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

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?

so is this directly correlated to structural cohesiveness?

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

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).

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?

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

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?

centralization: power in the hands of a few

small world phenomenon

clustering: tendency towards shared interactions based on homophily

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.

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

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

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

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

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?

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

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.

https://en.wikipedia.org/wiki/Clustering_coefficient

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?

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

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?

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

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?

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

Step 1.

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

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

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

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

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

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.

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?

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?

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?

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

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.

This is incredible! Thank you for sharing this link.

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).

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

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?

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

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

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.

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

I like this definition of SNA

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

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.

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.

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

positional different from relational because there could be structural holes

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.

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???

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.

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?

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.

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

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.

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?

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.

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.

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

SNAEd folks only need to scan this section :)

Please don't do this!

The link is not working.

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

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?

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?

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.

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

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

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.

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

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

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

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.

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.

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?

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.

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?

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.

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

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?

stroke?

I claim this one--Tianhui

stroke?

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?

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?

Very useful visualization of what data looks like.

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.

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

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.

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.

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

Excited to learn more about this!

Ego, I guess that is a good descriptor

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.

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

Interesting

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”).

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

Applied quantitative SNA methodology

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.

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

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.

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.

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!

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

Great example of SNA at work.

I think this will occur a lot in SNA

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.

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

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

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

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.

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.

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?

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.

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

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.

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?

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

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.

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.

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.

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!

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.

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.

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?)

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.

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

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.

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

Ties are connections between actors

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

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...

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!

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

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.

annotation about this piece of text.

• item 1
• item 2

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

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.

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.

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.

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?

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

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.

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?

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...

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.

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?

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

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?

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.

spelling mistake?

there is an extra blog

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

Let's annotate!