When writing your thesis, the process of analyzing data and working with statistics can be pretty hard at first. This is true whether you’re using specialized data analysis software, like SPSS, or a more descriptive approach. But there are a few guidelines you can follow to make things simpler.
1. Choose the Best Analytical Method for Your Project
The sheer variety of techniques available for data analysis can be confusing! If you are writing a thesis on internet marketing, for instance, your approach to analysis will be very different to someone writing about biochemistry. As such it is important to adopt an approach appropriate to your research.
2. Double Check Your Methodology
If you are working with quantitative data, it is important to make sure that your analytical techniques are compatible with the methods used to gather your data. Having a clear understanding of what you have done so far will ensure that you achieve accurate results.
For instance, when performing statistical analysis, you may have to choose between parametric and non-parametric testing. If your data is sampled from a population with a broadly Gaussian (i.e., normal) distribution, you will almost always want to use some form of non-parametric testing.
But if you can’t remember or aren’t sure how you selected your sample, you won’t necessarily know the best test to use!
3. Familiarize Yourself with Statistical Analysis and Analytical Software
Thanks to various clever computer programs, you no longer have to be a math genius to conduct top-grade statistical analysis. Nevertheless, learning the basics will help you make informed choices when designing your research and prevent you from making basic mistakes.
Find this useful?
Subscribe to our newsletter and get writing tips from our editors straight to your inbox.
Likewise, trying out different software packages will allow you to pick the one best suited to your needs on your current project.
4. Present Your Data Clearly and Consistently
This is possibly one of the most important parts of writing up your results. Even if your data and statistics are perfect, failure to present your analysis clearly will make it difficult for your reader to follow.
Ask yourself how your analysis would look to someone unfamiliar with your project. If they would be able to understand your analysis, you’re on the right track!
5. Make It Relevant!
Finally, remember that data analysis is about more than just presenting your data. You should also relate your analysis back to your research objectives, discussing its relevance and justifying your interpretations.
This will ensure that your work is easy to follow and demonstrate your understanding of the methods used. So no matter what you are writing about, the analysis is a great time to show off how clever you are!