1. Relevance. Do not blindly follow the data you have collected; make sure your original research objectives inform which data does and does not make it into your analysis. All data presented should be relevant and appropriate to your aims. Irrelevant data will indicate a lack of focus and incoherence of thought. In other words, it is important that you show the same level of scrutiny when it comes to the data you include as you did in the literature review. By telling the reader the academic reasoning behind your data selection and analysis, you show that you are able to think critically and get to the core of an issue. This lies at the very heart of higher academia.
2. Analysis. It is important that you use methods appropriate both to the type of data collected and the aims of your research. You should explain and justify these methods with the same rigour with which your collection methods were justified. Remember that you always have to show the reader that you didn’t choose your method haphazardly, rather arrived at it as the best choice based on prolonged research and critical reasoning. The overarching aim is to identify significant patterns and trends in the data and display these findings meaningfully.
3. Quantitative work. Quantitative data, which is typical of scientific and technical research, and to some extent sociological and other disciplines, requires rigorous statistical analysis. By collecting and analysing quantitative data, you will be able to draw conclusions that can be generalised beyond the sample (assuming that it is representative – which is one of the basic checks to carry out in your analysis) to a wider population. In social sciences, this approach is sometimes referred to as the “scientific method,” as it has its roots in the natural sciences.
4. Qualitative work. Qualitative data is generally, but not always, non-numerical and sometimes referred to as ‘soft’. However, that doesn’t mean that it requires less analytical acuity – you still need to carry out thorough analysis of the data collected (e.g. through thematic coding or discourse analysis). This can be a time consuming endeavour, as analysing qualitative data is an iterative process, sometimes even requiring the application hermeneutics. It is important to note that the aim of research utilising a qualitative approach is not to generate statistically representative or valid findings, but to uncover deeper, transferable knowledge.
5. Thoroughness. The data never just ‘speaks for itself’. Believing it does is a particularly common mistake in qualitative studies, where students often present a selection of quotes and believe this to be sufficient – it is not. Rather, you should thoroughly analyse all data which you intend to use to support or refute academic positions, demonstrating in all areas a complete engagement and critical perspective, especially with regard to potential biases and sources of error. It is important that you acknowledge the limitations as well as the strengths of your data, as this shows academic credibility.
6. Presentational devices. It can be difficult to represent large volumes of data in intelligible ways. In order to address this problem, consider all possible means of presenting what you have collected. Charts, graphs, diagrams, quotes and formulae all provide unique advantages in certain situations. Tables are another excellent way of presenting data, whether qualitative or quantitative, in a succinct manner. The key thing to keep in mind is that you should always keep your reader in mind when you present your data – not yourself. While a particular layout may be clear to you, ask yourself whether it will be equally clear to someone who is less familiar with your research. Quite often the answer will be “no,” at least for your first draft, and you may need to rethink your presentation.
7. Appendix. You may find your data analysis chapter becoming cluttered, yet feel yourself unwilling to cut down too heavily the data which you have spent such a long time collecting. If data is relevant but hard to organise within the text, you might want to move it to an appendix. Data sheets, sample questionnaires and transcripts of interviews and focus groups should be placed in the appendix. Only the most relevant snippets of information, whether that be statistical analyses or quotes from an interviewee, should be used in the dissertation itself.
8. Discussion. In discussing your data, you will need to demonstrate a capacity to identify trends, patterns and themes within the data. Consider various theoretical interpretations and balance the pros and cons of these different perspectives. Discuss anomalies as well consistencies, assessing the significance and impact of each. If you are using interviews, make sure to include representative quotes to in your discussion.
9. Findings. What are the essential points that emerge after the analysis of your data? These findings should be clearly stated, their assertions supported with tightly argued reasoning and empirical backing.
10. Relation with literature. Towards the end of your data analysis, it is advisable to begin comparing your data with that published by other academics, considering points of agreement and difference. Are your findings consistent with expectations, or do they make up a controversial or marginal position? Discuss reasons as well as implications. At this stage it is important to remember what, exactly, you said in your literature review. What were the key themes you identified? What were the gaps? How does this relate to your own findings? If you aren’t able to link your findings to your literature review, something is wrong – your data should always fit with your research question(s), and your question(s) should stem from the literature. It is very important that you show this link clearly and explicitly.