Data Analysis: Do’s and Don’ts

In order to answer your research question, you must be able to do data analysis. Data analysis isn’t just about gathering data, which is its initial part. Data analysis includes the idea that what you do with the data collected and how do you come to findings. It isn’t a child’s play to go for data analysis, which is different in its nature from the whole dissertation project, involving data analysis techniques and statistical representation of data. Below we are going to discuss the do’s and don’ts while executing data analysis.

  • Keep it Relevant to Research Objective

You should never forget the reason why you started your research project. Neglecting your research objective while data analysis would be a big blunder. You must keep in view that your data analysis chapter should be aligned with the research objective.

  • Skim your Data

While collecting data, one gathers data from maximum and different people to get the best results out of it. It is not necessary that all the data collected must be used within the analysis section. There would be data which wouldn’t fall under the given research question or would be an outlier. You should make sure that you always skim your data before actually start working on it. If you want to know more about dissertation and its data analysis, you can search for Dissertation primary data services.

  • Keep it Focused

You should be aware of the misconception which a lot of researchers have their mind while undergoing data analysis, that is the data analysis chapter is being driven by the researcher and he can come up with his opinions about it. Well, that is not the case with data analysis. A free research doesn’t mean that you impose all your irrational and impartial opinions as data analysis. This is true that you need to guide your audience till the start that what would be the possible outcomes but that doesn’t mean that you publish something which portrays like a wish, not a fact.

  • Know your Research Type

You can’t be going for using same data analysis techniques for different research types. Thematic and descriptive analysis are being used for qualitative research. This type of research further uses interview methods and focus group for deep insights into the research questions. This type of analysis would be by the researcher without use of any software. Students will come to conclude analysis based on their own understandings which should be logical and rational in their approach.

SPSS is used for analyzing data having research type of quantitative data. This type of research includes numbers and stats for its outcomes. Students aren’t required to conclude results from their own understandings, rather the software will bring results for you provided that you have input correct information. 

  • Look For Assumptions Before Analyzing 

Before doing data analysis, one should always look for the possible assumptions. This will refrain the researcher from any time-wastage. In addition, it will guide the researcher itself that what the possible results could be. Invest enough time in assumptions as if you get results in contrast of the assumptions, you might have to start all this step again.

  • Getting Few Weird Results

You need to give yourself the liberty to fail in analyzing data. This is not a compulsion that you have to be bang-on the first time you are searching for data analysis. Getting failed for a few times while conducting data analysis is a normal practice and shouldn’t be a thing to get worried. Even a minor mistake can cause you to get inappropriate results. In such a case, review your data analysis techniques and consult your supervisor in order to get right outcome.


In order to execute a smooth data analysis, you should make sure that you align your data analysis section with the research objective. In addition, look for the research type which is suitable for your research project and avoid using irrelevant data as it can distract you a great deal.


What do you think?


Written by Whitney Hart

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