Suchergebnisse

One way to study the politics of big data is the inspection of their assemblages. By opening up the "black boxes" of data assemblages, it is possible to reconstruct the choices, compromises, conflicts and agree-ments that contributed to the construction of a given datum. Leaning on in-depth interviews and focus groups with experts and specialists who work within three European data centers, we unveil the interde-pendence between social and technical aspects and between a series of internal and external actors, which all contribute to the data assemblage. Results show that communities of experts, technologies, stakeholders and end-users are entwined components that interact amongst them in a contingent and complex web of negotiations and constraints and frame what is possible, desirable and expected by data.

Structural equation modeling (SEM), especially partial least squares path modeling (PLS-PM) has become a mainstream method in many fields of research. In the last years it has been increasingly disseminated in a variety of disciplines. The researchers have been promoting this new statistical methods for the evaluation of policies. Generally, policy evaluation applies evaluation principles and methods to examine the content, implementation or impact of a policy. To better understand and characterize this trend, a bibliometric study of international papers on this subject has been developed in order to describe the use of SEM and PLS-PM approaches in the policy evaluation in the almost last 20 years. A total of 450 articles from 2000 to 2020 have been selected and analyzed in order to discover the research trends in this field and the main dimensions and words related to the terms "decision making" and "SEM-PLS" approach, that are most commonly employed in the scientific literature. The research has been conducted in theWeb of Science from ISI Web of Knowledge database and Scopus database, with the aim of identifying the major themes, authors, areas, types of the sources, titles, years of publication and countries of these publications, as well as the main themes related to the two topic analyzed

Intro -- Preface -- Contents -- 1 Introduction -- References -- Epistemology -- 2 On Data, Big Data and Social Research. Is It a Real Revolution? -- Abstract -- 1 Data-Bases Are not a Repository -- 2 Some Consequences for Building a Data Science for Social Research -- References -- 3 New Data Science: The Sociological Point of View -- Abstract -- 1 The Reframing of Social Sciences -- 2 Data as Signification Acts -- 3 Data Culture, Data Assemblage and Data Science -- 4 Sense Making, Small Decisions and Social Actors -- 5 Final Remarks -- References -- 4 Data Revolutions in Sociology -- Abstract -- References -- 5 Blurry Boundaries: Internet, Big-New Data, and Mixed-Method Approach -- Abstract -- 1 Introduction -- 2 New Contexts and New Data -- 3 The Study of the Online Textual Data -- 3.1 What Is New in Approaches and Analytics? -- 3.2 Concerns and Boundaries -- 4 Open Questions -- References -- 6 Social Media and the Challenge of Big Data/Deep Data Approach -- Abstract -- 1 The Field of Observation: The Nature of the Data and the Perspective of Collection -- 2 Computational Social Sciences and Big Data: New Research Questions -- 3 Big Data: Critical Issues -- 3.1 The Contingency of the UGC: The Dimension of Time Is Relevant -- 3.2 The Architecture of the Platforms Counts -- 3.3 Making Sense of Network -- 3.4 Deep Data and the Context of the Facts -- 4 Conclusions -- References -- 7 Governing by Data: Some Considerations on the Role of Learning Analytics in Education -- Abstract -- 1 Introduction -- 2 The Transformative Power of Data -- 3 System of Measurement -- 3.1 Description -- 3.2 Prediction/Prescription -- 3.3 Accountability -- 4 Culture of Evidence -- 4.1 Short Circuits -- 4.2 Collateral Risks -- 4.3 Methods -- 5 System of Influence -- 5.1 Hybrid Actors -- 5.2 Software Space -- 5.3 Transactional Pedagogies

This edited volume lays the groundwork for Social Data Science, addressing epistemological issues, methods, technologies, software and applications of data science in the social sciences. It presents data science techniques for the collection, analysis and use of both online and offline new (big) data in social research and related applications. Among others, the individual contributions cover topics like social media, learning analytics, clustering, statistical literacy, recurrence analysis and network analysis. Data science is a multidisciplinary approach based mainly on the methods of statistics and computer science, and its aim is to develop appropriate methodologies for forecasting and decision-making in response to an increasingly complex reality often characterized by large amounts of data (big data) of various types (numeric, ordinal and nominal variables, symbolic data, texts, images, data streams, multi-way data, social networks etc.) and from diverse sources. This book presents selected papers from the international conference on Data Science & Social Research, held in Naples, Italy in February 2016, and will appeal to researchers in the social sciences working in academia as well as in statistical institutes and offices.--