41 Matching Annotations
  1. Apr 2017
    1. methodological transactionalism

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

    2. binary

      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.

    3. you will determine whom you plan to study

      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.

    4. Research questions or hypotheses flow directly from your preferred theoretical framework—assuming, of course, you are operating in a deductive manner

      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.

    5. it is critical that you immerse yourself in the relevant literature that has developed and tested various components related to these and other network-based theories.

      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.

    6. Table 12.1

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

    7. social capital

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

    8. patterns of missingness

      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?

  2. Mar 2017
    1. Summary

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

    2. advantage

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

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

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

    4. This translates into the number of “steps” that separate the two most distant actors in an ego's network.

      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.

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

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

    6. The size of ego networks typically ranges from 0 to 6, since a name generator typically limits the number of alters that ego can list.

      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?

    7. Table 7.2

      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?

    8. Table 7.1

      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.

    1. something that Position 4 seems to possess. Position 3 (Students 5, 3, and 9) has a reciprocated relationship with this position, suggesting that this actor, too, may reap some advantages by having a mutual tie with these actors; no other position receives a tie from them

      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.

    2. In Figure 6.5, there are actually five automorphic equivalence positions: {A}, {B, D}, {C}, {E, F, H, I}, and {G}

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

    3. Though each procedure will provide you with a different take on the network's substructures, they are all based on the ways in which actors are interconnecte

      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?

    4. By relaxing the criteria for group membership, the number of cliques has increased from 11 to 20, with Student 1, for example, being a member of 16 clique

      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?

    5. In general, GN subgroups are identified by first calculating betweenness centrality (this version of centrality is discussed in Chapter 7) on the ties, and, second, determining if there are any components revealed once ties with the highest betweenness scores are removed. This process is repeated until the number of desired groups is obtained

      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.

    6. The number of “blocks” to enter is up to you, and after working upward from two, it was decided that three was a meaningful number of blocks.

      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?

  3. Feb 2017
    1. 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.


    2. 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?

    3. 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?

    4. 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?

    5. 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.

    1. 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.

    1. 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?

    2. 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.

    3. complete-networks

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

  4. Jan 2017
    1. 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.

    2. 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?

    1. 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.

    1. 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.

    2. 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.

    3. 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?

    4. 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...

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

      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!

    2. 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?

    3. 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.