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.--

AbstractThe concept of Regional Competitiveness (RC), in the last years, had a considerable influence on the development of strategies and policies focused on sub-national areas from an economic and social perspective. In particular, RC attracted the substantial attention of policymakers due to the possibility of evaluating and comparing different territories without any established political or conceptual framework. Conversely, scholars have long debated the meaningfulness of territorial competitiveness itself and, in case, which dimensions have to be included. This study reviewed the most recent scientific literature concerning RC with a bibliometric approach based on topic modelling. The use of a textual-based statistical approach offered an interesting insight into the RC research domain. We highlighted the topics discussed by scholars, showing the patterns emerging across years and from different publication types, and marked the differences between the vocabulary used by authors coming from EU and non-EU countries. Finally, a comparison between the RC issues coming from the literature analysis with the RC issues defined by policymakers tried to link the two standpoints followed by the institutions and the academia in a more comprehensive conceptual framework.

AbstractPartial least squares path modeling (PLS-PM) has become very popular in recent years, for measuring concepts that depend on different aspects and that are based on different types of relationships. PLS-PM represents a useful tool to explore relationships and to analyze the influence of the different aspects on the complex phenomenon analyzed. In particular, the use of higher-order constructs has allowed researchers to extend the application of PLS-PM to more advanced and complex models. In this work, our attention is focused on higher-order constructs that include reflective or formative relationships. Even if the dispute between formative models and reflective models is not exactly recent, it is still alive in current literature, for the most part within the context of structural equation models. This paper focuses attention on theoretical and mathematical differences between formative and reflective measurement models within the context of the PLS-PM approach. A simulation study is proposed in order to show how these approaches fit well in different modeling situations. The approaches have been compared using empirical application in a sustainability context. The findings from the simulation and the empirical application can help researchers to estimate and to use the higher-order PLS-PM approach in reflective and formative type models.