scholarly journals Linking Quantitative and Qualitative Network Approaches: A Review of Mixed Methods Social Network Analysis in Education Research

2020 ◽  
Vol 44 (1) ◽  
pp. 244-268 ◽  
Author(s):  
Dominik E. Froehlich ◽  
Sara Van Waes ◽  
Hannah Schäfer

Social network analysis (SNA) is becoming a prevalent method in education research and practice. But criticism has been voiced against the heavy reliance on quantification within SNA. Recent work suggests combining quantitative and qualitative approaches in SNA—mixed methods social network analysis (MMSNA)—as a remedy. MMSNA is helpful for addressing research questions related to the formal or structural side of relationships and networks, but it also attends to more qualitative questions such as the meaning of interactions or the variability of social relationships. In this chapter, we describe how researchers have applied and presented MMSNA in publications from the perspective of general mixed methods research. Based on a systematic review, we summarize the different applications within the field of education and learning research, point to potential shortcomings of the methods and its presentation, and develop an agenda to support researchers in conducting future MMSNA research.

2019 ◽  
Author(s):  
Dominik Emanuel Froehlich ◽  
Sara van Waes ◽  
Hannah Schäfer

Over the past three decades, educational research, policy, and practice have become increasingly interested in relationships and collaboration. In response, social network analysis (SNA) emerged as a theoretical and methodological framework, offering tools to explore relationships in depth. Compared to then existing approaches, SNA allows capturing relationships in a more nuanced way, by focusing on the patterns and qualities of relationships (Borgatti, Mehra, Brass, & Labianca, 2009). SNA offers a valuable perspective for examining whether and to what degree interaction and collaboration take place in education. Another key strength of SNA is that it offers several tools to visualize relationships (Hogan, Carrasco, & Wellman, 2007), which not only creates opportunities for (visual) research but also for practice (e.g., for intervention and feedback purposes). The potential of SNA is reflected in a surge in publications from 37 in 2003 to more than 400 a decade later in the Education Resources Information Center (ERIC; Froehlich, Rehm, & Rienties, 2019). SNA has established its usefulness in various educational sub-fields, for instance, in examining the role of relationships for student achievement (Moolenaar, Sleegers, & Daly, 2012), reform and improvement (Penuel, Bell, Bevan, Buffington, & Falk, 2016), policy implementation (Coburn, Russell, Kaufman, & Stein, 2012), and leadership (Spillane & Shirrell, 2017). No other methodological framework is that much focused on the in-depth exploration of the roles of relationships and structures in learning and instruction (Moolenaar, 2012; Sweet, 2016). The surge in SNA publications across the academic disciplines is largely driven by quantitative SNA studies (Freeman, 2004). Despite its merits, this formalized approach to network analysis has been criticized for a lack of attention to the qualitative aspects of relationships (Fuhse & Mützel, 2011; Hollstein, 2011). Recent work convincingly addresses these concerns by combining quantitative and qualitative approaches. These approaches succeed in addressing research questions not only related to the formal or structural side of relationships and networks. They also attend to questions related to the actual content and meaning of interactions, the (day to day) variability of social relationships, the developments of nodes and ties, and the idea of agency (Crossley, 2010; Crossley & Edwards, 2016).In this article, we posit that mixing methods within SNA is an original innovation that will help to answer new sets of research questions in education research (Bolíbar, 2015; Domínguez & Hollstein, 2014). We argue that a systematic review of mixed method social network analysis (MMSNA) is needed (1) to offer an overview of the existing body of work in education, (2) to show the merits of this approach, and (3) to develop a set of pointers for conducting rigorous MMSNA research and to support scholars in conducting future MMSNA research.


INTEGRITAS ◽  
2021 ◽  
Vol 7 (1) ◽  
pp. 121-142
Author(s):  
Wigke Capri ◽  
Devy Dhian Cahyati ◽  
Mahesti Hasanah ◽  
Dias Prasongko ◽  
Wegik Prasetyo

Corruption action develops way more advance compare to corruption studies in Indonesia. Corruption studies are mostly focusing on institutional corruption or using an institutional approach to understand corruption. This research offers to understand corruption better using actor-based and network approaches. Utilising social network analysis (SNA), researchers unpacking corrupt relational actors in natural resources, especially in oil and gas and forestry in Indonesia. We collected six important findings;  corruption creates dependencies amongst actors; to be corrupt, an actor must have a strong network and resources that can offer and deliver multi-interests. Corrupt action is a repeated action that creates interlocking relations amongst actors. Interlocking relation serves as a safety belt for each chauffeur. Institutionalisation of corrupt networks only requires a strong corrupt network. The institutionalised corrupt networks shape a shortcut both for the private and public sectors-a short cut that makes bribery and exchange permits possible.


E-Marketing ◽  
2012 ◽  
pp. 185-197
Author(s):  
Przemyslaw Kazienko ◽  
Piotr Doskocz ◽  
Tomasz Kajdanowicz

The chapter describes a method how to perform a classification task without any demographic features and based only on the social network data. The concept of such collective classification facilitates to identify potential customers by means of services used or products purchased by the current customers, i.e. classes they belong to as well as using social relationships between the known and potential customers. As a result, a personalized offer can be prepared for the new clients. This innovative marketing method can boost targeted marketing campaigns.


Data Mining ◽  
2013 ◽  
pp. 326-335
Author(s):  
Roberto Marmo

Research on social networks has advanced significantly due to wide variety of on-line social websites and very popular Web 2.0 application. Social network analysis views social relationships in terms of network and graph theory about nodes (individual actors within the network) and ties (relationships between the actors). Using web mining techniques and social networks analysis it is possible to process and analyze large amount of social data (such as blogtagging, online game playing, instant messenger etc.) and by this to discover valuable information from data. In this way, we can understand the social structure, social relationships and social behaviors. This new approach is also denoted as social network mining. These algorithms differ from established set of data mining algorithms developed to analyze individual records, because social network datasets are called relational due to centrality of relations among entities. This chapter also sets out a process to apply web mining.


Author(s):  
Przemyslaw Kazienko ◽  
Piotr Doskocz ◽  
Tomasz Kajdanowicz

The chapter describes a method how to perform a classification task without any demographic features and based only on the social network data. The concept of such collective classification facilitates to identify potential customers by means of services used or products purchased by the current customers, i.e. classes they belong to as well as using social relationships between the known and potential customers. As a result, a personalized offer can be prepared for the new clients. This innovative marketing method can boost targeted marketing campaigns.


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