5 Data Analysis Tips for Your Dissertation

Writing your dissertation is challenging. Along the way, you’ll have to battle with impostor syndrome, burnout, procrastination, and life getting in the way—all of which lead to delays. Without a crystal ball, it can be difficult to accurately predict when you will finish writing your dissertation and submit it to your supervisor. In every course, some students seem to breeze through writing the entire process and complete their dissertations on time. Knowing what they did right allows you to apply the same strategies when writing your dissertation. You can keep going even when you’re running low on motivation until you complete your final draft.

In this post, we’ll delve into data analysis tips for your dissertation to help you craft solid research results and discussion chapters. Our tips will help you jump through hurdles on the long and challenging road to completing your dissertation.

Before we get into the data analysis tips, let's cover a few basics:
 

What is Data Analysis in a Dissertation?

Data analysis involves examining, sorting, and modeling data. The ultimate goal of data analysis is to uncover patterns and valuable information. Then this supports informed actions and decisions. When analyzing the data you’ve collected for your dissertation, you will rely on logical and statistical approaches to evaluate your data.

Evaluating your data will then allow you to describe and illustrate, predict future behavior or trends and make recommendations based on the analysis you have conducted.
 

Why is Data Analysis Important in a Dissertation?

When writing a dissertation paper, you will need to collect data to help you answer your research questions. Analyzing the data you collect will help you understand and interpret what your data means and its implications. It also helps you to structure your results and discussion chapter. Here are five data analysis tips to help you achieve a high grade:
 

Focus on energy management, not time management

Data analysis involves sifting through the data you collect to get rid of statistical errors, missing data, and inaccuracies so that you can present reliable data. And when it comes to what you need to pay attention to during data analysis, a key yet ignored component of data analysis is the amount of time and energy it takes to conduct data analysis, write a discussion, and finish your dissertation on time. To avoid burnout, go beyond time management. Instead, focus on energy management to help you optimize your energy levels during data analysis.

For example, if you’ve collected data from reluctant respondents, you’ll have difficulty analyzing their responses. No matter how much time you dedicate to analyzing these responses, you will end up frustrated. To get around this, focus on analyzing your responses whenever you’re full of energy to help you work through the difficult parts and move closer to finishing your analysis. You might even consider hiring a data entry assistant to help with organization and collection so that you can spend your time honing your ideas. This saves you time and makes the entire analysis process less challenging.
 

Make sure your data aligns with your research question

By now, you already have an established research question, so you now need to review it and see if you’re still on track with the data you have collected. The data you collect could be either first-party, second party, or third party data.

First party data is the data you’ve collected directly from your respondents. First-party data is usually structured and defined, for example, survey responses.

Second-party data is data that someone else has collected—for example, by census bureaus, world health organization data, and government institutions. It might be less relevant to your research question than your first-party data but is often reliable.

Third-party data comes from different sources and has been collected and put together by a specific source. These could be industry reports of an organization in the field you’re studying, e.g., Gartner and Statista.

So, does the data you have still align with your research question? Did you collect your data from the right sources? Or, do you need to collect more data before you start analyzing it?
 

Clean your data before analysis

You need to clean and sort your data to get rid of unnecessary data, which will muddle up your good data and cause confusion during analysis. Cleaning your data makes it easier to organize, manage and store it well in case you need it in the future, even after you’ve analyzed it.

Cleaning your data involves doing any (or all) of the following things:
 

1. Remove duplicates and errors from collecting data from third-party sources. You’re also likely to find outliers in your data, so for consistency, eliminate outliers too.
2. Remove unwanted data that is not relevant to your research question. This includes biased and irrelevant responses that will slow down your analysis.
3. Get rid of typos, layout issues, and general poor organization of your data.
4. Fill in the gaps in your data. For example, if you're conducting qualitative analysis, you rarely have a fixed sample size, so you may choose a smaller sample that won’t be viable for analysis.
5. Conduct exploratory analysis to identify trends and characteristics and how they relate to your hypothesis.

 

Analyze your data thoroughly

Depending on the data you have, you’re going to use different methods to analyze it. This will depend on the kind of data you have, your preferences, and any other requirements you’ve received from your supervisors.

Here are some of the methods you’ll use to analyze your data:
 

1. Descriptive analysis: explain what has already happened in your samples.
2. Diagnostic analysis: explain why something happened on your samples.
3. Predictive analysis: predict what is likely to happen in the future based on the data you have from your sample.
4. Prescriptive analysis: make recommendations based on your findings from your sample.

Thoroughly analyzing your data means that you won’t only present graphs or charts without relevant discussions to accompany them. Ideally, in your results and discussion chapter, you need to describe your data and provide context for each graph or chart. Make sure you paraphrase, rather than copy, this text when including them in your final dissertation.

Your qualitative analysis needs to focus on specific themes, not those you identified during your literature review, but rather those that your data presents. Here, you want to rely on inductive intelligence to identify the themes you want to focus on.
 

Know when to stop data analysis

Once you’ve answered your research hypotheses, it's time to stop analyzing your data. However, there’s always a tendency to go back to your data and dig deeper to identify if there are more differences or correlations between different variables, even to the extent of creating a new set of hypotheses to test. While that sounds like a great initiative, you’re likely not an expert in statistics. You're limited to several data analysis methods, most of which you’ve likely learned about in class.

Instead of digging deeper into your data, spend more time fine-tuning your analysis and findings. To do this, consider reaching out to a peer or an advisor to review your data and findings. You can use document collaboration tools to work asynchronously with your advisors when they’re reviewing your work.
 

Conclusion

How well you can analyze your data will make or break your dissertation. Proper data analysis means that your results and discussion chapter will be solid enough to defend successfully during your presentation.

The tips we’ve discussed above will help you work through data analysis effectively and save time.

If you’re looking for professional help with your dissertation, reach out to us, and we will help you work through the challenging aspects and help you to complete it on time.