• 3-minute read
• 11th August 2015

How to Streamline Your Data Analysis

Every chapter of your dissertation or PhD thesis is important, but the data analysis is something to which you should pay particular attention.

Many people find this aspect of writing an essay difficult or think that it’s enough to simply describe the data collected. But proper analysis involves assessing the significance of your data and spelling out its implications for your reader.

How you do this will depend on the nature of your project and your research aims, so it’s important to plan your analysis. To help you do this, we’ve prepared a guide to key considerations to keep in mind when it comes to data analysis and statistics.

1. Aims and Objectives

Everything about your data analysis should be designed with the stated aims and objectives of your research in mind.

This includes the analysis itself, in which you explain the significance of your results, but also the way in which data are presented and the structure of your analysis section. You should, for example, open the analysis with an introduction setting out how you’ve organised the analysis within.

2. Research Approach

Probably the most important thing to remember when conducting data analysis is that the methods you adopt should reflect the specifics of your project: The statistical analysis used to assess data from an empirical experiment, for example, will not be any good for an in-depth qualitative case study conducted using a series of interviews.

3. Methodology

It’s vital to pick analytical techniques that are appropriate to your project. It is therefore advisable to include consideration of data analysis as part of your methodology chapter.

In addition, you should consider your methodology when composing your analysis chapter. If you have performed experimental tests, you should explain how they were conducted in relation to your research aims. Likewise, if you have chosen a particular form of statistical analysis to fit with the sampling method you adopted, this should be noted in your analysis chapter.

4. Learn the Basics!

Statistical analysis can seem very complicated to the untrained eye (and even to the trained eye sometimes). But with the help of various software packages – such as SPSS and Linux – it’s much simpler than it might seem.

First, though, you need to familiarise yourself with the techniques and programs you want to use in your analysis. Understanding the basics of how to use these will let you make informed decisions while designing your research, ensuring that you avoid errors when analysing your data.

5. Present Your Analysis Clearly and Consistently

This might not seem as important as the technical side of things, but unless you present your analysis clearly and consistently, your hard work elsewhere will be severely undermined. Remember that your reader will not be as familiar with your work as you are, so you need to make sure that it’s easy to follow!