Theme 6 discussed the dimensions of both qualitative and case study research. These are my reflections based on the lecture, seminar and reading. Read my previous blog post on this theme here.
First of all, despite the familiarity of the concept of qualitative research, I felt it interestingly refreshing to process probably the most well-known scientific method (at least for me) as the last one in this course. Perhaps because of the order of themes discussed, even qualitative research gained new dimensions in my thinking. As a summary, qualitative methods are a good choice when descriptively explaining a phenomena in detail and in depth. Also, in contrast to quantitative research, qualitative is also more flexible – it actually allows the researcher to revise the study when new findings occur. This is a feature that is shares with design-driven research as well! As for the limitations, qualitative methods often are very time consuming, the possible influence of researchers in the process is higher (both when gathering the data and analyzing it) and the sample can be smaller and less statistically generalizable. It is therefore often benefitting to use mixed methods, of which I was happy to make a concrete observation in the article of my choice. Another thing that I gave more thought after this theme was the expenses – because of its time consuming and complex nature, qualitative research can be extremely expensive. Surely that can have an effect on the choice of a method, at least in some scientific communities.
As for the nature of case study, it is not so much a method but rather an approach which often combines both qualitative and quantitative methods. In the seminar we described the context of case studies by stating that it is conducted in a specific time and space or even across time and space. How a community copes after an earthquake or how an organization changes as a new management model is brought into use. We discussed that a case study doesn't have a hypothesis, but it does have a research question as a focus that is narrowed down in the process. Sometimes you don't have enough existing theory to formulate a decent research question, and those case studies are called theory building case studies. Another important feature is that data is overlapping with analysis during the entire process, and the process of conducting the study doesn't even have to be a chronological one.
I find it accurate when Ilias described case studies as a "metamethod", a sort of higher level of scientific research, that is not a method you can apply but a method relying on other methods. This definition is similar to the one I wrote in the second paragraph. Case studies also remind me of qualitative research as they too provide in-depth description of a phenomena, and as a matter of fact case study papers don't necessarily even state that they are case studies. This is something we discussed during the seminar as well, that the study presented by Hanna Hasselqvist on the lecture was actually a case study even though not stating it on the paper.
Ilias also mentioned that neurosciences is a good example of using case studies as an approach, because there are many interesting cases concerning the processes of human brain and the affect of brain damages on behavior, for example. But just as much the approach can be applied to any context on a micro or macro level. An interesting thought occurred to me as I started to further analyze the generalizability, which was brought up on the seminar as well. For me it seems that in the context of neuroscience, the aim is to often gain new knowledge of the matter and then try to generalize this to other similar cases and brain research. Understanding the process is still in the very core of it, but the desired outcome in a longer run can be to invent a new medicine, for example. In contrast to this idea of generalizability, we also discussed that the findings often are limited to the specific circumstances. Perhaps it could be identified that the generalizability of a case study also depends on the organism it examines: whether it is an organization, community or the complex human mind. Whatever the case (clever, huh?), a case study describes the cause and effect relationship of a specific phenomena and then provides seeds for further research.
maanantai 17. lokakuuta 2016
Theme 5: Comments
These are my comments on theme 5:
1. https://u12vkokq.blogspot.se/2016/10/reflections-on-theme-5-design-research.html?showComment=1476709583713#c91388029556165816
2. https://u1818rgq.blogspot.se/2016/10/theme-5-design-research-post-2.html?showComment=1476711517566#c7030913970800220680
2. https://u1818rgq.blogspot.se/2016/10/theme-5-design-research-post-2.html?showComment=1476711517566#c7030913970800220680
3. https://u1j8du7c.blogspot.se/2016/10/theme-52.html?showComment=1476713935378#c3765180653724282665
maanantai 10. lokakuuta 2016
Theme 5: Reflection
Theme 5 focused on design research and we had both a great lecture and a seminar in addition to the reading. This is my reflection on this theme.
To be honest, this theme was the one I was most unfamiliar with at the beginning. Since my background is in communication, psychology and economics (and little bit of digital media as well), I had very little knowledge of especially the tech domains in design driven research. However, that is also the reason why this theme felt even more relevant and important to dive in to.
In the beginning of the lecture we got a nice introduction to methods and empirical data in RtD. We are surrounded by design every day in all of its senses, whether it is the inner technological micro systems or surface design. In the center of RtD is the interactive system itself, but the focus is even more in the human aspects around it: interaction between humans and devices. Cognitive abilities, culture, emotions, physical bodies, social behavior and senses all provide a broad ground for research in several fields, all of which are equally needed when trying to gain a comprehensive understanding of the subject.
During the seminar we discussed our discoveries about the subject in relation to our perceptions before the theme. Starting from designing the concept of 'empirical data' we discussed about Lundström's paper "Differentiated Driving Range" and what is the empirical data in that. In that case we reached a consensus of it being the driving range (quantitative data) and further on with the help of Lundström we concluded that empirical data doesn't necessarily have to be experiments, it could have just as easily been interviews or observations. Lundström encouraged us to think all the options it can be instead of settling with one definition. A good reminder was also to remember that collecting empirical data is not yet research, although people often may be confused about it. For example, governmental institutions often gather data and publish statistics, but they are not to be mixed up with scientific research.
As we moved on discussing about the replicability of design research, we had a few interesting thoughts about the effects of historical setting, tools, skills of the researchers etc. on the studies. I had previously maybe a little too black and white perception about research needing to be so objective, that anyone could reproduce it with the knowledge gained from the paper and end up with similar results. At some extent this does indeed apply, when we think one particular study or for example coding of data: the point of several researchers coding same data is to increase the objectivity. However, while we did agree that design driven research is replicable, we also stated that the results are dependent on the current context. By this I mean both the technical development and cultural and social aspects, which always have an impact on the context. Of course one could argue that the same tools can be used and the study can be conducted exactly the same way as the original study guides, but we also discussed the little things that are always left outside of the paper – no matter how carefully one tries to describe the process.
To be honest, this theme was the one I was most unfamiliar with at the beginning. Since my background is in communication, psychology and economics (and little bit of digital media as well), I had very little knowledge of especially the tech domains in design driven research. However, that is also the reason why this theme felt even more relevant and important to dive in to.
In the beginning of the lecture we got a nice introduction to methods and empirical data in RtD. We are surrounded by design every day in all of its senses, whether it is the inner technological micro systems or surface design. In the center of RtD is the interactive system itself, but the focus is even more in the human aspects around it: interaction between humans and devices. Cognitive abilities, culture, emotions, physical bodies, social behavior and senses all provide a broad ground for research in several fields, all of which are equally needed when trying to gain a comprehensive understanding of the subject.
One particularly memorable detail was the rule of 4 D's in RtD: Discover (insight into the problem), Define (the area to focus upon), Develop (potential solutions) and finally Deliver (solutions that work). Another important message from the lecture was the thought that designing systems happens in the context of other systems, such as technical platforms and social systems. So 'design' as a term is so much more than the interface. The concept of design is so wide that each of the dimensions is an extensive area of study in itself. In addition to what I wrote above about the focus on interaction in RtD: then again, with a focus on technology, constructive activities are the core method of investigation.
During the seminar we discussed our discoveries about the subject in relation to our perceptions before the theme. Starting from designing the concept of 'empirical data' we discussed about Lundström's paper "Differentiated Driving Range" and what is the empirical data in that. In that case we reached a consensus of it being the driving range (quantitative data) and further on with the help of Lundström we concluded that empirical data doesn't necessarily have to be experiments, it could have just as easily been interviews or observations. Lundström encouraged us to think all the options it can be instead of settling with one definition. A good reminder was also to remember that collecting empirical data is not yet research, although people often may be confused about it. For example, governmental institutions often gather data and publish statistics, but they are not to be mixed up with scientific research.
As we moved on discussing about the replicability of design research, we had a few interesting thoughts about the effects of historical setting, tools, skills of the researchers etc. on the studies. I had previously maybe a little too black and white perception about research needing to be so objective, that anyone could reproduce it with the knowledge gained from the paper and end up with similar results. At some extent this does indeed apply, when we think one particular study or for example coding of data: the point of several researchers coding same data is to increase the objectivity. However, while we did agree that design driven research is replicable, we also stated that the results are dependent on the current context. By this I mean both the technical development and cultural and social aspects, which always have an impact on the context. Of course one could argue that the same tools can be used and the study can be conducted exactly the same way as the original study guides, but we also discussed the little things that are always left outside of the paper – no matter how carefully one tries to describe the process.
Theme 4: Comments
1. https://dm2572-16.blogspot.se/2016/10/theme-42.html?showComment=1476109904328
2. https://omg-dm2572.blogspot.se/2016/10/after-theme-4.html showComment=1476112160484#c8705817781878205621
3. https://u1ci4ejx.blogspot.se/2016/10/theme-4-review.html showComment=1476131876761#c2777317191776341822
4. https://u1eqtjc8.blogspot.se/2016/10/theme-4-reflection.html?showComment=1476134735551#c8431193030632598782
5. https://u11873yx.blogspot.se/2016/09/theme-3-second-post.html?showComment=1476135743446#c8765666847017634489
6. https://u1bauz11.blogspot.se/2016/09/theme-4-reflection.html?showComment=1476136965344#c191466614569887413
7. https://u1h02pv3.blogspot.se/2016/09/reflection-on-theme-3-quantitative.html?showComment=1476137833573#c8344185536235713445
8. https://scarsickbg.blogspot.se/2016/10/theme-4-blog-post-1-quantitative.html?showComment=1476138449654#c895421984201654116
9. https://u1c051gg.blogspot.se/2016/09/42-quantitative-research-reflection-310.html?showComment=1476139134454#c4315792381652307883
2. https://omg-dm2572.blogspot.se/2016/10/after-theme-4.html showComment=1476112160484#c8705817781878205621
3. https://u1ci4ejx.blogspot.se/2016/10/theme-4-review.html showComment=1476131876761#c2777317191776341822
4. https://u1eqtjc8.blogspot.se/2016/10/theme-4-reflection.html?showComment=1476134735551#c8431193030632598782
5. https://u11873yx.blogspot.se/2016/09/theme-3-second-post.html?showComment=1476135743446#c8765666847017634489
6. https://u1bauz11.blogspot.se/2016/09/theme-4-reflection.html?showComment=1476136965344#c191466614569887413
7. https://u1h02pv3.blogspot.se/2016/09/reflection-on-theme-3-quantitative.html?showComment=1476137833573#c8344185536235713445
8. https://scarsickbg.blogspot.se/2016/10/theme-4-blog-post-1-quantitative.html?showComment=1476138449654#c895421984201654116
9. https://u1c051gg.blogspot.se/2016/09/42-quantitative-research-reflection-310.html?showComment=1476139134454#c4315792381652307883
perjantai 7. lokakuuta 2016
Theme 6: Qualitative and case study research
My choice for the article is "Bringing Technological Frames to Work: How Previous Experience with Social Media Shapes the Technology's Meaning in an Organization" by Jeffrey W. Treem, Stephanie L. Dailey, Casey S. Pierce and Paul M. Leonardi. The article was published in the Journal of Communication, which has an impact factor of 2.895.
The article represents a study which examined the expectations that workers have regarding enterprise social media (ESM) in one the largest financial service companies in the United States, American Financial. American Financial decided to adopt an ESM platform called A-Life, because they wanted to encourage their employees to interaction and professional networking. The sample was limited to workers within a particular leadership program, which consisted of young social media oriented employees working in different teams, who had worked only a short period of time for the company (born between 1980 and 1990). That way they were thought not to be influenced too strongly by the organizational culture.
The data was collected through interviews of 58 employees in the leadership program in American Financial. The first interviews followed a semi structured interview protocol regarding each employee’s use and perceptions of various technologies. The second part of the interviews resembled a free-response questioning and its aim was to find out what impressions the participants had of a number of different publicly available social media technologies and platforms and also other digital communication technologies used in the workplace. At the final part of the interview, employees were presented with a hypothetical scenario about an American Financial sponsored social media technology and they were asked about their anticipated use of this hypothetical technology.
The data was first analyzed using the constant comparative technique, where three authors coded the interviews until a broad structure of themes emerged. Two of the authors also engaged in open line-by-line coding. They also coded for individual attributes and further on did classification. Later on they used axial coding grouping the raw text segments into similar responses. Finally, the researchers used also selective coding in the process, which was done blind to the attributes.
The researchers used several qualitative methods when analyzing the data, which was very benefitting considering the amount of the material they had. They explained the process thoroughly and justified their decisions. One of the limitations in the methods however, could be the semi-structured interview protocol, which can imperceptibly include a hidden agenda of the researchers because of its unstructured nature.
I would also have to criticize the demographic features of the respondents, since they were a very homogeneous group. In addition, they were all also from the same company, so it would have been perhaps benefitting to examine the same expectations in other companies as well.
Briefly explain to a first year university student what a case study is.
Case study research is a way of studying a specific group, situation, organization, individual or phenomenon in a specific period of time. Case study method in science aims at understanding the dynamics in specific settings and it usually combines several data collecting methods. The data used in case study research can be qualitative, quantitative or both. Case studies can be used in accomplishing for example the following: to provide description, test a theory as well as to generate a theory.
Use the "Process of Building Theory from Case Study Research" (Eisenhardt, summarized in Table 1) to analyze the strengths and weaknesses of your selected paper.
The case study paper of my choice is From Diagnosis to Death: A Case Study of Coping With Breast Cancer as Seen Through Online Discussion Group Messages by Kuang-Yi Wen, Fiona McTavish, Gary Kreps, Meg Wise and David Gustafson. It was published in the Journal of Computer-Mediated Communication (impact factor of 3.541). The chosen case study conducted a study by using an online narrative analysis as an innovative approach to investigate changing message themes across the cancer trajectory.
This case study was about a chosen individual on an online discussion group, and her 202 messages where the sample. The researchers stated clearly the theoretical framework: the model of coping (Lasarus & Folkman) and the fact that no previous research had examined how the messages of a breast cancer patient change across her medical timeline. The analyzing of the data was decided to do with using a qualitative approach, considering it was best for the aim. According to Eisenhardt's table, this case study was neither theory nor hypothesis, which retained the theoretical flexibility. The study has a strong justification, but it doesn't state a single research question or hypothesis: "the purpose of this analysis was to identify the patterns of online discussion group message themes in relation to the medical timeline of one woman's breast cancer journey from the time of her diagnosis to the time of her death." So this could perhaps be seen as a weakness: the lack of a clear research question.
The strengths in this case study could be the within-case analysis, since the study has a strong framework and also a database to which the chosen individual's messages are rather easy to compare. This case study also had multiple investigators: the open coding was done by two of the researchers. The study also manages to discuss well with the previous studies and answers to a question that is left open in previous studies, as stated above.
It actually felt quite hard to find the weaknesses of this study in respect of Eisenhardt's table. The study entered a rather new field with the online environment, and used the results of previous face-to-face studies as a framework, not so much as contradicting them. They reached a conclusion and left suggestions for future studies. They even answered the causalities and helped the reader to understand why.
The article represents a study which examined the expectations that workers have regarding enterprise social media (ESM) in one the largest financial service companies in the United States, American Financial. American Financial decided to adopt an ESM platform called A-Life, because they wanted to encourage their employees to interaction and professional networking. The sample was limited to workers within a particular leadership program, which consisted of young social media oriented employees working in different teams, who had worked only a short period of time for the company (born between 1980 and 1990). That way they were thought not to be influenced too strongly by the organizational culture.
The data was collected through interviews of 58 employees in the leadership program in American Financial. The first interviews followed a semi structured interview protocol regarding each employee’s use and perceptions of various technologies. The second part of the interviews resembled a free-response questioning and its aim was to find out what impressions the participants had of a number of different publicly available social media technologies and platforms and also other digital communication technologies used in the workplace. At the final part of the interview, employees were presented with a hypothetical scenario about an American Financial sponsored social media technology and they were asked about their anticipated use of this hypothetical technology.
The data was first analyzed using the constant comparative technique, where three authors coded the interviews until a broad structure of themes emerged. Two of the authors also engaged in open line-by-line coding. They also coded for individual attributes and further on did classification. Later on they used axial coding grouping the raw text segments into similar responses. Finally, the researchers used also selective coding in the process, which was done blind to the attributes.
The researchers used several qualitative methods when analyzing the data, which was very benefitting considering the amount of the material they had. They explained the process thoroughly and justified their decisions. One of the limitations in the methods however, could be the semi-structured interview protocol, which can imperceptibly include a hidden agenda of the researchers because of its unstructured nature.
I would also have to criticize the demographic features of the respondents, since they were a very homogeneous group. In addition, they were all also from the same company, so it would have been perhaps benefitting to examine the same expectations in other companies as well.
Briefly explain to a first year university student what a case study is.
Case study research is a way of studying a specific group, situation, organization, individual or phenomenon in a specific period of time. Case study method in science aims at understanding the dynamics in specific settings and it usually combines several data collecting methods. The data used in case study research can be qualitative, quantitative or both. Case studies can be used in accomplishing for example the following: to provide description, test a theory as well as to generate a theory.
Use the "Process of Building Theory from Case Study Research" (Eisenhardt, summarized in Table 1) to analyze the strengths and weaknesses of your selected paper.
The case study paper of my choice is From Diagnosis to Death: A Case Study of Coping With Breast Cancer as Seen Through Online Discussion Group Messages by Kuang-Yi Wen, Fiona McTavish, Gary Kreps, Meg Wise and David Gustafson. It was published in the Journal of Computer-Mediated Communication (impact factor of 3.541). The chosen case study conducted a study by using an online narrative analysis as an innovative approach to investigate changing message themes across the cancer trajectory.
This case study was about a chosen individual on an online discussion group, and her 202 messages where the sample. The researchers stated clearly the theoretical framework: the model of coping (Lasarus & Folkman) and the fact that no previous research had examined how the messages of a breast cancer patient change across her medical timeline. The analyzing of the data was decided to do with using a qualitative approach, considering it was best for the aim. According to Eisenhardt's table, this case study was neither theory nor hypothesis, which retained the theoretical flexibility. The study has a strong justification, but it doesn't state a single research question or hypothesis: "the purpose of this analysis was to identify the patterns of online discussion group message themes in relation to the medical timeline of one woman's breast cancer journey from the time of her diagnosis to the time of her death." So this could perhaps be seen as a weakness: the lack of a clear research question.
The strengths in this case study could be the within-case analysis, since the study has a strong framework and also a database to which the chosen individual's messages are rather easy to compare. This case study also had multiple investigators: the open coding was done by two of the researchers. The study also manages to discuss well with the previous studies and answers to a question that is left open in previous studies, as stated above.
It actually felt quite hard to find the weaknesses of this study in respect of Eisenhardt's table. The study entered a rather new field with the online environment, and used the results of previous face-to-face studies as a framework, not so much as contradicting them. They reached a conclusion and left suggestions for future studies. They even answered the causalities and helped the reader to understand why.
keskiviikko 5. lokakuuta 2016
Theme 4: Reflection
Theme 4 handled quantitative methods in research. This is my reflection after the lecture and reading the chosen article as well as the "IEEE VR 2012 - Drumming in Immersive Virtual Reality" written by Ilias Bergström and colleagues. Unfortunately I was not able to attend the rescheduled seminar for this theme, so I will base my reflections on the lecture and reading.
Before the lecture I knew the basics of quantitative method in research, but perhaps perceived it a bit too strictly as a numeric method and "pure statistics". Now, after processing the subject, I understand the extend and diversity of it as well as the possibilities the method has in science. On the lecture we discussed the nature of quantitative method and how it usually is perceived. The basic idea of quantitative method in science is numerical measurements, and further on statistical observations about the data. One could say that when choosing a quantitative method, we're not actually measuring the object or phenomenon itself but rather the features that it has. The features in turn are measured by using chosen indicators, such as questionnaires or interviews.
Sometimes there's maybe even an unnecessary confrontation seen between the different kinds of research approaches. It would be more benefitting to see them more as supplementing each other, since you can't catch the true nature of a phenomenon with too strict an approach. That is why quantitative and qualitative method go very much hand in hand – not always is the quantitative method the best option, even though some people tend to say that "numbers don't lie".
Before the lecture I knew the basics of quantitative method in research, but perhaps perceived it a bit too strictly as a numeric method and "pure statistics". Now, after processing the subject, I understand the extend and diversity of it as well as the possibilities the method has in science. On the lecture we discussed the nature of quantitative method and how it usually is perceived. The basic idea of quantitative method in science is numerical measurements, and further on statistical observations about the data. One could say that when choosing a quantitative method, we're not actually measuring the object or phenomenon itself but rather the features that it has. The features in turn are measured by using chosen indicators, such as questionnaires or interviews.
Sometimes there's maybe even an unnecessary confrontation seen between the different kinds of research approaches. It would be more benefitting to see them more as supplementing each other, since you can't catch the true nature of a phenomenon with too strict an approach. That is why quantitative and qualitative method go very much hand in hand – not always is the quantitative method the best option, even though some people tend to say that "numbers don't lie".
As we talked in the lecture, it is not the data that is talking. That is why it's extremely important to know statistics and have a good understanding of them, since the data itself does not bring any value if one doesn't know how to read it. Sometimes we are shown diagrams and bar charts, but they are simply useless without the ability to understand the meaning behind them. This brings us to the important role of researchers who build their theories by arguing the data and even explain the data to the scientific community. Finally, the apprehensive knowledge reaches the great publics and results as changes in behavior. This is where also media has an important role as an "enlightener".
In the process of studying for this theme, I came across an interesting Swedish statistician named Hans Rosling. He's also a medical doctor, an academic and a public speaker. At the latest, the great public got to know him after a Ted Talk about the use of data to explore development issues. The reason why I would like to bring him up in this post is that he is the co-founder and chairman of the Gapminder Foundation, which is a non-profit venture promoting the popularization of science. Its mission statement in short is to increase the understanding of statistics as well as other information about social, economic and environmental development at many levels – locally, nationally and globally.
I find the Gapminder Foundation extremely interesting, since it is trying to find a solution to a problem that I feel is urgent. The ineptitude of understanding the world behind statistics results as false beliefs, which at the most extreme results as very absurd decisions in politics, for example. Just by taking a look to the voters of Brexit or the ongoing presidental elections in the U.S., one could easily argue on behalf of better understanding about the "realities". Of course, in the spirit of questioning absolute truths I'm not saying there is facts even in statistics, but that's the whole point – one would at least know how to question things.
I truly recommend to read more about the venture: https://www.gapminder.org/
Or even take a look at the Ted Talk: http://www.bbc.com/future/story/20120528-the-best-stats-youve-ever-seen
In the process of studying for this theme, I came across an interesting Swedish statistician named Hans Rosling. He's also a medical doctor, an academic and a public speaker. At the latest, the great public got to know him after a Ted Talk about the use of data to explore development issues. The reason why I would like to bring him up in this post is that he is the co-founder and chairman of the Gapminder Foundation, which is a non-profit venture promoting the popularization of science. Its mission statement in short is to increase the understanding of statistics as well as other information about social, economic and environmental development at many levels – locally, nationally and globally.
I find the Gapminder Foundation extremely interesting, since it is trying to find a solution to a problem that I feel is urgent. The ineptitude of understanding the world behind statistics results as false beliefs, which at the most extreme results as very absurd decisions in politics, for example. Just by taking a look to the voters of Brexit or the ongoing presidental elections in the U.S., one could easily argue on behalf of better understanding about the "realities". Of course, in the spirit of questioning absolute truths I'm not saying there is facts even in statistics, but that's the whole point – one would at least know how to question things.
I truly recommend to read more about the venture: https://www.gapminder.org/
Or even take a look at the Ted Talk: http://www.bbc.com/future/story/20120528-the-best-stats-youve-ever-seen
maanantai 3. lokakuuta 2016
Theme 3: Comments
Here are my comments on the 3rd theme:
1. https://u1h02pv3.blogspot.se/2016/09/reflection-on-theme-3-research-and.html?showComment=1475524422401#c458218479796443231
2. https://u17fpbu5.blogspot.se/2016/09/post-theme-3-research-and-theory.html?showComment=1475411315367#c6074481560210462903
3. https://u1dn0y6t.blogspot.se/2016/09/post-reflection-theme-3.html?showComment=1475477047381#c6504251351992113196
4. https://u11zdo9t.blogspot.se/2016/09/theme-3-post-2.html?showComment=1475503217771#c3992245858406317676
5. https://u1cq6h0z.blogspot.se/2016/09/theme-3reflection-research-and-theory.html?showComment=1475505549303#c7510759396829607562
6. https://scarsickbg.blogspot.se/2016/09/theme-3-blog-post-2-research-and-theory.html?showComment=1475507146042#c1893475211183894478
7. https://pgd7117.blogspot.se/2016/09/theme-3-reflection.html?showComment=1475508289531#c1821301551550846816
8. https://u1bauz11.blogspot.se/2016/09/theme-3-reflection.html?showComment=1475409424764#c2055792936335131152
1. https://u1h02pv3.blogspot.se/2016/09/reflection-on-theme-3-research-and.html?showComment=1475524422401#c458218479796443231
2. https://u17fpbu5.blogspot.se/2016/09/post-theme-3-research-and-theory.html?showComment=1475411315367#c6074481560210462903
3. https://u1dn0y6t.blogspot.se/2016/09/post-reflection-theme-3.html?showComment=1475477047381#c6504251351992113196
4. https://u11zdo9t.blogspot.se/2016/09/theme-3-post-2.html?showComment=1475503217771#c3992245858406317676
5. https://u1cq6h0z.blogspot.se/2016/09/theme-3reflection-research-and-theory.html?showComment=1475505549303#c7510759396829607562
6. https://scarsickbg.blogspot.se/2016/09/theme-3-blog-post-2-research-and-theory.html?showComment=1475507146042#c1893475211183894478
7. https://pgd7117.blogspot.se/2016/09/theme-3-reflection.html?showComment=1475508289531#c1821301551550846816
8. https://u1bauz11.blogspot.se/2016/09/theme-3-reflection.html?showComment=1475409424764#c2055792936335131152
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