What is Data Analysis in Dissertation?
Taking a large amount of data to analyze and understand every part of it by gathering and compiling all data to find the similarities in result and pattern followed by them, this is known as data analysis of dissertation. Also to check the rationale of the figure and facts accommodated by it completely.
To prove your research result you should perform dissertation of data analysis even though if you have used figures and facts to explain it. The dissertation of data analysis helps you to provide with scientific support for conclusion and thesis of your research.
How to do data analysis in dissertation
For a dissertation you should do data analysis to produce a better conclusion from the output of your research. The below given are the steps to be followed for doing dissertation data analysis:
For doing data analysis you should have an organized data, to manage it make use of database or spreadsheets. Every data you use should be clearly mentioned and also those dataset should be error free and maintain consistency to ensure integrity.
Choose a methodology for doing your work by considering the data nature of your research problem which you are going to address. If you are working on numerical data then select quantitative method of approach like regression analysis, descriptive statistics or inferential statistics (ANOVA, t-tests). If you are working on qualitative data then choose methods such as content analysis, ground theory or thematic analysis.
To briefly explain about the main points from your dataset, use descriptive statistics to perform it. To do that you have to measure the central tendency such as mean, mode and median and also the dispersion measure such as range and standard deviation. This will provide an overview of collected data at your initial stage.
Perform statistical test to analyze the data and to find similarities between the variables, you can follow either regression analysis or correlation coefficients for doing that. For instance, that way can help you in understanding about the relationship between variables so that you can produce a high quality output with your data.
By doing inferential statistics based on the design of study and nature of data, you can bring a better conclusion about particular population. The tests included in inferential statistics are hypothesis testing, chi-square test, variance analysis (ANOVA) etc.
Do thematic and coding analysis if you are performing a qualitative research. First organize your qualitative data and then analyze them to find their recurring patterns, trends or themes. By making use of examples and quotes you can increase the credibility of your result.
When you are presenting your research work do it in a creative way by making use of visual presentations like graphs, tables and chats which will make others clearly understand about the patterns and trends of your data.
Make sure that the method you chose for data analysis matches the assumptions from statistical test. Check whether your data have undergone any deviation, verify about the variance homogeneity and assess normality. If you feel like violating an assumption then choose alternative methods.
Explain clearly about the process you used for data analysis in the dissertation to brief about the steps you used and the justification of your choice. It will show that the problem you addressed and the objective of research matches with your analysis method.
Find the limitations from the data analysis method you chose and discuss about it in your dissertation. Make sure to ensure transparency in your research findings regarding challenges, constrains or potential biases which could damage their validity.
Demonstrate the similarities between your hypothesis or research question and the result you found from data analysis. Produce a clear cut outline that your research produces a solution for the problem you addressed.
To provide a more general and stable result, ensure to do cross validation techniques. You have to follow this when working in predictive modeling and when handling sample pieces of smaller size to produce a quality result.
Ask feedbacks from your advisors, colleagues or peers in analyzing your data which can help you identify your mistake or the area in which you need improvement for your analysis. This will enhance the robustness in your dissertation.
You should clearly document all your data analysis process to help others who is trying to replicate your work. Make it more transparent and ensure reproducibility by adding details such as software used for coding or any modifications made in data used to it.
You should always recheck your methods, refine analysis and perform more analysis when you find a new problem while doing paper writing process because data analysis is an iterative procedure. By repeatedly checking your paper you can make your findings more reliable.
Maintain the ethical standards of your data analysis while working with human subjects. Get necessary permission and approvals from the respective review board of institution to ensure the privacy and confidentiality of a participant in your whole process.
The research question, nature of data and research design will decide the methods and process of your data analysis. Get necessary help from your instructor if you face any challenges or working on a complex analyses.
Useful Tips for Dissertation Data Analysis
PhD students and doctoral students have to do data analysis on their dissertation to get their degree. But many of the students face difficulties in it, because of lack of training. Here are some tips to crack it:
>> Dissertation Data Analysis Services:
Many professional services are available to provide you with assistance from starting to end, with that you can make an analyzed and organized dissertation.
>> Relevance of Collected Data:
Collect only data’s which are more relevant to the research objective to focus more on research work and to avoid distraction and complication of work.
>> Data Analysis:
Adapt a qualitative or quantitative technique for your research to maintain trends and patterns of data.
>> Qualitative Data Analysis:
While analyzing qualitative data, use methods such as inductive or deductive approach, to produce better findings.
>> Data Presentation Tools:
Make your presentations more attractive and clearly understand by people with the help of tools like charts, graphs and tables.
>> Include Addendum or Appendix:
To make your paper more neat and clear make use of appendix to arrange your extensive data and keep the key quotes and statistical data in dissertation.
>> Thoroughness of Data:
Discuss about the data after completely analyzing it and finding its anomalies, potential errors and strengths to make the output more credible.
>> Discussing Data:
While discussing about data explain about its trends, reliability and patterns.
>> Findings and Results:
Make sure that your research findings and objective of your research matches and they provide logical reasoning.
>> Connection with Literature Review:
Do a comparison check with your research findings and already existing literatures to find similarities and differences between them.
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