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So, you have gathered all the data for your dissertation. Good. Do you know what the next step in your dissertation writing is? Yes, it is about conducting data analysis for the dissertation. Data analysis is a tough job that takes a lot of time and energy from students. Although it is rough and tough work, it must be done perfectly because the dissertation's success depends on your analysis section. By conducting the right data analysis for the dissertation, you are much closer to getting the right results.
However, many students do not know how to perform data analysis perfectly. They make mistakes in their analysis, thus minimising their chances of getting a degree on time. Remember, today's article is about 10 things you should never do in your data analysis. So, let's learn what those things are.
10 Things You Should Never Do In Data Analysis for a Dissertation
The little things count in life, which is very much the case with data analysis. Failing to pay attention to minor details can cost you heavily during your degree. So, here are the top 10 data analysis mistakes that students must not do at any cost. These all can easily be avoided in a dissertation, so do keep them in mind all the time.
Using An Unsystematic Approach
Data analysis for the dissertation should be based on a systematic approach. It should follow a defined path. Going into the analysis without planning and understanding what to do and how to do it is not wise. Such a thing can create many problems for you in the long run. Therefore, as a researcher, you should never jump into data analysis without having a systematic approach in mind. Understand the problem fully, develop a plan, and then analyse.
Also, Read This: 10 Step Guide for Conducting Preliminary Research – Things You Must Do
Not Having A Defined Goal
Why do you want to analyse the collected data? Why is it important to do it objectively? Have you ever thought about these things during your research? If yes, then you know your data analysis's goal. Having a defined goal is important for data analysis because it allows one to remain on track and avoid any distractions coming the way. Therefore, you should never start analyses without a clear goal in mind.
Not Preparing Data Well
Regarding data analysis, you should be extra careful about what data goes into the software and what remains. It means preparing the data well for analysis. If the data you are going to analyse has not been analysed and cleaned before the analysis, there is a high chance that the analysis you conduct will be nothing more than "rubbish." So, prepare well.
Failing To Keep The Log Of The Data Analysis for Dissertation
Even if you have a Guinness World Record for memory, do not try to skip keeping the log of your analysis. Always keep a log of your actions, tasks, and decisions about the data. This point is particularly relevant to qualitative data, where the researcher takes many subjective decisions. So, it would help if you recorded why you had combined certain variables or coded the data before the analysis.
Also, Read This: 7 Ways Mixed Method Approach Is Helpful In Dissertation Research
Losing Sight Of The Research Questions
Any data analysis for your dissertation needs to focus on the research questions. It seems obvious advice to give to a student; it is astonishing to see how many researchers lose sight of research questions during the analysis. So, the first thing you should do before running the analysis is to see the research questions you have to answer through this analysis. Do not lose sight of them.
Failing To Be Consistent Throughout The Analysis
Conducting an analysis is hard because you have to deal with lots of files. What do many students do? They fail to be consistent in their analysis. As a huge amount of data is stored in different files to be analysed, the researcher loses consistency. Consistency is important in the data analysis for dissertation. Ensure to name files consistently using the same pattern.
Thinking, "Software Is A Magic Wand."
No single software can run the whole analysis for you and make you extraordinary graphs, charts and tables. As a data analyst, there are a lot of things that you have to do on your own. For example, formatting the tables and graphs to the required format and style. So, it would help if you did not think of software as a magic machine. Undoubtedly, it can help you greatly, but you also have to work on many things.
Also Read This: What Is Discriminant Analysis? An Overview With Guidelines?
Conducting The Data Analysis for Dissertation In A Biased Manner
Bias is not acceptable at all by teachers or supervisors. Bias is a disproportionate inclination of a researcher towards something. Many students choose analysis methods that are not the best fit for the data. They only choose them because they know how to use those methods. It is wrong, and you should never do this in your analysis. Always use appropriate analysis methods.
Ignoring Extraneous Variables
An extraneous variable is one you are not investigating or analysing, but it can harm the overall data analysis for the dissertation. Such variables are bad for your analysis as they can harm the results without your knowledge. It would be best if you never ignored them in your analysis and always kept an eye on them. If you have difficulty recognising such variables, the dissertation writing services can help you greatly.
Using The Wrong Analysis Technique
One of the rarest mistakes, but still, many researchers make it. They use analysis methods that are not the best fit for the data. Doing this can affect the results of your study severely and sometimes does not let you get your degree on time. Therefore, you should never use mismatched analysis methods. Always go for the methods that are the best fit for the problem.
Conclusion
Conclusively, data analysis for a dissertation is a hard job. It requires you to acquire lots of skills before running the analysis. In addition to the skills, knowing the things to avoid is also necessary. Therefore, I have mentioned the top 10 things you must avoid in your analysis. Please read them and craft your analysis based on these 10 things.
