The Process of Data Collection

The Process of Data Collection

Data collection is a systematic process. The process of data collection is universal. It may vary slightly according to the subject, but the basic steps remain the same. The type of research you are undertaking will affect how you organize this collected data. The goal is to create a database, meaning structured data. The process of data collection is a significant part of any research. It has some obstacles which you must know to be mentally prepared. Once you know the steps, the obstacles will be easier to overcome. Thus, a lot of emphasis is laid on it in data science. Data collection allows us to gather, organize and structure data according to our interests. It allows us to test theories and hypotheses. It also enables us to analyze and critically evaluate the results. Hence, it is a crucial step in the scientific process of proving any theory.

The Steps of Collecting Data are as follows:

Draw the boundaries of your data

The first step in the process of data collection should be delimitation. Outline the information you want to collect via questions or statements. For instance, if you want to find the correlation between children and the popularity of comic books, then you should focus your questions to determine the age group of children who read comic books, their nationality, etc. The sharper your boundaries, the more reliable your research. Vague questions can lead to low reliability or validity of the research. So, try to understand what information you want precisely before collecting data. 

Data Collection Method

Depending upon the type of research you are conducting, i.e., quantitative or qualitative, you can pick your tools to collect data. There are numerous methods to collect data. Choosing one may depend upon the following factors:

• Geographical accessibility – Data collection can have challenges, such as geographical availability. The data you want may not be easily accessible to you from your location. Researchers often use assistance for this purpose from locals of the research areas. Another option you can explore is collecting data remotely through digital means. That way, your physical presence will not be mandatory for data collection.
• Quantitative Data Collection – Methods employed for quantitative research are objective. It means that the same information is needed from a sample (a group of people). Surveys, experiments, and observational studies can be used for this purpose. If you are conducting secondary research, other research can be assessed. 
• Qualitative Data Collection – Qualitative research is more subjective. They are best conducted through personal interviews, case studies, focus groups, etc. The idea is to limit the quantity and research in depth. Such methods are used frequently for sociological research, as subjectivity is higher in such kinds of research.

Data Sizing 

Another critical step in data collection is determining the research scale and, consequently, the size of the data to be collected. The larger the data size, the more reliable it will be. In qualitative research, the data size is usually smaller. It varies according to the research topic and the nature of the data. 

A Flexible Time Frame

Data collection can take an indefinite amount of time. It can delay the research for years. So, make a flexible time frame for data collection. The data collection process will not completely fit your schedule, but trying to achieve it might save a lot of time. A report of your progress can also indicate the time required to complete the data collection.  

Grassroot data collection

Data collection is a rigorous process. While implementing your plan, you must avoid loopholes because there is scope for plenty. Thorough work while sorting and organizing makes your data polished. As for qualitative data, the clerical work is more, and there is little interpretation. Such data will integrate the opinion of the researcher. Since the data size is small, it is possible in a limited time.

Obstacles of data collection

• One of the biggest problems in data collection is the lack of universal standards. Every researcher faces the issue of which framework to follow. But a quick study can help you identify which standard applies to your research. Maintaining a uniform standard while collecting data is a must.
• Data collection might not be the organization’s primary function, which is collecting the data. In this case, the obstacle becomes prioritizing other primary functions over data collection. For example, if the organization conducting the data collection is a hospital or a fire station, it would be a priority to save lives. Then data collection would be their secondary function and may be given less importance. It may generate inaccurate research results.
• Another common obstacle is when business organizations collect data. If it is not related to the core business, this data is disregarded. For example, they will collect data in the name and personal details and healthcare histories in the healthcare business. But they will not collect data regarding cultural background, higher education, etc. This gives the collected data a very narrow scope.
• Data collection is a complex process. It is challenging to collect single data items that adequately represent all aspects of the required information. For example, it is difficult for a person with disabilities to group all forms of disabilities into one data item called “disability.” This questions the integrity of the data collection.
• A common obstacle faced in the research field is the inadequately trained staff due to a shortage of trained front-line workers. Nursing staff, receptionists, interns, NGOs, and other volunteers often do not understand why the data is being collected and for what purpose. They may not be qualified enough to understand the nuances of the process of data collection. For instance, if data is being collected to check domestic abuse cases specific to a particular neighborhood, the staff may not have the sensitivity or qualification to get accurate data from the public. Hence, the data collected will be inaccurate.
• Even if other obstacles are not present, a low budget is expected towards collecting and maintaining data records. The proper IT equipment is often not available for collecting and storing large amounts of data required for adequate research maintenance is an economic liability for the organization. Everyone doesn’t need to understand the need for primary data integrity data.

Conclusion

There is much scope for improving data collection, executing, and maintaining organization. To make it an absolute priority, businesses need to truly understand the potential data collection holds in contributing to their growth and success. While we have a frame data collection method, we still have a long way to go to improve data accuracy, data collection staff training, and maintaining data integrity. With the help of more accurate data, we can have thorough and effective research and thus better and faster results in the scientific fields. Unfortunately, data collection is not presently given its due importance in India, because of which most research is inaccurate. Therefore, it is a massive waste of our resources. If data collection is done right, it will save time, money, effort, and lives.