Writing a dissertation can be tough because you do must collect information and analyze it the right way to make the text more attractive and presentable to the readers. Data collection is a systematic process of gathering observations or measurements. Whether you are performing research for business, government, or academic purposes, data collection allows you to gather firsthand knowledge and original insights into the research problem.
While working on the dissertations, your aims and methods may differ but the overall process of data collection remains the same. Before you begin collecting the data, you will have to keep the aim of research in mind, the type of data that you will collect as well as the methods and procedures you will use for the collection, storage, and processing of data. Collecting high-quality data is relevant to your purpose; you must follow these steps shared by experts of dissertation writing services to collect the best data:
- Define the aim of the research
- Choose the data collection method
- Plan the data collection procedures
- Collect the data
- Frequently asked questions about data collection
To gather data, you have to choose first what type of data you want and what purpose it will serve. A dissertation is no easy task and students need to work hard to come up with the most relevant information that delivers the most efficient results. There are two options; qualitative or quantitative data. Data can be gathered through observations, interviews, focus groups, or with surveys, tests, and existing databases. Data can be grouped into four main types based on data collection methods. They include:
When collecting information for a dissertation, you need to first all develop a good idea. Dissertation ideas can come from many places, depending on the subject and the type of research you are conducting. After you have an idea, it is best to develop a good question. A good question can be answered by the research and for this purpose; any of the research methods could be utilized.
It is important to 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 consistency with which your collection methods were justified. It is important to remember that you will always have to show the reader that you did not choose the research method haphazardly but arrived at it as the best choice based on research and critical reasoning. The aim is to identify significant patterns and trends in the data and display these findings meaningfully
Data Analysis Strategies:
Data analysis is defined as a process of cleaning, transforming, and modeling data to come up with useful information for business decision-making. The purpose of data analysis is to extract useful information from data and taking the decision based upon the analysis obtained.
According to researchers, various analytic procedures “provide a way of drawing inductive inferences from data and distinguishing the signal (the phenomenon of interest) from the noise (statistical fluctuations) present in the data”. While data analysis in qualitative research can include statistical procedures, there are times when analysis becomes an ongoing iterative process where data is continuously collected and analyzed almost simultaneously.
Researchers generally analyze for patterns in observations through the entire data collection phase. The form of the analysis is determined by the specific qualitative approach taken, whether it is a field study, ethnography content analysis, oral history, biography, unobtrusive research, and the form of the data such as field notes, documents, audiotape and videotape. Discussed here are the top strategies for analysis of the collected information:
- Begin With The Right Questions – Beginning with the right questions is very important as it will set you in the right direction and you will be able to analyze the collected information most efficiently
- Establish Clear Measurement Priorities – You need to understand and decide what you intend to measure and finalize the strategy that will be used for measuring it to achieve desired results
- Source Data – You need to work out how the data has been obtained and how to organize it the best way to validate the findings as you progress.
Deep data analysis is very important and it can be done by converting the data in several ways such as plotting it on a graph, examining the correlations, or creating a pivot table in Excel to sort and filter data over different variables to calculate the mean, minimum, maximum and standard deviation of data.
Once the data has been analyzed, it is time to interpret the results and see how it relates to the information you obtained and if it is proving the validity of the hypothesis.