Ego-Net Analysis in Educational Contexts

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Student Guide

Introduction This example dataset introduces ego-net analysis in educational contexts. This method allows educational researchers to examine the network of contacts (alters) that form around a particular student (ego) or educator or indeed any other player in education. Ego-net analysis may be described as a unique branch of social network analysis, in addition to whole and two-mode network analysis (Borgatti et al., 2013). Social network analysis takes as its starting point the premise that social life is created primarily by relations and the patterns formed by these relations (Wasserman & Faust, 1994). Social networks are formally defined as a set of nodes (or network members) that are tied by one or more types of relations.

This example examines the ego nets of two Grade 4 students in Southern California, aiming to exemplify the use of ego-net analysis in educational contexts. Data were collected during the 2016–2017 school year, as part of a larger study in the context of special and inclusive education. The primary aim of the study was to understand students’ social networks, friendships, and peer relationships, particularly those who have been identified as having special educational needs and disabilities (SEND). In many cases, students with SEND in particular remain marginalized and socially isolated (Mamas, 2012), therefore studying their friendhsips through ego-net analysis may provide significant insights into the wider field as well as education policy. Additionally, we wanted to explore the size of our participants’ ego nets as well as the dispersion and diversity of their alters

Page 2 of 20 Ego-Net Analysis in Educational Contexts SAGE SAGE Research Methods Datasets Part 2019 SAGE Publications, Ltd. All Rights Reserved. 2 in an effort to begin to unravel their main sources of social support and social capital. For this reason, the data presented here include a student with SEND and a student without SEND. The three authors conducted the data collection and analysis.

Ego-Net Analysis Ego-net analysis is one of the approaches to social network analysis. It is also referred to as “actor-centered” or “personal network” analysis. An ego net is the network which is formed around an actor, in our case a student. As it is a network, it involves other actors or “alters” with whom the student or “ego” forms relational ties. A relational tie may reflect a “connection” between individuals through which “resources” may flow (Lin, 2002), such as a friendship, loaning money, or information. In our study, we are primarily interested in friendship connections as sources of academic, emotional, and social support. Crossley et al. (2015, p. 2) define an ego net as “simply a list of alters with whom a target individual (ego) enjoys a particular type of relation.” Methodologically, the ego net of a student can also be extracted from a whole classroom network and can be visualized as in Figure 1. An advantage of ego-net analysis is that we can examine the network of contacts of a student both inside and outside the classroom. In the whole network analysis, this is not possible as usually the classroom is the boundary of the network, and students cannot select any actor outside of their classroom. In ego nets, however, this is possible. For example, students may select their friends in a variety of contexts without restrictions, such as the classroom, school, neighbourhood, and other. It is important to think of friendships outside the context of the classroom. We live in an ecosystem of relations that are not necessarily bounded by formal structures, such as classrooms. We wanted to provide the broadest net within which to capture important others that we may not be aware of a priori the study—as such we are trying to better reflect the larger system from the individual’s perspective rather than imposing upon the student

Figure 1: Ego-Net Visualization.

Data Exemplar: Ego-Nets in Examining Students’ Friendships A participatory visual mapping technique was used to collect the ego-net data. This method is seen by some as a highly engaging method for children, as it provides the opportunity to them to draw their network and then talk about it (Crossley et al., 2015). Student participants were asked to create their ego network by writing and/or drawing on a white board or a flip chart paper. Three concentric circles (see Figure 2) were pre-drawn, and participants were asked to write the names of and/or draw the alters that are important to them within the three circles by elaborating particularly on their friends. The concentric circles are advantageous as they can provide insights into the strength or quality of ties, by asking students to place contacts within the three different rings, with those closest to them at the center.

Figure 2: Concentric Circles.

We employed participatory visual mapping to collect the ego-net data. Students were asked to draw and/or write the names of the important people in their lives and this comprised our name-generator tool (Halgin & Borgatti, 2012). A name generator usually involves three elements, namely, alters, structure, and alter attributes. Therefore, we used this technique to compile the names of others (alters) in the student’s ego network, to collect information about relationships between these alters (structure), and to collect basic information about the alters (alter attributes). To do so, we interviewed the students after they were done with drawing/writing and asked them about the alters in their network (who they were, how they were connected to them, whether they knew each other, and other).

Page 5 of 20 Ego-Net Analysis in Educational Contexts SAGE SAGE Research Methods Datasets Part 2019 SAGE Publications, Ltd. All Rights Reserved. 2 All interviews were audio recorded. Before any data collection occurred, students’ parents provided informed consent and students themselves gave their full assent for participation. Interviews with students were audio recorded, and full verbatim transcripts have been developed for analysis purposes. The participatory visual mapping technique presents a comprehensive qualitative approach in collecting ego-net data. It allows for both quantitative and qualitative analyses to occur. On the one hand, ego-net measures, such as tie central tendency, tie dispersion, and alter central tendency may be calculated. We provide definitions for each of those measures in Stage 3. On the other, qualitative interview analysis techniques may be used to analyze the interview data to capture the subjective perspective of the students in terms of the research objectives (e.g., Stage 4). In our case, we employed grounded theory analysis (Corbin & Strauss, 1990).

Ego-Net Analysis In conducting ego-net analysis, we propose four stages:

1. Preparation of dataset 2. Analysis of visual maps 3. Calculation of ego-net measures 4. Analysis of interviews

Stage 1: Preparation of Dataset Stage 1 consists of two steps: (a) development of ego-net data grids and (b) production of interview transcripts. These two elements along with the students’ concentric circle visual maps comprise the dataset. It is important to have a system of managing the data. In our case, we developed an ego data matrix (see Table 1) and ego-net data grids for each student (Tables 2 and 3), which are called name interpreters (Halgin & Borgatti, 2012). Name interpreters are basically used to elicit information about the ego’s alters, such as their gender, age, race/

Page 6 of 20 Ego-Net Analysis in Educational Contexts SAGE SAGE Research Methods Datasets Part 2019 SAGE Publications, Ltd. All Rights Reserved. 2 ethnicity, and other. Table 1 shows the study participants, their grade, gender, and SEND status. Of course, the wider study has many more participants, but for the purposes of this example, we present data from only two participants to talk the users through how they might go about analyzing the data we provided. Depending on the study you are undertaking, Table 1 may include more columns to capture more demographic information that is relevant to your participants. In Tables 2 and 3, information about the students’ alters is recorded, for example, alter gender, alter relation, and alter closeness. Alter closeness reflects the position of alters in the ego-net map, for example, if the alter is within the first, second, or third circles. This is usually reflective of the tie strength between the ego and its alters. More columns can also be added to these tables to record other alter attributes, such as age, income, and so on.

Table 1. Ego Data Matrix.

ID Grade Gender SEND

S1 4 Female Yes

S2 4 Female No

Table 2. Name Interpreter Grid for Student 1 (S1).

Ego ID Alter ID Alter gender Alter relation Alter closeness

S1 KA Female Friend 1

S1 LU Female Friend 1

S1 AL Female Friend 1

S1 SO Female Cousin 1

S1 JO Male Brother 1

S1 JA Female Teacher 1

S1 MOM Female Parent 2

S1 DAD Male Parent 2

Table 3. Name Interpreter Grid for Student 2 (S2).

Ego ID Alter ID Alter gender Alter relation Alter closeness

S2 DA Female Best friend/classmate 1

S2 SI Male Cousin 1

S2 JO Male Cousin 1

S2 BT Male Uncle 1

S2 MOM Female Parent 1

S2 DAD Male Parent 1

S2 ST Male Uncle 1

S2 EL Female Friend 1

S2 GI Female Sister 1

S2 LA Female Sister 1

S2 Ms. BE Female Teacher 1

S2 LU Dog Pet 1

S2 BE Dog Pet 1

S2 GI1 Female Friend 1

S2 EL2 Female Friend 1

S2 DO Male Sister’s friend 2

S2 GR Male Dad’s boss 2

S2 LY Female Friend/Girl’s scout 2

S2 MA Female Best friend/Girl’s scout 2

S2 LA Female Friend/Girl’s scout 2

S2 RE Female Friend/Girl’s scout 2

S2 EL Female Imaginary best friend 2

S2 NE Female Untie 2

S2 ZA Female Friend 2

S2 Ms. SV Female Teacher 2

S2 EM3 Female Friend 3

S2 PI Male Friend 3

S2 JI Horse Animal 3

S2 BI Horse Animal 3

S2 CH Female Friend 3

It is important to read interview transcripts carefully so the information recorded in the tables above is correct and matches the ego-net visual maps (see next stage).

Stage 2: Analysis of Visual Maps Stage 2 is about presenting and descriptively analyzing the visual maps of our participants (see Figures 3 and 4). The visual maps for the two students reflect the name interpreter grids (Tables 1 and 2). The ego net of Student 1 is considerably smaller with fewer alters than the ego net of Student 2. This is immediately visible

Page 9 of 20 Ego-Net Analysis in Educational Contexts SAGE SAGE Research Methods Datasets Part 2019 SAGE Publications, Ltd. All Rights Reserved. 2 on both the visual maps and the name interpreter grids.

Figure 3: Student 1 Ego-Net Visual Map.

Figure 4: Student 2 Ego-Net Visual Map.

As shown in both visual maps, we asked students to use different colors to group their relations to alters. For example, one color was used to represent family members, another color for friends, and so on. We also asked students to draw lines to show how alters are related to them and each other. This is particularly visible within the Student 2 visual map. It should be noted here that there is software available, such as E-Net, VennMaker, EgoNet, and others, which can be used to turn name generators into visual maps. In our case, we employed the participatory visual mapping technique to collect the ego-net data where participants themselves develop their ego-net visual maps. We think this is a more appropriate method to use with children, as children tend to be more inclined to engage with hands-on activities rather than completing a survey or taking part in an interview asking them who the important people in their lives are. This is just one way of undertaking ego-net analysis in education. You may decide to provide

Page 11 of 20 Ego-Net Analysis in Educational Contexts SAGE SAGE Research Methods Datasets Part 2019 SAGE Publications, Ltd. All Rights Reserved. 2 a survey, or any other suitable method, asking your participants to identify alters and their relation to them. In this case, using a software to develop the visual maps would be recommended.

In terms of presenting the data, it is very important to anonymize the participants by “cleaning” the visual maps from any personal identifying information, such as names. As shown in Figures 3 and 4, we have deleted the full names of alters and only kept the two first letters of their names. Additionally, interview transcripts have been cleaned by removing all information with regard to names and places.

Stage 3: Calculation of Ego-Net Measures At this stage, we will show you how we have calculated some basic ego-net measures in further addressing our study’s aims. We have calculated three measures; tie central tendency or “degree,” tie dispersion, and alter central tendency (Borgatti et al., 2013; Crossley et al., 2015). These terms are the formal terms used in the method, and as such, we want to reflect the parlance of the approach.

As we are interested in exploring the social capital of our participants, we are measuring the size of their ego nets. Lin (2002) defined social capital as access to resources through network ties. We have measured network size by calculating the degree or tie central tendency, which is the number of alters in the network. It is quite straightforward to calculate the degree of the two students’ ego nets by summing up all alters that they have. Therefore, Student 1 has a degree of 8 and Student 2 has a degree of 32, when we include pets and the imaginary friend. Even without these, the network size of Student 2 (degree = 27) is considerably bigger than Student 1’s. The size of the network may elicit insights into the social capital that students may possess through their networks of interactions and may provide access to social support, resources, and information. This access may be more restricted for Student 1 due to the low tie central tendency or degree,

Page 12 of 20 Ego-Net Analysis in Educational Contexts SAGE SAGE Research Methods Datasets Part 2019 SAGE Publications, Ltd. All Rights Reserved. 2 meaning that student does not have many relative ties to others. Therefore, from a social capital perspective, we may argue that more or stronger ties can be counted on for providing help or support when needed (Borgatti et al., 2013).

The second measure, tie dispersion, examines how ties are spread in the network. In our study, we are particularly interested in the number of friendship ties and the number of family ties as possible sources of social support and social capital. This measure is about the composition of the network and only requires information on ego-alter ties. For binary data, like ours, tie dispersion refers to measuring the extent to which a student’s ties are equally distributed across different types of relationships, such as friendships, family ties, teacher ties, and so on. We may hypothesize that students with more friends and family ties may have access to more emotional support or resources. A summary measure for dispersion is Blau’s index H (Borgatti et al., 2013; Crossley et al., 2015) and for Student 1 is calculated by applying the formula:

H = 1 – P12 – P22 – P32 – … Pr2

Therefore, we calculate Blau’s H as below:

• Total number of alters is P1 3 (friends) + P2 4 (family) + P3 1 (teacher) = 8 • Hence proportions are 3/8, 4/8, 1/8, i.e., 0.375, 0.5, 0.125

• From the formula H = 1 – 0.3752 – 0.52 – 0.1252 = 0.5975 • H = 0.5975

This measure has a value of 0 if all ties are in one group (i.e., family) and a maximum 1 − 1/r if each group has the same number of ties (Crossley et al., 2015). In our case, Student 1’s H value is 0.5975, which shows a relatively good dispersion of ties across the three groups of relational ties (friends, family, and teachers). The H value for Student 2 is 0.6426, which is slightly higher than Student 1’s but still very close, again showing some dispersion of ties within the

The third measure is alter central tendency, which is analogous to tie central tendency (Measure 1) but using the alter attributes this time. In our case, we have one categorical attribute which is gender of alters. Other attributes that may be of interest include race/ethnicity, socio-economic status, disability, and so forth. To calculate alter central tendency, we simply count the number or proportions of each alter in each of the categories, in our case gender. Therefore, for Student 1, we have six female (75%) and two male (25%) alters. For Student 2, we have 20 female (71.4%) alters, including the imaginary friend but excluding animals, and 8 male (28.6%) alters. As a result, both students seem to have primarily female alters within their ego nets. We could then hypothesize that female students may have a tendency to associate more with female alters. A position generator can be used to elicit more attributes about alters; however, we should be careful not to overwhelm our participants with questions. Especially, in large ego nets, it may take considerable amount of time to go through each alter with the child asking them about alters’ attributes. Older kids may be able to do this, but in our case, the kids are relatively young (Grade 4). In our case, we identified the gender of alters and in some cases, their occupation but did not want to become too intrusive. All three measures enabled us to get insights into the social support available or not to the egos.

Stage 4: Analysis of Interviews At Stage 4, we explain the data analysis of the interviews by using the grounded theory methodology (Corbin & Strauss, 1990). The analytical focus of the qualitative interviews is on the students’ subjective perceptions about their social networks and friend and peer relationships with the aim to understand (a) more about the quality of the construct of friendship and (b) its function as a source of social capital and social support. We employed grounded theory because

Page 14 of 20 Ego-Net Analysis in Educational Contexts SAGE SAGE Research Methods Datasets Part 2019 SAGE Publications, Ltd. All Rights Reserved. 2 this approach operates inductively, meaning themes emerged from and are “grounded” in the data. Taking the specific procedures for data analysis from this approach into account, the interpretative-analytical process included the three basic types of open, axial, and selective coding (Corbin & Strauss, 1990) as well as the idea of a constant comparison, which was key to the investigation in order to determine the value of different characteristics and conditions in relation to the objectives (quality of friendship connection and its potential sources of social capital and social support).

The first step of the analysis process was to import the two transcripts of the interviews into the qualitative computer program MAXQDA for coding and organization purposes(MAXQDA, 2016). In the interpretive open coding process, we broke down the interview data analytically by labelling concepts, grouping them into categories, and writing memos (Corbin & Strauss, 1990). This stage of analysis was largely descriptive, where descriptive labels were attached to discrete instances of phenomena (e.g., Table 4). Both interviews were at first coded separately. The phenomenon friendship was one code that emerged from all the empirical material in different facets due to the research objective of understanding the quality of students’ friendship connections. Hence, the analytical lenses focus on explanations about friendship and we coded therefore all relevant sections. Student 1, for example, described a close friend like this: “She is a really nice friend, she helps me if I get hurt!” Out of this quote, the label help emerges as an attachment of the phenomenon friendship. This phenomenon occurs to have various dimensions, whose complexity we tried to capture. In Student 2’s narratives, the label similarity can be seen as a descriptive label of the phenomenon friendship when she says: “We had everything in common, we loved the same things, we had the same hair color, and we had everything”! For this quote in particular, we wrote a theoretical memo since in theory, common ground as well as similarity are seen as essential conditions in friendship relations (Hartup, 1993, 1996). In the following phrase, Student 2 adds a dimension of the

Page 15 of 20 Ego-Net Analysis in Educational Contexts SAGE SAGE Research Methods Datasets Part 2019 SAGE Publications, Ltd. All Rights Reserved. 2 relation between closeness and distance when she explains: “Then, one day she moves! We were all really sad but she’s still a part of my life”! Friendship seems to be vulnerable when it comes to physical distance because in this case, the friend’s move and separation causes feelings of sadness and loss. However, this example also shows that even without having a friend physically close, it still can be emotionally close and important. By doing this first step of open coding, it is key to search for low-level categories in the material prevalently on an explicit level of meaning (e.g., “friendship”, “help”, “similarity” “proximity vs. distance”).

Table 4. Open Coding.

Label/code Interview section Category Theoretical memo (subcategory)

Description/ Help as an important S1: “[ … ] she is a really nice friend, she Friend as important emotional Definition of dimension of helps me if I get hurt”! resource (e.g., Hartup, 1993) friendship friendship

Similarity as a Common interests and affiliation are S2: “We had everything in common, we loved condition of friendship main themes in friendships relations the same things, we had the same hair color, Description/ (e.g., Hartup, 1996) Physical distance and we had everything”! Definition of friendship Separation from friends sometimes “Then, one day she moves! We were all really Dimension of the provokes anxiety and a sense of sad but she’s still a part of my life”! relation: closeness/ loss (e.g., Hartup, 1996) proximity and distance

The next step of the analysis process involves axial coding, which serves to reassemble the categories generated during open coding in a new way. This process of exploring relationships among categories is done by identifying links between a category and its subcategories (Corbin & Strauss, 1990). For this, it is important to look for the so-called axis categories, which appear most promising for further elaboration. These are condensed with as many matching passages as possible to finally establish the relationships between these axis categories and other categories. As coding progresses and while comparing the two interviews, we were able to identify higher level categories (e.g., social support) that

Page 16 of 20 Ego-Net Analysis in Educational Contexts SAGE SAGE Research Methods Datasets Part 2019 SAGE Publications, Ltd. All Rights Reserved. 2 systematically integrate low-level categories (e.g., help) into meaningful units. By doing so, analytical categories are introduced. In our material, for example, social support emerged during this stage as an important analytical (axis) category in terms of our objectives. It occurred that social support is on an explicit as well as on a latent level grounded in the data as an important function of friendships. Hence, we aimed to identify its subcategories which correspond to aspects of social capital as a way of providing access to different dimensions of social support. Socioemotional support, for example, was one prominent dimension of social capital. Both students implied this kind of support in their responses. Student 1 explains for instance: “When someone makes me cry, she always helps me”. Being asked what she would do, if she is having a bad day at school, Student 2 says: “I would go to my friends and they’ll make me laugh.” In these examples, friends seem to function as an important source of socioemotional support. Friends are sources of trust, caring for each other, protection, and encouragement. It seems that social–emotional support is unique as students access resources and support through reciprocated social ties generated from their membership in their social networks. This shows the potentially tremendous value of their social networks and social capital as sources of social support. For instance, the students’ close friends seem to enhance the emotional well-being and alleviate loneliness.

Academic (learning) support turned out to be another important dimension that the two students referred to. We defined academic support as a provision of information, suggestions, and advice that is used to address problems in a learning context (e.g., Dumont & Provost, 1999; House, 1981). This dimension came up when Student 1 explained that her very best friend helps her with her homework or when Student 2 outlined: “If I have a problem at school, I would ask my best friend, she is a mathematic, so I would ask her initially to explain it to me”. Friends in this example are a source of academic support in a way that they assist with school questions. It seems as a common practice, getting help from friends

These two subcategories (socioemotional support and academic support) characterize two different functions of social support as deriving from social networks and social capital.

The final stage refers to the selective coding, in which axial coding is continued at a higher level of abstraction (Corbin & Strauss, 1990). The aim of this procedure is to define the central phenomenon and to set out the core categories. In other words, we choose to highlight the discovered patterns in the data and captured the conditions under which they applied (e.g., the axial coding process). Because we are interested in the quality of friendship connection and its potential sources of social capital and social support, we emphasized the core category friendship and social support and its dimension socio-emotional and academic support. There were other categories coming up during the coding process, like favorite subject or feedback evaluation. But since they were not relevant or related to our objectives, we didn’t include them in writing up the interpretation.

Reflective Questions

1. What are the main strengths/advantages of ego-net analysis? 2. What new did you learn from ego-net analysis within the context of educational research? 3. By engaging further with the dataset (interview transcripts), could you expand on grounded theory analysis?

Further Readings Borgatti, S. P., Everett, M. G., & Johnson, J. C. (2013). Analyzing social networks. London, UK: SAGE.

Page 18 of 20 Ego-Net Analysis in Educational Contexts SAGE SAGE Research Methods Datasets Part 2019 SAGE Publications, Ltd. All Rights Reserved. 2 Corbin, J. M., & Strauss, A. (1990). Grounded theory research: Procedures, canons, and evaluative criteria. Qualitative Sociology, 13(1), 3–21.

Crossley, N., Bellotti, E., Edwards, G., Everett, M. G., Koskinen, J., & Tranmer, M. (2015). Social network analysis for ego-nets: Social network analysis for actor-centred networks. London, UK: SAGE.

Dumont, M., & Provost, M. A. (1999). Resilience in adolescents: Protective role of social support, coping strategies, self-esteem, and social activities on experience of stress and depression. Journal of Youth and Adolescence, 28(3), 343–363.

Halgin, D. S., & Borgatti, S. P. (2012). An introduction to personal network analysis and tie churn statistics using E-NET. Connections, 32(1), 37–48.

Hartup, W. W. (1993). Friendships and their developmental significance. In H. MacGurk (Ed.), Childhood social development. Contemporary perspectives (Reprint, pp. 175–205). Hove, UK: Erlbaum Associates.

Hartup, W. W. (1996). Cooperation, close relationships ans cognitive development. In W. M. Bukowski (Ed.), The company they keep. Friendship in childhood and adolescence (Reprint, pp. 213–237). Cambridge, UK: Cambridge University Press.

House, J. S. (1981). Work stress and social support. Reading, MA: Addison- Wesley.

Kaimi, I., & Mamas, C. (2018). Graphical modeling. In B. B. Frey (Ed.), The SAGE encyclopedia of educational research, measurement, and evaluation (pp. 747–750). Thousand Oaks, CA: SAGE.

Lin, N. (2002). Social capital: A theory of social structure and action (Vol. 19). Cambridge, UK: Cambridge University Press.

Page 19 of 20 Ego-Net Analysis in Educational Contexts SAGE SAGE Research Methods Datasets Part 2019 SAGE Publications, Ltd. All Rights Reserved. 2 Mamas, C. (2012). Pedagogy, social status and inclusion in Cypriot schools. International Journal of Inclusive Education, 16(11), 1223–1239.

Mamas, C. (2018). Matrices (in Social Network Analysis). In B. B. Frey (Ed.), The SAGE encyclopedia of educational research, measurement, and evaluation (p. 1028). Thousand Oaks, CA: SAGE.

MAXQDA. (2016). MAXQDA, software for qualitative data analysis, 1989–2018, VERBI Software – Consult – Sozialforschung GmbH, Berlin, Germany. Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications (Vol. 8). Cambridge, UK: Cambridge University Press.

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