106 Matching Annotations
  1. Jan 2019
    1. Now, you could ask questions such as, how does being on the outside (periphery) or inside (center) of a friendship network correlate with one's grade point average? Or, do birds of a feather flock together? That is, are students with similar grade point averages likely to nominate each other?

      What variables are more important in the determination or change of friendship in different stages, grades, gender, interest, or similar economic background or experience? What self-identity cognition would be reflected from it?

    2. strength

      what constitute a tie? direction, same-sex, other-sex, specific subgroups, strength(very subjective to define the strength of a friendship).

  2. Apr 2017
    1. Appendix. Technical demonstration of the SOMprocedure

      This is a great example of Kohonen's Self-organizing Maps and the use of the U-Matrix. The authors were very thorough in explaining how it can be used.

    2. The SOM consisted of 360 neurons on a 24by 15 map grid, with hexagonal lattice andGaussian neighborhood function.

      Data Structure

    1. Ryu, S., & Lombardi, D. (2015). Coding Classroom Interactions for Collective and Individual Engagement. Educational Psychologist, 50(1), 70–83. http://doi.org/10.1080/00461520.2014.1001891 (Note: An attempt to combine SNA with critical discourse analysis.)

      I really like this concept and application of SNA with integration of critical discourse analysis (CDA), which is a course I am taking in the fall!! In another class, we read about critical quantitative approach, and I now think what makes it truly critical might be the integration of a mixed method approach, where something like ethnographic case studies or in this case discourse analysis is used in combination with SNA/statistical analyses to give it more robust findings.

    1. methodological transactionalism

      This paradigm shift may be supported by a similar shift from individualized learning theories to socio-cultural theorning theories too.

    2. whether even inferential results are generalizable to other populations

      a huge limitation to the application of SNA in general, in my opinion. while it's fascinating to look at particular networks and see some other structures in some of the studies i was reading to better understand my own issues, the findings weren't something i would be comfortable applying to my situation. people are so unique and add a social network of other individuals on top of that, plus environment, plus any number of other factors and it feels very limiting.

    3. isn't too large and dense

      yeah, this wasn't so useful for an interconnected network of over 400 individuals.....

    4. You will be somewhat surprised how expanding your search in these ways will yield a bigger, better, and deeper pool of literature that can be used to inform your research questions and design

      Since no one had "done" what I was looking to do I also found it helpful to look for studies that had the same general goal as mine. Even if we were talking about departments in a huge corporation or different professional development groups and how they shared knowledge it was pretty easy to find things in common with my own information.

    5. analyses presented throughout this book

      I would be interested in trying this especially because this textbook provides some good step-by-step examples using this package.

    6. hypothetical example of a questionnaire

      I would love to see more of these - in published papers, for example - or just tutorials that take us from raw data to tidy data to analysis/computations in R.

    7. Likert-type scales

      I hope to use survey design as a component of my data collection for a future project.

    8. If you are observing faculty members in a teachers’ lounge, are you interested in who speaks with whom, or are you also interested in who initiated the conversation? Who initiated the conversation and who responded might be more interesting than just recording the pairs of teachers who engaged in conversations.

      This is certainly applicable to my own study. My original thought was that my data could be undirected, but I'm now questioning that. For example, it would be interesting to see if an initial contact between two students was then reciprocated, or if there were other contacts that seemed to follow from the initial.

    1. In this respect, these models are closely related to logistic regression in that they analyze a dichotomous dependent variable (1/0) that is assumed to follow a binomial distribution.

      This makes a connection to my prior knowledge about traditional statistics, binary Logistic regression(dependent variable is binary, 0 or 1) and multiple Logistic regression(dependent variable has multiple levels, just like the school leader network example, four possible outcomes).

    2. we cannot reject that null hypothesis that the number of collaboration ties sent in year 1 does not vary by the level of leaders’ trust.

      This is (unfortunately) reminding me that I need to finish my stats exam due later this week and try to come to some conclusions like this after spending probably the next 8 hours staring at awesome output in R... but I digress... yes, this seems like it would be very applicable to my study. :)

    3. This hypothesis can be tested by comparing the average out-degree (number of dichotomized collaboration ties sent) of district- and school-level leaders.

      I have a background in quantitative statistics. The use of a t-test assumes normality of data. I know that normality does not matter for SNA, but is this particular section referring to a nonparametric test that works like a t-test or is it actually referring to the use of a t-test and just ignoring the assumption of normality?

    4. This section shifts the analytical lens to predict an individual actor's outcome, whether it is an attribute variable (e.g., a student's test score) or a structural variable (e.g., a teacher's betweenness centrality score), using relational data. For example: does a teacher's gender predict his or her influence (as measured by degree centrality)?

      Ok... So now this is making a little more sense for my own data. In my case, I could analyze the countries geographic loaction as an attribute varibale that predicts the probability of certain industries having multiple companies in a given country. Does this make sense?

    5. the focal variable is the actor's group membership expressed as an unobserved, latent variable whose value is the result of the observed ties among actors

      Ahhhh... This makes sense. I asked about this in the previous chapter.

    6. Do a leader's collaboration ties vary by their level of trust?

      Ooh! This seems like it would be very useful for my divvided up SP and CP, which I have categorized at L, M, and H as well. So, "Do students' ties vary by their level of SP?" would be the question I would ask.

    7. How well does a leader's perceived level of trust in his or her colleagues predict the number of alters to whom the person sent a collaboration tie in year 1, controlling for gender and the level at which the person works (district vs. school)? This question requires three vectors of independent variables (trust score, an indicator for gender, and an indicator for level) and one dependent variable vector (collaboration year 1 out-degree).

      Oy. This (multiple linear regression) is much harder for me to wrap my brain around, but I'm going to give it a go (especially since I have so many variables and it might be useful for tying them together). Again, I have SP, CP, and discussion forum type. If I did the surveys, I'd also have perceived sense of belonging. How well does a student's perceived sense of belonging predict his/her level of CP in discussion posts, controlling for discussion forum type (for example, only looking at large group discussions)? That doesn't get the SP in there though, and may not actually be a great model of regression. :/ Help?

    8. We might even be interested in the relationship between two individual attributes among a set of actors who are connected in a network. For example, in a school classroom, is there an association between students’ engagement and their academic achievement?

      This is very similar to what my research is doing, where engagement is defined as SP and academic achievement as CP. :)

    9. P* models are often employed to take this further by including actor- and network-level covariates.

      Again, if I were to try to apply this (p) to my own data, I could look at things like gender, race, etc, but it would make more sense to see if SP was tied in--in other words, take the same scenario from p1 (if a student respond to another (say, in a large group discussion), is that student more likely to respond back when higher levels of SP are involved? I think* I applied that correctly.

    10. Using directed and dichotomized relational data, a p1 model can be used, for example, to test whether school leaders tend to reciprocate relationship choices.

      Again, attempting to apply to my own study. This one (p1)seems straightforward. If one student responds to another (in a large group discussion), is the other student more likely to reciprocate by responding to that student in future discussions?

    11. The question, therefore, is whether school leaders prefer to collaborate with those with whom they have collaborated in the past or with those that they have turned to discuss confidential issues.

      I have next to no stats knowledge, so I'm going to try to extrapolate this out in regards to my own research to try to better understand it (hopefully!). In using my own research with SP (social presence) and CP (cognitive presence). I'm going to start with the varibles: levels of SP, levels of CP, discussion forum type. A question I have been asking is whether discussion forum type affects SP and/or CP. Modeling the question the same way as this one, it might be whether students are more likely to show higher CP with students they were in a programmatic small group discussion with versus just large group. I think this models this line of questioning, at least. This probably doesn't get the SNA part in. So, trying again... Are students more likely to respond to a student in a large group discussion that they formed a connection with in a programmatic small group discussion or a random small group discussion? This doesn't get the SP or CP working in there, but it gets the SNA. So part of what I'm studying. But, since I'm graphing SP as a weighted measure for SNA, maybe it could be whether students are more likely to demonstrate higher SP in an ensuing large-group discussion with students they were in small programmatic group discussion with in a previous module. Does that get all the parts working approporiately in a MR-QAP-procedure question??

    1. networks change, and in some instances quickly

      this is something i've been struggling with this entire course. in particular, for my network, one or two members who are always participating and holding these networks together could very easily get a new job, move onto a new opportunity etc. how to keep the networks so intertwined that one or two people leaving wouldn't make the whole thing fall apart is what interests me.

    2. Even the tools of predictive modeling are commonly applied to network data (e.g. correlation and regression)

      Would running such tests require a need for assessing latent variables that emerge from network analysis? I will keep reading, but from what I know of SNA, it seems like you are only analyzing observable variables and it would be difficult to obtain a correlation from such unique variables. Am I way off here?

    3. limiting the types of questions that could be asked,

      I have been dealing with this issue all semester. I am really struggling to determine which questions are appropriate and which can be answered using SNA techniques. Perhaps this is due to the type of data I am working with, but I am still working on developing sound research questions.

  3. Mar 2017
    1. These measures convey the image of a fixed network with attitudes or behaviors passing through it,

      Since my own study takes these measures weekly, I'm curious to see how the statistical aspect may help draw connections between weeks and predict what could happen in future weeks.

      Although, I will say I think this is only true in a broad sense. For example, I think I may be able to predict from a couple weeks of SNA data who the central players in future weeks will be (rock star students tend to retain that status throughout), but it may not be able to predict who responds to whom. Though such a measure may not be all that useful anyway.

      Coming back to this after reading the chapter, I'm having a difficult time deciding whether statistical approaches make sense for me. I am trying to make generalizations, but it's not around categories (like gender, race, etc.). Unless maybe my category is SP (social presence)? To make a prediction about students with high SP having high CP (cognitive presence)?

    2. Explain in plain language how simulations are used to create a probability distribution that enables you to make a statistical inference with network data.

      If your network differs from a typical random creation, then you can perhaps make a claim about unique properties.

    3. According to this emphasis, the main question asked is: If a study is repeated on a different sample (drawn by the same method), how likely is it that you would get the same answer about what is going on in the whole population from which both samples have been drawn?

      For my own study, this would be making a claim about the connection of SP to CP in certain discussion forum types to suggest that you can get the biggest bang for your SP and CP buck by adopting a certain discussion forum type.

    4. So, let's say you are interested in the number of collaborative exchanges that occur between teachers from two different grade levels in a complete network of teachers within one elementary school. First, you count the number of times these types of exchanges occur in the observed network and then permute these relational data lots and lots of times. With each permutation, you calculate the number of times this type of tie (collaborative exchanges between teachers from two different grade levels) occurs and compare this result to the original observed network. After this process of permuting and comparing, you can see how often the results of these permutations are the same as the original observed results: The more often the results of the permutations are the same as your observed data, the more likely that the pattern of exchanges in the observed data was due to chance. If, however, the results from the observed data are so unlikely when compared to the results of the permutations, then you are to conclude that your results are not the byproduct of chance. Therefore, this result would be considered statistically significant.

      In terms of my project (looking at racial and gender-based biases in communication between undergarduate students in an online class), then I could use this same rationale and process in order to make generalizations to a broader population?

    1. How ego-centric networks could be applied to your research projects?

      In my case, ego-centric networks are particularly interesting. I am looking forward to see how different individuals from first language groups interact with learners from other first language groups. Also, how does this correlate to their English proficiency, grades etc.

    2. definition(s) of the neighborhood

      Since my groups are small with four to six participants, a one-step neighborhood probably makes most sense, but if I were to look at an egocentric analysis with the moderator as the ego, the larger or complete network would be more applicable?

    3. What definition(s) of the neighborhood will make sense for your research projects?

      I would prefer to define the "neighborhood" for my ego-centric analysis to one-step, because I am most interested in who that person is directly connected to, instead of looking at the larger network that the ego is nearly connected to.

    4. How ego-centric networks could be applied to your research projects?

      If I were to apply ego-centric networks to my project, I would focus in on studying members of the game development community that have gone through a public education program to see if they have become more well-connected in the community since going through the program.

    1. Alternately, you would also use an egocentric approach if your research question is about different patterns of interaction within defined groups

      Comparing different groups with the same or similar structure? Or differing patterns of interaction within the groups? Or both?

    2. Summary

      When looking at my data set, I'm most curious to analyze the ego networks of particular students that either do not feel connected to the school or regularly struggle to academically achieve. Perhaps individual interventions can be designed to aid the educational experience of these students.

    3. advantage

      The term 'advantage' seems to be used loosely. I understand the power that A may be able to exert over students B & C, but given the various values of weak/strong ties and dense/disperse networks, I do not totally see how the term 'advantage' always applies in this scenario.

    4. Stated another way, weak ties are important for transmitting information but less so for transmitting behavioral influence (Valente, 2010)

      This is the most succinct description of the value of strong and weak ties I have heard yet.

    5. Viewed from this perspective, dense networks reinforce prevailing norms and behaviors and insulate one from outside influences (these can be, however, either good or bad)

      A similar theoretical foundation may apply to 'echo chambers' noted recently throughout social media.

    6. Brokerage

      This whole concept is a huge part of what I want to get out of my data set! This is exciting and also terrifying because I will have to figure out how to actually get these measurements....

    7. so that the ego networks of tenured teachers could be compared to the ego networks of untenured teachers

      I am going to see if it makes sense to do something like this with my data in terms of OIT v. non-OIT or instructional designers v. other types of employees. So many potential ideas!!!

    8. Ego actors can be individual persons, groups, or even some larger entity

      I had not thought of that until they spelled this out. that actually makes a lot of sense.

    9. The first is an “out neighborhood,” which includes all the actors to whom ego sends a tie. Conversely, a directed ego network can be defined as an “in neighborhood,” which simply includes all those actors who send ties to ego.

      Thinking about direction makes sense for my study. It could be interesting to compare an ego-centric network to look at one student (Student A) to see if Student A's out and in neighborhoods are the same or mostly overlap. From what I've seen in my data so far, this has only been the case when students are placed into small groups. When it's the discussion forum as a full class, there is significantly less overlap.

      It could be interesting to compare students' sense of belonging/community on the basis of whether they have a strong in or out neighborhood. I could see students who have high out but low in as feeling disconnected, and students with high in but low out maybe not even paying attention/not really caring about sense of belonging. Methinks I need to add a survey to my research...

    10. Why Study Ego Networks?

      This may or may not make sense for my research. Since my network is small (19 students; 20 if you count me, the instructor), it's easy enough to look at the whole network (socio-centric) and then decide if it makes sense to zero in on particular students. Reasons I may want to use ego-centric: To study high-performing (vs low-performing) students' networks; to isolate students showing high cognitive presence (one of the aspects I'm researching) to see what their individual networks look like; to explore students with high social presence to see what their networks look like--does quality of SP go along with quantity of SP?

      If I were administering surveys (which I do not believe I'll have time to do), then I might want to look at egos based on survey results, for example, to a ask like "Do you feel a sense of belonging in this online class?" to study how students' perceptions of belonging align with their actual quantity and quality of ties.

    11. Table 7.1

      The data presented in these tables is often kind of confusing, if it is generated from the program they are using.

    12. Whereas the previous chapter focused on concepts and measures most appropriate for complete network analysis, this chapter shifts the perspective to the analytical level of a sole focal actor—ego.

      Is it common to perform both types of analyses on a dataset?

    13. This was calculated by summing the geodesic distances between School Leader 1 and the other 42 actors in the network, dividing this by 1, and then multiplying it by 42, (g – 1).

      I think we calculated closeness centrality of vertices (and also the closeness centralization of complete network) two week s ago, it used the similar computation method as this egocentric network closeness centrality. It seems like that when calculate closeness and betweenness, you have to include indirect connections of a given ego, not just direct connections in the egocentric network.

    14. Tie strength has been a core idea throughout the network field, with weak ties serving as important bridges between different groups and strong ties being influential in behavioral adoption. Generally, weak ties are important for the spread of instrumental resources (e.g., work-related advice), while strong ties are important for expressive resources (e.g., guidance on personal matters) (Lin, 2001a). Stated another way, weak ties are important for transmitting information but less so for transmitting behavioral influence (Valente, 2010). Granovetter's (1973) classic work has laid much of the foundation for much of the work that has focused on the tie strength.

      I can see some application of tie strength both in my project for this course (analyzing the effects of gender and race on interaction between actors) and beyond my dissertation research: how do technology integration processes disseminate through a network of teachers?

    15. Egocentric network data generated in this manner, however, cannot be used to describe the overall embeddedness of the networks in some larger population.

      Seems similar to a caution when using Case Study qualitative research methods - it's best to use caution when generalizing from case study research.

    16. These less dense networks, often referred to as radial networks, can also be favorable or unfavorable, depending on the behavior or attitude that you are interesting in studying

      This reminds me of strong ties theory and weak ties theory(also called structure hole) we had read before which are competitive theories , but both theories can explain some certain social phenomenon. Strong ties theory can explain how strong ties affect people's behavior or attitudes etc., and weak ties play a role of bridge to disseminate (non-redundant) information. So I think what matters is your research question/interest, your research question will drive you to apply appropriate theory and interpretation.

    17. able 7.2 Types of Questions Used to Elicit Egocentric Network Data.

      This is useful. Sometimes I wish different studies would just show us the surveys they used so I can get a clear idea of types of questions and what is visualized.

    1. There is no assumption that groups are the building blocks of society: the approach is open to studyingless-bounded social systems, from nonlocal communities to links among websites.Rather than treating individuals (persons, organizations, states) as discrete units of analysis, it focuses on howthe structure of ties affects individuals and their relationships.In contrast to analyses that assume that socialization into norms determines behavior, network analysis looksto see the extent to which the structure and composition of ties affect norms.

      This is important for my own understanding of the "Why" of SNA. It made sense to disengage the social portion of the practice and focus on the composition of the ties. An ah-ha.

    1. It should be informed by theory

      I'm not sure what theory I should be leveraging to make cut-offs in my analysis of Slack interactions.

    2. However, you might also want to reveal how groups are distributed in the network and which actors belong to which groups. A clique analysis is one way to satisfy these purposes. A clique is a maximally connected subgraph of nodes (> 2) in which all nodes are connected to each other.

      It may be interesting to apply this after I've had students in the same small groups for a couple weeks for their discussions and then return to a whole-class discussion (to see if the students respond more so to the students they had been in a group with previously).

    3. Therefore, a 3K-core is a substructure, a subset of actors, in which each node is connected to at least three other nodes; a 2K-core would be a subset in which a node is connected to two others, and so forth. Those nodes that do not meet K, which is defined by you, are dropped from the network

      I could see this being useful for looking at my own research. Not many of the students are interacting beyond the minimum requirements for responses (typically, 2), so I could use this to separate out those who are a bit more easily (so doing a 3K-core).

    4. A Visual Comparison of Structural, Automorphic, and Regular Equivalence. Consider this a graph of a hypothetical hierarchy of a school district's organizational chart, which consists of three levels linked by supervisory relation. Depending on your preferred definition of equivalence, different positions will be identified.

      Moving forward into post-dissertation work, I can see how the idea of equivalence might apply in looking at technology integration processes across schools. But, for my research questions posed in this class, this isn't very useful.

  4. Feb 2017
    1. Reciprocity

      This one was easy! Getting good a good directed network to play around with it with into R and able to be modified was... way harder than getting this info.

      reciprocity(g, ignore.loops = TRUE)

      There is an additional mode operator where if you put the mode = ("ratio") it calculates (unordered) vertex pairs are classified into three groups: (1) not-connected, (2) non-reciprocaly connected, (3) reciprocally connected. The result is the size of group (3), divided by the sum of group sizes (2)+(3).

    2. The length of the longest path between two actors is five. To “get from” Student 15 to 16 in this directed network requires five steps: 15 ? 5 ? 17 ? 4 ? 2 ? 16. This is the only five-step path in the network and is the maximum distance between any two actors.

      This reminds me of 7 degrees of Kevin Bacon or using Facebook to find how connected you are to a complete stranger.

    3. First, you identify the main structural properties of a network, including those related to its size, density, and connectivity.

      Step 1.

    4. Centralization

      Centralization interests me for analyzing discussion forums--are there key players, and do these key players show higher degrees of cognitive presence?

      Calculating for centralization by number of connections seems quite straightforward in R: centralization.degree

    5. it is more sensible to report what is referred to as effective density, which is the number of lines (ties) multiplied by the number of possible alters:where L again is the number of lines (directed ties) in the network, N is network size, and λ is the maximum number of alters requested or permitted. Using this formula, the density of the Fraternity Data is 1.0: All possible relations are present, which is unsurprising given that the original ranked data were recoded (1–3 = 1, all others = 0) and each respondent had the maximum number of three friendship nominations.

      For examining either my original project idea or my new one, I think this will be a useful equation. For example, in my current project idea, it would be interesting to see the density of a given network where I have limited them to two responses total within a given assignment.

    6. Clustering

      I am very interested in clustering measures, because I plan to analyze data from a Slack group that I am a part of, where I suspect there are many subgroups who only interact with each other.

      After looking around for some different clustering algorithms, I found the "cluster_label_prop" function in the igraph package, which seems to do what I would like to do. To summarize, this function automatically detects groups within a network by initially labeling every node with a unique label and at every step each node adopts the label that most of its neighbors currently have. In this iterative process densely connected groups of nodes form a consensus on a unique label to form communities.

      There seem to be many different ways to define clustering though, so I am sure that I will need to do more research on the topic of clustering as I move forward with my research project.

    1. betweenness centrality

      This video was a great and simple explanation for calculating Betweenness centrality. It will be interesting to look at this in my data set (eventually).

    1. If an individual opts out, this should mean that their name appears nowhere on the social network diagram (even if they are identified by another individual as being part of their social network). For instance, in the sample map, you can see that the map would be very disjointed if John and Holly opted out of the SNA.

      Are we allowed to include nodes for John and Holly if they are identified by others, but without using their name? For example, we would refer to John as Anonymous 1 and Holly as Anonymous 2. Or would we have to exclude any data that involves them, regardless of anonymity?

    2. the purpose of the network analysis may be to identify areas of the firm that just aren’t critical to its mission, vision, and strategy. As social network researchers Steve Borgatti and Jose-Luis Molina note, “This introduces dangers for the respondents because management may make job or personnel changes (e.g., firing non-central workers) based on the network analysis. In fact, in the case of a consulting engagement, this may be the explicit purpose of the research, at least from the point of view of management (Borghatti & Molina, 2005).”

      This seems similar to conducting a program evaluation. I guess the only difference would be the application of network analysis.

    1. For example, using the public Facebook profiles of a set of students in the same school, it is possible to construct a network of who bullies whom, so long as you can precisely define how bullying is measured.

      I think this is a really good example of SNA research. It would take a lot of thinking to operationally define bullying as identified on facebook, but I bet the results would be interesting. I wonder if something of this nature has already been studied.

    2. It is possible to then collapse participants across all events, thereby producing a more representative network that can address the study's guiding question(s)

      This is the hard part! Who/what participants at the University represent range from "college" to "department" to "faculty groups" to "student union" to "tight knit group of people who happen to teach online courses". how to collapse them is hard to quantify, even if you know who they are and how they're related to various things.

    3. For example, you may include an event that is insignificant or have an event (or set of events) that excludes important players. So, while you may be interested in studying how a community's interests are actualized in the context of local school district policies, the local board of education meetings may not be the right event to uncover these relational dynamics.

      this is my problem. most of my ability to collect data relies on this (i.e. I know who attended an event) but the event attendees are the people I don't need to reach or concentrate on. They already attended! How to study the real social networks of people who are interested or invested in an event but can't/won't/don't attend is what I need.

    4. network's boundaries

      If planning to use SNA within a case study, it's important to know if you're a Yin, Stake, or Merriam-oriented case study person.

    5. there are few, if any, examples of their use in educational research.

      It is common to use students' family income or parents' education to predict students' academic performances. Educational researchers also usually talked about culture capital and social capital, but I think It could be more interesting employing SNA method to explore what resources certain student can access except from parents and how those resources impact students' achievements. In addition, I think we need more research investigating the extent to which social capital is independent from socioeconomic status.

    6. Managing Relational Data

      Your summary of this area was very helpful Bodong! Your visuals were very useful to understand the structure of data necessary for SNA.

    1. On the other hand, when it comes to searching for and obtaining resources that one doesn't possess, such as finding out about college opportunities or getting help with your homework, accessing and extending bridges in the network may be more preferable.

      Now this helps me to understand the stroke patients case, stroke people who go to hospital early have weak ties with other people, maybe because the other actors in the stroke people's networks are more likely to have resources (knowledge or experience refer to stroke symptom) which stroke people's family members don't have.

    2. For example, does a child benefit from having parents, teachers, neighborhood adults, and so on communicate with each other, or do these relations constrain that child?

      This is such an interesting question! My area is in college students, so I don't have to worry so much about parental involvement, but for those in K-12, the assumption that this sort of close communication is good. I wonder if it's like the stroke victims where having more weak ties among the network is a good thing. Though I can't fathom why that would be the case in this situation.

    3. One take on this is that bridges or access to bridges [Page 224]facilitates returns on one's investment, thereby indicating social capital. This was elaborated on and formalized by Burt (1992/2004) in his notions of structural holes and constraints

      This would relate to the talk we watched on stroke victims. In that, there was more constraint because of how close-knit the stroke victims who came in late were to their networks (presumably, families). If I understand it correctly, there would be few bridges in these networks because the nodes are already connected to each other, so there's not a "bridge" to a non-centralized group.

    1. Table 3.4

      Is it possible to indicate other types of relationships between actors, other than a 0/1? Or do you just need multiple tables identifying different relationships between the different actors?

    2. Table 3.1

      Very useful visualization of what data looks like.

    3. The advantages of representing network data in this fashion will become clear, but for now, keep in mind that a matrix is simply an array of data.

      This is an application of SNA that I haven't considered before; when I think of SNA I generally think of network graphs. Though this is definitely an interesting application of SNA data.

    1. the tendency to “reduce” individual actors to a collection of attributes removed from context

      The social factors are at least as important as the individual attributes when studying a human phenomenon.

  5. Jan 2017
    1. Graphing ego-level neighborhoods and comparing them can provide hints into the similarities and differences among the network's actors. For example, using the Peer Groups data, you could ask whether the ego neighborhoods of high-achieving students are bigger than those of low-achieving students or which students have neighborhoods that consist mainly of reciprocated ties.

      I could apply ego graphing to mentor networks. I could establish an ego network for each mentor and compare them for interaction and relationship to student interest and motivation.

    2. Pittinsky asked each student (ego) to rate their friendship on six-point scale (1 = best friend; 2 = friend, 3 = know-like; 4 = know, 5 = know-dislike; 6 = strongly dislike) with every other student (alter) in the class. In addition, the teacher was asked [Page 45]to do the same.

      Applied quantitative SNA methodology

    3. As noted by Hanneman and Riddle (2011a), a well-constructed graph can be very useful, perhaps even more useful than words, for communicating a network's properties.

      Is there a tool to model SNA over time? In other words, while the graphic snapshot these graphs represent is useful, I think being able to see change over time would be a yet more powerful tool.

    4. single relationship among actors: friendship, support, advice, and the like

      An interesting relationship for my research is L1 (first language). Most EL students drift towards students that share their L1 and a similar cultural heritage. However, it is always interesting to see Somali students in class using commands in Spanish during class!

    1. Examining 36 classes in two schools over the course of 1 year, McFarland's analysis, which includes a number of student-level social network measures, reveals that students’ background characteristics only partly influence students’ decision to defy

      Great example of SNA at work.

    2. social network analysis is the area of diffusion, particularly how innovations spread through and across organizations such as schools.

      I am very interested in studying the diffusion of knowledge from mentors to mentees as well as between peers in an astronomy related forum

    3. For example, social network analysis and its underpinnings in relational realism have helped reframe teaching and learning by focusing attention on the role of trust (Bryk & Schneider, 2002), relations among teachers (Coburn & Russell, 2008), and the relationship between social capital and student outcomes

      Interesting analysis: The triangulation of trust, relations among teachers and the relationship between social capital and student outcomes.

    4. For example, social network analysis and its related models can now handle millions of actors, and new methods for dynamic and temporal features of networks continue to be at the forefront of the field (e.g., Boyack, Börner, & Klavens, 2009)

      This seems exciting and full of possibility. I'm curious what the proliferation of this type of research will do to SNA of small communities, such as individual classrooms. I imagine studying both scales can be very beneficial. I hope both continue to grown in their influence and research stature.

    5. A second area in which educational research has generated theoretical insights through social network analysis is the area of diffusion, particularly how innovations spread through and across organizations such as schools

      Once I get more resources together, I have a website, 26ers.com, that I would love to attach to an SNA like this. The site is aimed at helping emerging readers explore literacies (traditional text, video, and graphic) through a mobile device and connecting that practice to their lived experiences.

    6. dynamic process that is not adequately explained by conventional social theory, nor do the methods most often used by social scientists capture [Page 36]these dynamics

      do the tools exist to model the dynamics of these relations in real time? i think it would be very powerful to show how these relationships change over time, as well as linking what the real-world implications would be.

    7. Since stronger friendships imply greater vulnerability to influence, students are likely influenced more by friends who are in the same track than by those in different tracks (Hallinan & Sorenson, 1985). Such social influences have obvious consequences for individual student outcomes.

      I would love to see how SNA and peer influence affect second language acquisition. I can see how this might help districts/schools support learners upon arrival. Connecting them to important social structures to support learning.

    8. the main story is that classroom social networks and instructional formats explain a great deal more about everyday acts of defiance than do background characteristics alone.

      I think background characteristics or students' attributes also affect how friendship networks form. Like attracts like, students with some attributes in common are more likely to form a friendship network, so I think attributes also play a critical role in this case. But I also agree it is a good way to tell a story from the social network perspective.

    9. economics

      This reminds me a course" the introduction of sociology", in that course, the instructor talked much about social relations embedding economy. He used Facebook (maturing cybernetworks) as an example to illustrate how Facebook uses social network to make money, it employs user data especially the friend relations to advertise and sell products to certain users. And a lot of vivid examples related to economic activities were presenting, such as how Chinese people or Jewish people use culture networks to conduct transactions abroad.

    10. Students’ friendship networks play an important role; the main story is that classroom social networks and instructional formats explain a great deal more about everyday acts of defiance than do background characteristics alone.

      By "defy," does McFarland mean to misbehave? If it's also about not completing work, I wonder how this same scenario would play out in the online realm.

    11. Relational realism, as described by Tilly, also rejects the quest for governing laws to explain large social processes ranging from war, revolution, urbanization, and class formation to the formation of nation-states. Instead, Tilly advocated a careful analysis of social relations, empirical examination of the chains of connections linking persons through time and space in larger compounds of relations. Consider, then, how this approach would view a process such as “school reform.”

      I hadn't really thought of SNA in these terms, though it does make sense. This reminds me of our current political situation. I wonder what work SNA has done to analyze this. I'm sure I could find some things, but if anyone's run across something they'd like to share, I know I'd be interested.

    1. The assumption here is that one student's behavior is independent of any other's. Social network analysis directly confronts this assumption.

      I think this is essential for teachers to understand when dealing with behavior, it is social. Many of my colleagues realize this, and deal with behavior by looking at who students interact with, but it seems like there is much more that could be done in this area.

    2. Adolescents often tend to turn to others and either mimic behavior or “act out” in ways to seek approval from select audiences. To best fully capture a description of the student's behavior, you should examine student-to-student relations. These relations might include membership in the same extracurricular groups, the frequency with which they communicate outside of school, joint course-taking patterns, friendship nominations, and others. To fully understand and model the phenomenon of student behavior, you need the relational data inherent to the social network perspective.

      I find this to be an interesting example because it draws ties to behavioural science, education, child development, psychology and SNA in a clear illustration of how SNA may be used to analyze adolescent behaviour in a school setting.

    3. Finally, also consider that the chance of any given teacher enforcing the policy increases with the number of others who enforce it. Under what conditions will [Page 15]enforcement of the policy spread to a nontrivial portion of the network? What percentage of teachers will ultimately enforce this policy? How does this depend on the network's structure and the individual's position in that structure as well as one's own individual attributes?

      Diffusion--as I understand it--is tracking how a particular phenomenon moves through a social network. This reminds me of a TEDTalk on how to start a movement: https://www.youtube.com/watch?v=RXMnDG3QzxE

    4. While you might assume that being on the periphery of a network is disadvantageous, often these peripheral members have ties to other people within or external to the network in which they may occupy important positions. In these instances, the actor serves as bridge to other groups or networks

      This makes an interesting point, one which is important to keep in mind when analyzing a network--you know only the bounds of that particular network and thus any hypothesized implications of, for example, peripheral members can only take you so far. Being able to fully analyze would mean access to information from other classes (in the context of education) and beyond. This already got more complex...

    5. Now, you could ask questions such as, how does being on the outside (periphery) or inside (center) of a friendship network correlate with one's grade point average? Or, do birds of a feather flock together? That is, are students with similar grade point averages likely to nominate each other?

      Suppose a de-tracked class at a racially integrated high school has a large variation in school performance (perhaps measured through GPA) within the class. I'd be curious to analyze the friendship nominations therein. I'm not quiet sure what question to ask, but I'd hope to better understand what barriers and bridges facilitate inter/intra-racial friendships in this scenario. The same question could be asked about gender, class, language, ect.

      As I read more about SNA, I continue to realize just how new this terrain is for me. There seems to be a lot of potential to study and learn interesting things. As of yet, I barely know enough about the tools within this methodology to even ask an appropriate question... haha!

    1. When a firm intentionally locks up a supplier to an exclusive contract, competitor firms are excluded from accessing that supplier, leaving them vulnerable in the marketplace.

      I had never considered this to be a type of "social network" or to be measurable in terms of the strength of ties between organizations.

    2. Bott found that the degree of segregation in the role-relationship of husband and wife varies directly with the connectedness (or density) of the family's social network. The more connected the network, the more likely the couple would maintain a traditional segregation of husband and wife roles, showing that the structure of the larger network can affect relations and behaviors within the dyad

      Can't help but think of the division of tasks within my own household. I find it interesting that stronger ties suggest more segregation between husband and wife. Maybe the thought is that when there are less strong ties in the family structure, they rely on each other more and hence work together more?

    3. A set of experiments (36) showed that nodes b and d have high bargaining power, whereas nodes a, c, and e have low power. Of special interest is the situation of node c, which is more central than, and has as many trading partners as, nodes b and d. However, nodes b and d are stronger because each have partners (nodes a and e) that are in weak positions (no alternative bargaining partners).

      What's an example of this type of "chain" network? I see the logic in the argument, but am having difficulty seeing its application.

    4. It was also noted that structurally equivalent individuals faced similar social environments and therefore could be expected to develop similar responses, such as similar attitudes or behaviors

      How might this show the ways in which teachers view their students? How might social environments in and around schools lead to teachers viewing their students with a deficit-thinking mindset? Or with an attribute-thinking mindset?

    5. From a social scientist's point of view, network research in the physical sciences can seem alarmingly simplistic and coarse-grained. And, no doubt, from a physical scientist's point of view, network research in the social sciences must appear oddly mired in the minute and the particular, using tiny data sets and treating every context as different

      What about the speed at which networks can now develop and morph? Is there a tool developed that we can use to analyze speed of network development, its efficacy in achieving its purpose (whatever that may be) and triangulating it with findings in network research done from a physical science point of view and the social science analysis? ...this is clumsily stated, actually.

    1. “Understanding these social factors will uncover pedagogical and technological constraints that have negative impacts to student persistence,”

      Probably critical to understand social factors from a cultural viewpoint, also. As online classes cross political boundaries, Cultures may have differing expectation that 21st century users may or may not understand about each other and that may or may not impact student persistence...Is this a fair observation?

    2. “Our MIT online course data already suggests students perform better when they have help and the social connection to support their learning,” Siemens said. “This connection contributes to their willingness to persevere through the course and could come in the form of interaction on the social network platform, experience in leveraging online social capital and personal motivation.”

      Interesting - connections among students contribute to their willingness to persist in online course. Social capital and personal motivation are also mentioned. Would love to read the final reports.