So the time has come where I need to consider how to measure innovation in my research (as well as other constructs I will be looking at). Making such decisions is part of the process of preparing my materials for data collection. I will be doing both qualitative and quantitative data collection via case studies (interviews, focus groups and a quantitative survey) so I will have lots of data to contend with once I am done.
Before Christmas I had given this some thought, but it did get side-lined a little so that my secondary data analyses could progress. I am now at the point where I need to bring up these notes and I need to consider how the whole thing will happen, with specific reference to how I am going to measure the constructs I want to.
So for my own PhD, I have a few main ‘constructs’ or areas I will be measuring. These are:
Innovative work behaviours (the main purpose of my PhD);
Other variables influencing innovation
In my own opinion, the most important one is the innovation in my study. If I don’t get this right, then the purpose can be lost and it might end up where I am actually measuring something I have no idea about. Hence the exploration of how innovation can be measured. You may also remember that I found out that there were two types of individual innovation:
Individual innovative behaviour: Evaluation of the approach and tools used with the aim of using new ideas and approaches within the workplace (Kleysen & Street, 2001, p.284);
Innovative work behaviour: Intentional generation of new ideas within a role, group or organisation whereby the idea is implemented within the organisation once created (Battistelli, Montani & Odardi, 2013, p.27).
I considered this in my PhD planning stages in year 1 and my specific innovation focus became apparent (and very clear) then. My research is exploring how workplace learning can enhance innovation because innovation is important on multiple levels (nationally, sectoral and organisational levels). However, I questioned how differences in innovation related to this. As you can see from the definitions above, innovative work behaviour has focus on intentional changes made to the person or organisation as the purpose for innovation. The innovator would know there and then that they wanted to make a change, and behave in a way to do so. Therefore, it was decided (by myself and agreed by my supervisors) that I would use innovative work behaviour as my main focus. Innovative work behaviour can be enhanced and it was my job to explore how.
Whilst doing a little background reading on scales and measurements, I came across some handy information. This information related well to innovation as it explained how innovative work behaviour was measured – this is the construct for me! So anyway, I stated reading a few articles and an important one appeared -Innovative Work behaviours: Measurement and Validation (de Jong & Den Hartog, 2008) which contained some very striking points about the measurement of innovative work behaviour itself.
By reading this paper and others in preparation for my RD5 review, I discovered that innovation is a multi-dimensional construct. That is, there are several processes involved in being able to innovate and measurements of innovative work behaviour need to consider all stages involved. de Jong & Den Hartog (2008) explore the multi-dimensionality in terms of how other researchers have defined and measured innovative work behaviours and agreed on their own stage process:
The person would realise something new needs creating, or an idea needs developing;
The person would then develop or generate an idea themselves;
Once the idea is generated, the person would need to champion this idea or have someone do it for them so that the idea can break down barriers that might stop it developing, and show the benefits of the idea itself;
The idea is then finally implemented and put into practice (wherever that may be).
For my PhD, I feel it’s important that I consider all stages of the innovation process. This is because I need to ensure that all process are accounted for as we understand that an idea cannot be called an innovation if it is not championed and implemented. Therefore, I need some form of measurement which considers all four stages and the paper by de Jong & Den Hartog (2008) does just that. Now I do have a few other papers to read too as I need to justify why I want to use this measurement and how. However, from reading this paper and some others, it is fairly clear that the objectives of de Jong & Den Hartog, (2008) addressed previous research and they developed the scale based on criticisms of what researchers had done before. Some of the criticisms are as follows:
Many researchers suggest that innovation is one single dimension, and develop scales to suit this belief. However if a process has four stages it cannot be singular in dimension and requires further exploration to develop a scale to reflect this.
Many researchers also do not test the validity of the scale. Now the validity is how well a test measures what it is designed to measure and this can come in many forms.
For me, why on earth would you develop a scale and not bother testing if it measures what you want it to? Validity helps to justify the development of the scale and whether it does what it is supposed to do. Testing out the convergent and discriminant validity of measurements mean you can see if related variables are supposed to be related, and whether separate variables are actually statistically separate. When measuring innovative work behaviour, this needs to be done to ensure all sages are distinct and that contributions to each stage are related within stages, but not related between stages. This can then help the researcher determine influencing factors in each stage (if required) and have a solid measurement of the stages people go through to be innovative. You can find a nice list of previous research and validity testing on page 8 and 9 of the article by de Jong & Den Hartog (2008), and this clearly demonstrates the need for a validated scale as well as one that is reliable.
So from reading just that one article (supported by others), I already know what I want to measure innovation. I need a measurement that reflects all stages involved from identification of idea need to implementation of the idea created. I need a scale that has been tested for both reliability and validity so that I know it measures what it is supposed to, and I can then have a discussion with my supervisors as to (a) what questions to use and; (b) whether my choices of measurement are fully justified.
Battistelli, A., Montani, F., & Odoardi, C. (2013). The impact of feedback from job and task autonomy in the relationship between dispositional resistance to change and innovative work behaviour. European Journal of Work and Organizational Psychology, 22(1), 26–41. http://doi.org/10.1080/1359432X.2011.616653
The paper has also received nearly 700 downloads since its publication too!
Not only has the paper won first prize and increased in popularity in terms of downloads, it has been noticed by Napier’s Media and Communications team. They have kindly offered to write an article about our research findings and you can find the article below.
I recently read an article which got me thinking about the domain my research is situated in. My PhD is a PhD in Information Science. It roots its knowledge and methods in information science to ensure that at the end of the three years of study (and the all-important viva), I have a comprehensive understanding of my PhD topic from the information science perspective. My PhD topic focuses primarily on workplace learning and innovation. I am exploring how innovative work behaviours can be learned on both the individual and collective levels. I am exploring this to identify determinants of successful workplace learning and develop a set of practical recommendations (or develop framework) on requirements to be able to learn to innovate. My research focuses mainly on organisational characteristics and how organisational may be able to set parameters to support learning in the workplace. I had considered looking at this from the perspective of the individual but I slowly realised that this would be a HUGEtopic to study and studying individual characteristics of people would mean my research was more of a Psychology PhD than an Information Science PhD. That is something me (and my research supervisors) did not want. For me this was quite hard to accept as my academic and employment background was in Psychology. However, I soon started to realise how important my previous study and experience were to my own research and how well my experiences fitted into a PhD in Information Science indeed.
I then began to realise that my previous career aspirations and academic study towards this (my undergraduate and masters BPS accredited degrees) were shattered by choosing to Information Science over Psychology. Bye bye accreditation!
It was just after New Year when I came across this article by Jeske & Stamov Roßnagel (2016) posted on LinkedIn and it took my eye straight away. It took my eye because it explores drivers for informal learning at work and this is something my PhD is focusing on too. I was pleased to be able to read this article as it got me thinking about my own research and the methods I will be using. It also got me wondering about the different perspectives research can take, and more importantly perspectives looking into concepts relating to my own PhD research too.
So in the article, the authors identify a number of factors that may influence informal learning in the workplace. Significance of such factors were tested using a cross-sectional survey and quantitative analysis of the survey answers. Results indicated that characteristics of individuals influenced the development of informal learning in the workplace. However, there was one result that surprised me when I read it: organisational characteristics had no influence on informal learning in the workplace (in the data collected by the authors). This surprised me as this is also one area of my research which I am exploring in detail and other articles have found the opposite results. The article made me consider whether I am doing the right thing or not but also made me think why the results of that article may differ from what I had expected myself. The results also got me thinking about the methods the authors used in comparison to my research and the domain of research the article lies compared to mine, which then helped me write the blog post you are reading now.
I am going to talk about a few things that came to mind when I read the article in terms of how some research could be similar to mine, and how my research could differ from others. I really enjoyed reading the article in depth and it definitely has a place in my thesis write up given that it has sparked some questions in my mind I did not think about happen. I explain these considerations below (note: these are not specific comments about the article I read, these are just aspects I need to consider in my own research, thoughts sparked by reading the article above).
Approach to research
The approach to research is really important, but I don’t mean the philosophical approach as such. Yes, we all know the philosophical approach is important and helps to set the grounds for the methodological choice, but the differences between top-down and bottom-up approaches came to mind for me. I am fully aware that the top-down and bottom-up approaches are types of deductive and inductive reasoning so they are technically philosophical approaches. But it is the differences between the two in research are differences I need to consider in order to answer the research questions I propose. So in simple terms:
A top-down approach takes a complex concept and breaks it down into smaller components to help understand what makes up each component;
A bottom-up approach helps to piece together the simpler components to gain an overall picture of what is happening. The final outcome can be seen when all the individual parts are explored and put together.
So the difference between the two can be quite major, and this difference can help shape how the research is carried out. For example, if I had a specific set (or large framework) of influencers on learning that I wanted to explore them as a whole, I could use the top-down approach. This would help me break down the larger component (framework) and see how these related to learning to innovate. However, this is not what I am going at all. I will be using a bottom-up approach to my research as I believe that learning to innovate starts with the individual. I am therefore going to explore smaller parts of the puzzle in detail and piece these together to gain an overall picture of how people learn to innovate. In a nutshell, I will be seeing what smaller components contribute to learning to innovate and then building these together in a framework of recommendations. However, part of my research does involve the top-down approach. My secondary data analysis uses a combination of both approaches. This is because: (1) I have the factors which could contribute to the development of innovation in the dataset which is the top-down approach but (2) exploring the dataset is using the bottom up approach to piece together which variables contribute to the development of innovation and which ones do not. A similar example is that by Frenz and Ietto‐Gillies (2007) who use the UK Innovation Survey data to answer their research questions.
Methods used to explore research questions
The approach then relates to the next thing I thought about… the methods used to explore research questions. So if a researcher was using the top-down approach, they may know then variables they wish to explore. Therefore, something like a quantitative survey (as in Jeske & Stamov Roßnagel, 2016) may be more appropriate to address the research questions as the researcher has an idea of the components that make up the larger picture, and wish to explore these in more detail. For my research however, I don’t know what components contribute to workplace learning and I don’t know what should. I am therefore using a bottom-up approach to build a picture of this and my research methods need to reflect this decision. I will be using a case study design where I will be interviewing employees of organisations (see King, 2008; Harbi, Anderson & Amamou, 2014; Mavin & Roth, 2015; Sykes & Dean, 2013, for examples). The interviews will be semi-structured but will allow for a lot of elaboration by the participants I talk to. I want my interviewees to talk about factors they feel contribute to their own learning so that I can see how individuals differ. I want to understand contributions to the development of workplace learning so the subjectivity of answers and differences between individuals and organisations will hopefully shine from the results I get. I can’t be restrictive on variables at the beginning as I have no idea about specific contributions to workplace learning (in the vast amount of literature I have read – there is far too much to discuss). However, once I gain an understanding of the relationships involved, I can then explore specific contribution relationships further and explore these quantitatively if I wish.
Different literature domains (perspectives on research)
So after I had thought about the different types of methods, I then began to wonder what happens about the literature domain? What happens when research questions are explored form multiple perspectives? Do they yield the same results or do they not?
The answer to this question is that we simply just don’t know everything. Every piece of research is situated within its own domain, so if research questions are explored from one perspective, a different perspective may approach the research differently and results may differ. This can be seen if I compare the article I read with my own research. Just because the researchers found no significant relationships quantitatively in this area does not mean I won’t too. I am using different methods to explore similar questions so the results I get could be completely different to research using methods not my own. The article helped me to understand the difference in approaches and methods and how these are important when considering research domains overall. My research is a prefect example of this as it could overlap with so many research domains, all explained below.
(1) The Educational perspective – this perspective focuses a lot on how people learn in the educational setting. My research could be situated here as people taking part in vocational education and training (such as those in Fuller & Unwin, 2003), or transitioning from education to employment may wish to build skills in innovation. However, my research is not situated in the Educational domain as my research does not focus on how people learn educationally. It focuses solely on the workplace so how people transfer knowledge form the classroom to the workplace is somewhat irrelevant in my case. The educational perspective may use methods such as observations, experiments and also tasks to test learning in the workplace.
(2) The Psychological perspective focuses a lot on individual learning and how people learn themselves. It often forgets about the collective levels of learning and knowledge acquisition, looking predominantly on individual characteristics as influencers to learning. As explained above, this idea was rejected a while back but parts of my research could be situated here if I find that individual characteristics may be important for learning in the workplace. The psychological perspective uses a variety of qualitative and quantitative methods (surveys, case studies, observations and so on) depending on the approach used and the research questions to be addressed. Examples of research using the psychological perspective include Axtell, Holman and Wall (2006), Battistelli, Montani and Odoardi (2013) and Holman, Totterdell, Axtell, Stride, and Port (2012).
(3) The Human Resource Management perspective is one of the most closely related perspectives to my research. Firstly there are many concepts researched from both the HRM or business perspective and the information science perspective (such as knowledge sharing: see Cabrera, Collins, & Salgado, 2006). However, this is rarely researched from multiple perspectives. So for example, learning in the workplace can be researched from this perspective as then formalised systems in the workplace can be put in place to support development of learning. Although this is very similar to my PhD domain, I am not looking to change the business world or theory in this domain. I am, however, looking to see how productivity and competitive advantage in organisations could be maximised through learning and how the effectiveness of employees are at the heart of such development. This is where the overlap between HRM and my research lies, even though I often don’t want to admit the similarities (e.g. Chan, Shaffer, & Snape, 2004; Crouse, Doyle & Young, 2011; Felstead, Gaille, Green & Zhou, 2010; De Vos, De Hauw & Willemse, 2015). The list is endless!
(4) The Work and Employment perspective – this perspective looks at the benefits of research for employment purposes. So although I will be (partly) exploring training and building innovation skills, this is not the only part of my research. Instead of focusing on employability, I am looking on how individual and learn to innovate and how this can then influence the organisation on the collective level. Regardless of employability the benefits will be to both individual and organisation and not just supporting the individual to get a job. You can see an example of the employment perspective in McAdam and McCreedy (2000) and Canny (2002) who all use the work and employment perspectives.
(5) The Organisational studies perspective – this perspective links partly with my research as my research explores how the individual can influence the collective in terms of processes that occur in the organisation like knowledge acquisition and sharing. This could be done through processes of collaboration and social relations that occur at work. However, organisaional studies in itself uses various approaches depending on what the research is looking into and whether it explores the organisation as a whole or individuals employed there. So I technically could be doing a PhD in organisational studies but the domain is too broad (in terms of organisational processes, behaviours, management and other topics covered within organisational studies) and I would struggle to differentiate and justify methods according to the research questions I want to address.
And finally, we have the Information Science perspective. The information science perspective (see Bawden & Robinson, 2012) differs from the perspectives explained above as my research will use the perspective to:
Study the use and application of knowledge in the workplace to facilitate learning
Study information behaviours and how people use information to support learning
Study interactions between people, information and behaviour and how this can influence learning in the workplace
… and this is what makes this perspective quite unique! More importantly, this is what makes my PhD quite unique. My PhD will make contributions to the information science field both practically and theoretically by applying a theoretical framework not used before in such a multidisciplinary research project on workplace learning and innovation. It will use a multi-method approach (both qualitative and quantitative methods) to triangulate data in exploring the relationship between workplace learning and the development of innovative work behaviours. It will also on the Social Informatics side of information science (see Smutney, 2015).
So to answer my initial question of ‘Is the domain of your PhD reallythat important?’… The answer is yes it is! The domain of research will help shape your approach and methods, but also help you justify why you are actually doing and why. I am not saying that domains of research cannot overlap, that would be wrong.
My research has a lot of crossover with HRM and organisational studies which I acknowledge but it roots its foundations in information science as that is the degree I hope to gain in two years’ time. I completely understand that multi-disciplinary work is very important as it demonstrates the ability to combine approaches and methods. However, it can also help to work out what is right or wrong for your research and help you justify your choices accordingly… and also see how other researchers approach questions you aim to explore! Many researchers have already adopted a multi-method approach to address issues with methodology. You’ve just got to look at articles such as De Vos, De Hauw & Willemse (2015), Giannopoulou, Gryszkiewkz & Barlatier (2014), Pattinson and Preece (2014) and Scott and Bruce (2016) for examples.
Axtell, C., Holman, D. and Wall, T.D. (2006). Promoting innovation: A change study. Journal of Occupational and Organizational Psychology, 79(3) 509-516.
Battistelli, A., Montani, F., & Odoardi, C. (2013). The impact of feedback from job and task autonomy in the relationship between dispositional resistance to change and innovative work behaviour. European Journal of Work and Organizational Psychology, 22(1), 26–41. http://doi.org/10.1080/1359432X.2011.616653
Bawden, D., & Robinson, L. (2012). Introduction to Information Science. London: Facet Publishing.
Cabrera, A., Collins, W. C., & Salgado, J. F. (2006). Determinants of individual engagement in knowledge sharing. The International Journal of Human Resource Management, 17(2), 245–264. http://doi.org/10.1080/09585190500404614
Chan, L. L. M., Shaffer, M. A., & Snape, E. (2004). In search of sustained competitive advantage: the impact of organizational culture, competitive strategy and human resource management practices on firm performance. International Journal of Human Resource Management, 15(1), 17–35.
Crouse, P., Doyle, W., & Young, J. D. (2011). Workplace learning strategies, barriers, facilitators and outcomes: A qualitative study among human resource management practitioners. Human Resource Development International, 14(1), 39–55. http://doi.org/10.1080/13678868.2011.542897
De Vos, A., De Hauw, S., & Willemse, I. (2015). An integrative model for competency development in organizations: the Flemish case. International Journal of Human Resource Management,26(20), 2543-2568.
Felstead, A., Gaille, D., Green, F., & Zhou. (2010). Employee involvement, the quality of training and the learning environment: an individual level analysis. The International Journal of Human Resource Management, 21(10), 1667-1688.
Frenz, M., & Ietto‐Gillies, G. (2007). Does Multinationality Affect the Propensity to Innovate? An Analysis of the Third UK Community Innovation Survey. International Review of Applied Economics, 21(1), 99-117, DOI: 10.1080/02692170601035033
Fuller, A., & Unwin, L. (2003). Fostering workplace learning: looking through the lens of apprenticeship. European Educational Research Journal, 2(1), 41 – 55.
Giannopoulou, E., Gryszkiewicz, L., & Barlatier, P.-J. (2014). Creativity for service innovation: a practice-based perspective. Managing Service Quality, 24(1), 23–44. http://doi.org/10.1108/MSQ-03-2013-0044
Harbi, S. El, Anderson, A. R., & Amamou, M. (2014). Innovation culture in small Tunisian ICT firms. Journal of Small Business and Enterprise Development, 21(2008), 132–151. http://doi.org/10.1108/JSBED-06-2013-0086
Holman, H., Totterdell, P., Axtell, C., Stride, C., & Port, R. (2012). Job Design and the Employee Innovation Process: The Mediating Role of Learning Strategies. Journal of Business and Psychology, 27(2), 177-191).
Jeske, D., & Stamov Roßnagel, C. (2016). Understanding What Drives Informal Learning at Work: An Application of the Resource-Based View. International Journal of Management, Knowledge and Learning, 5(2), 145-165.
King, N. (2008) Redesign: enhancing informal learning at American Express. Training and Development in Australia, 35(5), 9-10.
Mavin, T. J., & Roth, W.-M. (2015). Optimizing a workplace learning pattern. A case study from aviation. Journal of Workplace Learning, 27, 112–127.
McAdam, R., & McCreedy, S. (2000). A Critique of Knowledge Management: Using a Social Constructivist model. New Technology, Work and Employment, 15(2), 155-168.
Pattinson, S., & Preece, D. (2014). Communities of practice, knowledge acquisition and innovation: A case study of science-based SMEs. Journal of Knowledge Management, 18(1), 107–120. http://doi.org/10.1108/jkm-05-2013-0168
Scott, S. G., & Bruce, R. A. (2016). Determinants of Innovative Behavior : A Path Model of Individual Innovation in the Workplace. The Academy of Management Journal, 37(3), 580–607.
Smutney, Z. (2015). Social informatics as a concept: Widening the discourse. Journal of Information Science, 41, 1-30.
Sykes, C., & Dean, B. A. (2013). Studies in Continuing Education A practice-based approach to student reflection in the workplace during a Work-Integrated Learning placement. Studies in Continuing Education, 35(2), 179–192.
You may remember that I have been spending the last few months carrying out some secondary data analysis of different surveys relating to innovation and other related concepts in the workplace. However, you may also remember that this has not been an easy task.
Initially, I was going to be analysing secondary data taken from surveys relating to innovation alone, but as you can see in my previous blog post this approach did not work. I got to the point (not long after writing that blog post) where I had has enough, and I questioned the purpose of doing the secondary data analysis at all. To me, it didn’t matter that this was a requirement of my original PhD proposal and that my PhD sponsors and funders wanted this type of analysis done. To me, it was getting me down and the only way I could see to fix it was to make my way out.
I decided to tell one of my supervisors this as it got to the point where my frustrations were getting me down. It turns out they did not know it was making me feel bad, but did know I had not had the support network present that I really needed for the analysis, and at times I just had to plod on. We decided to have a supervision meeting to chat about the purpose of my secondary data analysis and to explore the next steps we could take. It turns out that this was one of the most productive meetings I have had with my supervisors as it meant from that point on, I knew what I was doing and made myself a plan of what I was going to do. In our meeting we decided that:
I was going to analyse some secondary data from international surveys. This would give information on trends occurring on the macro level (between international countries);
This would then lead to further exploration of UK survey data (on the micro level) where I could explore specific contributions to the development of innovation from data taken in the UK;
I could then draw conclusions about what was happening at international level and how this compares to countries within the UK, and finally I would have my European and UK comparison of secondary data.
We also formulated a plan of the types of exploratory analyses I could do and how these were carried out on the programme I was using (the joy of SPSS). And this is how it went…
I accessed some open-access data where I did not require specific permission to use it. My supervisor was great in finding lots of links and I spent a week searching through these, looking for variables associated with innovation and also variables which could contribute to innovation development. I accessed most of my data through Eurostat so that I had variables there and then. This meant I did not have to apply to use the data or have my project approved specifically either, which eased the tension a little. I did use other websites to access information on things such as education provision, gross domestic product and related variables, something I highly recommend PhD students do. The more data sources you access (reasonably) the more information you may find on variables related to concepts you are exploring. I found that this reduced my data access anxiety almost immediately as I was able to create a fully comprehensive dataset – a dataset containing over 70 variables I must admit. But this meant I had a starting point and a starting point was looking forward to explore. More importantly, I was able to send my variables to my supervisor and they gave advice on other data I may like to find to help fill the gaps in the analysis I was about to do.
Preparing the dataset
One thing that people don’t realise is that secondary data is not always in the best condition. There are often countries with missing data, countries where data that is incorrect and data which makes no sense at all. As part of my data preparation and cleaning I had to make sure I had a full dataset (by replacing missing values with means), remove errors and full stops before putting it into an SPSS file. I then I had to make sure I labelled, coded and checked all variables again for errors so the analysis could be performed properly. Some students don’t realise that this takes time, and that this time is precious. It meant I got to understand all of my variables and where they came from, meaning I understood what they actually meant!
The preparation also included clustering my data into levels of innovation within countries. I wanted to make comparisons between high, medium and low levels of innovation so I had to use hierarchical cluster analysis to get this done. I managed to successfully cluster my data and was able to make plans for further analysis from the results I got.
And then the analysis bit…
Firstly, I carried out some exploration of descriptive statistics. This has given me a picture of innovation and employment trends across 28 countries in the EU. It also highlighted some interesting trends in terms of how the UK compares to countries across Europe and I have found that this can help justify the purpose of my PhD – quite good for me.
Secondly, I then carried out come correlation analysis for two main reason. Initially I wanted to see if there were any relationships between variables that could be explored later, and after that I wanted to help justify my choice of exploratory analysis I would use next. I found that there were some correlations between variables and these correlations seemed to make sense. When reporting them in my write-up I started to find myself trying to explain the patterns in my head to try and explain what was happening and why. The interpretation part of statistics is one part that I love, regardless of how significant results seem to be.
Thirdly I then carried out something called a Principal Components Analysis (PCA). This was so that I could see if any variables were repeated and whether the variables could be narrowed down to less components. I found that this was quite hard. I had not run this type of analysis in almost 8 years and could not recall the point. But a great book and some statistics notes form my undergraduate and masters years helped me get through what I needed to do. In my opinion my analysis did not go as planned, but I was still able to see why. I was able to see why the components were not appropriate and made justifications to carrying out the next steps of my analysis.
I explored differences in means between low, medium and high innovative countries on the variables I had categorised as ‘influencers of innovation’;
From step 1, I then reversed some of the analysis. I explore the difference in means of the influencing factors on the amount of innovation present in countries. This helped me determine whether significant relationships in stage 1 were repeated and which ones were not significant at all;
I then took all significant variables in stage 2 and entered these into two-way analysis of variance analyses. This determined if there were any main effects of the independent variable (innovation influencer) present on the dependent variable (amount of innovation). It was at this point I considered covariates, variables that may influence relationships between other variables and ones that you can account for. I entered these into the analysis before getting my results.
It was at the point that I felt my analysis has been a success. I found that there are some patterns in my data worth reporting, and some that are not so helpful. I have found that this analysis WILL help me answer my research question initially and then my next analysis will go further in detail. I am hoping I can explore the UK survey data in as much depth as here but that will be a challenge for 2017 I think!
However, going tough the process of the analysis on my own (with slight help from my wonderful colleagues and supervisors when I got stuck) has given me a sense of achievement. I have managed to progress through stages of secondary data analysis on my own (pretty much), tackled problems I faced and fought hard to win. And winning the fight of my data analysis was key to my PhD 2016 success.
For now, that is all. I am sure that I will write a blog post on my findings at some point in 2017. However, I will leave that until my secondary data analysis is completely done.
Last week I attended some training designed to highlight the importance of the Researcher Development Framework and how PhD students can use the resources on the Vitae website to support their own development throughout the PhD. This is why it’s more than just a PhD.
Now I was quite skeptical about this training as I was concerned that I has heard it before as I have attended some similar training events in the not so distant past. However, the training got me thinking a little about my own PhD and what I am (and hoping to) get out of the whole process.
We do take it for granted that we will get a PhD as some of us actually don’t. Some of us leave, some of us fail and some of us struggle through. However, going into a PhD does have one ultimate goal – carrying out a research project and writing it up successfully so that we can graduate with smiles on our faces and be proud of our achievements, right? Sometimes not! For me, getting the PhD is a goal, but I have other ones too. I want to make sure I am confident enough to move on after my PhD, don’t dwell on things that have brought me down over the past few PhD years and most importantly, I want to make the outcome of my PhD worth it. To me, this means I want to use it in the career and make sure I do something worthwhile so that I know the last three years have been worth it. I know they will have been worth it anyway, but I want to know I’m putting my studies to use when I’m done and know I am doing that too.
What about the considerations to what we do after the PhD? Where do we go? Do we stay in academia, or not? Do we go into practice or do we forget about our research completely, decide we have had enough and take a whole new direction. for you, this is not up to me!
Now the careers adviser in me is the thing that keeps me going in this when I think I have not done enough. The career adviser tells me that I still have two years to go and that I can work towards me end goal without having a final destination in mind (ie, a job). However, the career adviser in me also tells me that it’s not the end goal that matters as much (although it still does), it’s more about what you do during your PhD years that will help. So this is there the Researcher Development Framework comes in – it can help you see how you are developing as a researcher rather than how you are not. It can help you point out skills and qualities that you want to work on as well as ones you are think you are good at. It can also help you plan ahead, and schedule in activities to help you work on those things you want to and most importantly, it can help you track progress so that you can actually see you are doing something more than ‘just a PhD’. It can help you work towards being a confident academic researcher with all the skills you need to survive, skills which employers want to see.
The afternoon of that training was a little weird and I started thinking about all the stuff I wanted to get out of my PhD that had nothing to do with the research itself. I then actually took a while to look at the Research Degrees Framework and have a 10 minute reality check that I’m doing okay and there is still stuff I’d like to work on – and that’s okay too!
We got talking in the training about how to ‘evidence’ out achievements in the RDF and this is something I am in two minds about. I wholeheartedly agree that things should be ‘evidenced’ to demonstrate capability in certain domains of the framework, however, I often don’t agree with this being recorded in one online place. For example, on day one of my PhD, I was given a Post Graduate Development Record by my supervisor and for me, this is my evidence. I am a very practical person so when there is the opportunity for me to ‘evidence’ my achievements physically I would normally choose that option anyway. My PDR file is full of bits and bobs that I have done – conference presentations, reviews, feedback and so on. For me, that is more important than nothing at all. For me, having all of my PhD stuff in one place is quite handy, not only to see feedback on something I did, but also to refer back to when I need to see what I have done.
I know this option is not for everyone and I do blame my careers and education background for my preference, but there are other options in how to record progress, and the RDF planner is one of them. Now this gadget keeps all your stuff in one place you can comment on evidence and upload evidence pretty much how you feel, but it does come at a price. Your educational institution should have a subscription to the service and as far as we know, the subscription ends as soon as you leave, but you can still pay as an individual, I think. You can always download your portfolio to keep and some people may prefer this way of doing things.
When I had my last review, I used my PDR file to see what I had done as I was feeling like I had not progressed much since. I actually proved myself very wrong in this case and realised that I had done more than just a PhD but that these things were helping my PhD too.
I found I had attended a lot of training events which was helping to develop my knowledge all of the way through, and also help me learn new things I did not know. I had helped to organise a 2016 doctoral colloquium earlier in the year and also a student conference in the School of Computing. I had also presented my work at Departmental level, Edinburgh Napier University level, Cross-University level and also internationally so I really now don’t see why I had a reason to complain. I’ve also published two articles with colleagues (one journal and one in conference proceedings) and worked with some wonderful people along the way. For my efforts I managed to win a few awards: best paper, second best poster and also best student presentation which rounded off my first academic year nicely in one. Then this year, I took on the role of School of Computing PhD student rep, and then I was chosen to be a SGSSS student rep too. I also started teaching which I found I love and have also been interviewed for a Scottish company on my graduate experiences so the second PhD year is quite fruitful too.
So for me, reflecting on my progress was a success. Using the RDF as a means for improvement (or development!), I then started to measure my own progress on a on the points scale of each domain. It worked for me knowing from my initial RD4 review to my RD5 review I had improved in some areas and there were some areas I felt I wanted to improve more. It means I have things I would like to work on and things I can say I’m okay with and other things I’m really really bad at!
As well as continuing with my secondary data analysis (which is now progressing quite well I might add), I have recently been interviewed by an organisation called Company Connecting about my experiences as a PhD student.
Company Connecting help to connect individuals and companies involved in IT, Tech and Business. As well as this, they publish a series of articles and blog posts about related topics, and one of those topics is a series on the graduate experience.
I was pleased to be contacted by Company Connecting to participate in an interview and you can find details of my interview on the links below:
This part of my PhD should have been one of the easiest, however, so far it has been one of the hardest. I’m doing SECONDARY DATA ANALYSIS but I’m not doing data analysis for any random reason though! I’m in the process of analysing some secondary data taken from different work and employment surveys so that I can see if there are any trends in training and skills that require further exploration in my case studies. Currently, I am focusing on two main ones form the UK:
I thought this part of my PhD would be quite straight forward as I am research methods and statistics trained. From working on my Bachelors and Masters degrees, I had substantial research methods training with a lot statistical elements to this. In a nutshell, I learned how to explore data (descriptive statistics, central tendency, spread of data), how to represent data (so things like frequencies and charts), how to explore relationships between variables (regressions and so on) and also how to compare means (ANOVA, MANOVA, ANCOVA). This combination of descriptive and inferential statistics means I understand (mostly) what to analyse and figure out how to analyse it. However, I have soon learned that this is not the easiest part of my PhD and may become the hardest.
I have ran into a few problems so far. Firstly, I cannot access the data I need easily. I had hoped to get the UK Innovation Survey as this is one of the main areas of my PhD. The survey would tell me about innovation activity of organisations within the UK. I had hoped to get this so that I could then compare data with the Community Innovation Survey. This is the European version of the innovation survey with the UK Innovation survey being part of the Community Innovation Survey. However, as both of these surveys are restricted in terms of who can have them (like much of the European data), I need to work with my supervisors to get them. This could take a while (up to two months for some!) so my plan of a wonderful UK/European innovation comparison will have to wait as I can’t write a paper without my data!
Secondly, some of the data is not all that useful. It’s great having over 80,000 respondents to surveys but when only 12 people give an answer to the question (or variable) you are looking to analyse then then the analysis of this hits a brick wall. I’m quite glad that I have other variables to explore, but it is making me question how representative secondary data is (in general, not just mine) in terms of representing the population of the surveyed country… or even whether there’s a point to doing this at all???
There is, I can assure you there is!
I’m even encountering problem with deciding what to analyse and don’t often go into analysis with no clue at all. This is exactly what I am doing for this data analysis and the lack of literature at the moment is becoming somewhat problematic. This is not only because I cannot see where my analysis story lies but also because I cant grasp an understanding of how the analysis might flow, or what the variables say at a glance.Its not my data, its someone else and this wads bound to happen sooner later, I just wish it had happened sooner.
Normally I would follow a pattern and:
(1) identify a problem for exploration (from the literature generally);
(2) identify hypotheses to test or variables to explore;
(3) explore the data in terms of central tendency and spread (and graphs, oh I love graphs!);
(4) decide on an analysis and justify this (ie, why am I doing this? What am I trying to find out?);
(5) carry out some simpler analysis and explore individual relationships among variables (to see if they are ‘worth’ exploring further);
(7) carry out the analyses (normally figure this is wrong and do another type of analysis) – and then do post-hoc comparisons depending on the data and analysis complexity
(8) see how wonderful the results are, report them and explain them in terms of the literature.
Even my tweets are showing the slow and frustrating process of exploring data to see what variables are of use and seeing if any patterns emerge *sigh*!
So for my next few weeks I have a plan! I am going to explore the data a little further to see if there are any more in-depth analyses that I can do, and comparisons I can make. Who knows, when I dig this may lead to something big (or not!). Either way, I will be able to write up my findings in a short report but whether it warrants anything publishable may not be known until the rest of my data is explored and I get my hands on the surveys I need the most.