With the incredible advancement of technology, our capabilities seem to be boundless with new innovations being developed constantly to make simple tasks as easy as possible for us. Speaking from a data perspective, it is evident that intelligently capturing data from paper forms greatly reduces time and costs by removing the need for manual entry. The basic principle to understand here is that it makes more logical sense to scan a document at the start of the process rather than the end.Then comes the moment that can make or break research projects; the analysis of the data. As I work primarily with members of the health sector, in particular clinical trial researchers, the analysis of the data can determine whether a potential treatment should advance into the latter phases or if it is deemed to have no effect on the patient and may require rethinking. Considering the amount of time and money that goes into the research from the start to the end, everyone hopes a positive outcome is the result so that valuable resources are not wasted.
Creating Excel graphs using your data is all well and good to find out the answers to your main questions, but what about all the data which goes unused, and could lead to more specific questions being developed to advance your research. As data collection can take several months or even years, it’s unfortunate that a lot of information is ignored, and not looked at in more detail.
By using Excel graphs, there is no real prompt to encourage you to explore segments of data more in depth when it comes to the analysis, unless of course you have already considered this. Instead, the main focus is finding out the answer to the key question you had at the start of the research, and once that is found, you may not have any further use of the data. In addition, to explore your data in more detail, you would be required to manually select the relevant information and create new graphs every time you wish to examine something new. As you can imagine, the amount of time completing this process can be lengthy, especially if you have a large number of participants.
Even Survey Monkey is seen to be a popular tool for collecting survey responses online, and as the service provides analysis tools automatically, many researchers are happy to use this option. After recently attending the UK Stroke Forum Conference, many of my conversations centred on being more interactive with research data, and utilising it to better allocate resources. Many delegates who I spoke to admitted that they were happy to use Survey Monkey or Excel for analysis, but failed to realise the true value of the data they have collected until I explained in more detail about a project we worked on.
The UK Stroke Association developed a campaign titled ‘Know Your Blood Pressure’ in which they collected blood pressure readings from members of the public at shopping centres and sports events, to highlight the risks of having high blood pressure. With the information collected, we developed an interactive dashboard which allowed the team to utilise the data they received more effectively.
By incorporating a Google Maps element into the dashboard, the research team were able to identify the areas in which high blood pressure was an issue (represented by larger green circles). As a result, the team was able to allocate resources more effectively and focus their efforts in these areas, rather than waste money targeting regions where high blood pressure was not much of an issue. This type of analysis would not have been possible using Excel, so this is merely one example where alternative methods prove to be more useful. In this case, it would be possible for the researchers to conduct further research into an area where blood pressure is low, to find out what the contributing factors are of this.
Of course this is simply just one alternative method to represent your data; there are many others out there which are beyond the conventional method of simple graphs created in Excel. For example when analysing free text comments, it may be worth focusing on word sentiments and developing a word cloud to build a clear picture of what the general thoughts are of the participants involved.
Using Excel may be the common method for the analysis of data, but by experimenting with different methods, it is possible to utilise more information and uncover trends which you may not have intentionally looked for at the start of your research. The end result may bring more value to your research and save you from wasting considerable amounts of funding and resources.