'Clique to play': Using Social Network Analysis (SNA) to map children's gaming relationships in face-to-face settings
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Sociograms of play patterns allowed me to develop an overview of the children’s gaming relationships in the two settings, and helped me to put particular examples of children’s play into perspective. They also provided a broader understanding of how particular games and gaming technologies were used in gendered ways in the two settings. These sociograms assisted analysis in that I was able to notice how the games and gaming platforms were used in gendered ways in the two settings, and identify dominant and marginalised groups or cliques of children and games to report on as play episodes. Presentation for an SNA workshop led by Marc Smith of the Social Media Research Foundation at UCT on 19 November 2014. Workshop description: https://medium.com/@marionwalton/social-network-analysis-workshop-6f12d90ddb2c Blog post at http://niccipallitt.wordpress.com/ tagged SNA
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'Clique to play': Using Social Network Analysis (SNA) to map children's gaming relationships in face-to-face settings
- 1. ''CClliiqquuee ttoo ppllaayy'':: MMaappppiinngg cchhiillddrreenn''ss ggaammiinngg rreellaattiioonnsshhiippss iinn ffaaccee--ttoo--ffaaccee sseettttiinnggss Dr Nicola Pallitt Lecturer, Centre for Innovation in Learning and Teaching (CILT), UCT Social Network Analysis workshop with Marc Smith 19 November 2014
- 2. Rationale for using SNA • Thesis on gendered play • ‘Play schedules’ – accidental quantifiable data • Sociograms for play patterns of gaming relationships in 2 settings • Identifying dominant cliques or marginalised groups of children • ‘Play episodes’ – unit of analysis
- 3. Fieldsite and timeframe Adults present Number of girls Number of boys Age of children Time spent Methods used Riverside Boys (pilot study in April 2010) 1 teacher 0 103 11–13 1 hour Questionnaire, drawings of favourite game Arts and Crafts club at St Mary’s co-ed school (Sep–Dec 2010) 1 teacher 1 research assistant 12 9 9–11 22 hours Video recordings, interviews, questionnaire, drawings of favourite games Holiday club at Riverside Boys (Dec 2010– Jan 2011) 2 club organisers 8 24 6–12 42 hours Video recordings, questionnaire, interviews, game demonstrations, play schedule Holiday club at Riverside Boys (April 2011) 2 club organisers 4 14 4–13 30 hours Video recordings, questionnaire, interviews, game demonstrations, play schedule An overview of research conducted in three settings, showing participants, time spent in the fieldsites, and methods used
- 4. The relationship between nodes and edges in this study
- 5. Gaming relationships and gendered play at St. Mary’s
- 6. Gaming relationships and gendered play at the December/January holiday club
- 7. Gaming relationships and gendered play at the April holiday club
- 8. Where SNA is useful for this kind of research • Making relationships in fieldsites visual • Enabled comparison of gendered play across fieldsites • Enhance traditional reception & ethnographic studies • Strengthens claims about contextual info – evidence • Methodological contribution to Childhood Studies in areas such as gendered play and cliques • Gaming as relationships rather than individual players • SNA combined with social theory – not just SNA as theory & method (eg. relational agency, interpretive reproduction in peer groups)
- 9. Things to be aware of when using SNA for this kind of work - limitations • Edge weights calculated based on how many times children played together (i.e. number of play episodes) and not how long they played for or which games they played • Can’t determine if they played a game for an hour or 5 minutes by looking at the sociogram • Generally 30 min play turns, but could switch games – not all games played for equal amount of time • Names of games along edges (links) very small – repeated in key • Can identify cliques but not clique overlap – need for fieldnotes • Eigenvector centrality applied in ethnographic way can be useful but research community you’re in needs to be SNA literate • Knowing what metric to use to enable you to make particular claims
- 10. Findings • SNA (WHO) helped next level of analysis = more focused on WHAT children played & HOW • Children selected ‘less-strongly-gendered’ games for cross sex play in both settings • Gendered gaming influenced by gender relations children encounter in same-sex and co-ed schools (school habitus) • Gaming preferences site-specific • SNA useful for broader overview of groups, friendships and cliques in fieldsites • Selection of data (ie. which play episodes to analyse in more detail) based on these patterns – shows up significant interactions and marginal ones
Editor's Notes
- To my knowledge, this is the first study to employ SNA to describe children’s digital gameplay in physical spaces. Sociograms of play relationships in the two settings help visualise social arrangements of the children and their gendered play patterns.
- This study combines traditional methods of researching children (participant observation, interviews, focus groups, video recording) with a novel approach to social network analyses of gaming relationships. In total, 53 children were observed playing games. Thus, a substantial amount of observational data was collected, making this a relatively large-scale study in comparison to other research on children and games. Sociograms of play patterns allowed me to develop an overview of the children’s gaming relationships in the two settings, and helped me to put particular examples of children’s play into perspective. They also provided a broader understanding of how particular games and gaming technologies were used in gendered ways in the two settings. These sociograms assisted analysis in that I was able to notice how the games and gaming platforms were used in gendered ways in the two settings, and identify dominant and marginalised groups or cliques of children and games to report on as play episodes. Sociograms (developed using Social Network Analysis) assisted me in not only constructing a visual representation of cross- and same-sex play, and what games children chose to play in the two settings, but also allowed me to view particular play episodes in relation to others, and compare gendered play patterns across the fieldsites. One of the strengths of this approach is that it prioritises attention to the play contexts studied. Consequently I could understand the situated nature of gameplay in the two main fieldwork sites. To understand gameplay, one needs to go beyond the textual level of theorising games based on their rules and representations to how they are played by particular people in specific gaming spaces. While this study of middle-class children’s gameplay has limited generalisability because of the small sample of participants, it nonetheless provides a new way of thinking about the gender and games nexus.
- Schedules were kept of the children’s choice of games and gaming partners in both settings. In the case of the holiday club children, such schedules helped to structure play-turns among large groups of children. During fieldwork I accorded great importance to observing actual gameplay, yet the play schedules became valuable data which I later used to construct the sociograms of children’s play patterns. Often the play schedules were a quick hand-drawn table with columns for the time (rows of 30 minute play-turns divided each day at the holiday club), names of the children, the game they wanted to play and its platform. The children then scribbled in their names and the game they wanted to play. As constructed from these schedules, the sociograms of the children’s play in the two settings affords this study a quantitative dimension through its use of SNA metrics, a feature often lacking in Cultural Studies of children and games, as well as in Game Studies more generally. I used a centrality metric to identify dominant peer relationships in the two settings, as well as the most popular game titles among same- and cross-sex play groups. It was important to identify these dominant relationships for sampling in this study, as it enabled me to select play episodes that exemplified routinised play in the fieldsites. SNA allows calculations based on relations between connected nodes, known as vertice metrics. A common metric is centrality, which can help one to identify dominant relationships within groups of actors. Friendships tend to involve a network of friends, a social group or clique. Cliques are important to this study, in that it allowed me to attend to homophily in children’s peer relationships beyond same- and cross-sex play, such as noting clusters of children who played together because of similar ages, being in the same class or extra-mural such as choir practice, sharing the same ethnicity, home language, and so forth. Cliques refer to groups of children, with between three and ten members, who have strong relationships. Brown and Klute (2003) argue that cliques are, at times, difficult to study because they are hard to identify. They mention three major ways of defining and assessing cliques: a researcher can gather information from informants about who interacts with who, use ethnographic methods, or SNA where one can “employ nominations of friends from all participants in a social context to identify the major clusters of individuals that comprise each friendship group” (2003, p. 339). I used a combination of SNA and observation. Prell (2011) argues that the important distinction of a clique from a larger network structure is the sense of strong cohesiveness which goes hand-in-hand with the subgroup developing its own set of norms and rules, different from the larger network in which it is embedded. Such cliques are important reference points for individuals and their identities. However, the limitation of software metrics that help us to identify cliques numerically is the possibility of clique overlaps, and this is where observation and fieldnotes are useful. One is able to confirm or disagree with the accuracy of relations represented in the computer-generated diagram, based on a record of observed interactions.
- I developed the following method of representing children’s play relationships. The children are represented in the sociograms as nodes or vertices, and the games that connect them are labelled on different-coloured edges to show the connection between same-sex and cross-sex peer relationships and children’s choice of games. Pink edges were used to represent same-sex female play, blue edges for same-sex male play, and green for cross-sex play. The edges were also labelled with the games that the children chose to play together. Edges that are bolder than others (edge weight) indicate strong relationships where a particular pair of children played together more often, while lighter edges suggest once-off play episodes. The strength of children’s gaming relationships (how many times a pair of children played together as a pair or part of a group or clique) were included as edge weights. Eigenvector centrality was chosen to identify the “influence scores” (Hansen et al., 2011, p. 41) of particular children. Hansen et al. describe this metric as follows: Eigenvector centrality is a more sophisticated view of centrality: a person with few connections could have a very high eigenvector centrality if those few connections were themselves very well connected. Eigenvector centrality allows for connections to have a variable value, so that connecting to some vertices has more benefit than connecting to others. The PageRank algorithm used by Google’s search engine is a variant of Eigenvector Centrality. (2011, p. 41) I used NodeXL to calculate this metric. Eigenvector centrality is used to draw the sociograms such that well-connected children (nodes) are drawn situated towards the middle of the graph. Those with fewer connections to others appear on the margins. This helped me to identify the most influential play relationships in each fieldsite.
- Children were divided by gender because of the large number of boys attending same-sex schools, as well as the lack of other shared interests. By contrast, gender differences assumed less significance at St. Mary’s, since the majority of the children were united by attendance at a co-ed school and its cross-sex extra-murals, such as singing in the choir. While shared interests or belonging to a shared network may qualify as ‘different criteria’, these may also be seen in relation to gender tactics. The St. Mary’s boys and girls who were part of the choir bonded over the singing games, but the boys who were not part of the choir played on the margins of the group as a whole. The relationships between the choir children were not equal either, and there were cliques within this group who connected because of race, language, being in the same class, being best friends at school, and so forth.
- New methods of data analysis and description, such as SNA, complement studies of gaming as relationships involving networks of friends or cliques. While SNA helped me to construct an overall ‘big picture’ of gendered play in the two settings, interviews and participant observation were vital in allowing me to investigate the micro-interaction of children’s digital gameplay, and how they interpreted ludic gendering in games and performed gender identities (discussed in the following chapters). Social Network Analysis can be a productive starting point for thinking about gender and games in a new way, combined with theories of relational agency and identity. This may help researchers to study games as contextually appropriated technologies, and gaming as relationships, allowing for theories less focused on individual players. Children’s same-sex and cross-sex play, friendships, cliques and gendered use of playground space have been widely researched by Childhood Studies researchers using ethnographic methods. While cliques can form between three and ten friends, cliques are different to groups because they maintain stability throughout the term or school year. Cliques tend to remain exclusive, not changing members often, and tend to not dissolve if a member leaves the group (Brown & Klute, 2003). Until recently, cliques have been hard to study because they are difficult to identify: There are three major ways of defining and assessing cliques: social network analyses that employ nominations of friends from all participants in a social context to identify the major clusters of individuals that comprise each friendship group; information from selected informants about who interacts with whom; or systematic direct observations of adolescents in their natural context, using ethnographic methods. (Brown & Klute, 2003, p. 339) Foot and Chapman (1995) note that ‘cliquing’ is rarely applied to children younger than nine years of age, although it is frequently applied to adolescent peer relations, and is biased towards girls.
- One of the limitations of SNA in this study is that edge weights were calculated based on how many times particular children played together, and do not represent these relationships in terms of time. Whether a pair of children played together for an hour or for five minutes as part of a larger group cannot be discerned from the sociograms. In selecting eigenvector centrality as a metric, am I imposing an assumption / ideology on the data? Yes – that there are hierarchies in children’s peer groups and some children are more connected than others.