Most importantly, it should point out the limitations as well as future perspective on your findings. The fact is you cannot represent loads of data in a brief thesis. However, this problem can easily be solved with the help of quotes, diagrams, graphs, charts, and formulas. This way you can easily sort out your data dissertation analysis. You can organize your data analysis section in a uniform manner; this requires you to put all sample questionnaires, data sheets, focus groups into the Appendix.
While discussing your data you should focus more on patterns and any inconsistency that have a significant impact on any theme of your subject.
This is probably the most important part of your research when you put on all results collected through data dissertation analysis that is backed by facts and figures. Before you conclude, it is recommended to compare your data with the previous search to point out the similarities and differences with your research as well as identify gaps in the respective field. The quality of my essay was worth the money I had paid. Oscar J London, UK. Excellent work and timely delivery!
Thank you very much, Great job! I was in such a hurry and within 12 deadline, my 8 pages essay was delivered on time. This company came in handy and they delivered my case study analysis on time. Analysis Always remember that no matter what methodology you are using, it must be able to justify the main objective of your research. Quantitative Work This type of data is usually collected in technical and sociological fields where the research requires a considerate dissertation data analysis of statistical facts and figures.
Qualitative Work On the contrary to quantitative data, a qualitative data requires less numerical data to conduct a research. Thoroughness In order to support your viewpoint and its relevance to the literature, you must carefully analyze it with a keen eye.
You should explain and justify these methods with the same rigour with which your collection methods were justified. The overarching aim is to identify significant patterns and trends in the data and display these findings meaningfully. Quantitative data, which is typical of scientific and technical research, and to some extent sociological and other disciplines, requires rigorous statistical analysis. By collecting and analysing quantitative data, you will be able to draw conclusions that can be generalised beyond the sample assuming that it is representative — which is one of the basic checks to carry out in your analysis to a wider population.
This can be a time consuming endeavour, as analysing qualitative data is an iterative process, sometimes even requiring the application hermeneutics. It is important to note that the aim of research utilising a qualitative approach is not to generate statistically representative or valid findings, but to uncover deeper, transferable knowledge.
Believing it does is a particularly common mistake in qualitative studies, where students often present a selection of quotes and believe this to be sufficient — it is not. Rather, you should thoroughly analyse all data which you intend to use to support or refute academic positions, demonstrating in all areas a complete engagement and critical perspective, especially with regard to potential biases and sources of error.
It is important that you acknowledge the limitations as well as the strengths of your data, as this shows academic credibility. It can be difficult to represent large volumes of data in intelligible ways. In order to address this problem, consider all possible means of presenting what you have collected. Charts, graphs, diagrams, quotes and formulae all provide unique advantages in certain situations. Tables are another excellent way of presenting data, whether qualitative or quantitative, in a succinct manner.
The key thing to keep in mind is that you should always keep your reader in mind when you present your data — not yourself. While a particular layout may be clear to you, ask yourself whether it will be equally clear to someone who is less familiar with your research.
You may find your data analysis chapter becoming cluttered, yet feel yourself unwilling to cut down too heavily the data which you have spent such a long time collecting. If data is relevant but hard to organise within the text, you might want to move it to an appendix. Data sheets, sample questionnaires and transcripts of interviews and focus groups should be placed in the appendix. Only the most relevant snippets of information, whether that be statistical analyses or quotes from an interviewee, should be used in the dissertation itself.
In discussing your data, you will need to demonstrate a capacity to identify trends, patterns and themes within the data. Consider various theoretical interpretations and balance the pros and cons of these different perspectives. Discuss anomalies as well consistencies, assessing the significance and impact of each. If you are using interviews, make sure to include representative quotes to in your discussion. What are the essential points that emerge after the analysis of your data? These findings should be clearly stated, their assertions supported with tightly argued reasoning and empirical backing.
Towards the end of your data analysis, it is advisable to begin comparing your data with that published by other academics, considering points of agreement and difference. Are your findings consistent with expectations, or do they make up a controversial or marginal position? Discuss reasons as well as implications. At this stage it is important to remember what, exactly, you said in your literature review.
What were the key themes you identified?
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