The halfbaked thesis template

It should include the following sections:

• clearly set out the issue/problem your thesis explores and say why that is important (e.g. addressing a gap in the literature)
• say a little about what you did (methodology)
• say what you found (your contribution to 'new knowledge')
• explain why it is significant (the 'so what' question)

I'd advise that you re-write your abstract on a regular basis - use it to track your thinking about your PhD (a bit like your elevator pitch).

Once you have finished writing the rest of your thesis you will need to re-write the abstract. It should be the last thing you re-draft in your thesis.

You still need to write out abbreviations in full the first time you use them in the body of the text. For key terms you also still need to explain these in the main body of the text. This section just makes it easier for folk to find those definitions/abbreviations later on.

If you know what your (initial) research questions (RQs) are then state them explicitly (but expect them to change later in the thesis!).

It should end with a summary of what is to come - which you will write when you have finished the rest of the thesis.

• tell us what is already known about the area you are investigating
  • it should identify 'gaps' in the literature which suggest RQs that you might want to investigate (or if you have already identified your RQs, confirms their importance)
  • if you have stated your RQs then it should tell us what is known about the answers to your RQs
• highlight other research that has explored similar issues/RQs (which will inform your methodology in Chapter 3 - if other people investigating similar issues have used a particular methodology successfully that suggests it might be appropriate for your study too)
• demonstrate that you have a reasonable grasp of relevant literature
• demonstrate that you can critically analyse and synthesise existing literature
• it may introduce and explain a framework or model that you intend to use to inform your data collection and analysis

Start your literature review by setting out what it will do (see the bullet points above) and what its scope/limits are (you can't include all the literature that exists so you have to clearly set out what is to be included).

When you first introduce a study provide a brief summary of it.
e.g. Twining et al. (2017) provided 44 pupils in 12 primary schools in the UK with digital cameras so that they could take photographs of their use of digital technology at home, which formed the basis for one to one interviews with the pupils and their parents. They found that blah blah ...

Did I mention that you need to be critical:

• What were the limitations of the studies you are referencing?
• Weigh up contrasting evidence (Study A said x. Study B said y. Study A's findings are more compelling because blah blah).

Whilst you will start to write your literature review near the beginning of your PhD, you should be continuing to read relevant literature throughout your study - so will need to go back and update your literature review (probably after you have completed your analysis/whilst you are writing your discussion chapter).

• set out your theoretical stance (briefly) - What are your ontological and epistemological positions?
• explain/justify the approach you adopted
• explain who your participants are and how they were selected/recruited
• describe the methods you used - including details of each of the instruments and show how they relate to your RQs (and to any framework/model you are using - see the last bullet point in the literature review chapter above)
• show the data generation timeline (what did you do, when, where, with whom?)
• discuss the ethics (for qualitative research you should also be evidencing your ethical stance throughout the thesis (e.g. reflexivity))

You may also have a section of this chapter that explains how you carried out your analysis - or this could be included as part of your findings chapter. Your analysis should demonstrate what you did to make sense of the data - in so doing you are aiming to show that you have been rigorous, critical, and systematic. You are trying to convince the reader (your examiners) that your analysis is credible and trustworthy. If you used a framework/model (see above) then you need to show how this informed your analysis.

The methodology chapter should enable someone else to replicate the process you went through to generate and analyse your data (that is not the same as replicating the study - because that isn't possible within qualitative research).

It is critical that there is consistency between your theoretical stance, your approach, your methods/instruments, your analysis, and any claims that you make (Twining, 2018). You can use a mix of methods, and generate both numerical and non-numerical data within a qualitative approach, underpinned by a relativist ontological and epistemological position - but don't call it mixed methods - it is a qualitative study. See Twining (2018) for a fuller explanation of terminology and the quantitative/qualitative/mixed methods (non-)debate (or better still read Twining, Heller, Nussbaum and Tsai, (2017)).

See Shaffer (2017) for some great advice on data analysis (that applies even if you are not doing what he (mistakenly in my view) calls Quantitative Ethnography) - really, this is one of the best books I've read on methodology/analysis.

You need to ensure that you clearly show that you have interrogated the data - what I tend to think of as destruction testing your findings. Once you have spotted a pattern or decided on an interpretation of the data you then need to go through and actively search for data that contradicts that finding - and revise your interpretation if need be. What other interpretations are possible? Why is your interpretation the most appropriate one?

You need to be reflexive - aware of your implicit biases (we all have them) and actively trying to ensure that they don't prevent you from spotting alternative interpretations or seeing contradictory evidence.

If you are using software to support your analysis then remember that you have to do the analysis - the software make help you do that more efficiently - but you have to interpret the data and destruction test your emerging findings. The decisions and judgements are your's, not the software's.

This chapter needs to convince the reader (your examiner) that you have been rigorous and systematic, and that your interpretations of the data are credible and trustworthy.

For a PhD it is important that you show how your findings extend our understanding - so I would tend to avoid highlighting where your findings confirm previous research - you want to play up how your findings challenge/extend/or otherwise move the field forward.

This chapter should clearly state what your key findings are - make sure that you don't over claim - anything you claim must be based on your data analysis. For qualitative research remember that you cannot generalise (see Twining (2018) again!).

Explain the limitations of your study - remember that a PhD (or masters) is research training - you are expected to make mistakes, but also to recognise and learn from them.

Inevitably you will identify further research that is needed in the light of your findings.

Don't put anything that it is important for your examiners to read into the Appendices - assume that they won't read the appendices and include all essential material in the main body of the thesis.

Your appendices should include all of the documentation associated with your methodology (e.g. instrument schedules, examples of information sheets and blank consent forms). You may want to include longer examples illustrating your data analysis and demonstrating the breadth and depth of your data - but you wouldn't normally include all your data.