Your data analysis will depend heavily on your area of study and the type of research you are conducting. Depending on the type of data you have collected for your dissertation topic, your analysis may just be a write up (consisting of text only) or a combination of graphs, tables and text. It is advisable to start your data analysis chapter with an introduction, outlining the organisation of your analysis, so that your analysis chapter is easy to navigate. Below is a guide to follow depending on the kind of study you are conducting (experimental, quantitative or qualitative).
Your data will be presented in the form of tables, graphs and diagrams, but you will also need to include text to guide your reader through your data. You will need to use the text to:
- Explain the tests you performed (and why)
- Explain how you gathered your data
- Indicate what results are significant, and how
- Make meaningful comparisons
- Draw any immediate conclusions
You will need to conduct statistical tests when analysing quantitative data, using SPSS, Eviews or a similar tool. Make sure that you present the results of your statistical analysis in an informative way. Use the text in your analysis to:
- Describe your sample
- Remind your reader of the research questions being addressed, or the hypothesis being tested
- State which differences are significant
- Highlight the important trends and comparisons/differences
- Show whether your hypothesis is proved, not proved or partially proved
Qualitative data will be analysed purely through a written description. Therefore, it is important to structure your data analysis so that your reader is easily guided through. Divide your analysis into sections and use headings as ‘signposts’ to direct your reader.
No matter what type of data you are analysing, every data analysis chapter should reflect:
- The aims or research question of your dissertation, including any hypotheses that have been tested
- The research methods and theoretical framework that have been outlined earlier in your dissertation
Do not simply describe the data. You need to interpret the data and make connections between results, and state your reasons for doing so.