Relational Concepts, Measurement, and Data Collection

2016 ◽  
Author(s):  
Justin H. Gross ◽  
Joshua M. Jansa
Keyword(s):  
Missing Data ◽  
Network Analysis ◽  
Data Collection ◽  
Network Measurement ◽  
Concept Development ◽  
Network Data ◽  
Measurement Models ◽  
Common Practices ◽  
Relational Concepts ◽  
Methodological Problems

Political phenomena are inherently relational, so it is natural that network analysis should come to play an important role in the study of politics. And yet relational data present special practical and methodological problems. The network data scholars would like to collect are often incomplete or altogether inaccessible. It is tempting to take whatever data are available and treat these as a proxy for the desired variables. This chapter reviews the most prominent relational concepts in political science and the operationalization strategies and data collection techniques typically employed. It then examines common practices for handling missing data and identifies recent innovations in this area. Finally, the chapter recommends that political scientists give more consideration to the concept development and measurement phases of research design and proposes possible directions for the development of network measurement models.

scholarly journals Potencialidades del análisis de redes para el estudio de las migraciones

2020 ◽  
pp. 179
Author(s):  
Verónica De Miguel Luken
Keyword(s):  
Network Analysis ◽  
Data Collection ◽  
Methodological Approach ◽  
The Other ◽  
Statistical Techniques ◽  
Network Data ◽  
Capital Social ◽  
Redes Sociales ◽  
Migration Networks ◽  
Dual Perspective

El presente trabajo examina las principales aportaciones realizadas a la investigación de la inmigración extranjera aplicando el análisis de redes sociales, fundamentalmente en el contexto español. Para ello, se emplea una doble perspectiva. Por un lado, se atiende a la aproximación metodológica particular utilizada en la recogida de datos y en las técnicas estadísticas usadas para su análisis. Por otro, se ubican los trabajos en los ejes temáticos principales identificados. Previamente, se contextualiza la cuestión en el debate más general sobre redes migratorias y capital social, se presentan algunos conceptos sobre el análisis de redes y se proporcionan algunas claves sobre la recogida de datos reticulares y las técnicas más apropiadas para su explotación. Por último, se sugieren las fortalezas de esta perspectiva metodológica y las limitaciones a las que se enfrenta su utilización.In this paper, the main contributions to the research on foreign migration through the application of social network analysis are examined, especially for the Spanish context. A dual perspective is used. On one hand, the focus is on the specific methodological approach applied for the data collection and the statistical techniques chosen for the analysis. On the other hand, the works are classified according to a proposed thematic division. Previously, the topic is framed in the more general debate about migration networks and social capital, some concepts on network analysis are introduced and some key facts about network data collection and the appropriate techniques for their exploitation are discussed. Finally, the strengths and the limitations of this methodological approach are suggested.

scholarly journals Estimating Contextual Effects from Ego Network Data

2020 ◽  
Vol 50 (1) ◽  
pp. 215-275
Author(s):  
Jeffrey A. Smith ◽  
G. Robin Gauthier
Keyword(s):  
Mental Health ◽  
Social Context ◽  
Network Analysis ◽  
Data Collection ◽  
Regression Models ◽  
Hierarchical Linear Model ◽  
Contextual Effects ◽  
Network Data ◽  
Data Sets ◽  
The Relationship

Network concepts are often used to characterize the features of a social context. For example, past work has asked if individuals in more socially cohesive neighborhoods have better mental health outcomes. Despite the ubiquity of use, it is relatively rare for contextual studies to use the methods of network analysis. This is the case, in part, because network data are difficult to collect, requiring information on all ties between all actors. In this article the authors ask whether it is possible to avoid such heavy data collection while still retaining the best features of a contextual-network study. The basic idea is to apply network sampling to the problem of contextual models, in which one uses sampled ego network data to infer the network features of each context and then uses the inferred network features as second-level predictors in a hierarchical linear model. The authors test the validity of this idea in the case of network cohesion. Using two complete data sets as a test, the authors find that ego network data are sufficient to capture the relationship between cohesion and important outcomes, such as attachment and deviance. The hope, going forward, is that researchers will find it easier to incorporate holistic network measures into traditional regression models.

Introduzione alla personal network analysis e alle statistiche tie churn attraverso l'uso di E-NET

2012 ◽  
pp. 27-54
Author(s):  
Daniel S. Halgin ◽  
Stephen P. Borgatti
Keyword(s):  
Network Analysis ◽  
Data Collection ◽  
Computer Program ◽  
Size Composition ◽  
Network Data ◽  
Personal Network ◽  
Network Change ◽  
Composition And Structure

In this article we review foundational aspects of personal network analysis (also called Ego network analysis) and introduce E-NET (Borgatti 2006), a computer program designed specifically for personal network analysis. We present the basic steps for personal network data collection and use E-NET to review key measures of personal network analysis such as size, composition and structure. We close by introducing longitudinal measures of personal network change, including tie churn, brokerage elasticity, and triad change. We argue that these measures can help reveal change patterns consistent with tie formation strategies that would otherwise be missed using more traditional analytic approaches.

Developing a sustainable Digital Health Innovation Ecosystem in a rural and resource-poor region: A monitored networking approach (Preprint)

2020 ◽  
Author(s):  
Arunangsu Chatterjee ◽  
Sebastian Stevens ◽  
Sheena Asthana ◽  
Ray B Jones
Keyword(s):  
Social Network ◽  
Network Analysis ◽  
Data Collection ◽  
Digital Health ◽  
Resource Poor Setting ◽  
Quadruple Helix ◽  
Innovation Ecosystem ◽  
Resource Poor ◽  
Quadruple Helix Model ◽  
Over Time

BACKGROUND Digital health (DH) innovation ecosystems (IE) are key to the development of new e-health products and services. Within an IE, third parties can help promote innovation by acting as knowledge brokers and the conduits for developing inter-organisational and interpersonal relations, particularly for smaller organisations. Kolehmainen’s quadruple helix model suggests who the critical IE actors are, and their roles. Within an affluent and largely urban setting, such ecosystems evolve and thrive organically with minimal intervention due to favourable economic and geographical conditions. Facilitating and sustaining a thriving DH IE within a resource-poor setting can be far more challenging even though far more important for such peripheral economics and the health and well-being of those communities. OBJECTIVE Taking a rural and remote region in the UK, as an instance of an IE in a peripheral economy, we adapt the quadruple helix model of innovation, apply a monitored social networking approach using McKinsey’s Three Horizons of growth to explore: • What patterns of connectivity between stakeholders develop within an emerging digital health IE? • How do networks develop over time in the DH IE? • In what ways could such networks be nurtured in order to build the capacity, capability and sustainability of the DH IE? METHODS Using an exploratory single case study design for a developing digital health IE, this study adopts a longitudinal social network analysis approach, enabling the authors to observe the development of the innovation ecosystem over time and evaluate the impact of targeted networking interventions on connectivity between stakeholders. Data collection was by an online survey and by a novel method, connection cards. RESULTS Self-reported connections between IE organisations increased between the two waves of data collection, with Small and Medium-sized Enterprises (SMEs) and academic institutions the most connected stakeholder groups. Patients involvement improved over time but still remains rather peripheral to the DH IE network. Connection cards as a monitoring tool worked really well during large events but required significant administrative overheads. Monitored networking information categorised using McKinsey’s Three Horizons proved to be an effective way to organise networking interventions ensuring sustained engagement. CONCLUSIONS The study reinforces the difficulty of developing and sustaining a DH IE in a resource-poor setting. It demonstrates the effective monitored networking approach supported by Social Network Analysis allows to map the networks and provide valuable information to plan future networking interventions (e.g. involving patients or service users). McKinsey’s Three Horizons of growth-based categorisation of the networking assets help ensure continued engagement in the DH IE contributing towards its long-term sustainability. Collecting ongoing data using survey or connection card method will become more labour intensive and ubiquitous ethically driven data collection methods can be used in future to make the process more agile and responsive.

Duality beyondPersons and Groups

2020 ◽  
pp. 391-413
Author(s):  
Sophie Mützel ◽  
Ronald Breiger
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Network Data ◽  
Future Directions ◽  
Affiliation Networks ◽  
The Social ◽  
Recent Developments ◽  
Principle Of Duality ◽  
Seminal Article

This chapter focuses on the general principle of duality, which was originally introduced by Simmel as the intersection of social circles. In a seminal article, Breiger formalized Simmel’s idea, showing how two-mode types of network data can be transformed into one-mode networks. This formal translation proved to be fundamental for social network analysis, which no longer needed data on who interacted with whom but could work with other types of data. In turn, it also proved fundamental for the analysis of how the social is structured in general, as many relations are dual (e.g. persons and groups, authors and articles, organizations and practices), and are thus susceptible to an analysis according to duality principles. The chapter locates the concept of duality within past and present sociology. It also discusses the use of duality in the analysis of culture as well as in affiliation networks. It closes with recent developments and future directions.

scholarly journals Scientific collaboration network ammong Brazilian universities

2013 ◽  
Vol 6 (1) ◽  
Author(s):  
Maria Isabel Escalona-Fernandez ◽  
Antonio Pulgarin-Guerrero ◽  
Ely Francina Tannuri de Oliveira ◽  
Maria Cláudia Cabrini Gracio
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Scientific Collaboration ◽  
Methodological Approach ◽  
Scientific Production ◽  
Collaboration Network ◽  
Network Data ◽  
New Knowledge

This paper analyses the scientific collaboration network formed by the Brazilian universities that investigate in dentistry area. The constructed network is based on the published documents in the Scopus (Elsevier) database covering a period of 10 (ten) years. It is used social network analysis as the best methodological approach to visualize the capacity for collaboration, dissemination and transmission of new knowledge among universities. Cohesion and density of the collaboration network is analyzed, as well as the centrality of the universities as key-actors and the occurrence of subgroups within the network. Data were analyzed using the software UCINET and NetDraw. The number of documents published by each university was used as an indicator of its scientific production.

scholarly journals Big Data and IT Network Data Visualization

2018 ◽  
Vol 3 (1) ◽  
pp. 9-16 ◽  
Author(s):  
Lidong Wang
Keyword(s):  
Big Data ◽  
Network Analysis ◽  
Graphics Processing Units ◽  
Data Analytics ◽  
High Performance ◽  
Big Data Analytics ◽  
Network Visualization ◽  
Network Data ◽  
Graphics Processing ◽  
Performance Computing

Visualization with graphs is popular in the data analysis of Information Technology (IT) networks or computer networks. An IT network is often modelled as a graph with hosts being nodes and traffic being flows on many edges. General visualization methods are introduced in this paper. Applications and technology progress of visualization in IT network analysis and big data in IT network visualization are presented. The challenges of visualization and Big Data analytics in IT network visualization are also discussed. Big Data analytics with High Performance Computing (HPC) techniques, especially Graphics Processing Units (GPUs) helps accelerate IT network analysis and visualization.

scholarly journals The Challenges of Effectively Anonymizing Network Data

2013 ◽  
pp. 15-22
Author(s):  
Ch. Himabindu
Keyword(s):  
Network Security ◽  
Information Security ◽  
Data Collection ◽  
Network Data ◽  
Sensitive Information ◽  
Security Testing ◽  
The Past ◽  
Technical Leadership ◽  
Security Research ◽  
Privacy Issues

The availability of realistic network data plays a significant role in fostering collaboration and ensuring U.S. technical leadership in network security research. Unfortunately, a host of technical, legal, policy, and privacy issues limit the ability of operators to produce datasets for information security testing. In an effort to help overcome these limitations, several data collection efforts (e.g., CRAWDAD[14], PREDICT [34]) have been established in the past few years. The key principle used in all of these efforts to assure low-risk, high-value data is that of trace anonymization—the process of sanitizing data before release so that potentially sensitive information cannot be extracted.

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