When writing your thesis, the process of analyzing data and working with statistics can be pretty hard at first. This is often true whether you’re using specialized data analysis software, like SPSS, or a more descriptive approach. But there are many guidelines you can follow to create things simpler. Data analysis is how researchers go from a mass of data to meaningful insights. There are many different data analysis methods, depending on the type of research. Here, experts of
dissertation writing services UK have discussed some of the few;
Relevance:
Do not blindly follow the data you have collected; make sure your original research objectives inform that data does and doesn't make it into your analysis. All data presented should be relevant and appropriate to your aims. Irrelevant data will indicate a lack of focus and incoherence of thought. In other words, it's important that you show the same level of scrutiny when it comes to the data you include as you did in the literature review. By telling the reader the academic reasoning behind your data selection and analysis, you show that you are able to think critically and get to the core of a problem. This lies at the very heart of higher academia.
Quantitative Work:
Quantitative data, which is typical of scientific and technical research, and to some extent social science and other disciplines, requires rigorous statistical analysis. By collecting and analyzing quantitative data, you will be able to draw conclusions that can be generalized beyond the sample (assuming that it's representative – that is one among the basic checks to carry out in your analysis) to a wider population. In social sciences, this approach is sometimes referred to as the “scientific method,” as it has its roots in the natural sciences.
Qualitative Work:
Qualitative data is generally, but not always, non-numerical and sometimes referred to as ‘soft’. However, that doesn’t mean that it needs less analytical acuity – you continue to need to carry out thorough analysis of the data collected (e.G. Through thematic coding or discourse analysis). This will be a time-consuming endeavor, as analyzing qualitative information is an iterative process, sometimes even requiring the application hermeneutics. It is important to note that the aim of research utilizing a qualitative approach isn't to generate statistically representative or valid findings, but to uncover deeper, transferable knowledge.
Analysis:
It is important that you use methods appropriate both to the type of data collected and the aims of your research. You should explain and justify these methods with the same rigor with which your collection methods were justified. Remember that you always have to show the reader that you simply didn’t select your method haphazardly, rather arrived at it as the best option based on prolonged research and critical reasoning. The overarching aim is to identify important patterns and trends in the data and show these findings meaningfully.
Appendix:
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 arrange within the text, you may 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.
Other Important Guidelines Are:
Findings:
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.
Discussion:
In discussing your data, you will need to demonstrate a capability to identify trends, patterns and themes within the data. Think about various theoretical interpretations and balance the pros and cons of those 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.
Relation With Literature:
Towards the end of your data analysis, it's advisable to start comparing your data with that published by different 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's important to remember what, exactly, you said in your literature review. What were the key themes you identified? What were the gaps? How will this relate to your own findings? If you aren’t able to link your findings to your literature review, something is wrong – your data should always fit with your research question(s), and your question(s) should stem from the literature. It's very important that you show this link clearly and explicitly.
