Last Updated on September 28, 2022 by admin
Introduction
Have you ever wondered why data analysis is necessary? Or, do you notice while teaching data analysis topics that our teachers take special time and elaborate on the topic more precisely? This is because of the importance of data. While collecting data from the web or any other resource, you may have noticed that not every piece of data is relevant to your research. That’s why we keep separating the relevant data from the irrelevant one. But why is this data important? Because relevant data helps in producing the most accurate results. That is the reason data must be analysed and processed accurately before using in a research work.
How to define dissertation data analysis?
For writing dissertation and conducting research, we need to have either qualitative or quantitative data. It depends upon the type of research which type of data to be employed; it can be qualitative or non-qualitative. But what does dissertation data analysis mean? The collection of data, interpreting it and finding out the most relevant patterns from it is called dissertation data analysis. We need to conduct an authentic dissertation data analysis to find accurate results and examine the facts and reasons behind the results. Let’s read the 10 important things you need to do for a perfect data analysis.
10 Things to Work on for dissertation data analysis
Use best data analysis tools:
It is often hectic to analyse data manually. A human can only analyse things to some extent because humans have greater chances of making errors and have limited energy. Moreover, humans cannot be more efficient than machines in analysing data, irrespective of the fact that humans have designed data analysis tools. So, in order to analyse data use relevant tools for quantitative data and qualitative data, you can take guidance from your supervisor about what type of tools to use for your data. You can also get dissertation help online if your supervisor is unavailable.
Ensure data relevancy:
Data relevancy means there must be consistency between the problem statement of your research work and the data. What does it mean? Let’s take an example. If you want to design a machine which can distinguish between males and females, and you collect data on fruits to train your machine on this data, will your machine be able to distinguish between males and females? No, right? Thus, to meet a good accuracy in your research results, make sure your data is relevant. Irrelevant data can distract you from the main goal of your research.
Select the relevant data analysis technique:
Data analysis is the most important task in the whole research work. For dissertation data analysis, make sure you have selected the relevant technique. While selecting relevant techniques, find out which type of data you have for your research work. As you find out the type, it will be easy for you to find out the relevant and most suited techniques for data analysis. While writing data analysis methods in a dissertation, make sure you highlight how you select the most relevant methods for research.
Learn how to represent data:
Presentation makes things more valuable. If you have completed a difficult task but do not know how to present it, your whole struggle will be of no use. Research work is not an easy task. Almost all students make their full effort to conduct research using their time and energy, but only a few are able to get the reward and recognition for their work. So what’s the difference between these two types of students? One major difference is presentation. Presenting your data using tables and charts helps to have a better understanding of it. Make sure you have presented your data well using charts, tables and diagrams.
Use appendix if needed:
Dissertation data analysis should include the most relevant data and information. But there is some information in most of the dissertations which needed to be in the paper, but in reality, it is not the part of the research paper. What can you do with that information? Add this information to the appendix. Yes, you can design an appendix in your dissertation for data that is not the part of your research, but is necessary to understand the dissertation.
Discuss your data:
Data discussion will involve the positive and negative aspects of your data. It would help if you elaborate on your data’s effectiveness, correctness and reliability. Also, it would help if you also write the reason behind your data collection and how you collected it. Moreover, in the discussion part, you will also write sources of data collection and the effectiveness of your data.
Results and findings of data
Results and findings of data involve the final results you find out after conducting the data analysis. This is sometimes the final part of dissertation data analysis, so make sure you understand and write the results as well as finding clearly and precisely. In this part, you will also add the outcome of your whole analysis.
Develop a connection between the literature review and your findings:
A literature review of a paper includes relevant research about the problem statement of your paper. So it would be incredible and effective for the reader to know what the relationship between your study and previous studies is. That is why make sure you include in the ending part of your dissertation data analysis the relation between previous studies and yours.
Make sure you have written your contributions:
While writing data analysis for a dissertation, you should include what contributions you have made in the already collected data in your relevant research. If you search on the internet, you will find there are many research studies based only on data collection and analysis. This means scientists do separate research on data collection. So adding your data analysis contribution would be a plus point.
Proofread and make sure consistency:
After writing your dissertation data analysis, make sure you proofread it and have added all the information needed. Also, keep in consideration your dissertation is consistent and understandable.
Conclusion:
A dissertation data analysis can make or break your thesis worth. That is why this section should be completely scripted and well written. You can include your contribution and techniques you have used while conducting data analysis.
