You might feel like your entire research project is falling apart and that you cannot move forward. However, rest assured that there are always ways to deal with unexpected data that will not only salvage your research, but also make important contributions to your field. In research, results can always still be useful in some way by telling you something important or interesting about either your data set, methods or methodology.
It is still possible to write up and publish this research, and to extract important information from the results you have obtained. You might find something very interesting and insightful in the methods you used. Or you might discover that the results tell you something novel, even groundbreaking, about that particular data set or the issue you are investigating.
Being able to clearly demonstrate and explain how and why a method does not work, or why a particular method produces undesirable outcomes, is itself a valuable contribution to the field. For example, in research around vaccines or medical treatments, having something not work out is not considered failure.
Instead, it can help researchers eliminate what is not effective, narrow down the scope of investigation, and allow them to rule out certain methods so they can proceed to work with others. Remember that throughout the history of scientific research, unexpected anomalies in results have often brought up surprising new discoveries or prompted scientists to investigate other novel issues. It is not uncommon for PhD students to panic when they get unexpected results.
They might then try to start their project from scratch or give up altogether. Your supervisor should be able to offer you more focused advice about what you can do to effectively address the specific issues arising with your data. You can talk to them about what you did during your data collection in either labwork or fieldwork and they can help you untangle where things may have gone wrong, how to recollect more data if necessary and if you have time , and what else you can do at this stage to move forward with the results that you have.
Getting different perspectives and troubleshooting your process with others might also reveal that the issues you are facing with your data are not as disastrous as you think.
When you talk to others, they can give you new ideas for how you can work with the data you currently have or offer suggestions for what else you can do in your situation. It is important not to try to solve everything on your own. Remember that you have the support of your supervisors and the research community in your department and university. All researchers understand that it is common for difficulties and issues to arise with research and data, and they are more than likely to have good advice and reassurance to help you.
Although it may not seem like it at the time, having to deal with difficult, unexpected results is an excellent opportunity for you to prove your strengths and resourcefulness as a researcher. As you address diverse and unexpected issues arising in your research, you demonstrate your knowledge of the field and discipline. You show that you understand a range of existing theory, methodology and analytical tools, and showcase your ability to employ and extract from previous work to manage your own research — whatever the challenges.
By doing this, you show how resourceful, adaptable and versatile you are as a researcher. You would also be exercising clear researcher reflexivity, by presenting conscientious awareness of how your decisions and actions affect your data, your analysis and the overall directions of your research. You will be able to show that you have a thorough understanding of what you have done, how you have done it, and what you could have done differently. You are commenting using your Twitter account. You are commenting using your Facebook account.
Notify me of new comments via email. Notify me of new posts via email. Skip to content Even though we have a long way to go on the Indicators project with regard to organizing our data sets I jumped the gun the other night and ran some queries that pulled data from a variety of sources into Perl and created a temporary database table on my Mac.
Naturally I expected to see student Staff Activity vs Student Performance results improving relatively to the participation rates of the teaching staff. Share this: Twitter Facebook. Like this: Like Loading Pingback: Indicators update February. Leave a Reply Cancel reply Enter your comment here Fill in your details below or click an icon to log in:. Email required Address never made public. Name required. Previous Previous post: LMS.
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