If you are new to the world of research and dissertation writing, writing the data analysis section can be very daunting. Whether it is qualitative data analysis or quantitative, the level of complexity and challenges are the same. The results of dissertation data analysis can be very disastrous if you get just one thing wrong in this important section. The reason is that it guides the readers about the completeness and effectiveness of your research by analysing how it answers the defined research questions.
Writing a dissertation data analysis is a whole new challenge for you because you have not worked on such a thing previously. You need to communicate the findings and their meaning very carefully. However, your knowledge about writing this section is so little that you could not get it done with flying colours. Therefore, today’s article is about writing a data analysis chapter. Here, I will list the top 10 best tips for writing this section. So, let’s get started.
10 Best Tips For Writing A Data Analysis Chapter
Dissertation data analysis is the process of understanding, analysing, and combining large amounts of data. Identifying the common themes and patterns present in the dissertation data is also a part of this section. The analysis provides scientific support to your collected data. Nevertheless, a brief description of the top 10 and the best tips are as follows:
Relevance of the data
Do not bombard your analysis section with data. Of course, the data is the most basic element for a good analysis, but it is the relevant data. You should not blindly follow and include every data you have collected in your dissertation. Make sure that only the data which relates to your research objectives and aims goes into the analysis. Adding irrelevant data represents a lack of focus on the research. Therefore, you must avoid it. However, in case of the non-availability of the exact data, you must opt to buy dissertation online.
Appropriate analysis methods
As described earlier, dissertation data analysis is about analysing and compiling the analysis results, so it is important that you use appropriate analysis methods. As a researcher, you must explain and justify the methods you have used in the analysis of data. It is important to do this because you do not want your reader to think that you have chosen the methods haphazardly. So, use appropriate analysis methods and justify them.
Quantitative data analysis
Normally, the data is of two types; qualitative and quantitative. Quantitative data is data that deals with numbers. If your dissertation data is quantitative, then know that it cannot be analysed using thematic analysis techniques. To analyse this data, you must use some statistical techniques like ANOVA tests, regression analysis, or chi-square tests. These analysis methods explain the meaning of the quantitative data well.
Qualitative data analysis
Not all the time, do you work with numbers or quantitative data. Sometimes, as a researcher, you have to deal with words, expressions, and other non-numeric data. This type of data is commonly referred to as soft data, but this does not mean it requires less attention. You still need to carry out an extensive and thorough dissertation data analysis to reach a conclusion. Some analysis methods commonly used for qualitative data are thematic analysis, content analysis, grounded theory etc.
The thoroughness of the data is the key
The thoroughness of the data in the data analysis section is the most important. Believing that your data speak for itself is not the way to go. Not data speaks for itself. It is you, the researcher, who gives it meaning by explaining it. Therefore, you should thoroughly analyse all the data that you want to use as a source for approval or disapproval of the hypothesis. Along with this, you must also acknowledge the limitations of your data wholeheartedly.
Make use of presentational devices
The use of presentational devices like bars, charts, and graphs in the dissertation data analysis section gives the analysis section a decent look. Just numbers and text make it boring for the readers. As a researcher, you must consider using all the possible means to make your data analysis section look beautiful and attractive to the eye. If you do not know how to create charts and graphs, then take the service of a dissertation writing service UK. Such a service knows how to make this section appealing to the eyes of a reader.
Put the excess data into an appendix
Sometimes, the data is so much that your analysis section looks cluttered with data. Such a thing greatly reduces the readability of your dissertation. So, you must not forget to put the excess data into an appendix if it is relevant. Some data sheets and questions that could not make it to the dissertation data analysis section can come into this section.
Discussing the data
The analysis section does not only include reporting of the analysis results. It is also about discussing the results in light of the research questions. Consider various theoretical interpretations of your data analysis and discuss its pros and cons. It is important that you discuss the anomalies in the analysis section because no analysis is perfect enough to have no anomalies in it.
Talk about the major findings
A perfectly conducted data analysis gives birth to some of the unexplored trends and patterns of the research problem. So, what are the essential themes that have emerged as a result of the data analysis? This question is important, and you must discuss the major findings in the analysis section carefully and precisely.
Relation to the literature
Lastly, when you are at the end of your data analysis section, it is important that you try to relate your analysis results with those published in the past. Consider the point of agreement and disagreement when you do this. If your findings are in line with those published in the past, it is good. If not, then discuss the potential reasons.
Conclusion
Writing a dissertation data analysis requires dedication, sound knowledge of the analysis methods, and proper planning. The top 10 and the best data analysis tips mentioned above can help you analyse the data effectively. So, read the article from start to end and let your analysis speak for its effectiveness and perfectness.
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