70 Matching Annotations
  1. Sep 2018
    1. UHnot implemented with care, they can also perpetuate, H[DFHUEDWHRUPDVNKDUPIXOGLVFULPLQDWLRQÝ

      This is what I was just posting about. :)

    2. ISDUWLFXODULQWHUHVWLVDQH[SORUDWLRQRIWKHHWKLFDOimplications for algorithmic decision making

      I saw a TEDTalk about this, but in HR versus higher ed. It discussed the issues with some AI/machine learning for choosing applicants, but that there could be unintended bias that is in the AI that weeds out groups based on gender, race, etc. I could easily see how something similar could be problematic in an algorithm for LA in higher ed.

    3. he right to correct wrong information (or inter-pretations arising from it)

      I like this idea, but I wonder how many students would actually go through the trouble of doing this.


      I agree that it's a good idea to have this before collecting data, but, depending on what you're doing, you might need to be more generic about the details. If you have an experimental study, for example, you don't want full knowledge of the study to affect behavior, which would taint results.


      This is related to something that I was thinking, which is the difficulty with student buy-in from a logistical standpoint, where someone may not check the box for buy-in because they didn't read your email asking for it. So they may be OK with your using their data, but they haven't let you know that. So it is considered "sneaky" to do an opt-out versus an opt-in? Can you assume that they're OK with your using their data unless they submit something telling you not to, or is that considered "sneaky"?

    6. understanding that demographic characteristics have and do impact support provided DQGDVVXPSWLRQVPDGHDQGWKHQHHGWRUHFRJQL]Hand address this)

      This gets to the point I was making before. Demographics can matter. Looking at the LA of them is not always so unethical.

    7. ccess, and storage (e.g., security issues and avoiding perpetuation of bias); and governance and resource allocation (including clarity around the key drivers IRUÜVXFFHVVÝ DQGZKDWVXFFHVVPHDQV H[LVWLQJFRQ-straints, and the conditions that must be met).

      All good points. Of particular note is benefits and to whom and also definition of success.

    8. Learning analytics as moral practice — focusing not only on what is effective, but on what is ap-propriate and morally necessary2. Students as agents — to be engaged as collabora-tors and not as mere recipients of interventions and services3. Student identity and performance as temporal G\QDPLFFRQVWUXFWVØUHFRJQL]LQJWKDWDQDO\WLFVprovides a snapshot view of a learner at a particular WLPHDQGFRQWH[W4. 6WXGHQWVXFFHVVDVDFRPSOH[PXOWLGLPHQVLRQDOphenomenon5. Transparency as important — regarding the pur-poses for which data will be used, under what conditions, access to data, and the protection of an individual’s identityThat higher education cannot afford not to use data

      This last point is an interesting one. I will be interested in the continued conversation of what sorts of data are acceptable. A few members of the class have expressed concern about demographic data being a part of this. Is there a case in which demographic data is ethical and helpful? I would argue yes.

  2. Apr 2017
    1. One potential harm is that it is more likely than not that a participant will know who else is participating in the study

      Unless, of course, no IRB is needed and no one knows they're being studied. Which holds its own ethical issues, of course.

    2. relational measures collected at separate time points)

      Which I'm already doing on my own through weekly data collection.

    3. RSiena

      OK. So maybe this could be a useful package in the future. Except my studies will likely just stick to the semester, given I have no way of tracking students beyond that.

    4. It is strongly advised that network data be collected through means such as these because they reduce respondents’ burden and errors in data entry

      No kidding!

    5. Organization Network Analysis

      Another one to keep tabs on.

      It occurs to me that I might've been able to use one of these instead of what I did (Google Forms) to create a survey for my students and then could've already had the data ready to export rather than needing to do the entry myself. Next time...

    6. Network Genie

      Another one to keep in mind for the future.

    7. These questionnaires can incorporate name generators and interpreters and provide a very helpful framework to configure and perform a survey interview

      Wait--does this mean it takes in the data from the surveys which can then be exported into a visualization tool without the researcher needing to import data separately?? That sounds awesome!

    8. E-Net

      This one has come up in a few studies I've read as well. Probably not as useful for my particular research.

    9. RSiena

      Sounds like what I would've been using a few years ago. Maybe not as relevant anymore.

    10. PNet

      Would need to have a firmer grasp on stats to understand which elements are most applicable to me. p tests might be, but I think I recall from that week they seemed less likely something I'd be interested in, so this program may not be for me.

    11. Foremost among its strengths is that it combines attribute and relational data into one model to perform a context analysis: It integrates data that describe people with data that describe relationships between people into a single analytic model

      I don't have a large network, but am I working with more complex data. Not super complex, but I like the idea of the integration it describes.

    12. NetMiner

      I haven't seen this one come up as much, but it sounds more powerful than UCINET and still has the phrase "user friendly" in it. May be a better option to look into.

    13. easily manipulate and transform data

      I like how often the word "easy" is used here. :)

    14. UCINET

      This one comes up a lot in the studies I've read. I guess I was under the impression it was more basic (like the SNA equivalent of web 1.0) but could be wrong.

    15. (1) ties between actors in complete networks; (2) certain individual attributes predicted from relational data; or (3) relations within and between groups.

      I had some difficulty in this area with my students because they're basically meeting minimum requirements for how many reply posts they do. I could ask questions around degree of my rockstar students or maybe look at repeated ties (suggesting the formation of a relationship of sorts rather than just a "reply to random person"). I need to see how many data shook out to start forming more of those "ah-ha!"s.

    16. Visualize the Network

      Getting better at this in R. I get the basic functions (more or less). Moving onto the fancier stuff! The Michael Marin videos on YouTube were really helpful for solidifying the basics, btw.

    17. Data Matrix with Three Vectors

      Mine ended up looking more like this (but with more vectors). Much better for R. :)

    18. adjacency matrix

      This was NOT a good route for me. I made the first few Excel sheets as adjacency matrices, and R was not happy. I will NOT be using this method in the future.

    19. Once your egocentric or complete network data are gathered, they are ready to be organized in a manner that is suitable for analysis

      It took a long time to get my data all tidy, but now that I know what that looks like, it would be much easier the second time around.

    20. Valued data reflect the relative strength, frequency, or duration of a relationship between a pair of actors. Different options for gathering valued network data include the use of Likert-type scales that assess the frequency with which one engages in a behavior with someone else: 1, 2, 3, or 4; 1 = never, 4 = frequently

      I used something similar to this for coding SP and CP, which was much more meaningful for me than just binary ties. However, I'm running into issues in graphing these because SP has 3 valued elements to graph.

    21. directed or undirected relational data

      This was an easy question for me to answer. Directional all the way. I'm interested in both directions too, as a student's sense of belonging is potentially affected by both incoming and outgoing posts.

    22. With a clear question, you can develop a better image of the type of data you are interested in gathering.

      I collected all the data! Because I may be using this for multiple studies, I collected everything on a weekly basis (after the close the discussion forum for the week). It made grading much easier because I was able to code at the same time I was grading, and it helped me give more detailed feedback to my students in the grading.

      More in next section.

    23. validity, reliability, accuracy, and patterns of missingness

      Continuation from previous post...

      I can see, however, that I either need to be doing the incredibly tedious thing of coding in one session, double checking coding, and/or having a more detailed rubric to follow. Because what I was applying was not totally clearcut, I can see where I may have (and probably did) code similar things differently when it came to levels of CP and SP. Not ideal, but also helpful for ensuring better consistency in future studies

    24. Select a Sample

      I was beginning to think my answers to everything was going to be "Could've done that better...," but this was one area I did pretty well. It was more of a given, since I was teaching the class. In future studies, I could look at pairing with another instructor. That might make my coding of things like CP and SP more objective. I found myself sometimes downgrading one student whose posts were at a higher CP level because I wasn't used to them doing that level of CP in their work. I had to go back a few times to compare coding. Triangulation in the form of another coder would be helpful, but I think objectivity would help too. It's hard to do study not your own class though, given access issues.

    25. yet this order could easily also be reversed.

      Which is something Tasha pointed out to me once for my own study. It was a good point.

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

      As Bodong knows all too well, my issue is in narrowing. I have way too many research questions currently because I'm trying to work too many elements together. I could do a better job of isolating elements rather than trying to find some grand way they all fit together. Just because they're related doesn't mean they belong in the same study.

    27. Develop a Theoretical Frame

      When I first read this, I thought it was going to be about the 30K level stuff, which I am also still wrestling with a bit--not like stats. Different sort of brain workout.

    28. social influence theory

      This one might be particularly useful for my research. While my study is set up as being causal, it's less causal on the SNA elements themselves. I would need a more comprehensive understanding of the stats before really delving into more deductive elements.

    29. This should happen even before you start formulating your own research questions.

      I had done some review of the lit before taking this course, but I didn't understand SNA well enough to "get" the studies. Other studies I've pulled have happened after my own research began. If I were doing this process again, I would do a better job in this first step of reviewing before formulating. I would also be a little more inclusive--there are not as many studies for SNA and CoI together (so great niche to publish in, but for lit review, I should cast a wider net).

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

    2. Contrast the aims of the mathematical and statistical approaches to social network analysis. For what reasons would educational researchers prefer one approach versus the other?

      Mathematical is descriptive (centrality, degree, etc), whereas statistical is inferative (allowing to make a prediction about the network). A researcher would be interested in the former if they do not need to make generlatizations about their network--if they are just interested in their network for its own sake. If the researcher wanted to say something about the population in general based on their network, then they would want to consider integrating statistical approaches.

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

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

    4. Why are these simulations necessary in order to make probabilistic inferences with network data?

      Simulations make things a lot easier. In even a modestly sized network, there could be thousands of permutations. Having a tool to create randomizations makes the process much less cumbersome.

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

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

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

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

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

    2. Why Study Ego Networks?

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

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

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

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

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

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

  4. Feb 2017
    1. 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

    2. 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?

    3. 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?

    4. 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.

    1. 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?

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

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

    3. 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?

    4. 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.

    5. 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?

    6. 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?

  5. Jan 2017
    1. 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. 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!

    2. 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.

    3. 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.

    4. 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. 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?

    1. 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

    2. 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...