Dissertation Analysis

What is a dissertation analysis?

The dissertation analysis also referred to as the analysis chapter of the dissertation, consists of data that has been gathered as a part of the research and the researcher's analysis of the data. The purpose of this chapter is to present the collected data and its analysis in a comprehensive manner which is easy to understand for the readers.

How to do Dissertation Analysis?

There are different approaches to do a dissertation analysis. However, you need to follow certain principles while preparing the analysis. Here are some tips on how to do dissertation analysis:

1. Keep it relevant:

It is a mistake to follow the collected data blindly. It is essential to make sure that the original research objectives inform which piece of data makes it the analysis and which does not. You need to present data that is relevant and appropriate to your goal.

2. Choose the right methods:

It is crucial to choose the methods that are appropriate for both the type of data you have gathered and the goal of your research. You need to justify the data collection method. It should be clear to the readers that your choice of method is significant to the research.

3. Gather quantitative data:

Quantitative data is absolutely essential for scientific and technical research as well as other research work in conducting statistical analysis. With the collection and analysis of quantitative data, you will be able to draw inference which can provide more insights into a wider population.

4. Focus on qualitative data:

Qualitative data is also significant for analytical acuity. Even though it can be a time-consuming task, you still need to carry out a thorough analysis of the gathered data. The analysis of qualitative data is an iterative process. This is essential to unveil deeper, transferable knowledge.

5. Be thorough with your data:

Having data is one thing and using it in your dissertation is different. If you think the data will just speak for itself, you are mistaken. You need to thoroughly analyse the data in order to support or refute academic positions or arguments. You need to acknowledge the strengths and limitations of the data.

6. Use different means of data presentation:

When you are dealing with a large amount of data, you need to consider all the possible means of presenting the collected data. Things like charts, graphs, quotes, and formulae often work in your favour. You can even use tables to present both quantitative and qualitative data.

7. Move the less important data to appendix:

If you use all the data you have collected in the analysis chapter, you may end up presenting a chapter that is too cluttered to read. If you are finding it difficult to organise the data within the text, you are allowed to move the data to the appendix. Keep the most relevant information and statistical analyses in the dissertation.

8. Discuss various aspects of the data:

While discussing the data, you need to identify trends, themes and patterns within the data. You should use various theoretical interpretations and balance the different perspectives based on their pros and cons. It means you need to discuss both anomalies and consistencies, evaluating the significance and impact of each.

9. Highlight the findings:

You need to focus on the essential points that come out of the analysis of the collected data. These are the findings of your analysis. You need to state them clearly with proper reasoning and empirical support.

10. Compare with the existing literature:

While you reach the end of the analysis chapter, you need to compare your data with similar data published by other academics. You need to consider their points of agreement and difference. In this step, you need to reflect on what you aid in the literature review section.

Follow these steps to prepare an insightful, concise data analysis chapter for your dissertation. In case you find the process to be challenging, you have the option to avail the much-needed assistance from MyAssignmenthelp.com.

What is data analysis in the dissertation?

As mentioned, data analysis in a dissertation is one of the most significant chapters in the entire paper. In this chapter, you need to include discussions about the critical analysis and interpretation of figures and numbers. With data analysis, we attempt to find the significance behind the emergence of the main findings.

Moreover, you need to compare primary findings of the research to the findings of the literature reviews, which is deemed important both qualitative and quantitative analyses. If the primary data is absent, the data analysis methods should involve the discussion of common patterns along with the controversies within secondary data.

What are the different methods of data analysis?

There are several types of data analysis methods. The major ones are discussed below: 

• Text analysis:

Text analysis, which is often referred to as data mining, is a method used for discovering a pattern in macro data sets using databases or data mining tools. Researchers use this technique to convert raw data into business information.

• Statistical analysis:

Statistical analysis allows you to demonstrate what happened by using data from the past. It includes collection, analysis, interpretation, presentation, and modeling of data. This type of analysis is classified further unto two different methods:

i. Descriptive analysis: It analyses complete data or the sample of summarised numerical data. It highlights mean and deviation for continuous data.

ii. Inferential analysis: It analyses samples from complete data. Interestingly, this method allows you to find different conclusions from the same data by choosing different samples.

• Diagnostic analysis:

The diagnostic analysis allows you to find out why something happened by studying the findings in statistical analysis. This analysis is useful in finding behavior patterns of data.

• Predictive analysis:

Predictive analysis allows you to find out what is likely to happen by studying past data. It is not exactly an easy method as you need to keep the circumstances and other elements in find.

• Prescriptive analysis:

The prescriptive analysis combines the insight from all the analysis methods mentioned above to figure out which action to choose to deal with a current problem or decision.

There are several other types of data analysis methods in practice. However, most researchers rely on these types of analysis mentioned above.

Why is analysis important in the dissertation?

There are numerous benefits of data analysis. However, the most important ones are discussed below:

a) Data analysis allows you to structure the findings from different sources of data collection like survey research.

b) It is also useful in breaking a macro problem into micro parts.

c) Data analysis also acts as a filter, while you are dealing with a large data-set, in finding meaningful insights.

d) And most importantly, data analysis keeps human bias away from the research conclusion as it uses proper statistical treatment.

Obviously, there are several other benefits of using analysis in a dissertation that you can figure out yourself while you use it for your dissertation. However, if you are finding it difficult to prepare the dissertation or any of its chapters, you can ask for assistance from the expert dissertation writers at MyAssignmenthelp.com.