Qualitative study
The paper I chose that is using a qualitative method is “Publicly Private and Privately Public: Social Networking on YouTube” written by Patricia G. Lange1. The goal of this paper is to explore how youth and young adults use Youtube and how they share and give access of the videos to friends. They use an ethnographic research method in the paper, among others it consists of semi-structured interviews and field-notes on observations on Youtube. The interviews and questions were adapted accordingly to every participants own interests and background based on their Youtube channel. The big benefit of using a method like semi-constructed interviews are that you will get all sorts of data that could be hard to know about before doing the research. It could also be data that is very hard to quantify like individual and complex answers to question like ‘why’ and ‘how’. I think that the limitation of this method is that you could en up with a lot of very individual answers that is really hard to see connections in and make logical sense of.
I am having a hard time understanding the scientific value in a bunch of interviews and answers from individual persons that do not connect to each other. At least, that is what it feels to me sometime. I am not saying that is how it is, I am just saying that I do not understand. For me it almost feel like a research prior to the real research that would consist of better and more precise questions were ‘real’ conclusions could be drawn. The analyse of the interviews are that persons personal interpretation of the participants personal answers and I feel like that is just to narrow to make any substantial conclusions. But, I guess that is not the point of qualitative research. The point is to find answers to complex questions. Questions that can not be answered with yes or no in two sentences. We also have to remember that everything is always an interpretation and we can probably never be truly objective. We are in a paradigm and we do our best with the knowledge we have right now.
Case study
A case study is a detailed and in-depth study of a subject. Different aspects of the subject are studied and analysed. Case studies are often used to develop new theories that are testable and empirically valid.
I have chosen to read “Treating small animal phobias using a projective-augmented reality system: A single-case study”2. In the article they are investigating how we can improve the effectiveness of augmented reality systems to “cure” small animal phobias. They start of by giving a good background of what virtual and augmented reality already does in this field. They then narrow the goal of the paper down to evaluating one new specific technics in this area. To collect data they have four participants with a phobia of cockroaches to study. Four participants seems low to me but the study can still contribute with (like they are saying in the text) new phenomenas and documenting of the efficacy of these new ideas. In the study, all the participants received the same treatment. They were exposed of cockroaches in projected augmented reality while they rated their stress level and talked about how they felt. The treatments function was to ease the participants into getting accustomed to the cockroaches and do not be afraid. The results show that a reduction in fear could be seen, even during the follow ups that occurred 3 and 12 months after the initial study. I think they did a good job of showing that the treatment worked but there is no explanation to why this treatment worked. I would have liked to read a bit more about the thoughts on what exactly in the treatment made it work. I would like to have a theory which there was no attempt at formulating. Also, they did not compare their data to other case studies which I think would have helped in order for them to value the impact and effectiveness of their own method.
I also think that they should have continued the study for longer, and iterate the process to make it more efficient. Now they just stopped after showing that this technique worked without trying to make it better. I feel that there was no real closure in the study and that they could have taken it much further.
References:
1: Patricia G. Lange, Publicly Private and Privately Public: Social Networking on YouTube, 2008
2: Wrzesien, M., Botella, C., Bretón-López, J., del Río González, E., Burkhardt, J.-M., Alcañiz, M., & Pérez-Ara, M. Á. (2015). Treating small animal phobias using a projective-augmented reality system: A single-case study, Computers in Human Behavior, 49, 343–353. doi:10.1016/j.chb.2015.01.065
Friday, 9 October 2015
Monday, 5 October 2015
Theme 3: comments
- http://duckyduckyducky.blogspot.se/2015/09/post-seminar-3_28.html?showComment=1444051961511#c9111653868714163686
- http://mediatechmishmash.blogspot.se/2015/09/theme-3-research-and-theory_23.html?showComment=1444053081201#c4506806621265147474
- http://dm2572elvira.blogspot.se/2015/09/theme-3-research-and-theory-post-seminar.html?showComment=1444053823754#c5690954590828377049
- http://mawnzblog.blogspot.se/2015/09/reflections-post-theme-3.html?showComment=1444054464761#c6536839415118921396
- http://vadfinnsegentligen.blogspot.se/2015/09/theme-3-reflection.html?showComment=1444055371168#c6458900648499568356
Theme 4: post seminar
Last week, we talked about the differences between quantitative and qualitative research. We also discussed when one method is better to use than the other. I think this theme was very concrete and straightforward. The seminar was interesting and I learned some new stuff as per usual. But it felt more like a confirmation of what I already knew compared to earlier seminars were I felt like I came to new insights all the time. This means that I am not going to write in detail what quantitative and qualitative research are in this post, since I have done it earlier.
Basically, quantitative data can be calculated and measured while qualitative data is more open and complex. We talked a lot about how one analyses qualitative data in a scientific way. At first I thought that qualitative data could be analysed quantitatively. How many participants answered in a specific way and so on. It is true that the qualitative data can be used like this, but after we spoke to the seminar leader we understood that qualitative data and research really shine in complex studies that has no easy answers. The example that was brought up was heavy metal listeners and their political opinions. If they generally have a different view on politics the interesting thing to know is why. That answer is so complex and has so many variables that it can not be reduced and answered with quantitative data. This is called ‘wicked problems’ and is solved with design research that later produces an artefact that contains new knowledge (a book for example).
Quantitative data are better suited for studies were you do not care about context or the before and after. It is better when you need a simple and concrete answer. We talked about why Illias used a quantitative method in his paper that we had to read last week, and he told us that since they were dealing with the subconsciousness of people they could not be sure to get an honest and precise answer from the participants if they would straight up ask them. Therefore, by measuring their movement it was easier to see the differences between the different states. He also told us that they used a lot of design research to develop their method which was not included in the paper. Among others, they used qualitative methods to come up with the quantitative method they used in the paper. Just like I wrote in my earlier blog post about this theme I think that it is good to use qualitative researches when you do not exactly know how you want to do your quantitative research.
Basically, quantitative data can be calculated and measured while qualitative data is more open and complex. We talked a lot about how one analyses qualitative data in a scientific way. At first I thought that qualitative data could be analysed quantitatively. How many participants answered in a specific way and so on. It is true that the qualitative data can be used like this, but after we spoke to the seminar leader we understood that qualitative data and research really shine in complex studies that has no easy answers. The example that was brought up was heavy metal listeners and their political opinions. If they generally have a different view on politics the interesting thing to know is why. That answer is so complex and has so many variables that it can not be reduced and answered with quantitative data. This is called ‘wicked problems’ and is solved with design research that later produces an artefact that contains new knowledge (a book for example).
Quantitative data are better suited for studies were you do not care about context or the before and after. It is better when you need a simple and concrete answer. We talked about why Illias used a quantitative method in his paper that we had to read last week, and he told us that since they were dealing with the subconsciousness of people they could not be sure to get an honest and precise answer from the participants if they would straight up ask them. Therefore, by measuring their movement it was easier to see the differences between the different states. He also told us that they used a lot of design research to develop their method which was not included in the paper. Among others, they used qualitative methods to come up with the quantitative method they used in the paper. Just like I wrote in my earlier blog post about this theme I think that it is good to use qualitative researches when you do not exactly know how you want to do your quantitative research.
Friday, 2 October 2015
Theme 5: Design research
There are of course a lot of different ways that media technology can be evaluated since it is a big area. But, since questions about user experience and usability are predominant in media technology, I think that testing and an iterative design process often are really important. To let the people that are your target audience test your product for example is critical in order to make it user friendly. Prototypes are an excellent way to gather data from users without having to develop a full product. It saves a lot of time and lets the researchers easily modify and redesign their product and method in an easy way. If your theory or concept is complex or maybe never has been done before a proof of concept prototype could be used. This prototype’s purpose is not to simulate the look and feel of the final product but rather just to prove that the theory is feasible.
One obvious limitation of prototypes are the simplicity of them. Because they are not the final product, they will not work like the final product and that could generate misleading data. It is important to be very distinct in how you present changes in the design research. Changes should make sense and reconnect with what the participants in the user test were saying and thinking. It is important that everyone understands the design changes that are made.
For the second part of this theme we had to read two texts, “Finding design qualities in a tangible programming space - Fernaeus & Tholander” and “Differentiated Driving Range - Lundström”. They are both good examples of articles about a design research. The empirical data are the knowledge about what works and what does not work. Also, why something works and why something else does not work. The common denominator in all these texts are that they are trying to convey information in the easiest way possible and I would say that an iterative design process is an excellent choice of method for this. We have to bear in mind that all these design decisions are just one way to do it that worked. It is not to say that the final product is the best way to do it, it is just the way that this particular research ended with. I would say that it is very unlikely that the research could be replicated since the user test input, design choices etc. will probably not be the same. Even though the research starts with exactly the same conditions it could end in a totally different way.
One obvious limitation of prototypes are the simplicity of them. Because they are not the final product, they will not work like the final product and that could generate misleading data. It is important to be very distinct in how you present changes in the design research. Changes should make sense and reconnect with what the participants in the user test were saying and thinking. It is important that everyone understands the design changes that are made.
For the second part of this theme we had to read two texts, “Finding design qualities in a tangible programming space - Fernaeus & Tholander” and “Differentiated Driving Range - Lundström”. They are both good examples of articles about a design research. The empirical data are the knowledge about what works and what does not work. Also, why something works and why something else does not work. The common denominator in all these texts are that they are trying to convey information in the easiest way possible and I would say that an iterative design process is an excellent choice of method for this. We have to bear in mind that all these design decisions are just one way to do it that worked. It is not to say that the final product is the best way to do it, it is just the way that this particular research ended with. I would say that it is very unlikely that the research could be replicated since the user test input, design choices etc. will probably not be the same. Even though the research starts with exactly the same conditions it could end in a totally different way.
Monday, 28 September 2015
Theme 2: Comments
1. http://elindm2572.blogspot.se/2015/09/theme-2-reflection.html?showComment=1443446496781
2. http://rchcc.blogspot.se/2015/09/reflection-of-theme2.html?showComment=1443447383841
3. http://capitalmyboy.blogspot.se/2015/09/theme-2-post-theme-post.html?showComment=1443448041840
4. http://ixxzw.blogspot.se/2015/09/theme-2-after-seminar.html?showComment=1443449560603
5. http://tamfmtol.blogspot.se/2015/09/theme-2-post-critical-media-studies.html?showComment=1443450074358
2. http://rchcc.blogspot.se/2015/09/reflection-of-theme2.html?showComment=1443447383841
3. http://capitalmyboy.blogspot.se/2015/09/theme-2-post-theme-post.html?showComment=1443448041840
4. http://ixxzw.blogspot.se/2015/09/theme-2-after-seminar.html?showComment=1443449560603
5. http://tamfmtol.blogspot.se/2015/09/theme-2-post-critical-media-studies.html?showComment=1443450074358
Sunday, 27 September 2015
Theme 3: post seminar
This week we mainly talked and discussed about what a theory is. This topic was much easier to grasp and understand than previous themes, and less abstract and weird. Therefore I also found it harder to discuss since there was not as much to work with.
I think this was an important theme to discuss since so many people (including me) confuse the word theory with hypothesis and think they mean the same. Just like they are saying in the text “What theory is not” it is very hard to pin down exactly what it is. But basically, the conclusion from our seminar is that theory is an attempt at an explanation of why something is.
We also discussed what the main differences between a weak and a strong theory is. I had no idea that a theory could be weak or strong before this theme. After this theme I found that it is very hard to draw the line where a theory goes from weak to strong. It is a subjective evaluation of the theory. Though, one should always strive for a strong theory since a strong theory is always better than a weak theory. An example of a weak theory from the seminar is that when more ice cream is sold at the beach, more people get attacked by sharks. It is a weak theory because even though it may be true, correlation does not imply causation. A stronger theory would be for example that more people at the beach makes for a greater risk of shark attacks.
During the seminar we also discussed if we can know that a theory tells the truth. The answer given was that it is very hard or maybe impossible to obtain truth. Truth is a priori knowledge, other than that it is hard to talk about. We are constantly in different paradigms that sets the standard for what is considered legitimate contributions to a science. Therefore when a paradigm shift is happening a lot of theories can become untrue. An example of a paradigm shift is how we started thinking differently when we discovered that earth is not the center of the universe and that we are in fact orbiting around the sun.
A theory is very important to have since all data is already filtered and not objective. Data without a theory is useless. I think that this knowledge about what a theory is and how important it is would have been nice to have before I wrote my bachelor thesis.
I think this was an important theme to discuss since so many people (including me) confuse the word theory with hypothesis and think they mean the same. Just like they are saying in the text “What theory is not” it is very hard to pin down exactly what it is. But basically, the conclusion from our seminar is that theory is an attempt at an explanation of why something is.
We also discussed what the main differences between a weak and a strong theory is. I had no idea that a theory could be weak or strong before this theme. After this theme I found that it is very hard to draw the line where a theory goes from weak to strong. It is a subjective evaluation of the theory. Though, one should always strive for a strong theory since a strong theory is always better than a weak theory. An example of a weak theory from the seminar is that when more ice cream is sold at the beach, more people get attacked by sharks. It is a weak theory because even though it may be true, correlation does not imply causation. A stronger theory would be for example that more people at the beach makes for a greater risk of shark attacks.
During the seminar we also discussed if we can know that a theory tells the truth. The answer given was that it is very hard or maybe impossible to obtain truth. Truth is a priori knowledge, other than that it is hard to talk about. We are constantly in different paradigms that sets the standard for what is considered legitimate contributions to a science. Therefore when a paradigm shift is happening a lot of theories can become untrue. An example of a paradigm shift is how we started thinking differently when we discovered that earth is not the center of the universe and that we are in fact orbiting around the sun.
A theory is very important to have since all data is already filtered and not objective. Data without a theory is useless. I think that this knowledge about what a theory is and how important it is would have been nice to have before I wrote my bachelor thesis.
Friday, 25 September 2015
Theme 4: Quantitative research
For this assignment I chose the article “Channeling Science Information Seekers' Attention? A Content Analysis of Top-Ranked vs. Lower-Ranked Sites in Google”. The study examines search engines to see if they are biased towards showing certain types of links and information higher up in the list of results. In the paper they specifically targeted results from the queries regarding nanotechnology.
With an automatic program they collected data once every week, for 60 weeks, from the american version of Google (www.google.com) by submitting a search query including the word nanotechnology and a word representing different categories, e.g. “nanotechnology AND environment”. They then selected one week from every month at random and collected the first 32 links. The final database consisted of 9120 parent links and 224,987 child links. The program then tracked the frequencies of root words, e.g. “security”, “toxin”, “energy” etc., that then represented a theme. This is used to determine what the link was about.
The benefit of using an automatic process that collects the links and quantitative data is, of course, that it is easier to collect large amounts of data. Large amounts of unbiased data will often be more accurate and is therefore preferred. The limitation of this method is the control of the data that is collected. It’s hard to be certain that these themes and root words are accurate enough to represent reality. Though the sheer amount of data can often compensate for these inaccuracies. The method in this paper could always been more accurate by collecting more data and use more search queries and root words to divide the themes more finely. But somewhere we have to draw a line in order for something to be done.
IEEE VR 2012 - Drumming in Immersive Virtual Reality
I feel that the authors of the article does a good job complementing the data from the scales in the questionnaire and the movement data. With this quantitative data they were able to rule out a lot of different interpretations and explain their main point in a logical and clear way. Even though they said that they had a semi-structured interview with every participant I could not find that they used it for anything important in the text. I think that since the article concerns a lot of stereotypes it was a smart move to lean towards using the quantitative data more. Since if the quantitative data is collected and interpreted in a good way it will show results in a very concrete and unbiased way.
Quantitative data is also a good tool for researchers to use if they want to take a step back from their own interpretation of the scene and just look at the numbers. Enough quantitative data therefore often allows for generalisations to an entire population. One disadvantage with quantitative data is that it does not tend to explain why something is done or why we perceive things in a certain way. Qualitative data is often much better when we want to understand how and why we feel, react and perceive something in a certain way. Qualitative data is also very good to use when we do not exactly know what we are looking for. Loose and descriptive answers could potentially lead to a bigger understanding of the underlying cause. One disadvantage of qualitative data is of course that it is very subjective and that must be taken into account when doing surveys that deals with this method.
References:
Channeling Science Information Seekers' Attention? A Content Analysis of Top-Ranked vs. Lower-Ranked Sites in Google
With an automatic program they collected data once every week, for 60 weeks, from the american version of Google (www.google.com) by submitting a search query including the word nanotechnology and a word representing different categories, e.g. “nanotechnology AND environment”. They then selected one week from every month at random and collected the first 32 links. The final database consisted of 9120 parent links and 224,987 child links. The program then tracked the frequencies of root words, e.g. “security”, “toxin”, “energy” etc., that then represented a theme. This is used to determine what the link was about.
The benefit of using an automatic process that collects the links and quantitative data is, of course, that it is easier to collect large amounts of data. Large amounts of unbiased data will often be more accurate and is therefore preferred. The limitation of this method is the control of the data that is collected. It’s hard to be certain that these themes and root words are accurate enough to represent reality. Though the sheer amount of data can often compensate for these inaccuracies. The method in this paper could always been more accurate by collecting more data and use more search queries and root words to divide the themes more finely. But somewhere we have to draw a line in order for something to be done.
IEEE VR 2012 - Drumming in Immersive Virtual Reality
I feel that the authors of the article does a good job complementing the data from the scales in the questionnaire and the movement data. With this quantitative data they were able to rule out a lot of different interpretations and explain their main point in a logical and clear way. Even though they said that they had a semi-structured interview with every participant I could not find that they used it for anything important in the text. I think that since the article concerns a lot of stereotypes it was a smart move to lean towards using the quantitative data more. Since if the quantitative data is collected and interpreted in a good way it will show results in a very concrete and unbiased way.
Quantitative data is also a good tool for researchers to use if they want to take a step back from their own interpretation of the scene and just look at the numbers. Enough quantitative data therefore often allows for generalisations to an entire population. One disadvantage with quantitative data is that it does not tend to explain why something is done or why we perceive things in a certain way. Qualitative data is often much better when we want to understand how and why we feel, react and perceive something in a certain way. Qualitative data is also very good to use when we do not exactly know what we are looking for. Loose and descriptive answers could potentially lead to a bigger understanding of the underlying cause. One disadvantage of qualitative data is of course that it is very subjective and that must be taken into account when doing surveys that deals with this method.
References:
Channeling Science Information Seekers' Attention? A Content Analysis of Top-Ranked vs. Lower-Ranked Sites in Google
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