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A Guide by Harvard Graduate to Create Dissertation Data Analysis Plan

Data Analysis Plan

Writing a dissertation is a tedious task that demands a lot of time, commitment and skills. Many students feel stuck when it comes to creating a dissertation data analysis plan. As a Harvard graduate, I have enlisted some helpful tips to help you easily get through this difficult process!

What is a Dissertation Data Analysis?

The process in which you understand, gather, collect, compile and process a large portion of data is termed dissertation data analysis. You identify common patterns in the results and critically examine all the facts and figures to sort out the rationale behind the results you get. The analysis provides scientific assistance to the dissertation and the research conclusion.

Importance of a Dissertation Data Plan

If you dread failing your dissertation, you should create a data analysis plan to help you systematise data, improve your vision of the research problem, and facilitate the research process. It will also help you improve your problem-solving skills, make a better conclusion and showcase your analytical skills to the supervisor. I have been through this and am aware that analysing one’s work is tough, especially if it’s about reviewing your dissertation – that’s why a dissertation plan becomes necessary!

Here are a few beneficial tips that will help you prepare and present your data analysis plan in a better way:

  1. Systemize the Data
  2. Structuring the Analysis
  3. Avoiding the Jargon
  4. Coherence of Data
  5. Keeping it Focussed
  6. Keeping it Simplified

Systemizing the Data

The first thing you need to do while writing a dissertation data analysis plan is systemising your data. You will need to gather your thoughts to determine which parts of the relevant data are necessary for your dissertation and which are nothing more than clutter. You will need to assess and analyse the true value of information or content to be used in your dissertation. Remember, it is quality above quantity! It doesn’t matter how many words you’ve written; the authenticity of the information you provide matters!

Structuring the Data

While making a dissertation data analysis plan, you will need to structure the data well. It would help if you kept in mind that everything you present in your dissertation must be clear to you and your reader. No matter how technical or complex your subject is, you should divide it into concise and elaborative sections. Information presented in sections that follow a perfect order is easy to grab and understand and saves you from cluttering your ideas. Each section should elaborate on the main points of the research question or problem, and every chapter must be written on a new aspect of the topic to avoid repetition.

Avoiding the Jargon

I cannot emphasise enough on this point: avoid the jargon at all costs!

While crafting your dissertation data analysis plan, keep it in my mind to present it as simply as possible. There should not be any part of the dissertation that you or your reader will understand. Make sure to avoid using complex terms, jargon and technical sentences. If technical information is required to be added, provide the reader with an explanation of complex terms or describe the analysis process in detail. There must not be any confusing material used in your dissertation – everything should be brief, relevant, and elaborate.

Ensuring Coherence of Data

This stage is where most students commit a fatal mistake, decreasing their chances of scoring higher in their dissertations. After you have collected, compiled and systemised the data relevant to the topic of your dissertation, you should stop for a while and analyse it on your own. Ask yourself questions like the following:

  • Is there any relevance between my collected data and the research problem?
  • Are the key points of my systemised data effective enough to be used in the final thesis papers?
  • Does my collected data answer my research questions in any way?
  • What’s the correlation between the cause and effect per the terms of your dissertation topic?
  • Is my data well-structured?

You will obtain a definite understanding of where you stand in the data collection and data analysis process. If you do this part well, the data used in your dissertation will be well-presented, coherent and easy to follow!

Keeping the Data Focussed

This point is an extension of step 4 in its original meaning. You have to think about the broader picture. Analysing the data with a myopic vision will only jeopardise your journey of writing a perfect dissertation data analysis plan. Consider the relevancy of the data you have collected and determine if it stays focussed on the research topic. Thinking about the main points of the dissertation, both in theory and praxis will help you greatly in this regard.

Keeping the Information Simplified

You are almost done with your dissertation data analysis plan when you reach this step! Everything must be pretty simple by this step; all you have to do is keep it like that. There isn’t any need to tamper with the simplified and systematic information already present in the dissertations. Let it be – don’t over analyse or overthink it!

Quick Tip:

It becomes pretty complex to perform all this hectic process and not get drained during it. Hire dissertation proposal writing services online to save you from the worries of writing an impeccable dissertation data analysis plan. You might be new to the dissertation writing process, but such academic services have vetted experts on board to assist you better with your requirements!

Bottom Line

It is a widely accepted misconception that the data you present is self-explanatory – it’s not! A majority of students provide the data and relevant citations and think it’s enough and does not need further explanation. When you craft a dissertation data analysis plan, you try to demonstrate the ideas and critically evaluate each aspect of the main points. Always remember to discuss and describe the weaknesses and strengths of the data you use to enhance the credibility of your research!

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