Critical Research Analysis – 10 Rules to Follow When Working on Undergraduate Dissertation

Critical Research Analysis

Introduction

The critical research analysis is not an easy process. Your undergraduate dissertation will have no value if you are unable to critically analyze the data you have collected. To do this analysis properly, you need to know the proper rules and guidelines. Unfortunately, many students do not know any proper guidelines for research analysis. That is why; they end up making big mistakes in the analysis and discussion chapter of their dissertation. This article is here to solve your problem. It has given ten rules for critical research analysis that you need to follow when working on an undergraduate dissertation.

Relevance

The first rule for critical research analysis is to use relevant information. It is important that you do not use all the data which you have gathered. Try to ensure that you keep in mind the objectives of the research to determine which data to use in the analysis. It is important that the data you use in your analysis must be appropriate and relevant to your research objectives. Using irrelevant information will show you the incoherence of thoughts and lack of focus. In simple words, it is crucial that you demonstrate a high level of scrutiny when it comes to using data in the analysis. You need to tell your readers the academic reasoning behind the selection of data. This will show your ability to think critically. It lies at the heart of critical analysis.

Analysis

Another rule that you need to follow for critical research analysis is that you use methods which are appropriate to the type of your collected data and the research objectives. You must justify and explain the methods in your analysis. It is important to remember that you should show the audience that you did not choose your analysis methods in a haphazard way. You should show that your methods were the best choice regarding your research aims and objectives. The main in the analysis is to identify important trends and patterns in your work.

Quantitative work

Quantitative is usually used in technical and scientific research. It is sometimes also used in sociology and other subjects. This type of data needs strong statistical analysis. By collecting and analyzing this data, you would be able to come up with meaningful findings which could be generalized beyond the selected sample. If you are facing any issues in drawing a meaningful conclusion in your dissertation, then you should look buy dissertation online.

Qualitative work

Qualitative work is usually non-numerical in nature. It is sometimes referred to as soft work. However, it does not mean it needs fewer analytical points. It is important that conduct a proper analysis of the whole data you have gathered through discourse analysis and thematic coding. It can be a very time-consuming process as you are required to uncover transferable and deeper insights from the collected data.

Thoroughness

In critical research analysis, the data will never speak for itself. It is a very common mistake that students do when they just state their data and assume that it will sufficient. It is important that you analyze the whole data thoroughly. You need to refute or support academic positions. It is best that you show critical perspective and complete engagement in all areas of your analysis. It is also important that you talk about the strength and limitations of your research as well to show your academic credibility.

Presentational devices

Presentation is very important in critical research analysis. It could be highly difficult for you to show the large volume of data in a presentable way. To address this issue, you need to use various means of presentation such as graphs, formulas, quotes and diagrams; all are unique ways to show certain situations. You can also use tables to present data in a succinct way. It is important to keep your audience in mind while presenting your data.

Appendix

You need to pay close attention to the appendix chapter. You may find that your analysis chapter might be cluttered. However, you might not want to remove a large amount of data which took a long time for  the collection. Sometimes, data is relevant, but they are very difficult to organize within the analysis chapter. In this case, you should move the data into an appendix. Focus groups, transcripts of interviews, sample questionnaires and data sheets must be placed in the appendix. Only the most relevant information and data should be used in the analysis section itself.

Discussion

While discussing your data for critical research analysis, you need to show your ability to identify themes, patterns and trends in the collected data. It is important that you use various theoretical interpretations and balance the positive and negative aspects of various perspectives. You need to discuss various anomalies and also consistencies by properly assessing the impact and significance of each. When you are using an interview, it is important to ensure that you include representative quotes in the discussion chapter.

Findings

The purpose of critical research analysis is to draw a meaningful conclusion. You need to write all the essential points which can emerge from the analysis of the data. You must need clearly state these findings and support them with empirical backing and tightly argued reasoning.

Relation with literature

The next thing you need to consider for your critical research analysis is the relation with the literature. At the end of the analysis, you should compare the data with resources published by other authors to show the points of difference and agreement. It is important to note that your analysis should always fit with your research questions. It is also important that you show that link explicitly and clearly,

Conclusion

The above guide can help you write a perfect critical research analysis for your undergraduate dissertation. You do not have to overthink while analyzing the data. All you need to do is to follow the above-given rules, and you are good to go. Good Luck!

By John Sonron

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