360 Matching Annotations
  1. Mar 2017
    1. There is, however, no current standard for an acceptable Q value.

      I imagine this is due to a focus on research question variability and the flexibility of the overall SNA processes?

    2. bottom-up approach starts first with the dyad and extends upward

      I 'm confused to tell the difference between "top-down" approach and "bottom-up" approach, it seems like they begin with different unit, "top-down" approach starts first with the whole network and then try to find its subgroups, and "bottom-up" approach starts first with more micro unit like dyad. Do I understand correctly?

  2. Feb 2017
    1. “keep things together.”

      so is this directly correlated to structural cohesiveness?

    2. connected subgraph in which [Page 114]there is a path between all pairs of nodes

      component definition; the graphic from Bodong's video clip is helpful in getting a (mental) picture of this definition.

    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. Which structural properties of the complete network might be of interest to you?
      1. Would it be right to say that a decentralized network would mean that more actors have a say in the evaluation process?
      2. Could a measure like high transitivity indicate more equitable practices in evaluation?
      3. Would high reciprocity mean that there was healthier communication (not just directed "at" the new teachers, but feedback loops and supportive avenues of communication)?
      4. And finally, what would high density scores indicate -- that resources are more accessible and basically better connections overall - so a more well-connected network, with fewer actors in isolated or peripheral positions?
    3. before the participants got to know each other

      If they were asked at a later point to similarly identify three friends, if reciprocity increases, would that leave more structural holes?

    4. cohesive network with minimal clustering.

      So if we have a large (size) cohesive network with minimal clustering, does it follow logically that there will be high transitivity and high reciprocity and low centralization?

    5. relations are focused on one or a small set of actors

      centralization: power in the hands of a few

    6. “Small worlds” are those that paradoxically have a low average path length but high clustering.

      small world phenomenon

    7. “group together” into pockets of dense connectivity

      clustering: tendency towards shared interactions based on homophily

    8. large diameter and small average path length suggests a structure in which there are parts of the network that some network actors may be unable to access.

      ah - this might answer my previous question: so cohesiveness in a large network might not mean the same thing as cohesiveness in a small network. Very interesting.

    9. hierarchy, equality, and exclusivity

      examples of forms of relationships (16 dif kinds according to Holland&Linehardt, 1979)

    10. teacher networks with high reciprocity would be positively associated with teachers’ perceptions of their schools’ innovative climate and trust

      But who would be excluded? Or is it possible to imagine a network with high reciprocity, where there is high inclusion as well?

    11. reciprocity is reported as the proportion of reciprocated ties in the network. Therefore, values closer to 1.0 indicate higher reciprocity

      reciprocity: appears in many studies and seems highly relevant in educational fields

    12. conceptual level, it influences the structure of relations

      size: affects structure of relations, reflects network's boundary, and measure of number of nodes in network

    13. Table 5.1 T

      Is this an acceptable format for an excel/csv file in R - or is it better to convert to weighted format, which is I think how LesMis data was presented?

    14. dichotomized

      How were the data changed from 1,2,3 to 1,0?

    15. network-level clustering coefficient of 0.24

      Is the formula they used to calculate this coefficient the same as on the link below? If so, I'd be curious to see how matricies can help make this calculation easier.


    16. A network's diameter refers to the longest path between any two actors

      I'm reminded of six degrees of seperation. Is this a helpful rule of thumb when considering whether a network has a large or small diameter?

    17. 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.

    18. structural holes (one actor connected to two others who, in turn, are not connected to each other)

      From my understanding of this, structural holes differ from triads since triads generally indicate that all three actors are connected in some way. Is this correct?

    19. The paradox, of course, is that the relative paucity of weak ties is what makes them “strong,” as they provide early access to diverse information

      Is it fair to say that networks with high reciprocity and transitivity might mitigate some of the negative effects of clustering?

    20. A high degree of reciprocity means that a network's actors choose one another. It could also mean that while some actors choose one another, they are not choosing others, which results in a high degree of clustering within the network

      Hmmmm, high reciprocity is associated with network stability and social equality, but it might also be associated with clique-i-ness (clustering). What is the nature of a social network with high reciprocity and low clustering? What is the value of this type of network?

    21. This was done simply for purposes of presentation; any manipulation of network data should have some theoretical or empirical basis.

      So are 'purposes of presentation' valid basis for data manipulation? I am left yearning for more explanation.

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

      Step 1.

    23. effective density,

      I like the explanation of effective density. I think it will be useful for my own "potential" research

    24. UCINET

      This is the software that was used by the authors of the article that I chose for our Week 3 assignment. Apparently, it's a fairly common choice for SNA research

    25. network's topography
    26. Density is intricately linked to network size

      ...and then: here is the connection to the previous note.

    27. the pattern of relations among the network's actors.

      (Mental Note) The concept that I have to focus on...

    28. 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

    29. 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.

    30. Equation 5.1

      I think this equation only can apply to calculate density of directed network, if we need to calculate a non-directed network density, it should be another equation, is that right?

    31. CDi indicates individual actors’ centrality scores

      Foundational math question: Does this CDi mean all other scores added together, or is this calculation meant to be applied between the actor with the highest degree against every other actor? So, in the scenario with the 17 frat brothers, is calculating the centrality a matter of doing Freeman's formula once, or 16 times?

    32. following measure of reciprocity

      Is there a typo in this equation? I would think that instead of having Aij in all 4 positions, of the equation, that Aji would be in 2 of them (as that would indicate the relationship of j to i instead of just i to j). Am I thinking about this wrong? Why are all 4 of the equation factors with a focus on actor i to j?

    33. The theory of cognitive dissonance grew out of this work, attempting to explain how people felt when their immediate environment was unbalanced

      This is interesting! Didn't realize cognitive dissonance had SNA roots.

    34. 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. See http://kateto.net/network-visualization↩

      This is incredible! Thank you for sharing this link.

    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. Social Network AnalysisTheory and Applications

      I like this text. Loads of useful information. Thanks!

    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. it may be possible to guess the names of individuals by virtue of their location in the network

      While I know this is possible in some situations, I wonder how often it actually occurs.

    3. 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.

    4. We close this section with some discussion of why it might actually be unethical to neglect the organization’s social network

      I have been thinking about the SNA project I'd like to work on. Based on the article I looked at last week, I might change directions and look at the social network of all 9th graders at my school. I'd be valuable to consider how structures that the administration puts in place affect social networks, how these networks build social, particularly racial, trust. Certainly, in schools, an absent and uncritical examination of race can be an unjust act.

    5. not managing relationships is managing them. The only choice is how to manage networks of relationships. To be an effective networker, we can’t directly pursue the benefits of networks, or focus on what we can get from our networks. In practice, using social capital means putting our networks into action and service for others. The great paradox is that by contributing to others, you are helped in return, often far in excess of what anyone would expect

      so applicable to how the best types of parenting work... and with very similar paradox(es)?!!

    6. mapping and measuring of relationships and flows between people, groups, organizations, computers, Web sites, and other information/knowledge processing entities

      I like this definition of SNA

    7. P-O-L-C

      http://catalog.flatworldknowledge.com/bookhub/5?e=carpenter-ch05_s01 Planning -Organizing - Leading - Controling

    1. snowball sampling

      Interesting. I am unfamiliar with this type of sampling. I have heard of it before, but I have not seen many studies that utilize this sampling approach.

    2. 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.

    3. K-core, discussed in more detail in the next chapter, is a subset of actors that has ties with at least K other actors

      useful in "empirically locating network boundaries" but not used widely

    4. positional approach generates a set of actors that occupy a similar position in some social structure. Each actor, however, need not be directly connected to every other actor

      positional different from relational because there could be structural holes

    5. 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.

    6. First, network structures uncovered through this approach look different from one another

      Good observation Travis @church251. I think so too, but I think I also did some ego analysis of individual actors using the Reingold-Tilford layout, maybe???

    7. 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.

    8. making an inference from one network to another is unwise

      I imagine there would be a high bar of proof/analysis required to wisely infer something about a certain network from other networks. Am I wrong in assuming that this isn't just an issue for networks bounded by positional attributes?

    9. How can you be assured that the [Page 71]respondents can really identify those charter school advocacy organizations that are most important?

      You can answer this question with confidence by defining 'most important'. It simply must be limited to having the best reputation. Reputation has very real consequences, which can be articulated and analyzed. The question is not whether respondents can identify those advocacy organizations that are most important. The question is whether you can build a strong case for these networks social centrality having physical, political, economic, ect consequences.

    10. complete-networks

      I suppose measures of centrality, as many people analyzed through Gephi and R, would be complete-network measurements, right?

    11. 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.

    12. This approach is either based on your knowledge about relations among a set of actors or relies on the actors themselves to nominate additional actors for inclusion.

      This is interesting for my own likely research needs/goals in looking at how technology integration processes spread. That said, it might also be difficult to track in a truly large-scale project. For example, there isn't one "universal" place for teachers to go and find technology integration processes. Perhaps this will have to be bound by the PLC/PLN I have in place?

    13. 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.

    14. you may be interested in observing participants at local board of education meetings, collecting relational and attribute data on only those participants who attended three consecutive meetings in the past 6 months.

      This reminds me of a question I have been being curious about, how do administrators of a university to make some big decisions especially when they attend board or other management meeting. I think it is interesting to explore this question using social network perspective.

    15. 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.

    16. Quality of Relational Data

      SNAEd folks only need to scan this section :)

    17. In the node-list format, the first node in each row is ego, and the remaining nodes in that row are the nodes to which ego is connected (alters).

      Please don't do this!

    1. He reaches a higher volume of information because he reaches more students indirectly.

      Could this be restated as "quantity over quality" of connections?

    2. For example, Coleman claims that social capital is any “social-structural resource” that generates returns for someone in a specific action. Accordingly, social capital can be captured only by its effect; whether it is an investment depends on the [Page 221]return to a specific individual in a specific action (Lin, 2001b). Obviously, it is impossible to theorize social capital when its causes and effects are folded into a single function.

      I have an issue with this as stated. Couldn't it be that the "return" of social capital, as it is considered here, is only looking for extrinsic motivation? What about intrinsic motivations, such as helping someone simply because it feels good or is seen as the "right" thing to do judged solely by one's moral compass?

    3. he concept of social capital is said to address all of these situations (Kadushin, 2004).

      I keep coming back to the "mind as rhizome" metaphor as we talk about social networks and the theories that surround it. It seems to me that we, as humans, often create things that are structurally or functionally similar to our own bodies (i.e., the ways that a computer works and it's internal hardware mimicking the structures of the brain). Perhaps the same is true with social capital and social media: are these actually extensions of our brain?

    4. You are at home doing your homework and have no idea how to solve a math problem. But you can call a classmate and ask that they help you work through the answer. It was worth being nice to that classmate even though you do not particularly like him. Do you have to return the favor? Maybe he can grab a ride with you the next time your sibling gives you a ride home from school. Maybe the value of his help was minor enough that you feel little need to reciprocate. Now, however, you need help preparing for a big math exam. Your classmate has made a digital copy of the notes that the teacher prepared for the whole class that you left in your locker. But there is really nothing to return except for the goodwill, because although he passes on this digital copy, your classmate still has it. Another classmate that you have never spoken with has heard from this first classmate that you are real good at writing computer code and asks you to help him. You are busy but feel obligated to at least try and help because you are all in the same math class. Someday you may even have to ask that classmate, maybe even a different one, for help in fixing that computer. What goes around comes around. In fact, you may be in a bad mood because you got that homework question wrong, failed the math exam, and your computer is busted. Someone with whom you have been friends for years and do not see often calls you and makes you feel much better.[Page 216]

      This really reinforces the concept of social capital from the neurology video in the course website, and provides a qualitative example of what diversity of information and connection might be experienced like.

    5. These kinds of situations give rise to an array of important, basic questions that confront educational researchers. On the dyadic level, why does one student help another? What are the consequences of this interaction? If all of your classmates have the same answer, should you try and reach out to others not in your class who may give you a better alternative answer? What are the costs associated with getting help on that exam? Suppose you kept asking for help on subsequent exams; is your relationship established enough to do so?

      Simply put, "What's in it for me?"

    6. Cultural capital theory

      This is my first time learning about cultural capital. It makes sense so I am surprised that I have not come across it before.

    7. human capital theory

      Human Capital is a big deal in the field of Human Resource Development. Supporters of the human capital theory argue that humans are the best asset of any organization. It's nice to see the theory mentioned in literature outside of business and HR fields.

    8. social closure vs. structural holes

      While I can make an educated guess as to what these terms mean, I am not very familiar with them.

    9. Social capital as a theory-generating concept is best conceived in the social network perspective (Lin, 2001b)

      I hadn't considered this before, but it makes sense! Interesting.

    10. “Social capital [Page 218]is defined by its function. It is not a single entity, but a variety of different entities having two characteristics in common: They all consist of some aspect of social structure, and they facilitate certain actions of the individuals who are within the structure” (Coleman, 1990, p. 302).

      This is the easiest definition to work with, in my opinion!

    11. this classical theory of capital consists of two distinct elements: value that is generated and pocketed by the capitalists and investment on the part of capitalists with expected returns in the marketplace. Therefore, capital is a surplus value and represents an investment in expected returns (Lin, 2001a).

      I find this interesting because it is being described as "classical theory of capital"... even though it is still used to this day as the dominant definition of capital, and fits well within contemporary applications of capital theory, including social capital theory.

    12. 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.

    13. bridges, brokers

      I don't think I adequately understand the difference between these two. They are both about connecting separate networks, and I thought at first the terms were interchangeable, but they're used together here, so clearly they're not. What's the difference?

    14. 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.

    15. Network resources refer to those that the individual ego can access through direct or indirect with alters. Measures that capture network resources focus on the range, quality, variety, and/or composition of these resources. Contact resources are slightly different in that they refer to the valued resources possessed by alters (e.g., power) and applied in specific ego actions, such as bullying, choosing a college, or finding a teaching job.

      OK. Now I'm confused. Can anyone help clarify the difference between network and contact resources? It sounds like contact resources are an attribute of one's network resources--is that accurate?

    16. 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.

    17. Brokerage opportunities are those in which an individual is located in a position in which he or she can broker the flow of information between people and control the tasks that bring different people together

      I believe this is also referred to as a bridge. Is that accurate?

    18. This idea that there is value in relations undermines the different ways in which the term social capital has been defined by a number of prominent theorists, who in the 1980s independently explored the concept in some detail (Lin, 2001a)

      I find it interesting that there isn't an agreed-upon definition. Is there one theorist's definition considered the most valid in educational SNA?

    1. stoke


    2. Rienties, B., Héliot, Y., & Jindal-Snape, D. (2013). Understanding social learning relations of international students in a large classroom using social network analysis. Higher Education, 66(4), 489–504. http://doi.org/10.1007/s10734-013-9617-9

      I claim this one--Tianhui

    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 3.2

      So in this example, if both actors have a 1 in relation to each other they share an edge (friends), but if one or the other actors does not have a 1, they have a unilateral relationship, and if they both have 0's then there is no relationship between the actors?

    3. Table 3.1

      Very useful visualization of what data looks like.

    4. 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.

    2. Therefore, in conducting social network analysis, it is crucial to do no harm to the participants by (1) emphasizing the voluntary nature of the data collection; (2) disclosing how the data will be used; and (3) disguising the data to maintain confidentiality (Daly, 2010). As the field is relatively new and often misunderstood by members of institutional review boards, further ethical concerns will surface and need to be addressed.

      Interesting point regarding confidentiality and the volunteer nature of SNA --> I wonder how this applies to non-consensual studies or research (for example, investigative journalism or information found online and adopted without consent). I can definitely see this flagging up some issues with a consent board.

    3. These structural relations—unlike “fixed” attributes such as gender, race, and age that do not vary in different contexts—exist only at a specific time–place and either disappear or recede when actors are elsewhere.

      Patterned social relations are structural relations that exist only at a specific time-place and either disappear or recede when actors are elsewhere. Relations vary significantly across contexts and condition the social actors apart from their attributes.

    1. Fig. 3.

      This figure is very helpful for identifying the types of data necessary for SNA

  3. Jan 2017
    1. The network formed by targeting one node, including all other nodes to which that node is connected (friends) and all the other connections among those other nodes (friends [Page 50]of friends), is referred to as the ego network, also called the one-step neighborhood of a node.

      Ego, I guess that is a good descriptor

    2. 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.

    3. directed graph. Conversely, an undirected graph

      These graphs are powerful tools, very similar to concept maps.

    4. Multiplex data, discussed later in this chapter, are those network data that measure more than one kind of relation, which most contemporary network studies incorporate.
    5. core-periphery structure:


    6. arcs or edges

      Here is a better explanation of the SNA terms Arcs and Edges. Arcs represent those relations that are directed from one student to another, meaning that the friendship nomination has not necessarily been reciprocated. Edges, on the other hand, are those lines that do not have arrowheads (since friendships are directed, there are no edges in Figure 3.1), which are appropriate when the relation is by definition reciprocated (e.g., “studies with”).

    7. arcs.

      I am unfamiliar with the term "arc" in this context since I am unsure what is meant by "directed lines".

    8. 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

    9. Two-Mode (Affiliation) Matrices

      While I certainly appreciate the highly quantitative nature of the matrices discussed here in chapter 3 (meaning, not just this particular heading), I hope we'll also talk a bit about tools that can be used to visualize the information, too.

    10. If schools are the actors, they too have multiple relations with other schools, including information exchange, alliances, partnerships, and other connections.

      It's important to remember that, if one considers schools as actors, that connections to student families are a huge factor in student behavior.

    11. 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.

    12. Social network analysis relies extensively on graphs to represent social structure.

      I'd like to know more about the rules that govern how to create these figures shown above. I intuit the meaning of centrality and connectedness, but I'd like to know more about why specific locations of actors, and distances between actors, are chosen.

    13. 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!

    14. sociometry

      This has come up a couple times now with no definition (that I've seen), so I looked it up on dictionary.com: the measurement of attitudes of social acceptance or rejection through expressed preferences among members of a social grouping

    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. relational thinking

      I think this will occur a lot in SNA

    3. nductive modeling strategies

      This is a really great SNA term/phrase. Generating big ideas from small observations is a nice description of SNA. It reminds me of the grounded theory approach in qualitative research.

    4. 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

    5. Diffusion

      I really appreciate how the term diffusion is used in SNA to describe the natural spread of knowledge through a network.

    6. 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.

    7. 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.

    8. 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.

    9. Unfortunately, this divide still polarizes the field of educational research

      At the risk of being "that guy" - why can't we all just get along? Why is it that so many in this field find it necessary to try and squelch the work of others?

    10. 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.

    11. CONCOR (for CONvergence of iterated CORrelations)

      I'm curious about this concept, as I suspect it's been the basis for other technologies we use today.

    12. therapeutic techniques

      I think the idea of research as therapy is really interesting. Beyond that, though, a major concept within LT is the idea of bringing theory into practice; this seems like a different application of that process.

    13. phenomenological individualism

      Is this ontology inherently incompatible with relational realism? If different educational researchers operate under each ontological paradigm and describe the same scenario in unique ways, what value would each bring to our understanding of the world? Is one inherently more valuable than the other?

    14. It is a comprehensive paradigmatic way of taking social structure seriously by studying directly how patterns of ties allocate resources in a social system”

      SNA is not just a methodology, rather an informed perspective that should affect every area of research.

    15. 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.

    16. 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.

    17. 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.

    18. Social network theorists and analysts go as far as to recognize that the inductive modeling strategies of social network analyses—that is, generating big ideas from small observations—are in opposition to the usual canonical assumptions of statistical methods, which prefer a deductive logic that operates from ideas to observations

      I can see where the inductive may align more with qualitative whereas deductive may be more quantitative, but SNA still strikes me as much more quant than qual. I'm still new to research ideas, but it seems that to move SNA from quant to qual, you'd need to include more than just the mathematical modeling, such as having interviews to add some color to the SNA. Without those interviews, though (or something like that), it seems despite being more inductive, it would still be considered quant, not qual. As I said, though, I'm newer to this, so if someone wants to provide opposing reasoning, I'm very open to it!

    19. 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.

    20. 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.

    21. Fourth, relational realism is the doctrine that interactions and social ties constitute the central existence of social life

      This seems to somewhat reflect a similar evolution in education theory (not directly, but somewhat), tying into the newer theory of connectivism, as I see it. Though this doesn't necessary indicate a progression (maybe though?)

    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. Groups

      A collection of actors on which ties are to be measured.

    3. 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.

    4. Relation

      The collection of the ties between actors, taken as a whole.

    5. Ties

      Ties are connections between actors

    6. 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

    7. 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...

    8. 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!

    9. These advances have enabled social network analysis to move beyond description and toward inference: predictions about what will likely happen to a network over time

      And the epistemological foundation of this development of SNA seems rather post-positive. Or would this still be consistent with phenomenology?

    10. This orienting chapter establishes this point by demonstrating how this theoretical and methodological approach differs from conventional approaches used in educational research, which often views individuals as mere collections of attributes

      What is the epistemological foundation of SNA? There seems to be a similarity between SNA and phenomenology, in that the interaction between people (or things) shape their behavior (or reality). Yet, it is still unclear to me whether phenomenology would be the epistemological foundation of SNA.

    11. After all, characteristics such as one's academic history or educational aspirations influence who one knows and spends time with.

      annotation about this piece of text.

      • item 1
      • item 2
    1. flow of social influence

      so, does SNA within an educational research context require one to have a social constructivist lens or framework in mind?

    2. whether through influence processes (e.g., individuals adopting their friends' occupational choices) or leveraging processes (e.g., an individual can get certain things done because of the connections she has to powerful others).

      I think, as information itself is generated more and more quickly through social pressures and technology, that the processes of knowledge creation and answer generation will become more and more important in everyday life.

    3. 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.

    4. A hundred years before Moreno, the social philosopher Comte hoped to found a new field of “social physics.” Fifty years after Comte, the French sociologist Durkheim had argued that human societies were like biological systems in that they were made up of interrelated components.

      I find it very interesting that the roots of SNA are this old!! Especially considering that the preamble of this text says that SNA is growing rapidly lately.

    5. 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?

    6. 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.

    7. Twenty years later, Stanley Milgram tested their propositions empirically, leading to the now popular notion of “six degrees of separation”

      I'd be curious if this principle stands the test of time. Has our society become more integrated? Perhaps it's 5 degrees of separation... Has our society become more polarized? Perhaps it's now 7 degrees of separation... Has this polarizing affect made it so that there is a larger spread in the degrees of separation between people? I suppose I don't totally understand Milgram's empirical principle, but I'd like to see someone repeat his methodology today and then again in 30 years.

    8. 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?

    9. It was soon discovered that the kinship systems of such peoples as the Arunda of Australia formed elegant mathematical structures that gave hope to the idea that deep lawlike regularities might underlie the apparent chaos of human social systems

      I'd be curious to study these mathematical structures. I imagine there could be a lot of interesting empirical analysis of social networks that would illuminate social themes and phenomenon. Just adding more to the list that I don't (yet, hopefully) understand...

    10. 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.

    11. The common conviction at the time was that urbanization destroyed community, and that cities played a central role in this drama.

      So, here is a thought: The historical theory about urbanization destroying community, has proven to be false...what theories that we hold today, about social networking and online communities, will be proven false in the future?

    12. understanding the antecedents and consequences of network phenomena

      I am curious about a few things packed in this sentence: network phenomena, and their antecedents and consequences.

    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.

    1. you can highlight the error and include two tags – snaEd and issues – in your annotation.

      an example of issue reporting. Don't forget to add two tags below.

  4. Nov 2016