Our Virtual Class is officially scheduled for the week of Tuesday March 27 - Tuesday April 3, with March 20- 27 designated as our class Reading Week.
However, I would encourage you to find times anywhere in the period from March 14 - April 1 to participate asynchronously in our Virtual Class discussion on this blog. That will give you more time to do the reading and viewing for this class, and to take time to reflect on the ideas and the ongoing discussion, without rushing things.
You are expected to:
I've chosen two things that everyone should read and view for our Virtual Class, with the hope that these will be helpful to you as you consider your own Masters research ideas for your graduating project, thesis or capstone project:
1) Please view this 16 minute TEDx video from 2016: Tricia Wang on The human insights missing from big data. This interesting and controversial video from the world of business research considers the relationship between data mining and ethnographic approaches (and other quantitative and qualitative approaches) in doing research that leads to making good decisions for the future. Although it comes from the world of business, we may be able to extend the ideas to educational settings as well.
(Optional: If you would also like to view another short video (6 min.) outlining ideas about research methodology in a straightforward way, you might take a look at Dr. Mark Peace's 'On Methodology' as well).
2) Please read this chapter from Phillips & Burbules (2000), Postpositivism and educational research.
I will not propose writing prompts for our virtual class, but will just ask you to pay attention to "stops" that you encountered in the video and the article, and then to respond to your classmates' responses as well.
The virtual class gives us the luxury of a longer, more slow-motion online discussion of some big ideas, with time to consider what we want to add and what others have said. I hope you enjoy it very much!
However, I would encourage you to find times anywhere in the period from March 14 - April 1 to participate asynchronously in our Virtual Class discussion on this blog. That will give you more time to do the reading and viewing for this class, and to take time to reflect on the ideas and the ongoing discussion, without rushing things.
You are expected to:
- Respond in the 'comments' section at the end of this post.
- Add at least four substantive responses over the course of the March 14 - April 1 period. (Please space your responses apart by at least a day or two, and read everything up to that point so that you understand where the discussion has been so far).
- By 'substantive', I mean 'full of ideas and meaning' -- they don't need to be very long, and each response should ideally be 1-2 medium length paragraphs long.
- Keep your responses closely connected to the video and article. While we may mention things from outside these two 'texts', each response should tie back to something in them -- a quote, an idea, etc.
I've chosen two things that everyone should read and view for our Virtual Class, with the hope that these will be helpful to you as you consider your own Masters research ideas for your graduating project, thesis or capstone project:
1) Please view this 16 minute TEDx video from 2016: Tricia Wang on The human insights missing from big data. This interesting and controversial video from the world of business research considers the relationship between data mining and ethnographic approaches (and other quantitative and qualitative approaches) in doing research that leads to making good decisions for the future. Although it comes from the world of business, we may be able to extend the ideas to educational settings as well.
(Optional: If you would also like to view another short video (6 min.) outlining ideas about research methodology in a straightforward way, you might take a look at Dr. Mark Peace's 'On Methodology' as well).
2) Please read this chapter from Phillips & Burbules (2000), Postpositivism and educational research.
I will not propose writing prompts for our virtual class, but will just ask you to pay attention to "stops" that you encountered in the video and the article, and then to respond to your classmates' responses as well.
The virtual class gives us the luxury of a longer, more slow-motion online discussion of some big ideas, with time to consider what we want to add and what others have said. I hope you enjoy it very much!
This comment has been removed by the author.
ReplyDeleteHey folks,
ReplyDeleteI hope you’ve all had a good week. I’d like to get the class discussion going by commenting on the TEDx video; the reading was really challenging and I’m looking forward to seeing what you guys got out of it, so we might be able to understand it better as a group.
The Tricia Wang video was helpful in providing a clear example of the difference between what she called BIG and THICK data. That BIG data is useful for analyzing information and providing actionable results within a contained system is quite easy to understand; the challenge comes when “human systems” make the interpretation more difficult. The example of Nokia in China seems to be a clear “can't see the wood for the trees” situation; the data Nokia had was only looking at the people from a detached perspective while Wang was embedded in the experience of those same people and so had a completely different perspective. The example of how Netflix capitalized on the market for binge-watching by using an ethnographer is another good example but, in this case, Netflix had both the BIG and THICK data which they used to create their business model. In a way, their approach was a mixed-method study of the market and this allowed them to understand more fully how to capture the market share they were pursuing.
What caught your attention in this video?
Hi Kieran! What impressed me a lot in this video is that Tricia Wang said“You think you know something, and then something unknown enters the picture”, and this reminded me of what Professor Hawking have said “the greatest enemy of knowledge is not ignorance, it is illusion of knowledge”. I think this is an very important reason for the failure of Nokia, who relied too much on big data, but ignored the flux of human being. Nokia and Netflix served as good examples that confirmed the significance of qualitative research. In my view, “big data” are absolutely important, but we have to recognize that individuals are different and flexible, and “thick data” are playing an indispensable role in solving problems.
DeleteHi,Yuxi!I like your quote from Hawking in particular! Yes, sometimes we are so confident in believing that we know something but that actually may well turn out to be ignorant. We used to think Nokia were too slow in reaction to the abrupt change of the market because it did not value understanding the market. However, it turned out that they valued the market very much: they spent large sum of money in big data and research. So it was not their wrong approach that led to their collapse.
DeleteI know that later Nokia has realized the problem. In its last bid, it invited many inguistics to determine a name for its phone and then send it to a large number of people to listen to their feelings about the name and finalized it to be Lumia.
As to its implication to educational research， I think we should be constantly reminded of the importance of listening to individual participants' voice. In many cases when we evaluate an area in education (curriculum, teaching materials, programs, teaching methods..,), we tend to rely on quanlitative data to analyze its outcome, to see whether it helps to improve our students' scores. But that would very likely expose ourselves to an “illusion of knowledge”. It is human that our research centered. So we should never ignore individual voices.
Hi Kieran，
DeleteI also got some inspirations from the TED talk video. I really like the two examples Wang gave in that talk which provided me with a clear view about the importance of big and thick data. I still remember that 10 years ago, it is the era of Nokia, most of people will choose this brand when they decide to buy a cell phone. However, when some brands started to produce smart phones, and Nokia did not do anything. Obviously, they lost the campaign at the end. I was confused at that time why they did not choose to produce smart phones as well. After watching this video I knew the truth. I used to think big data is like a skyscraper which is hard to reach and understand for normal people, but giving this case as an example, I understand the importance of it.
In addition, I also think that we should pay more attention to how to collect data. For instance, the sample should be various so we can realize the different opinions and perspectives of different people. We should also consider the further use of the data, when we collect it, something we should question ourselves, will the situation change in the future? how could we deal with the change? For example, if we want to investigate people's perspectives towards smart phones we should also collect some data about in several years, to what extend can it change to portable and durable one?
Crystal
Hey guys, I agree that Tricia Wang's Nokia example is certainly intriguing and made people think twice about big data. The way she presented it worked well to support her arguments. However, I do not believe the size of Tricia's sample was the only reason behind Nokia's decision to put off investment in smartphones. At that time Nokia was one of the market leader in the mobile phone business, famous for durable and inexpensive handsets. To venture beyond their existing share of the market, and to develop in technologies that may be detrimental to their existing competitive advantage may just be a risk too big to take on at that time.
DeleteI have only listened and watched the TED interview once so far...can only take in little bits at a time...but there were 2 things that she said that caught my interest right away...they were "understanding the human narrative" and "quantification bias". Certainly trying to understand the human narrative is something we all need to be aware of...for we are all involved, one way or another, in the human narrative. What confounds me is how researchers can get big data, thick data or even realistic data when they are dealing with those of us who claim to be part of the human narrative...feeding folk dumplings in the street or picking through garbage certainly brought the human narrative to an earthy level.
ReplyDeleteNow about quantification bias...how do researchers work that into their data...or do they...and how can we tell when we are reading a research paper if that has been part of the results unless it is noted in the paper...I need to go back to the TED interview again to see what I have missed on my initial go-through.
Well, Dear Colleagues, here is my first response to "What is postpositivism?"
ReplyDeleteI had a hard time understanding most of it! And it has made me more sure that I never want to in any way think of becoming any kind of a researcher. It seems to me that there are a lot of people defining things that are not understandable in their definitions and I became more muddled as I read on. Some ideas I understood but what I have been left with a this point is a sense of bewilderment. Of course, I shall read and read and read again to try to determine what the paper is all about but I would be grateful if those of you who understand all this stuff would bring it to a level that I can comprehend. With loving thoughts for you all. Jennette
Hi Jennette，
DeleteI just finished reading “What is Postpositivism” and also found it quite difficult to read as it involves many ideas concerning philosophy and logic. Actually, this article mainly talks about positivism, the central idea of which is the importance it attaches to scientific evidence and also the difference between claim and knowledge. According to the scholars who support positivism, speculations without observational evidence was non-scientific and cannot be believed as knowledge, and consequently tings like philosophy should be restricted to research about behaviour instead of inner factors of person. In addition, they also claim that a concept is meaningless unless the researcher can specify how it can be measured, so abstract concepts like creativity can not be investigated. However, there are lots of problems in positivism. (1) The reality of reason; (2) Theory-laden perspective; (3) Theory is underdetermined by evidence. This is why postpositivism was proposed by scholars which admits that (1) sometimes reason can be defective; (2) people’s perspective is influenced by their background knowledge and (3) one evidence might support different theories. To sum up, postpositivism admits the flexibility and multiple sources of knowledge; there is not an authoritative answer to a question.
Yuxi
Hi Jennette and Yuxi, I feel you after reading the Phillips and Burbues chapter. I found it easier to understand when I compare it to positivism. Positivists seek to eliminate any types of speculation from scientific research, including the researcher. Postpositivists seem to embrace the idea that experiences/knowledge/theoretical background of the researcher will influence the subject of inquiry. Similar to previous class discussions of "truth" and "objectivity of research" where we talked about there's no such thing as absolute objectiveness in social science, postpositivism seeks to justify the "subjectivity" of researchers.
DeleteDear folks,
DeleteI also find the positivism reading quite challenging and refrained from commenting on it due to lack of understanding, really. Well, I encountered this term throughout my final assignment writing and got to learn that positivism is based on the idea that science is the only way to learn about the truth. This was a big stop to me. I came across te following definition while I was looking into research methodologies:
"As a philosophy, positivism adheres to the view that only “factual” knowledge gained through observation (the senses), including measurement, is trustworthy. In positivism studies the role of the researcher is limited to data collection and interpretation in an objective way. In these types of studies research findings are usually observable and quantifiable."
How is it possible that truth can be only learned through science? What are the perks of considering that the basis of knowledge should depend exclusively on scientific methods? First thing that comes to my mind is that positivists are way to inflexible... What role does creativity play in this approach?
Jennette,
ReplyDeleteI have just finished reading the 'What is postpositivism?' article - it is a difficult read! But a very useful one as I start to form more a concrete idea about where my research is taking me.
To understand postpostivism, it seems to me I need to get my head round positivism first. For me, some key concepts or terms that emerged as I read that would help me define or explain positivism are: rationalism; experience; observable; behaviour; phenomena; definitive. Positivists, in various guises, believe there is a definitive (of concrete?) way of making knowledge claims. Postpositivists are a mixed bunch and they reject the idea of definitive knowledge claims based on observable behaviour or phenomena that can be explained by rational thought or experience.
My two lingering thoughts at this moment are:
How to extract the useful or helpful elements of positivistic thinking (which the authors of the article do concede there are) for my own research without being labelled as a positivist (which it would appear is a bad thing!)
From a postpositivistic perspective, if I am not looking to make a definitive knowledge claim - what 'type' of knowledge claim am I looking to make. And why? Why will it be useful?
Kieran - I haven't got to the TEDx video yet!
Hey Katie, I found the following quote useful in explaining the difference between positivism and postpositivism: “While positivists emphasize independence between the researcher and the researched person, postpositivists accept that theories, background, knowledge and values of the researcher can influence what is observed” [Robson, C. (2002)]. For me, it seems obvious that researchers would find it near impossible to remain independent during inquiry; try as we might, our bias and perspectives will color whatever we try to do – if we try to counteract this, there’s the risk of “overcompensating” in our approaches in order to maintain our independence / neutrality which simply skews our inquiry in a different, but opposite, direction. This seems to highlight the importance of peer-review in research so that our methods and findings can be questioned and validated from other perspectives.
DeleteI really liked Dewey’s phrase “warranted assertibility” in substitution for “truth”; we discussed in class what truth is and how we might be able to justifiably claim that something is true. Many of us fell back on trusting the “experts” and on what we personally observe ourselves to provide truth. You noted that “Looking specifically from a historical perspective - truth can never be objective” while Lilian observed that “Illusion is what is most similar to reality”. Sarah explained that “Like an iceberg, 'truth' is only what we see on the top, but the part we don't see is made up of years of lived experiences, perceptions, worldview and beliefs”. The idea of the grey areas between perspective, politics, illusion, propaganda, perceptions, and beliefs make it difficult to justifiably assign the label of truth in many cases. However, the term a “warranted assertibility” seems like a fair way to split the difference between an outright claim and a healthy skepticism of our own subjective beliefs.
Kieran,
DeleteI too appreciate the term "warranted assertibility" and see that as a healthy medium between assuming truth and accepting skepticism. In Social Studies, I frequently ask my own students "How do you know?" in response to their assertions or statements. My question is often matched with uncomfortable silence or "the textbook says". It can be uncomfortable to accept that what we hold to be 'true' is not true for all, and that our own beliefs may not be correct, or conditionally true, that is until further research undermines or reaffirms our beliefs. In my estimation, it is dangerous to hold tight to one's beliefs without questioning, but equally dangerous to hold no beliefs at all.
Hi everyone,
DeleteAll of your comments are so thought provoking! Full circle, I ended up going back to my blog post to remind myself what I thought to be truth, and in some ways I also emphasized the need of experts or a verifiable basis to confirm my understanding of truth. There was a hint of acknowledging that lived experiences can help form my understanding of what is true, but it is certainly clear that class after class I have learned and gained a much stronger appreciation in the subjectivity of truth. This article, and your comments help to solidify this learning.
Katy - I too am pondering the question you asked: "what 'type' of knowledge claim am I looking to make. And why? Why will it be useful?"
In addition to your comments on warranted assertibility, the article explains that "...we must seek beliefs that are well warranted (in a more conventional language, believes that are strongly enough supported to be confidently acted on), for of course false beliefs are likely to let us down when we act on them to solve the problems that face us!"
So as long as we have enough conviction and enough evidence based on our experience (learning and lived) that pushes us to feel responsible to investigate and research, then we should act.
I found Tricia Wang's video very interesting. First, because it clearly shows how collecting data is complex and involves several variables. I specially liked the part where she mentions that the surveys and methods conducted by Nokia had been designed to optimize an existing business model, showing the audience the importance of considering data that can not be easily quantified.It somehow tells me that we do have to go the extra mile and try out new ways to conduct research and even question the methods we chose to employ. This video also reminded me of the course on video ethnography I took last term, where we discussed the importance to implement thick description, that is, researchers are to analyze not only the behaviour but also the context in which that specific behaviour happens so as to make it easily understandable to an outsider.
ReplyDeleteWith regard to the surveys and methods conducted by Nokia, the second video notes that “The inquirer inevitably produces a version of reality” through the inquiry. In Nokia’s case, their version of reality was developed through the analysis of BIG data while Wang’s ethnographic research, using qualitative evidence, showed her a different (more accurate / actionable, is seems) version of reality. As Lilian points out, Nokia were focused on the BEHAVIOUR of the consumers while Wang was looking specifically at the CONTEXT in which this behavior took place. Nokia dismissed Wang’s research because it wasn’t BIG data which meant that they were unable to appreciate the change was coming with regard to low-income Chinese people’s purchasing behavior, specifically that they were being enticed by the ads for iPhones which represented a symbolic “arrival” at or an “embracing” of the new technological world. In essence, their understanding of the reality of the phenomenon failed to take into account the “on the ground” situation what Wang was able to investigate.
DeleteThis comment has been removed by the author.
DeleteNokia's failure in responding positively to Tricia Wang's findings is unsurprising. The reliance of big data and "market dynamics" is conducive to maturing corporate models. As a company grows, there is more reliance on validating results and ensuring that benefits outweigh the risks in pursuing decisions. If I put my business cap on, the proposal offered by Tricia Wang to enter a new market with little data to validate would seem too risky to Nokia. There are shareholders the corporation needed to be accountable to, and so the decision was possibly made for the benefit of the shareholders (to provide the highest returns possible). It also may have not even been so thought out. It could very well be that the need for big data and validation was just ingrained in their corporate culture. Regardless, in hindsight this story highlights that their shortsightedness possibly led to their failure. What I'm interested in is the tension Tricia Wang was confronted with. What could Tricia Wang have said to change their position? What would it have taken Nokia to understand that this thick contextualized data is essential and could in fact have changed the corporation's trajectory? Probably more importantly, why is statistical validation so over-valued in institutions, and what needs to be done to change this way of thinking?
DeleteTaking into consideration Lilian, Keiran and my thoughts above, I've been thinking about how our interpretations of the video relate to positivism and postpositivism (if I understood the reading correctly!)
DeleteLilian describes the distinction between Nokia's methods of using quantifiable/ verifiable (big) data to "optimize" the existing business model and Tricia Wang's method of building thick data by understanding behaviour and context. Keiran builds on this tension between the two methods- specifically how Nokia couldn't appreciate Tricia Wang's approach because it wasn't big data (in other words, the methods they use to grow their business). I attempted to ask why there is this conflict (to understand how we can resolve the conflict), and attempted to introduce some reasons for Nokia's position that are rooted in culture/ world views.
To further this inquiry, the following excerpt in our reading reminded me of the the tensions between big and thick data:
"But what if no observational evidence or data could conceivably be collected? A small group of philosophers, physical scientists, social scientists and mathematicians working around Vienna in the 1920s and 1930s - a group that became known as logical positivists - took an extremely hard line on the matter, asserting that speculation about such things was non scientific and nonsensical. They devised a criterion of meaning whereby it was literally meaningless to make statements about things that could not be verified in terms of possible sense experience..."
Pg 9
In the case of Tricia Wang's presentation of her findings to Nokia, in one sense Nokia perception aligns very much to positivist thinking. Sure, Tricia Wang was able to learn the experience of a hundred or so people, but the method that she used (of contextualizing and learning about experiences) cannot be verified and extrapolated against the entire market/ population, and therefore was not sensible for Nokia to invest their money into. In other words, although Tricia could verify individual experiences, the experience of the entire market could not be verified. Postpositivist theories express that quantifiable and verifiable data isn't always necessary, and actually limits one's ability to understand the broader unknown. Postpositivist theory acknowledges that "data can be accounted for in more than one way" and so understanding knowkedge cannot just be a linear process. Tricia Wang embraced this idea by not just collecting surveys to support big data but walked off the linear path by digging deep and embracing experience.
Relating the video to our reading offers confirmation of two totally different approaches that are founded in different world views that in fact have criticisms against each other. From the view of the positivists, unverifiable data is meaningless data and therefore postpositivism is not credible. From the view of the postpositivists, thinking just big data can be shortsighted (and the article offers a few reasons why).
Hi Naureen!
DeleteI was also trying to connect Nokia’s case with positivism and postpositivism theory. When I was reading the very beginning of the article introducing positivism, especially when it talks about logical positivists who attach very importance to scientific evidence, I thought positivism was closely connected to quantitative study. When I continue reading, however, I found that one misconception of positivists is that they can be recognized by their adherence to the use of quantitative data and statistical analysis which is considered as evident. So now I realized that the connection lies in the interpretation of data. As advocated by both Nokia and positivists, data must be scientific and valid, and can only be interpreted in one authoritative way. Conversely, postpositivists believe in the flexibility of data and the influence people’s experience and beliefs on the interpretation. In addition, postpositivists do not stress authority of one methods or one way of interpretation and admit many possibilities in the interpretation process.
Yuxi
Hi Naureen，
DeleteI was thinking about that why Nokia did not receive Wang's suggestions at time. Does it just because they could not predict what will happen in the near future, or Wang's data is not sufficient enough for them to believe. So as a researcher, you are not only have the responsibility to collect and analyze the data or complete the research, you would better cultivate the ability to make you audiences to trust your results, or make it seems more convinced. In this case, they also had some misunderstanding between each other I think. Nokia tried to keep their opinions about the high prices of smart phones that poor people could not afford it but Wang investigated lots of different levels of people and collected information from different resources.
In addition, since Wang didn't mention how she present those thick data to Nokia, maybe the form of her presentation was not the most suitable one for Nokia's staff to understand. When we talk about why Nokia, which was regarded as an unwise company in this video, did not make a correct decision, we also need to think about whether the data is acceptable or not.
Crystal
I found Wang’s video on big and thick data is quite intriguing. In today’s society, there seems to be a dominant preference of quantitative over qualitative and other methods in various research and academic fields. A general belief is that more is better than less. Enterprise or corporation is all about efficiency and time. In the Nokia’s case, I can’t help myself from wondering why Nokia is neglecting Wang’s finding. It is very unfortunate that Nokia made its decision just because there are more participants or numbers invovled in the big data research. Nokia’s story is a great example showing that having more numbers does not point to the correct answers all the time. The thick data, as referred by Wang, adds the context to the big data by allowing the data to tell the complete story. The thick data, I think it could also be referred as in-depth data as it adds more layers and in-depth information to allow the researchers to capture things which may be easily missed or overlooked by the big data. I am also quite impressed with Wang in terms of how she embedded herself in the living experience of the people she was investigating. I also wonder how her personality, enthnicity, and her own cultural background impact her research experience.
ReplyDeleteI am still in the process of digesting reading chapter on positivism which is complex and challenging one.
Hui - I think your point about the impact of her personality, ethnicity, and own cultural background and how it impacted her research experience is a very important. Wang essentially lived her research, rather than lived a life whilst conducting research, for a number of years. A huge undertaking! There must be some limitations to this immersive type of research (perhaps a reason why Nokia was dismissive?) But, as Kieran points out, it meant Wang was very aware of the context in which certain behaviours was occurring.
DeleteNokia appear to have dismissed Wang's findings as they had pre-conceptions about the low-income Chinese purchasing power - that being, they had limited purchasing power. Even though Wang's findings contradicted this, the very fact that she was focusing on low-income communities meant Nokia were not as interested, perhaps. What I am trying to say (in a rambling way!) is that dismissal of qualitative "thick" data clearly played a role here, but the bottom-line and profit steered the course rather than an actual dislike or distrust of different types of data. As ever, money is guiding big business decisions not simply a trust or favouring of big data. I would be very interested to know what types of companies employ ethnographers - is it just tech companies?
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DeleteHi Katy. I like your idea about the true factors affecting Nokia's reaction to the data. We are all commenting the company's decision from the hindsight. But what if we were participants back there at the scene: I have been the top of the business for years. My theories and methodologies (looking at big data) have won me countless battles. Now a girl suddenly came to me and told me she spent some time with a group people in a village in a remote country and tell me that our company should be sheered in a completly different way, which meant that we need to spend huge money changing all our designed or maybe executed strategies. Should I buy it? I hesitated.
DeleteThe situation also reminds me the post positivist article. In p17, it says that "theory is underdetermined by evidence" because an evidence could be explained by different other theories. Wang spent time living with those people and gained evidences and then concluded that "even the poorest in China would want a smartphone and they would do almmost everything to get their hands on one". BUT can the evidences be explained in other ways? What if a theorist sucesscully interpreted them to be "even the poorest in China would want a phone that is firm, durable and mechanic"? By the same token, Nokia, should they have taken Wang's advice, may have come up with a different "smartphone" and still faced its fall.
So I think one of the important insight from the postpositivists is that data, big or thick or not, are very important, but we should at the same time always be critical and be vigilant to think that these data make my thoughts unarguably true.
Hi Katy and Haynam,
DeleteI think being time consuming and the amount of time, commitment, and efforts invested in this type of research is enormous. In the business world, corporations need to be able to make quick decisions and big data may seem to be more appealing as the data could be obtained in a more “effective” way; however, efficiency or effectiveness does not win all the time.
I think you all brought an excellent point about whether or not we are ready or prepared to whatever the outcome. As researchers, we should be cautions and be aware of our own bias.
Hi Katy, your question about what kind of company employs ethnographers is quite interesting-I also wonder if big data or other quantative research may be more applicable or relevant in certain type of research or fields (may be more in the scientific research like medicine) where qualitative or other alternative research methods such as ethnography is more commonly used is humanity or social science subject?
DeleteHi Hui，
DeleteI was also impressed by Wang's ideas of big data and thick data. Actually, I think why big data is so powerful and popular in today's society is because it is quite convenient and compared with thick data, it is easy to obtain. It has lots of advantages since the sample is extremely large and the analytical method is advanced. People even do not need to spend time on collecting and analyzing it, because technologies could deal with it. I think the convenience and relative accuracy of big data encouraged people to rely on it. As for thick data, it is like a qualitative study, people have to categorize the sample and analyze it in different ways. In addition, when people consider a lot, more things we need to be considered will appear. The research could be even less objective, so how to avoid those aspects?
Crystal
Hi Folks,
DeleteThis BIG /+ THICK data issue is interesting. Crystal mentioned that BIG data is attractive as an analytical method because of its perceived accuracy while Hui pointed out its attractiveness in terms of efficiency/ effectiveness. Also, Katy noted that our pre-conceptions (specifically in Nokia's case) can cloud our interpretation of THICK data while Haynam points out that it is only with the benefit of hindsight that we can see how Nokia missed their chance in the smartphone market in China.
All of these comments made me think of magic! I read some time ago how children are better than adults at figuring out how magic tricks are done. Part of this is due to their size; because they are smaller, they physically see things from a different perspective and so might notice a magician's sleight of hand more easily than adults. Another reason is that they lack many of the preconceptions that adults have with regard to "how things work"; adults try to understand a magic tricks through the application of their own accumulated experiences and rule out explanations "because they don't seem sophisticated enough" [https://read.bi/2pM1cuv] whereas children are better able to see the truth more easily by (usually) looking for the simplest possible solution. Children aren't bogged down with BIG data, useful as it can be, whereas adults feel the need to run their thoughts through this to seek an answer. In short, children are often able to see the "truth" more easily specifically because of their LACK of preconceptions and in the ABSENCE of hindsight. (follow the link above for more on this).
Hi Hui, you're absolutely spot on about the preferences for quantitative over qualitative/other research methods. The obsession with big data certainly has its reasons. The recent Facebook data scandal, whether or not proven to have swayed the results of the latest US election, further contested to the "effectiveness" of large data.
DeleteLooking more closely at academia, in another class a couple of weeks ago, we discussed academic publication and how publication influences academic career, university rankings, and so much more. Time-consuming research methodology probably wouldn't play well within the bigger picture.
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DeleteAfter reading your comments on big and thick data I started wondering about how important it is to effectively integrate both in order to achieve better results in research paradigms. While big data provides quantitative information, from which researchers can build up new and innovative ideas, thick data aids in supplementing the understanding of how things happen the way they do.
DeleteI found a blog created by Tricia Wang and a post dated back to 2013 in which she affirms we do need big data, and must engage with it. Thick data, then, would play an important role in understanding areas and facts that are still unknown. Check her blog here: http://ethnographymatters.net/blog/2013/05/13/big-data-needs-thick-data/
March 24, 2018.
ReplyDeleteAlthough this doesn't really relate to the content of either the TED talk or the text on postpositivism, it does relate to the way in which the content is delivered. Why is it that I have less difficulty in understanding a talk rather than reading an academic text? Could it be that the author whether in talk or text has a different level of language that is used. It often seems to me that the verbal presenter considers more user-friendly words than the academic author. It has been my experience if one reads aloud what is to be printed or spoken that language becomes less ponderous and predicts greater clarity. I cannot back that assumption up with any research methodology, however, it is just a thought. I certainly understood the speaker more easily than the writer.
Haha I got you Jennette. We discussed this in our linguistics class as well when we were all 'disgusted' by one piece of the assigned readings. One of our assumption is that the written form allows the writer as well as the reader much more time to produce/process more cognitively demanding texts; also, we often find the academic writing full of jargons may because we are not a member of the circle yet (as we learned that languages vary even among the tiniest community, a small group of friends or families for example). But this article does not look like exclusive to any specific discipline so we can feel free to think that the writer is just too used to being a jargonic jerk (laugh). Finally I think on top of the language, the article involves many philosophical concepts and thoughts which make the article even harder to understand.
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DeleteDear Jennette,
DeleteI absolutely agree with your reflection. There are many reasons why Tricia Wang's presentation resonated with me more than the reading. One of those reasons is because she presented her argument in a short period of time yet clearly, and applied a pragmatic real-life context. Also, the imagery and visuals used helped to really empathize with and understand her learning experiences. In addition to presenting the concept of thick data, I believe the video on it's own is a form of thick data (by simply learning Tricia Wang's experience). And the audience of the video is us, so the learning experience is customized for us.
In steep contrast, the article is much more technical and using language that academic experts in the field are more inclined to understand (I'm not joking, I googled about 10 words to get me through the article!). Of course, after reading the article a couple of times, it started to make more sense. But in real life (that is, outside of the classroom and in the workplace) unfortunate to say, but how do we have time to deconstruct this important learning? Personally, I feel we need more translators, more story tellers to teach us.
Hi Jennette, excellently put! I would have to guess it has something to do with the intended audiences. Tricia Wang's talk was produced to reach out to a much larger audience than Phillips and Burbules's book. Moreover, as pointed out by Naureen, Tricia was telling stories, with the help of examples that the audience can easily relate to, where the chapter was under the scrutiny of academic excellence and prepared to encounter criticism from like-minded academics.
DeleteMarch 26, 2018.
ReplyDeleteDear Classmates,
As I was pondering our readings, I came across this poem that has always given me reassurance when faced with difficult questions. I provide it here for your consideration.
The Three Goals by David Budbill
The first goal is to see the thing itself in and for itself, to see it simply and clearly for what it is. No symbolism, please.
The second goal is to see each individual thing as unified, as one, with all the other ten thousand things. In this regard, a little wine helps a lot.
The third goal is to grasp the first and second goals, to see the universal and the particular, simultaneously. Regarding this one, call me when you get it.
With affection, Jennette
Dear Jennette,
DeleteI deeply appreciate how you manage to connect literature to ideas as a means of extending your own understanding and working through a problem. In response, I offer you a quote from Lewis Carroll, whose writing I find to be frustrating for its immense ambiguity, but whose writing also aligns nicely with the Postpositivism article and connection of philosophical validation of social and educational research:
“If I had a world of my own, everything would be nonsense. Nothing would be what it is, because everything would be what it isn't. And contrary wise, what is, it wouldn't be. And what it wouldn't be, it would. You see?”
This seems to be the fate of the novice researcher embarking on a quest to navigate the many theories and methodologies of research. Just when we find one that seems to sync with our own 'beliefs', we read that "If [we] are to contribute to the ...educational policies and educational practices--[we] need to raise [our] sights a little higher than expressing [our]fervent beliefs or feelings of personal enlightenment, no matter how compelling these beliefs are felt to be." (Philips&Burbules p.3)
I'm reminded of a novel I read when I was 13 that negated everything I thought I knew. It is called "Sophie's World" by Jostein Gaarder and much like Descartes and Locke, I had a moment where I had to undo all I thought I 'knew' in order to take in this new information presented in the novel. Really, the novel should have come with a warning, as I was not prepared for an existential crisis at the age of 13 :) Since beginning this course, I have had many moments of "self-examination" (p.6) and have found another quote from Carroll that seems appropriate here:
"I knew who I was this morning, but I've changed a few times since then." (Alice in Wonderland)
As I continue to work through the big ideas in this article, I take away the small comfort that what I believe to be 'true' at breakfast may cease to be 'true' by dinner time, and that understanding human knowledge as being "conjectural" (p.26) is helpful is recognizing the complexities of researchers and research in general as not ever having one answer, but many answers that simultaneously can be considered to be 'true'.
Wishing you the best in your attainment of the second and third goals.
Hi Jennette and Sarah,
DeleteJennette, I really like your idea about 3 goals, we see things in the world as different individuals at the very beginning stage, and then we could classify similar things as a group and we could use these categories to explore other things in this world, which makes the life much more easier.
Hi Sarah I also read the book "Sophie's World" when I was 14 years old . At that time we started to learn philosophy and we realize the meaning of materialism and idealism and some names such as Kant, Hegel, and Marx. Actually, the post positivism belongs to idealism, and I do think it is quite abstract, since they just use what they have learned to examine the things they meet.
Crystal
Hello all,
ReplyDeleteIt's been fascinating reading through your responses to both the readings and the TedTalk, and I'm looking forward to responding to them shortly. Here I'd like to discuss some of the thoughts I had while watching Tricia Wang's TedTalk on Big Data and Thick Data. I will post again with my comment on the reading so as not to overload with text!
I was immediately struck by the construction of Wang's speech, and the way she hooked me in as a viewer from the beginning. I visited Delphi many years ago while in Greece and learned the history of the oracle (and how she was often, as Wang stated "high as a kite") and from my own lived experience, wondered how this anecdote tied into Wang's main argument. As the talk progressed, I realized that the structure of TedTalks in general is similar to some of the papers we have recently read in class where narrative, travelogue, quotations and poetry is used throughout to engage the reader and make it more personal---. Combining research and thesis with narrative and story. In order to make something personal, there must be a connection between the producer and the consumer, which is where Big Data fails. As Nokia found out, their Big Data failed to take into account the lived experience and personal motivation of users, which Wang predicted would occur. Wang's data, Thick Data, accounted for the personal needs of consumers, but not the big data of a large sample pool. If we think about it, we as students seek both Big Data and Thick Data when doing research for papers. We seek to find studies that prove what we already believe (as Nokia relied on) and as we progress, try to figure out the 'why' behind the data we have. We search more, perhaps conduct research of our own, discuss ideas with colleagues, and work through similar struggles or questions. If we only used Big Data in research, we miss out on the "complex and unpredictable" forces that Thick Data provides. Likewise, if we only used Thick Data in our papers, we would be ignorant of the bigger picture past the scope of our own experience.
I think this idea goes beyond research and the progression of technology as well. As I watched the video, I thought about education and assessment practices, particularly with regards to standardized testing and teacher evaluation in the U.S. The big data received from mass standardized tests reveals general trends of accomplishment and weakness across a particular grade level in school. In U.S schools, this Big Data is also used to reward teachers whose students are successful, and punish those whose students are not. Standardized testing fails to take into account Thick Data, however, and therefore does not provide a full and complete picture of the capabilities of groups of students. Like Nokia, are school systems that rely on Standardized Tests doomed to fail?
Returning to the discussion of Netflix, the ethnographer's findings about binge-watching were crucial to the progression and success of Netflix-- armed with this information, the company was able to give people more of what they wanted....not different shows, but more of the same-- the gratification of watching 3 years of a show over the span of 3 days. It led me to wonder about TedTalks and the growing popularity over the format over the last 10 years, and I went to their website to see if they also used Thick Data and/or Big Data to curate content. The link to their history is here, if you are interested: https://www.ted.com/about/our-organization/history-of-ted
It's been about 10 years since TedTalks really expanded, and in this course and education in general TedTalks are a common source of academic information. I watched the 'most popular' ones as recommended, and do you know what they all had in common? Narrative (the personal) mixed with quantitative research. Big and Thick. Gives me something to think about as I continue to write my own papers going forward..
Hi Sarah!
DeleteI really like your observation about how big and thick data are applied in current studies. I agree that quantitative research and qualitative research are equally important! Considering Nokia’s case, actually it is not big data’s fault. The real reason of the failure of Nokia is the decision makers’ ignorance of thick data. So for those decision makers, it is very important to take into account both quantitative data and qualitative data. This can be a significant implication for our research as well, in which quantitative methods and qualitative methods can be used as triangulation to ensure the reliability of the results. When the results from the two types of methods are different, it is necessary to figure out the reason, which might be an more important finding of our paper!
Yuxi
Hi Sarah, thank you for pointing out some big and thick data in research. In my discipline (Teaching English as a Second Language), there is also a large body of research looking at big data and thick data. Research looking at the effectiveness of a pedagogical model usually collects big data while research looking at the sociolinguistics perspective of people like thick data more. But I’ve seldom seen any research taking both into account at the same time. Maybe that is one thing needs doing.
DeleteHi Haynam,
DeleteThat's an interesting point that you bring up about the separation of big and thick data depending on the research model. It would be interesting to look at the triangulation of research if big and thick data was used for understanding the effectiveness of pedagogical models, as it could reveal variables that big data is unable to account for. I wonder how someone would go about using both? That gives me a lot to think about!
Hi Sarah,
DeleteI really enjoyed reading your comment and your point about the structure of Wang's presentation using narrative is quite interesting. Something may not be directly relevant is that how narrative is used in business advertisement and its effectiveness. In my opinion, I am more inclined to the advertisements with a story other than just using a set of numbers. This is especially true when you are listing to ads on the radio channel. The ads with a storytelling style will quickly catch my attention rather than just telling me a set of different numbers. Maybe this is another great example of how powerful narratives or other alternative qualitative research approaches are.
Hi Yuxi,
DeleteI think you have raised a good question. As researchers, are we ready to accept the results which may totally be different from what we anticipate? In Nokia's case, the cost of learning this lesson is quite high. We all have our own bias, our values and beliefs are fed on our knowledge we have learned. It actually reminds me of what was mentioned in the "What is Postpositivism" article, "the human knowledge is not based on unchallengable, rock-solid foundations'.
I think the points being made here about using big and thick data in our own research are really interesting. At present, my research project is focused on thick data as I am going to be focusing on my own teaching practice and (hopefully) contacting some of me old students to consider the narratives that I teach in my own classroom. I am now wondering how I could include big data in my research? It is challenging to know how to fit it in and also how to include it with some meaning, rather than just including big data for the sake of being able to say I've used quantitative research elements. I don't want to resort to tick-boxing. So I am left with a decision to make - find a way of including big data for triangulation purposes and because the combination of quantitate and qualitative works well or focus purely on qualitative data because it fits with my research focus? I'm not sure what my decision will be at this point!
ReplyDeleteSarah,
ReplyDeleteI am very interested in your comments about narrative in TedTalks. As mentioned above I am focusing now on research on my own teaching practice and the narratives I may weave in my in own classroom. I am interested not in the historical narratives provided in textbooks that I might tell (often referred to as "grand narratives" and often referring to nation stories) but more to the narrative I construct over a series of lessons. It is interesting that you point out that most of the TedTalks use narrative. My reading into the concept of narrative has caused me to reflect on how much narrative underpins every day life and human existence in general. The idea that we are all living our own 'story' is a potent one. There is still much more for me to read on the subject but I am fascinated by the power that a narrative can hold. Hopefully I will be able to expand a little on these musings on my presentation tomorrow!
Hi Katy,
DeleteI'm looking forward to hearing your presentation and your findings about narrative in your own practice. My department head often recounts her experience in University and one particular professor who would begin every lesson with a story. She says the act of storytelling at the start of the lesson immediately hooked the class in and set the stage for the content that was to come. In her estimate, people are naturally inclined to listen to personal narratives and are better able to connect content when there is a story behind it.