Changing PhD Supervisors: Help or Hindrance?

I used to think the latter.

In an ideal world, one is assigned to two or three supervisor’s right at the beginning of one’s PhD, who would see you through to the very end. Most of us know however, that doing a PhD never really goes perfectly to plan. I know now this is just another part of the fun (!)

I have changed my main supervisor 3 times now – through no fault of my own I hasten to add. Well at least as far as I know. I ‘lost’ my supervisors simply due to them leaving for pastures new. Although I had the option of continuing and being supervised from a distant, I decided (after thinking long and hard) that this wasn’t something that I felt would be good for me. Thankfully, maintaining a certain level of stability, one co-supervisor has stayed with me from the beginning. I have to appreciate also, that having 3 different principle supervisors has also been a tough for her too – but hats off to her, she is still with me!

Admittedly, this has been far from easy as I have noted previously in a previous blog post. Indeed, I am not ashamed to admit that I have shed many tears over it. I admit that for totally selfish reasons, I felt abandoned and often felt I was being forced to take a path that wasn’t on my agenda.

My first supervisor change was extremely difficult as my new supervisors challenged me in a way I hadn’t been challenged before. I was unable to defend many things. This then led me down a backwards path for quite a long time. In time however, I realised that I needed to be led down this path in order to be able to move forward.

My second supervisor change was daunting, as I expected to be led onto this backwards path again. This however, was not the case. Sure I have been challenged, but this time it was welcome. I was now sure of what I had done, why I had done it, how I had done it and how it has contributed, therefore the challenging questions were welcome. Yes, of course, the reins have been pulled a little while I re-think some things and go back and forth to revisit work, but I can see how much stronger this is making my work. Although…. My literature reviews are a pain in the ass – they have always been a pain in the ass. I thought in time, my literature reviews and I would develop a mutual understanding, but it looks doubtful….

Anyway, I digress…..   Now that the dust has settled, I look back upon my supervision changes and challenges and have come up with this:

• Supervision is subjective – accept that. However, there is more than one way to skin a cat… That said, also respect each supervisor in their own right. More about styles of supervision here
• Set ground rules together: What was expected from previous supervisors may be different to what your new supervisor expects.  Similarly, establish what your supervisor will do for you in terms of reviewing work, supervision sessions etc. It may not be the same as your previous supervisor
• Don’t say “well my last supervisor said……”
• Embrace differences of opinion (no matter how hard this is). If your supervisor doesn’t like or agree with what you say or have been doing, then defend it. If you can’t defend it, then revisiting is probably what you need to do
• Keep a research journal. Your PhD is an iterative process. I don’t know about you, but sometimes I can’t even remember what I did said yesterday. You need to know what you did, when you did it, the reasons why you did it and what the outcome was throughout the whole process. This is your research journal
• Don’t be frightened of going back and revisiting/redoing work. It’s tough, really tough – but it’s an important part of the process. By the time you come to your viva, you will be able to justify what you tried, what didn’t work, what you did about it etc.
• If you don’t understand, don’t be afraid to say so!
• Remember all your supervisors in your thesis acknowledgements – they all helped you get where you did

So, I’m not going to lie, changing supervisors has been difficult, really difficult at times. However, on reflection, I feel very fortunate. I know I have had an amazing opportunity working with different very well respected professionals, all with different expertise and all with very different approaches to supervision. I know I can articulate my argument and be very clear about different approaches and processes, not just the ones I took, but of all the others I was challenged about. I feel confident when being asked g questions – in fact I welcome them (most of the time!). This I know will (I hope!) stand me in good stead come viva time. So to answer my initial question  – (only) from my experience, this has been a help rather than a hindrance.

More about changing PhD supervisors by @eljeejavier

If all the above fails and you really cannot continue with your supervision team, you may find this post @thesiswhisper  helpful: How to tell your supervisor you want a divorce

I would love to hear from you so please do leave comments. Have you changed PhD supervisors? How was it for you? Did you have similar or different experiences from mine? Are you going through a challenging time with changing supervisors? Is this just an initial transitional challenge or perhaps it goes deeper than that?

It would also be great to hear from supervisors – although I have blogged from a student’s perspective, I have absolutely no doubt that from a supervisor’s perspective, gaining a student who has had previous supervisors must be very challenging!

Member checking v’s dissemination focus groups in qualitative research

Historically, member checking (also known as member/participant validation) qualitative research findings has been viewed as an important aspect of establishing accuracy, credibility and validity (Koelsch 2013). Simply, member checking occurs when the researcher returns to participants to seek approval that the researcher has accurately reported their narratives and to gain further comments.

I hadn’t given member checking much thought (I conducted focus groups with members of the public and healthcare professionals in addition to one-to-one interviews with newspaper journalists and editors). It wasn’t until I had finished my preliminary data analysis when it was suggested to me by my supervisors.  This, I admit wasn’t a welcomed suggestion mainly due to the challenges it would likely cause. However, not being one to dismiss supervisor suggestions, I took myself off to explore this concept further. The outcome of this exploration was that I would not conduct member checks as I could not see a clear benefit. Here was my rationale:

• As some of my focus groups were opportunistically undertaken from already-formed social groups, locating the same participants was likely to be impossible. They were also conducted quite some time ago
• Geographically, my focus groups were conducted in another part of Scotland. I didn’t have the time or the energy at this stage of my research to travel back there for this purpose, especially when I questioned the effectiveness
• Just say I was able to locate my participants, I could potentially cause them discomfort having to listen to sensitive issues being discussed, especially around my interpretation of their narratives
• My participants could also feel uncomfortable hearing their own words
• Exposing my preliminary findings and interpretation to my participants could make me feel uncomfortable (not a big deal but nevertheless the potential is there)
• My participants may have forgotten they have said things therefore not be able to validate them. Alternatively, they may unintentionally wrongly recall what they have said and change the nature of the discussion that actually took place
• My participants may request the removal of valuable data from the focus group. Also, they may have changed their perceptions about something and request that their narrative or part of their narrative is removed
• The same group dynamics can never be recreated. Since group dynamics and interaction is a key component in my  focus group data analysis, it was deemed impossible to recreate the same group dynamics

However, this then left me with a gap.  Although I made the decision not to conduct member checks, it didn’t mean I could ignore the issue. This meant further reading and exploration. I also took to twitter to help me and received some excellent responses, in particular from Dr Bronwyn Hemsely @BronwynHemsley who had similar experiences.

Taking into consideration all the above points, and also importantly, keeping my epistemological stance of weak social constructionism and methodological approach (Interpretive descriptive methodology) at the forefront of my mind, I knew I wasn’t looking to ‘validate’ my findings, nor did I want to seek confirmation of a ‘truth’. Rather, I wanted to present my conceptual thinking and seek thoughts and ideas as to how I could be further develop them. Equally, I wanted to explore whether I had missed something important.  I therefore went down the route of dissemination focus groups. This is advocated by Rose Barbour (2005) as a more useful method to feedback preliminary findings than member checking.

So what I did was generate one focus group (6 people (two of whom were original participants)) with a mixture of members of the public and healthcare professionals (reflecting the characteristics of my participants). I prepared a Prezi and presented my key categories from my findings then asked specific questions for further discussion. The focus group was recorded with permission from the group and lasted just over one hour. I also provided light refreshments and gave small gifts as a token of my appreciation. Over all, I found this experience hugely beneficial as it:

• Helped me explain and contextualise my study as a whole concisely and succinctly (something which has never come easy for me!)
• Enhanced my analytical and interpretational sophistication through agreement and offers of further considerations
• Crystallised similar and different perspectives from both the public and healthcare professionals
• Helped further consider my findings in terms of what they mean in relation to informing practice and policy today and for the future
• Was fun for me and those who took part

If you are considering member checking for qualitative research, I would definitely recommend dissemination sessions as an alternative. I’m not however, saying this is the right way and member checking is the wrong way , or indeed the way I did it was the right way – we know there is no right or wrong in qualitative research. What I am saying is that this was the right way for me and my research. I imagine there are various and innovative ways in which this can be done, but hopefully sharing how I did mine gives food for thought. I would be very interested to hear from others their experiences of either member checks or dissemination sessions (interviews or focus groups). Were they helpful or a hindrance?

Qualitative data analysis: data display

The first thing I want to say is that data display was lots of fun!

So my last blog post finished after I had developed and played around with my propositions before moving onto data display.

Miles et al (2014) dedicates 6 chapters to data display (part 2 of their book). I read and re-read these chapters a number of times before I could get my head around everything. Had I not done this, I can see how I may have gone down an inappropriate avenue. Miles et al provide various suggestions along with some smashing examples about how data can be displayed – mainly though matrices and network displays.

For my study, I created matrices (with defined rows and columns). Miles et al describe matric construction as “a creative yet systematic tasks that furthers your understanding of the substance and meaning of your database” (p.113). A key point that resonated with me was that it’s not about building correct matrices – it’s about building ones that will help give answers to the questions you’re asking. To do this, they advise us to “adapt and invest formats that will serve you best” (p.114).

An important conclusion I came to? I didn’t need to use (or fully understand) all the matrices/network displays. I took what I needed to (role-ordered matrices) and combined it with a little of something else (Framework matrices) to allow me to display my data in a way that helped me move on with analysis and progress through to interpretation – always with my research questions at the forefront of my mind (and pinned to my office door).

So here’s what I did: I created a matrix for each main theme (n=4) and each focus group (n=15). In total I created 60 matrices.

My participants were entered along the first row and within each participant cell I also identified key demographic characteristics.  Each subtheme was a column heading. I can’t provide an example of one of my matrices in NVivo as the data is legible, so the image below is a QSR example from their volunteering study.

The beauty (and massive time saver) of NVivo is that when you click in each cell (number 4 ), the data that you have coded (for the individual within that theme) is displayed on the right of your matrix (number 3). This is referred to as the ‘associated view’. Obviously when you first create your matrix all the cells in the middle will be empty so from the coded data (associated view) a summary needs to be entered into each cell.

For my study, I read through all my coded data and my summaries were developed using the following:

• Including sufficient detail that was understandable and not overly cryptic
• Retaining my participants language
• Sometimes including short verbatim excerpts if I thought it was necessary. All quotes were kept in italics
• Including my commentaries (in a different colour) about context and focus group interaction

A simple, but important thing I noted when writing my summaries is that not all cells were coded, therefore no summary was required. I always wrote ‘NC’ in those cells so I knew that cells were not empty due to an unintended oversight.

Not surprisingly, as with all stages of data analysis, this process was extremely time consuming. However, by the time I completed it, I had so much more insight into what my data was telling me – for example, the similarities, the differences, the unsurprising and the surprising.  I generally gained a much deeper understanding of what was going on.

However, it didn’t end there. I wanted to compare and contrast my data not only within focus groups, but between focus groups. This I found difficult on a computer screen as I had to jump back and forth across so many matrices.  So….. similar to my propositions, I left my PC and went back to flip chart paper. To be honest, it was a nice break from sitting at my PC.

Another beauty of NVivo is that the matrices can be exported into excel. I did this then transferred them again into a word document (I like prettifying my tables with colours etc. and could only do that the way I wanted in word). It cost me a little more time, but nevertheless, it was worth it. I then printed my matrices out (all 60 of them). For each main theme and subthemes I sellotaped 3 flipchart paper sheets together (so that they were long enough to display all 8 focus groups matrices down both sides) and glued my public focus group matrices down the left hand side and my healthcare professionals’ focus group matrices down the right hand side.

These matrices on the flip chat paper then became my focus for a few weeks. I read them, compared them, returned to the literature, returned to my memos, reflected and took time away to think (long dog walks on the beach helped hugely with this). While I did this, I used the white space in the centre of my flipchart paper (between the matrices) to scribble down my thoughts and concepts. For me, this stage enabled the progression from description to interpretation. I even took them to one of my supervision sessions so I could talk through some of my thoughts and illustrate the process I took to get there. I can show you an image of this as the text is not legible – this is one main theme (with 4 subthemes (column headings (in blue)). The peach rows are my participants:

 

So in a nut shell, my data display process helped me to get my creative thinking underway for interpretation. I then used these matrices to help me write up my first draft of my findings.

I hope this has been helpful. Qualitative data analysis is so diverse and complex and depends upon a number of variables, particularly your methodological approach so there really is no ‘one size   fits all’. Please do respond to this post and share your experience of the process you took and how it worked for you. Or did you do something similar to me? 🙂

Qualitative data analysis: data condensation (aka reduction)

Rather than trying to squeeze my thoughts and experience of all my data analysis in one blog post, I intend to write shorter (!) posts of different stages as I progress. My last blog post was about developing my analysis plan.  Knowing what I know now, I am so thankful I spent the time doing that!

I am following Miles and Huberman’s approach to data analysis and have the 3rd edition ~ Miles et al., (2014). There are lots of similarities in this edition but also some differences. I prefer  this edition.

The purpose of this post is to share with your my first step of data analysis – data condensation. This used to be called data reduction (Miles and Huberman 1994) but it was changed because data reduction implies “weakening or losing something in the process”.

So immediately following my focus groups and interviews, I took extensive notes about salient factors (more about that in another blog post). From these notes I created a contact summary form as advocated by Miles and Huberman and one of my supervisors which synthesised all this information. This is  a very simple and highly valuable thing to do. I have repeatedly referred back to my contact summary forms throughout this process (if anyone wants the template, just ask). I also transcribed verbatim all my focus groups and interviews myself as soon as I could after data collection. This was a very, very long and at times, laborious task, but again highly valuable for really getting to know my data.

I listened to each audio recording (listening only ~ no note taking). Then I read each transcript (reading only ~ no note taking). Then I listened to each audio re-coding again whilst reading my transcripts. This time I scribbled notes down on a pad and drew various mind maps and diagrams. After all that, I was pretty sure I had immersed myself in my data (even though I hated listening to myself!).

I prepared transcripts for importing into NVivo 10. This involved ensuring consistent format and style and anonymising my participants by allocating each of them a pseudonym and a code to differentiate public, healthcare and media professionals (a blog post about this here). This process took quite a bit of time, but if not done thoroughly, I can see how this could have caused me many problems later on.

1st level coding: I developed a starting coding list based on my theoretical framework and wider literature to get me started (initial deductive approach). I listed these codes onto a coding framework with clear operational definitions so I had a clear understanding of what type of data needed to be assigned to each code. Throughout this stage, codes were revised or removed and additional codes and subcodes were created as new themes emerged from the data (inductive approach). As I revised my codes, each transcript was re-read and re-coded. I made sure at this stage I didn’t try to force my data into anything and that codes and sub-codes were all kept very descriptive.

Pattern coding: This was about working with the 1^st level codes and sub-codes so that they could be grouped into more meaning full and general patterns. This process was a little more challenging for me because at times I was aware that my thinking was going a little too fast and that I needed to remain fairly descriptive. I was also frightened about condensing too much and losing some of what I had. However, the beauty of NVivo is that you have an audit trail so if you do need to go back, everything is still there (I saved a copy of my NVivo project at the end of every day). While pattern coding, I examined my data carefully and asked a number of key questions such as: What is happening here? What is trying to be conveyed? What are the similarities? What are the differences? In doing so, I also explored not only the similarities but also the idiosyncrasies and differences. This process took quite a number of iterations before I was happy to move on.

Memoing: to help me through the process of coding, I created LOTS of memos which captured a wide range of my thoughts and concepts. I was also able to link my memos to my data and any external resources such as websites or literature. Again, I cannot stress enough how valuable this has been (and still is). My research journal was also created as a memo.

Propositions: it took me a little while to get my head around what I needed to do here as I have always associated propositions with case study research. This was another lengthy process but has helped so much as I started to gently move from the descriptive stage to a more conceptual and interpretive stage. I went through all my coded data and developed propositions from them – so basically a summary or synthesis of my data. I initially developed 613 propositions then reduced this to 479 following removal of duplications. In order to have a better visualisation of these, I left my computer and turned to flip chart paper. I printed each proposition (within their pattern codes) on different coloured post-it notes and arranged and re-arranged them (lots of times!). This then led me to revise my pattern codes again. Of course, with that, I revisited all my data and yet again re-coded into my final revised pattern codes and sub-codes. Just to say at this point – this doesn’t mean these pattern codes are written in stone. They can be (and will likely be) altered again as my interpretation progresses.

So in a nut shell – that was my data condensation. Obviously we know that qualitative data analysis is not a linear process and requires many, many iterations. While at times, this may be frustrating, it’s necessary and can be fun!

On a final note, if anyone is thinking about a CAQDAS programme, I cannot recommend NVivo 10 enough – I absolutely love it (I cannot comment on any other CAQDAS programme as I have only used NVivo) I know many people prefer manual analysis for a number of reasons, which is absolutely fine. NVivo has helped me hugely to store, manage and interrogate my data (of course it won’t interpret or write up my findings though!). The support you receive from QSR International via many ways is first class also.

I am now in the throes of data display and developing lots of Framework Matrices. Another really exciting stage and one that is continually challenges me on my current thinking.  That will be my next blog post. If you want to ask me any questions about my experience of data condensation, please ask away. Any comments would also be very welcome! I’m really trying to keep my blog posts short, but as you can see, I’m not doing well with that!

Qualitative research: pseudonyms or no pseudonyms?

As I prepared my focus group and interview transcripts for entering onto NVivo, ready to being my data analysis, it occurred just how many participants I had (82 in total – members of the public, healthcare professionals and media professionals). I started to think about how my participants would be represented in my thesis. Obviously to maintain the principle of beneficence, they had to remain anonymous. The use of pseudonyms is as we know recommended, but I wondered if this was a unified strategy or if anyone had other thoughts. So I posed this question on twitter to explore further:

Some interesting discussions followed and some issues arose that I hadn’t previously considered.

There was a general consensus from the responses (PhD students, researchers and a PhD supervisor) that using pseudonyms was a good idea as it allowed participants to feel like real people.  Consequently, it helped researchers portray their story effectively and maintain that human element.  This of course is key in qualitative research.

However, there were some important considerations raised.  Not everyone used pseudonyms and someone felt that codes were easier for the reader to track and relate to, whereas names could perhaps be easily forgotten.  Others stated that it was important to use both – the pseudonym for the human element, but also codes to differentiate between groups of people.

So how do people choose their pseudonyms? Suggestions included using a random baby name generator from the internet or Google the most common baby names which related to their date of birth (I can see why allocating a ‘Chantelle’ to an 80 year old lady probably wouldn’t be the best choice!). Someone also suggested choosing similar sounding names to their own. I know others have asked their participants to choose their pseudonym, but this can be challenging if the same name is chosen by a number of participants. I also wonder what the implications of this are if that participant is able to identify him or herself in the research findings?

So the outcome of this twitter conversation in relation to my study is that I am using pseudonyms, in addition to codes (which is also what one of my supervisors did). This is because I really want to keep the human element, but as I am analysing my public, healthcare professional and media professional data together and will be reporting my findings in one chapter, it is imperative that the groups are differentiated.

Another critical aspect that needs to be considered is that even when using pseudonyms, participants can still be identifiable, especially if they are from small communities. This is something that I need to be mindful of as some of my participants live in a small community which had experienced a traumatic event and some are journalists working for specific newspapers. Just because I have given them different names, I need to ensure that no one can be personally identifiable (or connected with a professional organisation)  in any way.

A special thank you to @strictlykaren , @Acrobat13, @merry30, @SarahLaneCawte, @gtombs, @Paully232000, @AbigailLocke, @VickiMcDermott and @CET47 for their insightful twitter comments and feedback 🙂

You may have your data, but do you have your data analysis plan?

Fast forward from 28th January (my last blog post) to today – supervision this morning with my data analysis plan in hand. The outcome? I can now get back to my data – nine months after I left it. I really can’t tell you how good that feels!

The development of my qualitative data analysis plan was that last little hurdle I needed to get over before I could return to my data, but it wasn’t straightforward. At times it even felt a little uncomfortable. However, forcing myself to ask very focused and specific questions about my research has enabled me to be pretty damn clear about how I am now going to deal with my data. Backtrack well over nine months ago when I had actually started analysing my data – I had jumped right in with little insight as to what I was looking for and why.

So… after writing my last blog post, I spent some time actually looking for examples of a qualitative analysis plan or even some indication of how to develop one. Despite lots of literature telling me that a data analysis plan is extremely important, I couldn’t actually find evidence of one. I found lots about different analytic strategies/frameworks to use – but this is not an analytic plan. From reading various papers and books and talking to people on twitter, I came up with some key questions that I needed to answer – which subsequently formed my analytical plan:

• What are my research questions? (Extremely important as we know, but I needed to put a lot more thought into them)

• What is the overall purpose of my analysis? (If I didn’t know the purpose of my analysis, how was I going to plan how to do it? This linked to my overarching research aim, philosophical and methodological approach in addition to the clinical relevance of my research – the so what question)

• How does each of my (large) qualitative data sets connect to my research questions? (I needed to ensure that by analysing my data sets, my research questions could be answered – this was easiest explained as a diagram). This part also made me realise and accept that I have a lot of data that will not feature in my PhD. This is quite ok though as I can use them for future potential publications)

• How am I going to conduct the analysis? (The trickiest one for me because I have 4 large (different) data sets, all gained by different methods – yet they are all connected in some way. Further discussion with my supervisors was required for this part. My two options were (1) Will I analyse each data set separately then pull them all together in a discussion? Or (2) Will I analyse them all together and present them in one findings chapter. Considering each option carefully, I listed pros and cons and talked through them in depth with my supervisors. Due to a number of reasons we all agreed on option 2. Within this section, I also had to consider my analytic framework. Again for a number of reasons, Miles and Huberman’s was the winner.

• What do I need to ask of my data? (This was also a tricky one because I need to ensure that I have both a deductive and inductive approach to my analysis. I must ensure that I am not ‘blinded’ by my theoretical framework and that it is only there as a scaffold for my analysis. The development of some initial broad propositions helped me with this. This also connects closely with how I am going to conduct my analysis – always keeping my research questions at the forefront of all my decisions).

• What resources do I need to conduct my analysis? (This was a simple one – time and patience! Also NVivo – lucky to have a licence with my institution and was fortunate to habe been funded to attend NVivo training).

• What is my timeline? (Given all the issues that I have had to address over the last few months, I do not wish to be a slave to the calendar. However, having a rough idea of time is not a bad thing. I am looking at analysing and interpreting for the next 10 months – but who knows! As a part time PhD student, I am extremely fortunate to have a generous amount of protected study leave each year (in addition to a whole semester during the end stages))

So here I have presented important questions that I studied carefully in order to develop my analysis plan. I found this exercise to be extremely beneficial and it also helped my vocalise and rationalise my thoughts and ideas with my supervisors. I am now very excited about finally being able to return to my data, reading my transcripts and listening to my audio recordings (even though I cringe hearing myself ask strange questions, ask 3 questions at once, not letting participants finish, laugh inappropriately, not pick up on a cue, sometimes not make any sense, or just sound ridiculous!)

On a final note, at the end of my analysis plan, I have this – which I like very much:

Lyn Richards (2005) and Bazeley (2009) list five key signs which indicate that the analysis is sufficient:

• Simplicity: a small ‘polished gem of a theory’, rather than a mere pebble of truism
• Elegance and balance: it is coherent
• Completeness: it explains all
• Robustness: it doesn’t fall over with new data
• It makes sense to relevant audiences

Thank you for taking the time to read. Did you develop a similar plan or have you approached this differently. I would be very interested in hearing different/similar approaches so please do leave a comment ….

Going backward is sometimes the only way to go forward

Eight months ago, in June 2012 I told how things had started to go wrong, then in August I really did try to be positive. That didn’t quite work out and things continued to go not-very-well. In my last blog post (November), I started to see a way out of what I referred to as the murky methodological jungle, although I recognised that I still had some way to go.

Fast forward to now: I believe I am out of that part of the jungle! Having turned around and trudged my way backwards again, I found an alternative, smoother route out. This route was quite different – it was not (as) difficult, it was not forced and I was able to walk down it quite comfortably. My complete re draft of my theoretical, philosophical and methodological chapter reflected this. While this is not a final draft by any means, it does allow me to park it and move forward.

I say move forward as a positive step – and it is. However, the point of this post is that as painful as it was (and it was really very painful at times), despite having spent the last 8 months going backwards (on more than one occasion), I realise that while going backwards I was actually moving forward. As Churchill once said: “The farther backward you can look, the farther forward you can see”

Had my supervisors not made me go through all this, then I dread to think where I would have been now. What I do know is that I would certainly have not been able to justify and defend the decisions I have taken to get to this stage. If I couldn’t have done that even at this stage, then goodness knows what would have happened during the rest of my PhD and indeed my viva.

So where to now? I can’t tell you how much I am looking forward to getting back to my data. However, following in-depth discussion with my supervisors, I know I still need to do some important work before I can go back to my data. Something else that I never did before jumping into my data last year – develop a clear robust analytic plan. I have 4 large data sets and I need to be crystal clear as to what I am going to do with them, how and why I am going to do it. Without having this clear plan, it is likely that I could find myself back in another part of that awful jungle.

There is a wealth of literature highlighting the importance of having a robust analytic plan. However, unfortunately, there is virtually nothing about how to develop one or even what one looks like. So, this is my next step. My supervisors have started me off, but this will take a little bit of time to get right. That’s ok though – I need this to be right so my data analysis goes smoothly. I will share with you the development of my analytic plan in due course.

Thank you for reading. Please do comment or share your experiences – I, (and others I’m sure) value them very much.

‘I’m a PhD student…..Get me out of here!’

So way back in June this year, I blogged about a really difficult situation I was faced with – for the second time during my PhD. I stopped data analysis and returned back to my methodological approach.  All explained in this post.

Although 2 months later, I was able to see some positives from the situation, as I posted here, seeing the positives beyond that post has been pretty damn tough. I still have not returned to my data. Rather, I have continued to read, reflect and write over and over again. I became fixated on trying to ‘find’ a qualitative methodology that I could ‘fit’ my study into – one that would please me, my supervisors and ultimately my external examiners. This continued for 6 painful months (although it feels longer). The outcome was (for many lovely reasons) that this study will be a multiple case study.

Ta da – problem solved!

So prior to my supervision last week, I created a philosophical, theoretical and methodological table so that I could identify key concepts and justify their uses for my study and demonstrate how they linked (thank you @planetchampion for that advice). Guess what – while talking it through with my supervisors, I quite clearly managed to explain that multiple case study actually did not and would not work!

After in-depth discussion, and going right back to the beginning, vocalising what exactly my study is about and what my aims and research questions are, it became clear. I was never going to be able to ‘fit’ my study into a neatly packaged qualitative approach at this stage and it was always going to be like trying to squeeze a ‘square plug into a round hole’. Oh, it could be done, but I would need to reconsider just about everything – almost starting over again. Believe me, after almost 3 years, this really wasn’t a route I was keen to go down.

So, the outcome of the discussion was that my methodological approach will stay along the same lines as it was initially because that is the way it was designed! However, while I have been painstakingly considering just about every other qualitative approach over the past few months, I recently came across Interpretive Description. This was first developed by Thorne in 1997 as a response to complex research needs within nursing and other applied health disciplines which are not readily answered by traditional qualitative methodologies (I have tweeted some informative papers about ID recently). This approach aligns with my initial approach, the structure and aims of my study, is consistent with my philosophical and theoretical stance and is considered rigorous and credible. It has also been used by many other qualitative researchers and PhD students. Of course there are limitations and they will of course be acknowledged, but limitations are congruent in all approaches

The positive side of this past year is that I can now very clearly justify why other approaches are not suitable for this study, defend the approach I am using and provide a rationale for every single step (forwards and backwards) I took to get to this stage!

So my title of this blog post reflects how I feel at the moment (not in terms of quitting, but finally getting out of the murky methodological jungle). The past few months have been the closest I have been to quitting. I feel I have been in this miserable, murky, methodological jungle for ages, with no map or compass to help me out (not that I’m any good at reading maps by the way!). This place, I suspect is probably not a million miles away from The Valley of Shit  (@thesiswhisperer)

Anyway, thanks to the support of my family, colleagues and everyone on #phdchat (there are some pretty special people on there!), I kept going.

For now, I think I have found a workable map and compass and am heading the right way to get myself out of this murky methodological jungle. I’m not quite there yet and only time will tell, but for the first time in a long time, I feel I know the direction I’m heading in….

 

Turning negatives into positives

So… my last blog outlined a not-very-nice PhD methodology-related situation I was unexpectedly faced with recently. As you know, this caused me quite a bit of distress, confusion and uncertainty.

Since then, taking the advice of my supervisors, I have done nothing but read, scribble notes and stepping back to reflect. My reading has been focused on the main qualitative methodologies and philosophical frameworks. While initially, this was rather painful to do at this stage of my PhD, it has been invaluable. It has forced me to revisit a number of difficult issues and consider a range of possibilities in order to address them. It has also made me take a step back and really consider the overall aims and objectives of my research. I still have some more reading and reflecting to do before I meet with my supervisors’ mid-August, which will be the first time since my last blog post.

Today, I had my TMC (thesis monitoring committee) meeting. This was my first TMC since my Transfer of Ordinance in February. Given my recent problems, I was very anxious about this meeting. I had no idea how I was going to explain what had happened and was concerned about what their response would be. I needn’t have been. Prior to the meeting, I considered all the ‘negatives’ of my situation, decided to turn each and every one into positives.  Surprisingly, it was not difficult to do! This would have been an impossible task to do two months ago! My TMC all listened, asked questions, offered advice and were extremely supportive. I came out of what could have been a difficult meeting, feeling good.

Although my issues are not resolved as yet, I firmly believe they can be. This I know will not be done overnight and there will still be many challenges ahead. That’s fine. I also know that what is happening at the moment will ultimately make me a stronger researcher; my PhD will be much more robust (I will not produce a mediocre thesis – I will produce a damn good one!) and I will be able to defend my work well in my viva.

So that’s just a quick update of progress – and although I couldn’t see it a couple of months ago, I definitely see this situation as progress. I am looking forward to continuing with what I am doing now then meeting with my supervisors later this month. When that day comes, I will be much more confident in talking about the issues that arose during my last supervision and importantly, I know I will be able to engage in a constructive critical discussion as to how I can move forward.

Just before I go, I would like to say a massive thank you to everyone on #phdchat and beyond, for the support, encouragement and advice that you have given me via this blog, twitter and email.  Thank you for helping me turn my negatives into positives…

 

 

 

 

When things don’t go to plan

I enjoy reading other people’s blogs and learn so much from them. I like reading people’s progress and their successes and I use this to help me progress. I also like to talk about the positives and share my experiences, as I have done in this blog. However, when things go wrong or don’t go to plan this affects most things in my life. As hard as it is, talking about the negatives and the challenges is also important, but there are few blogs which do that.

At the moment I feel like I have an impossible task ahead of me. This is hard for me to take as I consider myself a glass half full kind of person. I wrote in my research journal last night with the intention of publically blogging about it, but something stopped me. Despite being in a good place with my PhD for quite some time, unexpectedly, I now feel vulnerable and lost. Perhaps the embarrassment of admitting this stopped me publishing it. Or perhaps it was because my supervisors know I blog and could read it. I’m not sure. I asked the question on twitter last night whether or not we should publically blog our struggles as they happen. Some thought yes, others thought perhaps, and others thought perhaps not. I will.

A quick recap: I’m in my 3^rd year of a part time PhD: Understanding risk perceptions and responses of the public, healthcare professionals and media professionals towards Clostridium difficile. My data is collected (media coverage analysis, public and healthcare professional’s focus groups and media professionals interviews) and analysis started. My principle supervisor moved away this time last year therefore I changed supervisors.

My problem is with my methodological approach. With my previous supervisor, my methodological approach was a broad qualitative interpretive approach underpinned by weak social constructionism. I never had to defend it or consider any other approach – this was what it was from day one. This I see is where my problem stems from. However, this isn’t the place for going into detail about it. My problem is that my current supervisors feel this is not a robust enough approach for my PhD. My written work is not strong enough to demonstrate that this is the right approach and my argument is not clear. If it is not clear, then it’s impossible to go ahead and analyse my data.  I found myself in exactly this position last year(explained in my earlier post dated 2^nd Sept) but believed I had defended it enough to continue.

It is important that I say here that I do understand where my supervisors are coming from.  I agree that these issues need to be addressed or the rest of my PhD and ultimately my viva will be an uphill struggle.  My supervisors are supportive and I trust them. I know I can’t do this without them.

So as of Monday, I have been advised to stop data analysis and go back to methodological reading for a few weeks. No writing, just reading and reflecting. On doing this, I need to put my data at the back of my mind and reflect only on my aims and research questions while I consider other approaches.

This is where I feel lost.  How can I ‘unlearn’ what I have lived with for the past 2½ years?  How can I go back and change my methodological approach having progressed so far? A methodological approach is supposed to inform your research design and so on.  If I do change it, how can I say it has informed my research when it was chosen retrospectively? Finally, having lived with so much data for so long, how can I try to ‘forget’ about it while I consider other approaches?

I’m not necessarily looking to you for answers to these questions, as I know I need to work this out for myself. So for now, I will go back to reading and take the time to think about my supervisor’s advice and reflect, in the hope that my next blog will be a positive one. I would love to hear from anyone with similar experiences (student or supervisor) or from anyone who wants to say anything at all!