Top 10 Factors and Approaches Which Affect Your Dissertation

Top 10 Factors and Approaches Which Affect Your Dissertation

Choosing a subject for your thesis, dissertation, or research project is the first step in ensuring that your study runs as smoothly as possible. We provide the best dissertation help from assignments sky when selecting a topic, keep the following factors in mind:

  1. The criteria of your institution and department
  2. Your areas of expertise and interest
  3. The significance in terms of science, society, or practice
  4. Data and information sources are readily available.
  5. Your dissertation’s duration and timeline

It might be difficult to know where to begin if you do not have dissertation ideas. To start limiting down your options, follow these steps.

1.   Relevance

Do not just follow the data you have gathered; ensure that your original research objectives guide which data makes it into and which does not make it into your analysis. All data supplied should be useful and pertinent to your objectives. Irrelevant facts will show a lack of attention and thinking incoherence. In other words, you must apply the same amount of attention to the data you provide as you did to the literature study. By explaining the academic rationale behind your data selection and analysis to the reader, you demonstrate your ability to think critically and get to the heart of a problem.

2.   Observation

It is critical that you choose procedures that are appropriate for the type of data collected as well as the goals of your study. You should describe and defend these techniques with the same rigor as you explained your collecting methods. Remember that you must always demonstrate to the reader that you did not select your strategy arbitrarily, but rather that it was the ideal decision based on extensive study and critical reasoning. The main goal is to detect important patterns and trends in the data and to present these results in a relevant way.

3.   Quantitative research

Quantitative data, which is common in scientific and technological studies, as well as social and other fields, need careful statistical analysis. By gathering and analyzing quantitative data, you will be able to make findings that can be generalized beyond the sample (provided that it is representative – which is one of the fundamental tests to do in your research) to a larger population. Because it has its roots in the natural sciences, this technique is frequently referred to as the “scientific method” in the social sciences.

4.   Work of high quality

Qualitative data is often non-numerical, although not necessarily, and is frequently referred to as soft data. However, this does not imply that it demands less analytical skill — you must still conduct a comprehensive study of the data obtained (e.g. through thematic coding or discourse analysis). This may be a time-consuming endeavor since analyzing qualitative data is an iterative process that sometimes necessitates the use of hermeneutics. It is vital to highlight that the goal of qualitative research is to unearth deeper, transferrable information rather than to create statistically representative or reliable conclusions.

5.   Extensiveness

The evidence never just “speaks for itself” students frequently provide a selection of quotes and assume this is adequate – it is not. Rather, you should extensively analyze the evidence that you want to utilize to support or refute academic viewpoints, exhibiting comprehensive participation and critical perspective in all areas, particularly with regard to potential biases and sources of inaccuracy. It is critical that you disclose both the limitations and strengths of your data since this demonstrates academic legitimacy.

6.   Presentational tools

It might be challenging to express enormous amounts of data in an understandable manner. Consider all conceivable ways of presenting information you have collected in order to meet this issue. In specific cases, charts, graphs, diagrams, quotations, and equations all have distinct merits. Tables are another great approach to show data, whether qualitative or quantitative, in a concise fashion. The most important thing to remember is to always keep the reader in mind while presenting your material – not yourself. While a specific arrangement may be apparent to you, consider whether it will be clear to someone unfamiliar with your study. Quite often, the response will be “no,” at least for the first draught, and you will need to reconsider your presentation.

7.   Appendix

You may see that your data analysis chapter is becoming congested, but you are hesitant to reduce the amount of data you have spent so much time collecting. If data is relevant but difficult to organize inside the narrative, consider moving it to an appendix. The appendix should contain data sheets, sample questionnaires, and transcripts of interviews and focus groups. Only the most pertinent information, whether statistical analysis or statements from interviewees, should be utilized in the dissertation.

8.   Deliberation

You will need to demonstrate your ability to recognize trends, patterns, and themes in your data while presenting it. Consider numerous theoretical interpretations and weigh the benefits and drawbacks of these distinct points of view. Discuss anomalies as well as consistency, weighing the importance and impact of each. If you use interviews, make sure to incorporate representative quotations into your discussion. We provide the best commerce assignment help At assignment sky

9.   Results

What are the key points that arise from your data analysis? These conclusions should be communicated clearly, with their statements supported by tightly articulated logic and empirical evidence.

10.                  Relationship with literature

Toward the end of your data analysis, begin comparing your data to that published by other academics, taking note of points of agreement and disagreement. Are your findings in line with expectations, or do they support a contentious or fringe viewpoint? Discuss the reasons as well as the consequences. It is critical at this point to recall exactly what you mentioned in your literature review. What were the main themes you discovered? What were the omissions? What does this have to do with your own findings? Something is amiss if you can’t connect your findings to your literature review — your data should always match with your research question(s), and your question(s) should come from the literature. It is critical that you demonstrate this relationship clearly and precisely.

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