I have come to my final blog entry for BUSN 8018. I really enjoyed this course and as well as doing this blog. I would like to make a few points in relation to the progress of my qualitative research project. I am analysing the interview data in addition to some documents. I can already see how ANT will provide some explanations for my results and hence 'fit' my data and research problem. It provides a language for my observed phenomena. For example, with the interview data on research teams it becomes obvious that to understand research teams they cannot be seen in isolation but must be placed within their larger network of relations. The ties with significant others helps shape and mould the success/failure of research teams. Also, the entry and exit of team members is important in shaping the direction. Their are many interrelated factors that unfold to produce the observed outcomes.
Particular aspects of ANT that I think I will draw on to explain my data include: network of relations/interactions (broader nexus of activity); the notion of performativity; and the importance of human as well as non-human actors. The key difference between these actors is that human actors have intentionality, however non-human actors do not. Therefore, while ANT advocates that non-human actors deserve the same attention as human actors, the differentiating feature of intentionality is expected to provide insight into why differences may exist between these actors. In the context of my study, particularly in the document analysis, one can argue that accounting and MCS are the non-human actors (accounting is a network effect and an interdependent actor) and that accounting is a mediator and transforms relationships. The importance of accounting as a non-human actor, performativity and employing a network perspective based on social relations and interactions are the tools that I will employ from this theoretical lens.
Based on my experience from conducting interviews with academic staff I realise that during an interview, the interviewer needs to do a lot of thinking. For example, in addition to your interview protocol you may tend to ask another question following from what an interviewee said. This shows you are listening to the interviewee and it seems more like a dialogue than simply an interview.
A preliminary document analysis reveals that the companies involved in such a coopetitive relationship attempt to implement mechanisms to cooperate during earlier parts of the value chain (research, design, development and production), however seek to compete with it comes to selling the final product to consumers. In particular, I noticed that they tend to implement mechanisms to manage the JV separately from the two organizations and that there is a strong emphasis on making sure that neither company knows the other companies marketing and sales strategies, with some separation agreements.
Overall, this course helped me to gain a deeper understanding about the practice of qualitative research, including qualitative methods and the qualitative methodology.
Tuesday 24 May 2011
Monday 23 May 2011
Writing Up
Apart from the substance of the paper, I think that the difference between a good and bad paper is the style, exposition, expression and intonation. In other words, how it is written. I know based on my experiences, even if you have the idea to begin with, it just does not come out the way you want it to immediately. Editing and re-writing is the touch that makes the paper sharper, tighter, clearer and shorter. Something that I will remember is that the clearer you can write the more confidence you show. The sign that a researcher knows their topic inside out is if they can clearly communicate it to others who do not know it.
As Kerry mentioned in class, this course has taught us the practice of qualitative research. The aim of this course was to induct a habitus in us - an understanding of how to conduct qualitative research and the implicit rules of the game. This is the practice of building a good argument that requires you construct it an argument initially to see if it works, break it down, change, swap, rework it and build a new one. Given that the process of doing qualitative research is a practice and the rules of the game in each field are tacit and evolving, is something that is learnt and continuously modified.
I have conducted my interviews with academic staff and I am now in the process of coming up with the best story I can given the data that I have. I have developed predictions/expectations based on my theory ANT and will see what argument I can best construct. Following Kerry's suggestions, I hope to find similarities, differences, surprises, and some data consistent with expectations. I think that the power of qualitative research is in showing how there are many alternative explanations for the same outcome, and also there can be many different outcomes. This diversity and variation is not a shortcoming, but rather, a strength as it would be too simplistic to believe that everything is the same and you could make sweeping generalisations or universal laws. For example, if there are some surprises it may be because the theory lacks something and must be extended, it is the wrong theory, or the data is wrong. Some data may fit one theory and other data may fit another theory.
There are two key styles to present your qualitative interview data. I am familar with the classic quote and comment and have used it previously. With this style, you can clearly see how the evidence fits with the claim. However, the analysis of a narrative has some appeal, although seems relatively more difficult. For our project I will see which style is the most appropriate to convey my argument. I need to always have in the back of my mind, do I have evidence to support my conclusions? With the interview data, you need to be true to what they say but you don't need to put everything in.
In addition, I have been looking at some documents (annual reports and media releases) on two companies and their dealings in relation to their proposed joint venture, which presents exactly the issues I am considering. I think that this source of data provides a more real life touch to my research problem.
During this course, Kerry has continually provided us with tips to help us along the way, such as
the style sheet, the difference between errors in substance and errors in communication, as well as many others which I have found very useful.
As Kerry mentioned in class, this course has taught us the practice of qualitative research. The aim of this course was to induct a habitus in us - an understanding of how to conduct qualitative research and the implicit rules of the game. This is the practice of building a good argument that requires you construct it an argument initially to see if it works, break it down, change, swap, rework it and build a new one. Given that the process of doing qualitative research is a practice and the rules of the game in each field are tacit and evolving, is something that is learnt and continuously modified.
I have conducted my interviews with academic staff and I am now in the process of coming up with the best story I can given the data that I have. I have developed predictions/expectations based on my theory ANT and will see what argument I can best construct. Following Kerry's suggestions, I hope to find similarities, differences, surprises, and some data consistent with expectations. I think that the power of qualitative research is in showing how there are many alternative explanations for the same outcome, and also there can be many different outcomes. This diversity and variation is not a shortcoming, but rather, a strength as it would be too simplistic to believe that everything is the same and you could make sweeping generalisations or universal laws. For example, if there are some surprises it may be because the theory lacks something and must be extended, it is the wrong theory, or the data is wrong. Some data may fit one theory and other data may fit another theory.
There are two key styles to present your qualitative interview data. I am familar with the classic quote and comment and have used it previously. With this style, you can clearly see how the evidence fits with the claim. However, the analysis of a narrative has some appeal, although seems relatively more difficult. For our project I will see which style is the most appropriate to convey my argument. I need to always have in the back of my mind, do I have evidence to support my conclusions? With the interview data, you need to be true to what they say but you don't need to put everything in.
In addition, I have been looking at some documents (annual reports and media releases) on two companies and their dealings in relation to their proposed joint venture, which presents exactly the issues I am considering. I think that this source of data provides a more real life touch to my research problem.
During this course, Kerry has continually provided us with tips to help us along the way, such as
the style sheet, the difference between errors in substance and errors in communication, as well as many others which I have found very useful.
Thoughst on this Reflective Blog
I have really enjoyed writing this blog because it has provided me with a relatively easy outlet to reflect on my thoughts as well as share my thoughts with others. I find it quite easy to write and usually write a lot so this blog gave the opportunity to express many of my thoughts.
I thank Kerry for suggesting that we do a blog during this course because it has helped me to develop and refine my thoughts during this learning process. It has been an interactive tool that facilitates ongoing thinking and highly recommend it to others, not only researchers but also other professionals. I also enjoyed using the mindmap software because apart from being comfortable with words and writing, I am also a graphical person and like to see pictures relating concepts to one other.
I thank Kerry for suggesting that we do a blog during this course because it has helped me to develop and refine my thoughts during this learning process. It has been an interactive tool that facilitates ongoing thinking and highly recommend it to others, not only researchers but also other professionals. I also enjoyed using the mindmap software because apart from being comfortable with words and writing, I am also a graphical person and like to see pictures relating concepts to one other.
Sociomateriality
This entry to my blog will be short compared to the others. I thought it was worth mentioning that in the process of my reading for my research I came across some papers on sociomateriality, a concept that seems to be highly relevant for my own research. A paper by Orlikowski and Scott (2008) entitled 'Sociomateriality: Challenging the Separation of Technology, Work and Organization' appeared to have a special significance given my theoretical lens of ANT. In fact, Orlikowski and Scott (2008, pg. 434) advance the view "that there is an inherent inseparability between the technical and the social...and "that a reconsideration of conventional views of technology may help us to more effectively study and understand the multiple, emergent, and dynamic sociomaterial configurations that constitute contemporary organizational practices". Technology (accounting systems) may be a principal mediator and not simply an intermediary. This paper also discusses actor-netork theory (ANT) as it is considered the most prominent body of literature concerning sociomateriality.
Another paper, Granovetter (1985) entitled 'Economic Action and Social Structure: The Problem of Embeddedness" also seems to be speaking my ANT language. Essentially this paper argues that economic activities/transactions are embedded within a network of social relations and this is the source of so called 'structure'. It also provides a critique of Williamson's 'market and hierarchies' research that is based on positivist, functional transaction cost economics.
Another paper, Granovetter (1985) entitled 'Economic Action and Social Structure: The Problem of Embeddedness" also seems to be speaking my ANT language. Essentially this paper argues that economic activities/transactions are embedded within a network of social relations and this is the source of so called 'structure'. It also provides a critique of Williamson's 'market and hierarchies' research that is based on positivist, functional transaction cost economics.
Articles from the Qualitative Research Report and Other Thoughts
I found some of the articles from the qualitative research report to be very useful in enhancing my understanding about qualitative research. For example, today there was an article that caught my attention as it was entitled 'From the Outside Looking In: How an Awareness of Difference Can Benefit in the Qualitative Research Process'. This paper problematised the insider/outsider dictohotomy and, in particular, some common criticisms of outsider research. It is argued that we are all insiders and outsiders to varying extents in every research setting. The issue is that notions of insiders and outsiders essentialise categories and therefore overlook important issues. There are inherent advantages and disadvantages with each approach. For instance, while an interviewee may be more comfortable to divulge information to an insider because they have similar characteristics (eg. background), an insider may take some things for granted and therefore neglect or overlook issues that an outsider would question. On the other hand, interviewees may not feel at ease speaking to an outsider.
The researcher's closeness to the subject of investigation may 'blunt' their criticality, causing them to overlook and take for granted aspects that appear familiar (Tinker and Armstrong, 2008). Closeness may hinder the researcher's ability to be rigorous in their analysis. The is akin to the notion of reflexivity. However, these potential biases may be conscious or subconscious. Tinker and Armstrong (2008, pg. 58) argue that being an outsider may be more advantagous than previously thought as "being on the outside looking in can provide a valuable sense of distance, which can allow the researcher an insight into other people's social worlds".
On the issue of subject/object, subjective/objective, for some types of research there is increasing blurring and fusion between these concepts. That is, they abandon notions of dualisms - such as ANT. I have found some really relevant articles within my disciplinary area about issues that I have been thinking about mor my research. Specifically, Ahrens (2008) in AOS is called 'Overcoming the Subjective-Objective Divide in Interpretive Management Accounting Research'.
I was reading the Qualitative Research Report and followed some of the links to other resources, which transported me to a paper called 'Qualitative Research: Validity and Reliability'. This is exactly something I was thinking about; my presumption was that in qualitative research there still exists the idea of reliability and validity, even though it is done differently and this is not what it is called. The article I read basically argued that the issue of reliability and validity in qualitative research is intertwined with the definitions of qualitative research. Contrary to scholars that claim that quantitative research is not as valid and reliable as quantitative research, this essay argued that it is possible for qualitative research to be properly valid and reliable by taking into consideration the qualitative criteria in social research including in its design and methods. This is a social research debate given that it is grounded in social theory.
A point that struck my attention in relation to qualitative research is that the inherent difficulty and diagreement comes about because we are trying to find answers about a subject matter that is in slow motion, however continuously changing. That is, we are trying to identify and observe a moving target: the social world. This issue is particularly acute in interpretive research. Interpretive qual seeks to interpret a socially constructed reality and given that perceptions and meanings play a crucial role, there may be multiple realities, not one concrete reality. It could be argued that qual and quant constitute different approaches to social investigation.
Qualitative validity has to do with the association between data and conclusion. What valid conclusions can one draw from the data that you have? It is claimed that to achieve validity in qualitative research, you need to reduce the gap between reality and representation; the more the data and conclusion are correspondent the more a piece of qualitative research is valid. Validity in qualitative research concerns the relationship between the data and the construct, the findings and the conclusion.
With reliability I think of consistency. Internal reliability may refer to a case in which more than one observer agree to what is seen and heard. External reliability refers to the degree to whcih the study can be replicated. Analogous to my thinking, this paper states that reliability refers to "the degree of consistency with which instances are assigned to the same category by different observers or by the same observers on different occasions" (Hammersley, 1992, pg. 67). Also, reliability may refer to "the degree to which the finding is independent of accidental circumstances in the research" (Kirk and Miller, 1986, pg. 20). To record the observations consistently is to have a reliable method. Reliability relates to the extent to which the concepts used appropriately describe what they ought to describe.
I am realizing the benefits of having both a qualitative and quantitative mindset. In qualitative research when you are looking for relationships between the concepts, this is like quantitative research when you are testing the structual model and path coefficients. In the first stage of coding qualitative data (when you are doing your open coding) you are breaking down your data into discrete parts to produce concepts, labelling data with concepts into categories based on similarities. This is analogous to quantitative research where the individual items in your survey instrument measuring a particular construct are the concepts in qual and the category in qual is the construct in quant.
When collecting qualitative data and trying to make sense of it to form meaningful conclusions, prediction is important. In terms of prediction in qualitative research, it is a softer prediction compared to quantitative research because you are not necessarily stipulating specific variables and predicting the nature/direction between them. Rather, your soft prediction is based on the theory that you use to provide a unique language and tools for how you explain the underlying processes behind the observed phenomena.
These are all issues that I hope to address in our qualitative research project.
The researcher's closeness to the subject of investigation may 'blunt' their criticality, causing them to overlook and take for granted aspects that appear familiar (Tinker and Armstrong, 2008). Closeness may hinder the researcher's ability to be rigorous in their analysis. The is akin to the notion of reflexivity. However, these potential biases may be conscious or subconscious. Tinker and Armstrong (2008, pg. 58) argue that being an outsider may be more advantagous than previously thought as "being on the outside looking in can provide a valuable sense of distance, which can allow the researcher an insight into other people's social worlds".
On the issue of subject/object, subjective/objective, for some types of research there is increasing blurring and fusion between these concepts. That is, they abandon notions of dualisms - such as ANT. I have found some really relevant articles within my disciplinary area about issues that I have been thinking about mor my research. Specifically, Ahrens (2008) in AOS is called 'Overcoming the Subjective-Objective Divide in Interpretive Management Accounting Research'.
I was reading the Qualitative Research Report and followed some of the links to other resources, which transported me to a paper called 'Qualitative Research: Validity and Reliability'. This is exactly something I was thinking about; my presumption was that in qualitative research there still exists the idea of reliability and validity, even though it is done differently and this is not what it is called. The article I read basically argued that the issue of reliability and validity in qualitative research is intertwined with the definitions of qualitative research. Contrary to scholars that claim that quantitative research is not as valid and reliable as quantitative research, this essay argued that it is possible for qualitative research to be properly valid and reliable by taking into consideration the qualitative criteria in social research including in its design and methods. This is a social research debate given that it is grounded in social theory.
A point that struck my attention in relation to qualitative research is that the inherent difficulty and diagreement comes about because we are trying to find answers about a subject matter that is in slow motion, however continuously changing. That is, we are trying to identify and observe a moving target: the social world. This issue is particularly acute in interpretive research. Interpretive qual seeks to interpret a socially constructed reality and given that perceptions and meanings play a crucial role, there may be multiple realities, not one concrete reality. It could be argued that qual and quant constitute different approaches to social investigation.
Qualitative validity has to do with the association between data and conclusion. What valid conclusions can one draw from the data that you have? It is claimed that to achieve validity in qualitative research, you need to reduce the gap between reality and representation; the more the data and conclusion are correspondent the more a piece of qualitative research is valid. Validity in qualitative research concerns the relationship between the data and the construct, the findings and the conclusion.
With reliability I think of consistency. Internal reliability may refer to a case in which more than one observer agree to what is seen and heard. External reliability refers to the degree to whcih the study can be replicated. Analogous to my thinking, this paper states that reliability refers to "the degree of consistency with which instances are assigned to the same category by different observers or by the same observers on different occasions" (Hammersley, 1992, pg. 67). Also, reliability may refer to "the degree to which the finding is independent of accidental circumstances in the research" (Kirk and Miller, 1986, pg. 20). To record the observations consistently is to have a reliable method. Reliability relates to the extent to which the concepts used appropriately describe what they ought to describe.
I am realizing the benefits of having both a qualitative and quantitative mindset. In qualitative research when you are looking for relationships between the concepts, this is like quantitative research when you are testing the structual model and path coefficients. In the first stage of coding qualitative data (when you are doing your open coding) you are breaking down your data into discrete parts to produce concepts, labelling data with concepts into categories based on similarities. This is analogous to quantitative research where the individual items in your survey instrument measuring a particular construct are the concepts in qual and the category in qual is the construct in quant.
When collecting qualitative data and trying to make sense of it to form meaningful conclusions, prediction is important. In terms of prediction in qualitative research, it is a softer prediction compared to quantitative research because you are not necessarily stipulating specific variables and predicting the nature/direction between them. Rather, your soft prediction is based on the theory that you use to provide a unique language and tools for how you explain the underlying processes behind the observed phenomena.
These are all issues that I hope to address in our qualitative research project.
Sunday 15 May 2011
NVivo9 and EndNote
This week we used we had a go at NVivo9 qualitative data analysis software. Prior to this class I had already attended a full day training session on NVivo software so I was familiar with what it does and how to use it. I think that NVivo is very useful in terms of storing all your data in one place as well as provide tools in order to help a researcher determine emerging themes and assign meaning from their data. I had a look at the tutorial videos on You Tube as well as the getting started guide. NVivo is one of the most popular qualitative data analysis tools used in business, government and academia.
NVivo is quite versatile in that a wide range of data can be imported and shared with colleagues on group projects, including documents, papers, articles, books, spreadsheets, databases, photos, video and audio files. In addition to analysing the various forms of data, NVivo can be used to manage bibliographical data or literature reviews. NVivo would be of great value when you are working on a significant project, such as a PhD. However, I think that in the end just like when you use a calculator, the researcher needs to do the real analysis because qualitative analysis involves reading, thinking, reflecting, writing, which all lead towards an argument.
I have also been attending some training courses and recently I went to an EndNote class to learn how to deal with bibliographical software. I think that these software packages all have some value, however it's all about finding something that works for you. I have a very simple and effective system of managing my list of sources - I have a word document and continuously add new references under different headings depending on what issues that relate to, ie. management control systems, actor-network theory, methodology, coopetition, etc. Either way, it involves the researcher doing the thinking work.
Unfortunately, NVivo does not transcribe verbal interviews from your digital recorder into a text document, and there are some other shortcomings of NVivo. Also, given that qualitative research, is a very iterative process/cycle I wonder if it is reflexible enough when the direction of your project changes and a researcher needs to change the structure of the project. For example, the framework of the nodes and categories need to be altered. However, in terms of running various queries in order to identify some characteristics in your data (which manually may be difficult), NVivo should be very helpful.
NVivo is quite versatile in that a wide range of data can be imported and shared with colleagues on group projects, including documents, papers, articles, books, spreadsheets, databases, photos, video and audio files. In addition to analysing the various forms of data, NVivo can be used to manage bibliographical data or literature reviews. NVivo would be of great value when you are working on a significant project, such as a PhD. However, I think that in the end just like when you use a calculator, the researcher needs to do the real analysis because qualitative analysis involves reading, thinking, reflecting, writing, which all lead towards an argument.
I have also been attending some training courses and recently I went to an EndNote class to learn how to deal with bibliographical software. I think that these software packages all have some value, however it's all about finding something that works for you. I have a very simple and effective system of managing my list of sources - I have a word document and continuously add new references under different headings depending on what issues that relate to, ie. management control systems, actor-network theory, methodology, coopetition, etc. Either way, it involves the researcher doing the thinking work.
Unfortunately, NVivo does not transcribe verbal interviews from your digital recorder into a text document, and there are some other shortcomings of NVivo. Also, given that qualitative research, is a very iterative process/cycle I wonder if it is reflexible enough when the direction of your project changes and a researcher needs to change the structure of the project. For example, the framework of the nodes and categories need to be altered. However, in terms of running various queries in order to identify some characteristics in your data (which manually may be difficult), NVivo should be very helpful.
Tuesday 10 May 2011
Analysing the Interview
It is about what is defensible and justifiable. Analysis in a way is not really analysis, it is about based on the data you have, what is the most defensible argument and position you can take. An issue I thought about in this class was do patterns emerge from the interviews or do we assign a pattern to the data? In a way, you see what you want to see or are trained to see. I think that all research is subjective as it always involves some kind of judgement call. Researchers try to be objective but they are only human, they are not machines. Just because it is a number people think that it is objective, but the question is how did you get that number. What choices did you make, why did they select a particular survey instrument over another, this is subjective. It is about what fits, can you defend your argument with your evidence and your theory.
Qualitative research is a very iterative, cyclical, at times, repetitive process. The process of fitting your data with your theory involves a judgement, a skill, and it is very much an art not a skill. I have noticed that Kerry speaks with metaphors when explaining key concepts. For example, in terms of analysing the data, the picture on the jigsaw puzzle is like the theory, if the pieces (your data) fit your theory, it works, otherwise you have the wrong theory.
During interviews it is very important to hear the full story of an interviewee because the moral (meaning) of the story comes at the end. However, we also need to create a story from what we see. You can think about this in terms of an argument in a paper. When someone tells a story they are taking you through a journey and presenting an argument or their point of view, the ending of the story is like the conclusion of a paper. In the story, the moral is related to all the things a person said. In a paper, the conclusion is related to their overall argument in the paper.
A good theory should explain and not just describe. For example, when collecting interview data you see certain themes emerging, such as organizations working together, alliances falling apart, MCS working in some cases and not in other cases, how the rules of the game are created and changed. ANT provides the reasons, explanations for why these observations take place. In terms of structure and power, ANT deals with these issues but in its own way. They are there, they exist, but ANT explains them in a different way, they use a different language. It is like a different way of seeing things, a different mindset. The issue is how can you convince others to see the way that you see.
So it's like your data are observations of what is happening in the world, and the theory is a possible explanation for why things are happening. The feature that makes qualitative research interesting is that a particular event can happen, but different people with different theories can have different explanations for why something happened. This shows the diversity of qualitative research and the way that even with the same data, two researchers may come up with different conclusions and theories about what is happening.
In relation to reflexivity, there are two issues. First, your own biases that you need to deal with. Second, there are the biases of the interviewee, you need to understand the way they think. The interviewee could have a particular bias, and the interviewer could have different biases, and the interpretation of what really happenedmay not be what really happened.
In terms of analysing your data, Kerry mentioned an interesting point that was about not ignoring your data. You don't want to only selectively use the data that supports your argument and ignore everything else. You need to decide how to address this other data. It may mean that your theory is inadequate and by changing the theory you can use all of your data. Or the data may be telling you that there is something more to the issue that you have overlooked. Whenever you interpret your data, you need to consider your context. If it's one thing that I have learnt when dealing with humans is that you can't apply some set criteria or assumptions to everyone just because they have some particular attributes.
In order to be reflexive in a sense you you need to accept the fact that you don't know. If you think you know, but you don't, there is a problem because you come up with the wrong conclusions.
Qualitative research is a very iterative, cyclical, at times, repetitive process. The process of fitting your data with your theory involves a judgement, a skill, and it is very much an art not a skill. I have noticed that Kerry speaks with metaphors when explaining key concepts. For example, in terms of analysing the data, the picture on the jigsaw puzzle is like the theory, if the pieces (your data) fit your theory, it works, otherwise you have the wrong theory.
During interviews it is very important to hear the full story of an interviewee because the moral (meaning) of the story comes at the end. However, we also need to create a story from what we see. You can think about this in terms of an argument in a paper. When someone tells a story they are taking you through a journey and presenting an argument or their point of view, the ending of the story is like the conclusion of a paper. In the story, the moral is related to all the things a person said. In a paper, the conclusion is related to their overall argument in the paper.
A good theory should explain and not just describe. For example, when collecting interview data you see certain themes emerging, such as organizations working together, alliances falling apart, MCS working in some cases and not in other cases, how the rules of the game are created and changed. ANT provides the reasons, explanations for why these observations take place. In terms of structure and power, ANT deals with these issues but in its own way. They are there, they exist, but ANT explains them in a different way, they use a different language. It is like a different way of seeing things, a different mindset. The issue is how can you convince others to see the way that you see.
So it's like your data are observations of what is happening in the world, and the theory is a possible explanation for why things are happening. The feature that makes qualitative research interesting is that a particular event can happen, but different people with different theories can have different explanations for why something happened. This shows the diversity of qualitative research and the way that even with the same data, two researchers may come up with different conclusions and theories about what is happening.
In relation to reflexivity, there are two issues. First, your own biases that you need to deal with. Second, there are the biases of the interviewee, you need to understand the way they think. The interviewee could have a particular bias, and the interviewer could have different biases, and the interpretation of what really happenedmay not be what really happened.
In terms of analysing your data, Kerry mentioned an interesting point that was about not ignoring your data. You don't want to only selectively use the data that supports your argument and ignore everything else. You need to decide how to address this other data. It may mean that your theory is inadequate and by changing the theory you can use all of your data. Or the data may be telling you that there is something more to the issue that you have overlooked. Whenever you interpret your data, you need to consider your context. If it's one thing that I have learnt when dealing with humans is that you can't apply some set criteria or assumptions to everyone just because they have some particular attributes.
In order to be reflexive in a sense you you need to accept the fact that you don't know. If you think you know, but you don't, there is a problem because you come up with the wrong conclusions.
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