Getting stuck into your dissertation can be stressful if you don’t know where to start. Your data analysis chapters are your chance to further argue your thesis’s main point by interpreting data to your audience. Data analysis connects the dots between your research and primary and secondary research.
Think carefully about which data you will include in your paper. Your dissertation’s research question should dictate which pieces of data get to play a part in your data analysis discussion. In other words, only data that speaks directly to your dissertation’s research aims should make it into your data analysis. Including irrelevant data in your discussion will take the reader’s attention away from the main point of your research and make your paper seem incoherent. You should be able to explain to your reader the academic reasoning behind your selection of each and every piece of evidence included in analysis. Keep this in mind when choosing data to include in this section.
Quantitative vs. Qualitative Data
While it may seem basic, knowing how to use qualitative and quantitative data will help you in planning the structure of your data analysis section. Quantitative data deals with measurable and objective qualities like amount, size, or frequency. You should use quantitative data in your analysis when you want to lay out the hard, cold facts of your research. Using quantitative data will also allow you to generalize your findings outside of just your research, which is valuable when tying conclusions to the wider world.
Qualitative data deals with non-measurable qualities. This is the type of research that can be collected from interviews or simply be observing the subject of your research. This type of data can be used to breathe life into mathematical data and help your audience see the real world applications to your quantitative data. Quantitative data should be used to help make decisions based on your data analysis by describing frequencies and forming parameters through which to view sets of data.
Use Your Voice
While the saying, “the data speaks for itself” may sound impressive in TV shows and movies, in practice it is an oversimplification. You need thoroughly analyze all pieces of data in your research and explain to the audience why it is important in relation to your thesis. This will be important in refuting counterpoints or backing up your research claims.
Presentation is Everything
Wadding through large walls of text can seem stale to even the most seasoned academic professional. A good way to keep your readers engaged is to transform your data into a series of interesting tables, graphs, and charts. Using visual representations of data can also be used a way to help your audience understand the key points your data is trying to get across. Alternatively, if your paper is a bit on the long side, using tables and graphs is an awesome method of keeping your paper succinct while still including important data to back up your article’s claim.