Friday 28 October 2016

Theory and Method for Media Technology Final Blog Post



To answer complex research questions is to achieve knowledge about the world and why it is the way it is. Therefore, it is first important to have some idea of what knowledge is. According to Bengt Molander, knowledge is drawing attention to something previously unknown. Having gone through all the themes in this course, I would add that knowledge is also reaching understanding of something previously unknown. 


Kant argues that a priori synthetic knowledge exists – the 2 forms of intuition (time and space) and the 12 categories of pure reason – and is even a necessary precondition for gathering additional knowledge. It is important to note that this a priori knowledge is not a property of things as they are in themselves, but a structure of our minds that helps us experience the surrounding world. In that sense, objective knowledge about things (as they are in themselves, independently of our minds) does not exist. Benjamin also adds to that notion by arguing that perception is also historically determined – we perceive things differently depending on our previous experience, background, gender, upbringing, religion, etc. 


Theory, another crucial element for answering a complex research question, is something we construct, that does not exist by itself, in order to explain, analyze and/or predict a phenomenon or event, or to offer instructions on how something should be done. Moreover, as Dr Michael Patton states, theories point us to the things we should pay attention to and why we should pay attention to them. Many studies are focused on testing existing theories (deductive research) – if, and to what extent, a theory holds to different groups of people, phenomena or events in different circumstances and/or places around the world and at different times. 

Deductive research requires an extensive theoretical framework that explains why a particular occurance is worth analyzing and explaining, what knowledge has already been produced of that, or a similar, occurance, what is still unknown about it, what research method(s) would best fill the knowledge gaps, what existing knowledge will inform the interpretation of the research results, etc. Other research aims at constructing new theories that could explain novel occurances in the world (inductive research).  

According to Dr Patton, deductive research usualy uses quantitative methods and aims to establish whether a theory can explain a different event, phenomenon or behavior (focus is on the occurance), and when testing the generalizability of a theory (focus is on generalizability). He states that one of the reasons for doing hypothesis-testing research is that one of the main goals of science is to generalize across time and space. As we have discussed, quantitative methods, such as tests, experiments and interviews, seek to gather numerical evidence from large amounts of data and, as such, are relatively objective in comparison to qualitative methods, which are more subjective and depend on the interpretation of the data by the author. 


Qualitative research, on the other hand, is more typical of studies that do not begin with a particular theory or research question in mind, but rather focus on the surrounding world and seek explanations and patterns in it that might help the researcher construct a theory. Qualitative methods, however, are also used in hypothesis-testing research when the author strives to gain a deeper understanding of a smaller set of data and find subtle patterns that might point to a theory or set of theories which might explain the occurance in focus. 



Case studies are a meta-method – a research strategy that makes use of both qualitative and quantitative methods. They are usually conducted in relation to novel phenomena what researchers want to shed light on and are, thus, not initiated with a specific research question in mind, but rather with a very broad question that might become more and more specific as the researcher acquires knowledge about that phenomenon. For that reason case studies usually start out with the use of qualitative methods on a small set of data, in order to first gain knowledge of the aspect of the world that is in focus and construct some sort of theory that might explain that aspect. Then, in a separate study, through the use of quantitative methods, the researcher can see whether that theory holds to a wider range of events, phenomena or groups of people (deductive research) by examining a much larger sample. 


Research through design is a much newer and very interesting method that stands out. It involves the designing of a tangible artifact that embodies the theories that have informed a given study, as well as the knowledge gathered (and mistakes made) by the researcher during the process of conducting the study. In that sense, design research is an approach in which the design process is itself the research method; one that produces a superior form of knowledge that can be experienced and felt – and, thus, enriches our imagination and perception, – instead of just communicated verbally. 

Nonetheless, it is important that the design artifact is accompanied by text so that the wider public can know what changes that artifact has undergone and why and what went through the researcher’s mind during the design process. Research through design is usually used when exploring the interaction between a group of people and a piece of technology; when striving to come up with a better design for a piece of technology; or when exploring the process of technological design. 


Of course, the choice of research method absolutely depends on the question that is being asked, as well as on the theoretical framework that serves as a spine for the study. It’s also important to remember that the fruits of these methods (the results) can be interpreted differently by different people because human perception is inherently subjective. Therefore, the methodology has more weight than the results it produces and, hence, its use has to be well-motivated and carefully thought through, planned step by step and tested, the effectiveness of the approach  established, and the plan has to be followed every step of the way. That way readers can decide for themselves whether the results are worth trusting or not.
   

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Monday 17 October 2016

Theme 6, Blog Post 2: Qualitative and Case Study Research


The seminar clarified a few things for me about case studies. We talked about them as a research strategy usually employed when exploring a novel and unknown phenomenon that a researcher wants to shed light on. And because the researcher himself/herself is not familiar with that phenomenon, in the beginning of the case study, he/she does not propose a specific hypothesis or research question, which I think is good, because it carries a risk of drifting too much towards one aspect and missing out on other important details, thus, limiting the study – particularly bad if you are unfamiliar with the phenomenon, as you can get very lost. The hypothesis comes along later, when the researcher has gathered a sufficient amount of data to inform the formulation of a narrower research question.

We also discussed cases studies as a research method that uses other research methods – qualitative, quantitative or both, depending on their ability to generate relevant results – which I think is a very good point to remember; and as a research strategy characterized by an overlap between the research data and the data-gathering process, which contributes to a high degree of flexibility. I thought this last bit was interesting and it kind of brought me back to design research where as strong overlap exists between data collection, the data itself and the design proposal/product and how the latter sort of embodies the research data and makes it tangible, but also how the researcher can constantly change the design proposal and improve it.

I am glad that, in the seminar, we noted that a research paper that contains the words “case study” might not necessarily be a case study, because after reading the case study on the Pokémon craze in the early 2000s I had chosen, I started to have doubts about whether it was actually a case study. The paper contains some analysis of different elements of the phenomenon – films, TV series, toys, cards, etc – from a business and pedagogical perspective, but is not really supported by any existing theories or concepts, other than consumer/producer activity described in terms of the opposition between structure and agency. In that sense, I did not find it informative or convincing enough to appear in a reputable academic journal.  

Hanna Hasselqvist’s research that we became familiar with during the lecture, I think, is a very good example of how different methods can be employed when doing in-depth research into a small number of cases – interviews, questionnaires with open questions, GPS data from mobile apps, observation and the car-trips card. The part I found most impressive, however, was that the research team did not limit itself to just studying these 3 families, but also took their findings to companies and politicians to actually make an attempt for a real, tangible difference for everyone.

Monday 10 October 2016

Theme 4, Blog post 3: Comments



Theme 5, Blog Post 2: Design Research


Prior to the lecture I had no idea neither what Research through Design (RtD) was, nor even that such a thing exists. Moreover, technology and I are not the best of friends, so it was very difficult for me to understand the questions we had to answer in our first blog post. But here’s what I learned last week:

RtD is a research approach in which the design process per se is a method for acquiring knowledge. RtD is different from other research methods in that its end product is a design artifact, which embodies a researcher’s thinking and the knowledge acquired in the design process. Moreover, Christopher Frayling, who coined the term in 1993, explains that the goal of RtD is not to acquire or create knowledge that can be verbally communicated, but rather experienced (hence, the design proposal or product is empirical data at its purest), which, I think, renders design work a very superior form of knowledge contribution, because our experience of the design artifact adds greatly to our perception and imagination. Especially given Bengt Molander’s (whom Lundström mentioned during the seminar) idea of knowledge as something that draws attention to something previously unknown.

RtD can be used to explore the interaction between people and an artifact; to propose a better design for an existing piece of technology; or to explore and improve a design process itself. Like in quantitative research methods, the design product of RtD is tightly linked to theory. The way every element of the design product is, as well as how the researcher changes the product throughout the design process, is grounded in theory – the theory is also embodied in the design proposal or product. Nonetheless, I think that the textual discussion and explanation of the theoretical framework behind a design product is extremely important and should always accompany that product. Like in art, where you have to be familiar with the times the artist lived in and the thoughts and emotions that inspired him/her, in order to understand his/her work, in RtD it is also very important to be familiar with the designer’s thinking and the theories behind the product, in order to understand the product itself.  

Unlike design in general, RtD is not about aesthetics, but about acquiring and producing knowledge. However, I think it is also somewhat occupied with functionality. Going back to Lundström’s proposal of a differentiated driving range application (Figure 1 from the article), we can see how the knowledge acquired during the research process (what affects the life of an electric car battery) is visualized and distributed in a way that gives ordinary drivers a basic understanding of the factors that influence the battery life, thus, providing them with tools and tips to find their own way of getting from point A to point B – by choosing a specific speed or by turning the heating on/off. Having said that, I find Lundström’s proposal very functional, because it erases previous confusion, it is easy to understand and gives drivers a choice of how to go about their journey.

One of the implications of RtD that I found most interesting is designing a product that challenges existing ways of thinking about the world and opens our minds to relationships we had not previously considered. A good example of this is British/Japanese artist and designer Hiromi Ozaki’s Menstruation machine. The machine is equipped with a “blood” dispensing mechanism and electrodes which trigger contractions in the lower abdomen to simulate the pain and bleeding of a menstruation process. This piece of technology makes menstruation “accessible” to men – they can get some idea of what happens to a woman’s body during menstruation, although the machine cannot imitate the hormonal changes women undergo. The experience of the Menstruation machine can make individual people and society as a whole re/consider questions and norms about gender, technology, culture, etc.

The replicability of a design research was a topic of great confusion in the lecture and seminar group discussions. In our little seminar discussion group we came to the agreement that the methodology of a design research may be replicated, but that does not guarantee the exact same results as before, especially when studying the way people interact with technology. In a study, which does not involve people as research subjects, replicability depends on the researchers’ level of knowledge and skill, the time at which the study is conducted and, thus, the technologies at their disposal. 

Perhaps the researchers were unable to find the same devices used in a study that was conducted 20 years ago and the use of more advanced technology gives the different results? Or, when people are involved and the researchers get access to the technology used in the original study, but get different results, because people’s thinking has changed greatly since? In short, to successfully replicate a study from A to Z, the researchers’ skills, methodology, technology used, and sample population’s thinking, habits and way of life have to be exactly the same as in the original study, which is practically impossible. 

To finish off with a useful laugh, here is knowledge distribution - https://www.youtube.com/watch?v=b81Cr97ANrk

Friday 7 October 2016

Theme 6, Blog Post 1: Qualitative and Case Study Research 


My first chosen article, Exploring emotional expressions on YouTubethrough the lens of Media System Dependency theory, by Chei Sian Lee was published in 2011 in New Media & Society (Impact Factor 3,110). It aims to answer the following questions about user reactions to the death of celebrity Michael Jackson:
  • What types of emotional responses are expressed on YouTube?
  • What are the effects of time and gender on the emotional responses expressed on YouTube?
  • What are other possible micro- and macro-level effects that may influence emotional responses expressed on YouTube?

1. Lee applies qualitative content analysis to a corpus of 3000 user comments on YouTube, written under 60 randomly chosen popular (having over 100 comments) videos, uploaded during a period of 3 weeks – between June 25th (when Michael Jackson died) and July 16th.  Lee states that a short period of time has been chosen because MSD is not as good at explaining long-term social phenomena. From the 6000 comments, 50 per video were randomly selected and information about the gender of their authors was extracted. When information about the gender of an author was not found, their comment was excluded from the sample. Comments that were not in English were also excluded.
The author employed 2 approaches in the study – direct content analysis (the use of prior research to develop the initial coding scheme before data analysis – and conventional content analysis – categories were derived from the data during the analysis.

2. I learned that qualitative content analysis is “a research method for the subjective interpretation of the content of text data through the systematic classifica­tion process of coding and identifying themes” (Lee, 2011:463) and that there are these two approaches in qualitative content analysis – direct content analysis and conventional content analysis, as well as the differences between them.


3. Initially, I thought the author should have selected a longer time period, but then a convincing argument was provided in favor of the author’s choice. I also thought that since it is hard to employ qualitative methods to large bodies of data, the author would have to gather a small sample, which was not the case – 3000 comments are a pretty impressive number. Lee has chosen an existing coding scheme with 19 categories developed by Macias et al (2005), but then actually applied it to 10% of the selected comments and adopted it to the corpus of the study – some categories were merged and others that bear no relevance to the study were dropped. A total of 10 categories were included in the coding scheme. Lee has also tested the reliability of the final coding scheme – the result stated is 93%, which is above the minimal 80% (Raffe et al, 1998). Overall, I could not find anything that could be improved with the study. 

Lee, however, does point out that further research should be done on the topic – in relation to the death of other celebrities and on other social media websites. The author also encourages others to replicate the study, taking into consideration not just the gender, but also the age, geographical location and personality of the reponsndents.  

4. After reading Eisenhardt’s article, I came up with this brief, but not at the least complete explanation: A case study is a research strategy tightly linked to the qualitative and/or quantitative empirical data it analyzes, with a carefully selected real-life case, which aims to construct a novel, testable and empirically valid theory that can closely mirror reality.

5. The case study I have chosen, Gotta catch ’em all: structure,agency and pedagogy in children’s media culture by Sefton-Green and Buckingham, was published in Media, Culture & Society (Impact factor: 1,128) in 2003.

Compared to Eisenhardt’s step-by-step article, Sefton-Green and Buckingham do not specify their choice of research method, not is there a clear research sample. They just talk about the whole “Pokémon” phenomenon, devoting some space to every element of it – from its profitability, to the films, to the computer games, the card game, the toys, the children who are into Pokémon, etc. Their use of academic sources is occasional. They do not come up with a new theory or even make attempts of constructing one. Sefton-Green and Buckingham just don’t agree that the relationship between the activity of the children and Pokémon’s producer, Nintendo, can be described in terms of the opposition between structure and agency, but rather turn to the notion of pedagogy to provide a more plausible explanation of that relationship.  

Sunday 2 October 2016

Theme 4, Blog Post 2: Quantitative research


What I understood from the lecture is that quantitative methods seek to gather numerical evidence in support of a hypothesis that is being tested. Quantitative data may be obtained in different ways, depending on the corpus/sample of the research.

For instance, if we would like to determine the variety of coverage in different news outlets (which is the case with the article I chose for my first blog post), we could gather a body of news/broadcast material, published by these outlets in a particular period of time, and count the number of topics covered by each news outlet and the number of articles dedicated to each topic. When working with people we could ask them to fill in one or more questionnaires that we have borrowed from previous research (if such questionnaires are available) or design a questionnaire that serves our particular purpose. Then we translate the volunteers’ answers into numerical data and use statistical tests to interpret it.

Of course, quantitative methods or data alone mean nothing without a theoretical background to justify why a particular research is important and should be conducted; why it is important to look at a problem from one angle and not from another (for instance, this angle has been overlooked by previous research; or previous research has failed to explain this or that aspect of the chosen angle; or previous research does not provide convincing proof, etc.); why a quantitative approach is more suitable for the aim of this research than, say, a qualitative approach.

Not to mention that theory is extremely important in determining where to look for evidence in the first place. For instance, why choose to study newspaper articles rather than TV news broadcasts. When I wrote my Bachelor’s thesis in 2014, I had to justify my use of quantitative content analysis on a total of 436 articles published in the 8 biggest daily newspapers in Britain for a period of 1 year. My goal was to prove that the social unrest prior to the opening of the British labour market for A2 (Bulgarian and Romanian) citizens was a classic example of a moral panic.

Theory also provides a foundation for the kind of evidence one should look for – what information is relevant to our hypothesis and what should be omitted due to irrelevance or danger of skewing the results – and determines each step of the research process in order to get valid and relevant results. For example, based on my literature review (the work of previous researchers on moral panic, Other-presentation in migration discourse, dehumanization of the “Other”, disaster metaphors, etc.), I carefully designed a coding schedule to filter through all the information and gather only that, which is relevant to my hypothesis, i.e. the use of metaphors; positive self-presentation and negative Other-presentation, etc. In the course of my research I had to redesign the coding schedule several times, because while reading all those articles I found new and relevant information that I had not come up with categories for.

Regardless of how thorough we are, there are often results that have to be omitted due to irrelevance to our study. We also need our theoretical framework to interpret and explain the acquired data and its importance. How do our results support or differ from the results of previous research? Do they support or disprove our hypothesis? Have we discovered something new and interesting that is worthy of conducting more research? In short (and in the immortal style of Kant), when it comes to knowledge production, theory without method is fruitless (unless we're trying to solve a metaphysical problem) and method without theory is empty :D.