The PhD dissertation is more and less similar to undergraduate and master’s dissertations, but what it demands from the PhD scholar is originality. The majority of the students think that the doctorate dissertation is all about complexity. It is true, to some extent, but even a simple dissertation will be accepted by the committee if it has no parallel. The main point to be pondered here is how one can strive for originality. In dissertation or academic research, originality can be ensured by using several ways, but the one that is most important is by collecting your own data and analyzing it on your own. The data analysis is a bit tricky. Thus, students often ask for expert help. Therefore, this article will help you conduct the dissertation data analysis by following six simple steps. Let’s get started:
Thank you for reading this post, don't forget to subscribe!Dissertation Data Analysis- A Brief Introduction
Typically, dissertation data analysis is a process of transforming, cleaning, modelling, and reducing data for making important research decisions. Basically, when someone collects the data from the participants or the already available scholarly work, it is mostly available in the widely scattered form. This data is considered raw data as it cannot give important information to solve a scientific mystery. Thereby, applying some data reduction, summarizing, and analyzing methods is necessary to extract the useful important from the collected data and to observe the trends in data.
Data analysis is broadly classified into two methods. The first is the statistical analysis, while the other is the non-statistical analysis. What will be the exact steps to conduct the analysis on your dataset are largely determined by the type of analysis or tools you decide to conduct analysis. However, whether the data can be analyzed by using statistical analysis or a non-statistical method depends on the type of data you have collected so far. (Statically analysis is used for the digital form of data and non-statistical analysis for the textual form of data). Moreover, if you conduct the right analysis by selecting the tools that give you precise results, then getting your work noticed by the research committee will become easy.
Steps To Plan And Write A Doctorate Dissertation Data Analysis:
For planning and writing a doctorate dissertation data analysis, the foremost important thing to estimate is the availability of time. Let’s suppose you do not find enough time to commit mistakes and revise them periodically; then, instead of following these six steps, you must place an order at the PhD dissertation writing service. Their experts will charge only a few pennies from you and, in return, serve you with the best dissertation data analysis chapter. In contrast, if you come to know that you can easily spare time, then you can follow the following step-by-step guide:
Make a wise decision about the type of data that seems most appropriate to conduct a unique study
Whether you are conducting analysis for the doctorate or master’s analysis, you must first make a plan. This plan must contain clear-cut answers to a few very simple questions, such as:
- What kind of data can best explain the situation under discussion?
- Whether the selected data type is the best (you must collect theoretical evidence)?
- Whether you are ready to take on new data analysis challenges by selecting a unique form of data?
Select the best tool to collect the desired form of data
This step is also a part of the planning phase of dissertation data analysis. It asks you to do preliminary research to get your hands on the right tool for data collection. Without collecting data, no one can complete the analysis; moreover, there are several ways to collect the data. Traditionally, qualitative data is collected by structured questionnaires, observations, interviews, and group discussions. Contrary to this, closed-ended questionnaires, experiments, and surveys are used to collect the quantitative data. Additionally, searching the already published article is one of the secondary methods of data collection.
Collect the data by following your plan
It is the common step in the data collection as well as the data analysis process. Actually, it is the main step in the data collection process and this step is part of the planning phase of the data analysis process. Before dissertation data analysis, you must have hands full of collected data so other decisions, such as analytical tools or technique selection, can be done appropriately.
Select the method or tool for dissertation data analysis
Again, it will be your data that decide which tool or method of dissertation data analysis can be used. For example, if you set an in vivo experiment to collect quantitative data, then choosing any sophisticated statistical analysis package such as SPSS or MATLAB is the main task to be completed at the fourth step of dissertation data analysis.
Run the analysis either by using mathematical formulae or an AI tool
For instance, if you are dealing with a small data set, then applying different mathematical formulae to the raw data is possible. But if the dataset is too large to be handled effectively, then getting your hands on some AI tool must be preferred. If you select the SPSS, then you must enter all the collected data in the datasheet by labelling all the variables appropriately LinkedIn profile writing service; it will provide you with all the possible results.
Write your finding in the form of tables, graphs, and text
At this step, you must proofread and revise your analysis and click on the plot graph option in your selected analytical tools. The resulting graphs, tables, or charts will help you write the dissertation analysis by adding a brief description.
Final thoughts:
In a nutshell, dissertation data analysis is a straightforward process that needs PhD scholars to make a few vital decisions at the very beginning of the process, called the planning phase of analysis. However, in light of these decisions, you can run the analysis and write it in chapter four as it is. In the end, it is highly recommended to all young analysts first select the type of analysis and then search for its specific step-by-step guide to ending their research on valid consequences.
