Tips for Writing a Dissertation Data Analysis

Tips for Writing a Dissertation Data Analysis

Top 10 Tips for Writing a Dissertation Data Analysis


When you have collected data, it is important that you consider reviewing your research objective.

This is important for the sake of using data that is relevant for your analysis.

Sometimes you will find yourself blindly using the data you have collected which in some cases you may analyse irrelevant data.

When you are analyzing your data, it is important that you ensure you have relevant and appropriate data to achieve your objectives.

presenting data that is not relevant will show a lack of focus in your work.

You are expected to show the same level of exploration when analyzing data that you will include in your dissertation as you did in the literature review.

Make sure the reader understands your academic reasoning concerning the data collection and analysis you took.

The ability to show the reader you know what you are highlighting shows that you have thought critically concerning your data analysis to ensure you have dealt with the issues of concern.


For analysis purposes, it is important to ensure you have used the appropriate methods for the type of data collected and the aims of your research.

You should be able to clearly and explicitly explain and justify the methods you have used.

It is also important that you provide critical reasoning on why you chose the method that you are using and not any other.

This will show the reader that you did not randomly choose a method for the sake.

The importance of analysis is to ensure you have identified the patterns and the trends in the data so that you can be able to provide meaningful finding.

3.Quantitative work

Quantitative data requires a meticulous statistical analysis.

Quantitative data is used in technical and scientific research.

After the collection and analysis of quantitative data, you will be able to draw conclusions that can be generalized to a wider population and not just a sample.

In social science quantitative data is also referred to as a scientific method.

This is because it has its roots in the natural sciences.

4.Qualitative work

Qualitative data is non- numerical and sometimes known as soft.

The fact that qualitative work is non-numerical does not mean it requires less analysis.

You are expected to carry out an analysis of the data you have collected in qualitative work.

You can do this through discourse analysis or thematic coding.

Analysis of qualitative data can be time-consuming as the process is involving.

Sometimes analysis of qualitative data requires hermeneutics applications.

As compared to quantitative data which is to generate statistical representative and valid findings the qualitative approach aims to provide a deeper and transferable knowledge.


Just because you have collected data it cannot speak for itself.

Most people think that after the collection of data they have done a qualitative study.

Data collection is not sufficient for analysis.

You should be able to thoroughly analyze all the data so that you can be able to have an academic position.

The importance of data analysis is to help you to have a perspective concerning your research.

You will be able to remove any errors and biases that are in your data when you analyze it.

You will also be able to understand limitations and the strength of the data you collect to show academic credibility.

6.Presentational devices

When you have large volumes of data it can be difficult to present it.

For you to be able to present this kind of data it is important that you look for ways you can do this.

For example, you can consider using graphs, diagrams, chartsm quotes and even formulas to present your data.

It is important that you ensure the kind of presentation you take is applicable in the specific situation you are at.

Whether you are doing qualitative or quantitative analysis you can consider using a table because they are excellent ways to help you present data.

When presenting your data it is important that you ensure you have the reader in mind.

You may have yourself in mind and what seems clear to you may not be clear to the reader who is less familiar with your research.


If you have relevant data that is not fitting to your analysis chapter it is important that you consider moving it to the appendix.

The appendix is a section where you are expected to put your sample questionnaires, transcripts of interview, data sheets and also the focus groups.

Only the most relevant information should be used in the dissertation.

The rest of the data and information should be indicated in the appendix.


When discussing your findings, identify the trends, the patterns and the themes in the data.

It is important to ensure you have balanced the advantages and disadvantages of a different theoretical interpretation.

Consider discussing the consistencies and the anomalies and ensure you have done an assessment of their significance and impacts of each.

In discussing the interviews it is important that you consider using representative quotes.


It is important to highlight the important points that you got when you analyzed your data.

Clearly state these findings by having 88 argued reasoning and empirical backing.

10. Relation with literature

As you complete your data analysis it is important to compare the data you collected with the ones that are published by other authors and academics.

While comparing it is important that you point out where you have agreed and where you have differences.

Get to know whether there is a consistency between your data and other literature or you have a controversy or imagine your position.

It is important that you discuss the reasons and their implications.

While doing this comparison it is important that you consider what you had highlighted in your literature review.

You should remember the themes and the gaps that you highlighted and get to know how this relates to your findings.

The inability to link up your findings with a literature review shows there is something wrong with your data.

When showing up the link between your findings and literature should make sure it is clear and explicit.

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