No matter how organised we are, the best laid plans can sometimes still go awry. Encountering various problems with data, during both data collection and analysis, is quite common among PhD students but no matter how frustrating it can seem, there is usually a way around these issues.
Having problems in your data does not necessarily mean you cannot still do good research and produce an excellent thesis. What is more important than the problems that arise is how you respond to them and the subsequent steps you take to address the issues.
Read on to find out about the common problems that researchers face with their data, and how you might deal with these situations.
Losing vital data
In this scenario, you might have physically lost a significant amount of data that is central and crucial to your project. This might arise because of, for example, issues in the lab or technical issues with the hard drives where you have stored your data.
The first thing to do is to speak to your supervisor about what can be done. In some instances, if you have the time and resources, it may be possible to replicate your data collection or fieldwork to try to obtain that data again.
If re-collecting that data is not possible, discuss with your supervisor how you can work that loss into the overall research project. For example, if the data loss was a direct result of the particular method you had employed (for example, an experiment goes very wrong and a significant part of your data is destroyed), this can make for very interesting and important discussion. You can address the implications of this data loss and the effects of this method in ways that still contribute valuable and original knowledge to the field.
Alternatively, you could look at whatever data you still have left and think about how you can rework your project slightly to work with just that data – that small amount of data may still yield some important results and offer interesting insights that could form a project of its own.
You might find that despite your best efforts, you might not be able to collect enough data for your PhD. It is best to address this problem as early on in the process of data collection as you can. Explain the issues you’re facing to your supervisor – they should be able to discuss with you alternative methods that you could use in the time you have left, or consider ways to work with whatever data you have already collected.
If your problem with obtaining data stems from a key issue with the methodology and methods you have employed, this issue could form a very important discussion within your thesis. For example, if you are working with a new method or testing a method against a different type of sample/subjects, then the difficulties in collecting data may tell you something significantly and valuable about that method or the sample/subjects you are working with.
This scenario provides an excellent opportunity for you to reflect upon the decisions you have made so far in your research, as well as to consider other factors that are impacting the data collection and results that you are able to generate.
Your data isn’t yielding the results you had expected, or your data is showing ‘negative’ results
First of all, try not to classify data and results as either ‘negative’ or positive’. In research, results can always still be useful in some way by telling you something that is important or interesting about either your data set or your methods.
If a ‘negative’ result means that your data disproves your hypothesis or does not answer your research questions in the way you expected, this does not necessarily have to mean that the results must be discarded or rendered useless. It is still possible to write up and publish this research, and to glean important information from the results you have obtained.
Remember that a ‘negative’ or unexpected result is still a result – trace your research backwards and try to examine what it is that caused this result. You might find something very interesting and insightful in the methods you used, or that the results tell you something novel, even ground-breaking, about that particular data set or that issue.
Even if there are problems with your data, or your results are not exactly what you expect, this does not have to mean that your research is not good enough for a PhD. Explaining the problems you encounter within your data and how these challenges have impacted the trajectory of your research are just as important as your findings. Demonstrating a clear understanding of what has happened during your research and how you have addressed these problems is crucial for proving your strengths and resourcefulness as a PhD researcher.
Again, it is very important to speak to your supervisor if you are uncertain about how to proceed with your results. They should be able to offer you more focused advice for what you can do to effectively address the specific issues arising within your data.
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