Mixed Methods Social Network Analysis

Theories and Methodologies in Learning and Education

Dominik E. Froehlich, Martin Rehm, Bart C. Rienties

• Publisher: Routledge
• Year: 2019
• ISBN:
  9780429557040
• Edition: 1
• Topic: Computer Science
• Subtopic: Computer Science General
• Index: Computer Science

Mixed Methods Social Network Analysis brings together diverse perspectives from 42 international experts on how to design, implement, and evaluate mixed methods social network analysis (MMSNA). There is an increased recognition that social networks can be important catalysts for change and transformation.

This edited book from leading experts in mixed methods and social network analysis describes how researchers can conceptualize, develop, mix, and intersect diverse approaches, concepts, and tools. In doing so, they can improve their understanding and insights into the complex change processes in social networks. Section 1 includes eight chapters that reflect on "Why should we do MMSNA?", providing a clear map of MMSNA research to date and why to consider MMSNA. In Section 2 the remaining 11 chapters are dedicated to the question "How do I do MMSNA?", illustrating how concentric circles, learning analytics, qualitative structured approaches, relational event modeling, and other approaches can empower researchers.

This book shows that mixing qualitative and quantitative approaches to social network analysis can empower people to understand the complexities of change in networks and relations between people. It shows how mixed analysis can be applied to a wide range of data generated by diverse global communities: American school children, Belgian teachers, Dutch medical professionals, Finnish consultants, French school children, and Swedish right-wing social media users, amongst others. It will be of great interest to researchers and postgraduate students in education and social sciences and mixed methods scholars.

1
MMSNA

An introduction of a tale of two communities

Dominik E. Froehlich, Martin Rehm, and Bart C. Rienties

1.1 Introduction

In an interconnected world where everyone seems to be connected via social media and smart devices, social networks seem to matter more than ever before. For example, when a major tragedy or natural disaster occurs anywhere on this planet, within seconds people will know this via formal news channels and informal channels such as Facebook, Twitter, YouTube, or WEBO. We live in a digital era where we all are connected to each other, or at least we could be if we would want to. In this digital era, people have unprecedented access to knowledge and insight. Historically, formal institutions, such as governments, universities, or media corporations, “knowledge and expertise” was carefully scrutinized, managed, and stored. However, in our era, anyone can share their perspectives on the internet, form new links with others, and learn from people literally on the other side of the planet.

While popular press and politicians often blame social media for many issues, such as the tinkering with elections, eating disorders among teenagers, and open blaming and shaming of people, there is an increased recognition that social networks are important catalysts for (positive) change and transformation. One of the first scholars who formally started to map these relations between people was Jacob Moreno and his colleagues (1934), who aimed to better understand human relations in groups to enhance group psychotherapy. Using graphs of so-called nodes (e.g., people) and edges (e.g., the relationships between people), Moreno (1934) developed basic sociometric tools for a field called social network analysis (SNA). For a useful historical overview of SNA, we refer to Onwuegbuzie (2020).

We loosely define SNA as an approach to investigate relations and social structures through the use of networks and graph theory. Others referred to SNA as a methodological framework specifically geared toward capturing and analyzing interrelated actors (Längler et al., 2020). It focuses on measuring and understanding the social interactions between entities (nodes), rather than focusing on individuals’ attributes (Längler, Brouwer, & Gruber, 2020; Sarazin, 2020).

In the social sciences, there has been a tremendous surge of interest in SNA. According to Borgatti et al. (2009), the use of the term “social network” has tripled in the last decade in the online repository Web of Science. Although initially hesitant, there is emerging traction within the field of education that SNA could be a useful approach to understand the complex relations in learning and teaching contexts (Froehlich, 2020a; Onwuegbuzie, 2020). Indeed, the Education Resources Information Center shows an increase of SNA-related publications in the domain of learning and education from 37 in 2003 to more than 400 a decade later.

1.2 SNA use in education

Educational research has recently developed a strong interest in relational and structural phenomena (e.g., Froehlich, Van Waes, Schoonenboom, & Schäfer, Forthcoming; Rienties, Tempelaar, Van den Bossche, Gijselaers, & Segers, 2009; Rehm, Gijselaers, & Segers, 2014). Rudat and Buder (2015) stipulate being part of a network for learning contributes to a so-called agent awareness. This refers to acknowledging one’s surrounding within a network and being able to position oneself in the wider context of the network. This awareness contributes to social interaction and the active sharing of information (Lefebvre, Sorenson, Henchion, & Gellynck, 2016), which fosters the development of informational overlap and shared language (Borgatti & Cross, 2003).

Previous educational research has found that students who have more learning relations relative to other students receive more opportunities to share and co-construct knowledge (Baldwin, Bedell, & Johnson, 1997) and receive more social support (Emery, Daniloski, & Hamby, 2011). Rienties and Nolan (2014) found that the primary predictor for (self-reported) learning relations at the end of the module was the students that they were assigned to small groups with at the beginning of the module. However, Rienties and Tempelaar (2018) found that although students benefited from learning interactions in their groups, those who maintained relatively more intergroup links performed even better. Focusing on informal learning among teachers in social media, Rehm and Notten (2016) discovered that individuals’ personal networks increased. Furthermore, some individuals could attain central positions within the overall network, enabling them to dominate discussions and steer the conversation into directions they deem more interesting or preferable. While method-related research about SNA is rapidly advancing, and “easy-to-use” software packages like UCINET or Gephi are now available for both novices and experienced SNA users, this reliance on quantitative data has lately been criticized (Froehlich, 2020a; Crossley, 2010).

1.3 Mixed methods social network analysis

Especially in the domain of learning and education, the ongoing quantification of concepts and relations in SNA seems overly simplistic. It is widely acknowledged that learning and education are rather messy concepts. Does the process of being subjected to education automatically lead to learning? Just because you have a chance to engage in collaborative learning within a network (e.g., LinkedIn, EARLI, friends from your previous school), do you really make use of this? Some authors have suggested that networked learning makes up a combination of personal learning spaces that are socially connected and provide a collaborative foundation for learning (Törnberg & Törnberg, 2020). However, when you enter such spaces, neither learning nor knowledge creation are guaranteed. Instead, they provide an opportunity for informal, professional development by enabling individuals to engage into discussions with a wide variety of other individuals (Thomas et al., 2020), and by stimulating them to critically reflect on their actions (Onwuegbuzie, 2020). These networks, therefore, constitute social opportunity spaces (Rehm & Notten, 2016), which provide the meta-context wherein knowledge creation is fostered and learning processes are stimulated by the complex interplay of various underlying relations and factors. For example, Akkerman and Bakker (2011, p. 133) termed this possibility “boundary crossing” to describe a situation where individuals expand their horizon and look outside of their narrow daily existence. Yet, by focusing on quantitative SNA, researchers are missing a vital element for understanding the underlying communication and learning patterns. While quantitative SNA can visualize the structure and interrelations among members of a given (learning) network, this particular methodological approach does not provide any insights on the reasons, motivations, and expectations of individuals when entering such an opportunity space. Hence, the call for the inclusion of qualitative methods – the root of SNA – has become louder and clearer. This book is a response to this call.

In this book, we formally define mixed methods social network analysis (MMSNA) as any SNA study that draws from both qualitative and quantitative data or uses qualitative and quantitative methods of analysis and thoughtfully integrates the different research strands with each other. This book brings together 20 exciting chapters of how 42 authors from 11 countries have conceptualized, designed, implemented, and evaluated MMSNA in their complex, diverse, and unique perspectives. As described later, a wide range of MMSNA approaches have been developed over the last decade, which is both exciting and promising. At the same time, there is a need to ask critical questions in terms of the conceptualization, ethics, methodology and “practicability” of MMSNA.

It is important to recognize that MMSNA cannot be the product of just one community of researchers. The name already hints at (a minimum of) two research communities that are stakeholders to MMSNA: mixed methods (MM) researchers and SNA researchers. Both academic communities are of great importance for the development of the concepts, and thus both should be involved in advancing this perspective. In this book, we strive to bring these two communities closer to each other and to initiate an active, bilateral academic debate. The major vision of this book is to create a strong tie between two important communities that previously lacked academic communication channels.

We know that a tie is most easily formed if two nodes work together. And this is what this book achieves. With contributions from international experts from both communities, this book seeks to not only further each domain on its own (e.g., present transferable insights from MMSNA research to the more general MM debate or strengthen the methodological base of using MM within the framework of SNA), but also to establish an informed theoretical and methodological basis for research using MMSNA and to provide an integrated and cohesive perspective of the affordances and limitations of integrating MM research with SNA. We build a bridge that connects these two research communities that draw theoretically, conceptually and analytically from each other, but have not always engaged in discussions to learn from each other’s perspectives.

1.4 Structure of the book

Although we have developed this book as a collaborative project with a particular and hopefully logical structure, each chapter in itself can be read as an individual piece of academic work. Through clear referencing throughout the book interested readers can delve into specific sections or chapters or read the book in a non-linear manner. Nonetheless, here we provide a brief overview of the book if you want to read this end to end, or want to cherry-pick particular narratives that are of interest to you.

In Section 1 of our book a range of authors discuss “Why to do mixed methods social network analysis?” from various angles and perspectives. In Chapter 2, Froehlich (2020a) provides an SNA-inspired overview of 44 MMSNA studies that have combined quantitative and qualitative approaches to understand social networks in learning and education. His literature review identifies three common patterns of MMSNA approaches, which provide clear and relevant guidelines for researchers how to design their next MMSNA study effectively. In particular, the less-traveled paths in MMSNA research could be fruitful to explore, while replication studies of existing studies would also be valuable.

In Chapter 3, Längler and colleagues (2020) provide an excellent introduction of the key concepts of SNA, boundary specification, sampling and the mixing of quantitative and qualitative approaches. They provide a “beginners-guide” of how to think about MMSNA in education, while at the same time providing guidelines for more experienced SNA and MM researchers who want to mix and integrate MMSNA approaches. By providing practical examples of studies and critically reflecting on lessons learned, this chapter is a must-read. The authors make a clear argument why MMSNA might not be the panacea for all research questions and contexts and needs to be carefully considered.

In Chapter 4, Froehlich, Mejeh, and colleagues (2020) provide a passionate plea why it is important to integrate various units of analysis when conducting SNA and MM analysis. Reflecting critically on three classic SNA works, the authors provide a conceptual framework of macro-meso-micro-nano MMSNA, and illustrate these concepts by discussing two case studies.

In Chapter 5, Shannon-Baker and Hilpert (2020) critically reflect and bring together the theoretical positioning of this book. They emphasize visual representations and link SNA with MM. The authors argue that visual methods can be helpful for MMSNA researchers during the research design process.

In Chapter 6, Törnberg and Törnberg (2020) provide a strong plea for bridging the gap between culture and connectivity. They suggest that while many scholars argue for an intrinsic connection between culture and networks, most studies have conceptually or empirically treated these concepts as separate entities. The authors propose a nexus analysis that allows researchers to explore the emergent relations between culture and networks. A range of practical examples are provided of how this could be achieved.

In Chapter 7, Sarazin (2020) provides a case study of ethnographical research of a music education program in a French primary school, whereby he critically reflects upon his lived experiences how SNA and more ethnographical approaches could be integrated. In this fine-grained study, the author indicates that ethnographic MMSNA designs could be specifically useful when researchers seek to understand how people experience the networks they are embedded in.

Chapter 8 builds on Shannon-Baker and Hilpert (2020) by highlighting the affordances and limitations of visualizations of MMSNA for consultancy practices. By using three case studies, Palonen and Froehlich (2020) highlight the power of visualizing complex organizational networks in three distinct organizations. The authors illustrate how combining quantitative SNA with in-depth discussions can help to identify good practice, innovative ideas and holes in the networks that might hamper organizations’ development.

Chapter 9 critically reviews Chapters 6 through 8, whereby Schoonenboom (2020) argues that social networks are complex systems in which identity development takes place. Most importantly, because change in social networks is non-linear, any quantitative differences (e.g., one moderate member leaving a group, one extreme member entering) can lead to irreversible qualitative change, and this often occurs through critical events. By focusing on identity, Schoonenboom (2020) makes a clear case of how people as agents both have and do not have agency in MMSNA.

In Section 2 of this book, the remaining 11 chapters are dedicated to the question: “How to do mixed methods social network analysis?”. While none of the authors would claim that there is just one best way to do MMSNA, they share their rich and diverse perspectives of how they have implemented MMSNA approaches.

In Chapter 10, Murphy and colleagues (2020) combine two potentially opposing conceptualizations of networks and cultures in terms of SNA and activity theory. In an energy company, they explore how employees are learning from each other to prevent, identify and act upon potential incidents. The authors provide a complex and detailed map using activity theory and SNA of how colleagues work together in a dynamic and potentially hostile work environment.

In Chapter 11, Van Waes and Van den Bossche (2020) provide an accessible and useful introduction into the use of concentric circles (CC) as a method of relational data collection amongst academics following a professional development program. This method allows researchers and practitioners to collect fine-grained and ...

Table of contents

• Cover
• Half Title
• Title
• Copyright
• Contents
• Foreword
• Acknowledgments
• 1 MMSNA: an introduction of a tale of two communities
• Section 1 Why do mixed methods social network analysis?
• Section 2 How do we do mixed methods social network analysis?
• Index