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

I have a lot of node attribue data that is binary (workday track), but I also have a lot of atttribute data that I force to be binary. I think it works, and I have reasons for each fit, but ultimately it simply eases the analysis.

To what degree can this be an iterative process? After I saw the network data that I collected, I became interested in interviewing a particular subset of students. Though I may have been able to predict the intrigue that the friendship network created, I didn't. Only after seeing the data, did I develop more finely tuned problems to study.

To what degree may questions develop after data has been collected? Depending on the type of data collected, these new quesitons may already have relevant data to support conclusions.

A question I have had for awhile relates to the timeframe for developing a theoretical framework, and how to present that experience in a manuscript. It seems to be a good idea to understand a theoretical framework well after consulting the literature before collecting data. Often enough, I suppose data will not match the theoretical framework, which may force an adjustment of somesort. How do you present this in a manuscript? This experience may lead to the richest conclusions, but I could see authors writing to different audiences and varying their emphasis on the theoretical shift.

Consult literature that is not solely focused on education. That'll be important for me to always remember.

Sounds interesting! I'd be curious to learn more about this.

I need to look more into how to address the absence of certain data points. If I recall correctly, 133 of 144 students in the 9th grade at my school participated. Reasons for absences vary, and I need to look into what to do about the missing nodes. Any suggestions?

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.

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.

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

My intuitive analysis suggests that there must be some easy way to convert between density and distance. I am probably missing a core difference between density and distance for ego networks. The difference here doesn't seem to be as valuable as the difference for full networks.

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

Is it not valualbe to analyze the size of ego networks for social media? This likely wouldn't limit the size of the networks to 6, right?

There seem to be two types of questions here: relational questions and attribute questions. Might it be worth categorizing these questions about eliciting egocentric network data into two seperate categoies?

I notice that Leader 35 has the smallest number of alters and the highest efficacy score. Did I read that correctly? I am sure this is too tiny of a data set to conclude anything, so I'm curious what would happen to that pattern with broader analysis.

When designating each of the eight positions, was there a structure to the numbering? In other words, why does Position 4 seem to possess more influence? Why wasn't this group named "Position 1"? It seems random. I am just curious if I'm missing something.

Though noteably different, it seems like {B, C, D} might also hold reasonably equivalant positions. What analysis would show such positionality?

What would result from analyzing the similarities and differences between the results from each of these methods of group analysis? Each is distinct, for good reason. Nevertheless, overlap may rightly be expected. Might there be a way to identify that overlap and then make mearning from the overlap?

This seems to be an unweildy number of cliques for a group of 17 people. How can this bottom-up analysis conclude similar findings as a simple ethnographic inquiry? Would more analysis be necessary? If so, what analysis?

This seems to answer my question about the authors decision to have three blocks in this data set. Nevertheless, I need to practice this calculation to shake the feeling that it is ultimately arbitrary.

I trust that there was a theoretical framework for this choice. Nevertheless, I am at a loss to understand that framework. Why were three blocks chosen?

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?

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.

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.

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?

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.

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?

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.

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...

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.