based decision-making is relevant data in digital form, resulting from data processing. Datafication is the process by which subjects, objects and procedures are transformed into digital data. Only after data collection can other natural steps occur to acquire knowledge to improve the company's results if we move in the industry's functioning context. The task of finding a set of attributes (selecting attributes from a set of available attributes) so that a suitable alternative can be determined in its decision-making is analogous to the task of classification. Decision trees are suitable for solving such a task. We verified the proposed method in the case of logistics tasks. The analysis subject was tasks from logistics and 80 well-described quantitative methods used in logistics to solve them. The result of the analysis is a matrix (table), in which the rows contain the values of individual attributes defining a specific logistic task. The columns contain the values of the given attribute for different tasks. We used Incremental Wrapper Subset Selection IWSS package Weka 3.8.4 to select attributes. The resulting classification model is suitable for use in DSS. The analysis of logistics tasks and the subsequent design of a classification model made it possible to reveal the contours of the relationship between the characteristics of a logistics problem explicitly expressed through a set of attributes and the classes of methods used to solve them.

Teaching stats: A crisis in Irish sociology?

2015 ◽  
Vol 24 (1) ◽  
pp. 102-111
Author(s):  
Carmel Hannan
Keyword(s):  
Big Data ◽  
Social Science ◽  
Mixed Methods Research ◽  
Quantitative Methods ◽  
Social Science Research ◽  
Science Research ◽  
Undergraduate Level ◽  
Sociology Departments ◽  
The Uk ◽  
Research Councils

There is now a lack of quantitative capacity among practitioners and teachers in sociology in Ireland. Yet interest in the value of quantitative methods among governments, funding organisations and society in general are on the increase. Social science research councils and funders in other countries, notably the UK, have realised there is a problem and are now attempting to remedy this through increased funding for the recruitment of quantitatively trained academics for example, Q-Step. The paper examines a number of developments notably Big Data, increases in transdisciplinary research and developments in mixed methods research which, it is argued, underline the need for more and better quantitative methods teaching in sociology. The paper calls for sociology departments to re-think their curricula and actively promote the teaching of a range of methods at the undergraduate level.

scholarly journals Digital platforms and Big Data in art education

2021 ◽  
Vol 5 (S3) ◽  
pp. 197-207
Author(s):  
Kseniia Prykhod’ko ◽  
Olena Khil ◽  
Olena Pobirchenko ◽  
Oksana Umrykhina ◽  
Vira Kalabska
Keyword(s):  
Big Data ◽  
Art Education ◽  
Quantitative Methods ◽  
Educational Process ◽  
Digital Platforms ◽  
Qualitative And Quantitative Methods ◽  
Educational Role ◽  
Data Collection And Analysis

The Big Data and digital platforms in art education play an important role, especially in the field of optimizing educational intelligence, determining the results of research and learning activities, helping to optimize and improve management systems, contributing to the quality of education, image positions. This is what determined the relevance of the problems investigated in the article. The paper presents a description and analysis of the benefits of implementation and features of the use of digital platforms and Big Data in the sector of art education. The article aims to establish the components and content components of Big Data and the educational role of digital platforms used in art education, as well as identifying the attitudes of participants in the educational process on the active use of Big Data and digital platforms. The methods in the study are based on a comprehensive approach, used descriptive methods, qualitative and quantitative methods of analysis. To obtain the data the method of questioning was used, the study of literature, data collection, and analysis, formation of conclusions. 

scholarly journals Big Data Analytics in Handling Mechanisms of Transforming Healthcare Performance: An Approach Technological Management in Addressing Nurse Workloads in Emergency Department

2021 ◽  
Author(s):  
M Chairul Basrun Umanailo
Keyword(s):  
Big Data ◽  
Work Experience ◽  
Quantitative Methods ◽  
Health Workers ◽  
Big Data Analytics ◽  
Recovery Process ◽  
P Value ◽  
Cross Sectional ◽  
Sectional Analysis ◽  
Technological Management

Background: The importance of science related to Big Data was a need in overcoming the performance problems of health professionals to overcome disease healing for clients in the hospital. The understanding mechanism of Big Data in nursing will certainly positively impact the client's recovery process during treatment in the hospital. Purpose: This study aims to find out how the Big Data mechanism can be applied in overcoming the performance of health workers in providing professional nursing care to clients. Methods: This research uses quantitative methods to measure Big Data's health mechanisms' degree of understanding. To obtain the common research finding, researchers used cross?sectional analysis. Meanwhile, to get valid results during the study, the researcher uses observational analytics techniques Result: Healthcare professionals were expected to understand Big Data and knowledge mechanisms in addressing client care. This can be seen from the research results that explain that (n) p-value (≤ 0.05), which means that understanding Big Data was essential in knowing how efficiently caring the clients' health. Conclusion: Big data analysis is indispensable in health sciences at this time. Health data recorded in the database can help clients overcome health problems, especially those in the community. In addition, the work experience and ability of nurses to analyze Big Data medical records will undoubtedly have an impact on the rapid recovery of patients in hospitals.

Comparative Process-oriented Research Using Social Media and Historical Text

2017 ◽  
Vol 22 (4) ◽  
pp. 3-26
Author(s):  
Dhiraj Murthy
Keyword(s):  
Social Media ◽  
Big Data ◽  
Quantitative Methods ◽  
Canonical Process ◽  
Visual Maps ◽  
Successful Case ◽  
Historical Text ◽  
Google Books ◽  
Marked Preference ◽  
Process Oriented

This article evaluates whether we can use process-oriented theory to conduct comparative, historical social media research. There is a lack of theoretically informed approaches to studying recently digitized historical text with contemporary social media. This article argues that such perspectives are needed and extends Norbert Elias’ notions of ‘sociogenesis’ and ‘psychogenesis’ into data-driven research. Canonical process-oriented researchers such as Elias used mixed-methods approaches, including visual maps and quantitative surveys. By comparing 17th-century digitized diaries and 5 million digitized books from Google Books with contemporary tweet data, this study provides a successful case of comparing tweets with historical printed text at a big data scale. Moreover, quantitative methods are important to process-oriented methodologies and can be extended to big data empirical sources. An important finding is that there are similarities in the curation of everyday life in elite historical diaries and in more democratic forms of contemporary social media. Although accessibility and volume of content have changed over time from historical text to tweets, we found that there is a marked preference for certain words associated with communal sentiment over the centuries.

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