I really like this concept and application of SNA with integration of critical discourse analysis (CDA), which is a course I am taking in the fall!! In another class, we read about critical quantitative approach, and I now think what makes it truly critical might be the integration of a mixed method approach, where something like ethnographic case studies or in this case discourse analysis is used in combination with SNA/statistical analyses to give it more robust findings.

This is a useful way to think about questions and especially when designing surveys in terms of desired objectives.

I would be interested in trying this especially because this textbook provides some good step-by-step examples using this package.

I just like the phrasing here. antecednets and determinants - strong, but true.

yes, but at least they are accounted for in some way. Traditionally, these are either ignored or mentioned as something that will be left out because it is too complex.

reciprocal relationships are very interesting and relevant to my research interests.

I really like how this chapter is written and how they take us through all the steps. Love the idea that looking at many networks will lead to an "intuitive feel" #snagoals :)

I would love to see more of these - in published papers, for example - or just tutorials that take us from raw data to tidy data to analysis/computations in R.

I hope to use survey design as a component of my data collection for a future project.

I hope this will play a big role in my future projects

I feel like I really appreciate how this step is more involved than I originally expected - mostly in terms of needing the data to be tidied up - organized appropriately before computations.

I like this line of thinking -- how the author suggests that we don't have to feel rigid about the questions - have a little more elbow room in exploring our area of interest while still maintaining focus.

I think even with inductive approaches, the theoretical framework lends itself to identifying emerging central themes. Just gives more room to incorporate other frameworks as well...

So this is a sub theory of network theory that focuses on concepts such as structural holes and brokers?

This is (unfortunately) reminding me that I need to finish my stats exam due later this week and try to come to some conclusions like this after spending probably the next 8 hours staring at awesome output in R... but I digress... yes, this seems like it would be very applicable to my study. :)

wow. I'm having a Don Quixote moment of imagining that my final class presentation will have a table that looks something like this... phew.

which is sort of amazing to me, because this is exactly what I think a qualitative approach like ethnographic case studies rely on as well!!!

This sounds like the answer to a question - wait, there's the question in the follow up paragraph below: why are simulations necessary...? :)

This seems key - especially when I think of how we study covariates in statistics and how predictor variables can be confounding, mediating or moderating...

Since my groups are small with four to six participants, a one-step neighborhood probably makes most sense, but if I were to look at an egocentric analysis with the moderator as the ego, the larger or complete network would be more applicable?

Since I am looking at teacher interactions across four groups in two years, I could use ego-centric networks to explore the role of particular actors -- to answer questions related to how particular actors influence interactions based on specific aspects of certain attributes - by looking at the quality/quantity of their initiated interactions for example. Another example is to look at the moderator's role -- each year there was a different but consistent moderator leading all four groups. Categorizing the moderator's interactions based on either quality or quantity could also be very revealing?

I think this also applies in financial sectors, right? good explanantion linking centrality and structural holes; I also like the breakdown in the bottom paragraph with five specific roles shown in fig. 7.7

density + pattern of relations among alters = constraints

wow. entropy sounds awesome. I want to be able to use this in my analysis just because it sounds so cool and I think it was one of the few concepts that really made sense when I was introduced to entropy in physics and chemistry!

This is useful. Sometimes I wish different studies would just show us the surveys they used so I can get a clear idea of types of questions and what is visualized.

focus of questions that try to examine individual entities across different networks and/or patterns of interaction within groups.

ego's social capital in the hood

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 is incredible! Thank you for sharing this link.

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?

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

positional different from relational because there could be structural holes

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

I like this definition of SNA