Research can be designed ideally when an in-depth understanding of the problem is reached and a central question that needs to be solved is identified. The researcher who clearly understands the research problem is the best at achieving a logical answer to the research question, as the data that the researcher will collect is closely related to his/her main problem and not far from it.
In this paper, I will discuss the research data collection plan, the methods used, the techniques for measuring this data, the difficulties of this process, and finally, the value of the research question in planning the research.
Research data collection plan
It is the process of obtaining the necessary information for research from different sources and in various forms that target the interest of the study. The data collection process must be legal and ethical to be accurate and workable research (Phillips & Stawarski, 2008). Several methods of data collection are qualitative, quantitative, and mixed.
- Qualitative data: It is non-statistical descriptive data that relies more on narration than numbers. There are several ways to collect this type of data, the most important of which are records, documents, interview records, focus groups, and observational research (Sgier, 2012).
- Quantitative data: Data in the form of numbers and numbers associated with a specific data set. There are several ways to collect this type of data, the most important of which are surveys, experiments, tests, benchmarks, and market reports (Cramer, 2003).
- Mixed data: This type of data combines qualitative and quantitative data that cannot be separated. Sometimes, this style is considered the best form of business research, distinguishing it from scientific research (Sayago, 2015).
Measurement techniques
According to (McKubre, Macdonald, Sayers & Macdonald, 2018), each researcher must conduct analyses to refine the data and ensure its quality. One of these techniques is diagnostic analysis, which answers why this happened. Predictive analysis answers the question of what is likely to occur. Statistical analysis answers the question, what happened? Descriptive analysis mixes other data and reformulates it into a narrative form suitable for research. Text analysis, in which user data for research is extracted and revised.
Obstacles and difficulties
According to (Merlino et al., 2019), some difficulties lie in the abundance of data, the inability to process it, or the insufficient time for it; on the contrary, some researchers have little or no data. There is also the problem of publishing false and misleading information for a specific purpose and relying on an incorrect database, such as searching within a particular country and searching for another country. The time and cost required to collect data are important factors. In the past, the time and expense of consumers to move between books and references made it difficult for them to conduct research within a specific time frame and financial budget. There is excellent ease of moving between electronic libraries, among the billions of data available for free or through academic subscriptions, such as the Touro University Library.
The value of the research question in planning the research design
It dramatically helps the research question in planning, as it helps collect data related to the research. In addition, it provides clear goals that can be achieved and determine what is valid or irrelevant for research. When reaching a specific research question, the research can be planned through particular points such as the research objective, the type of data needed, and methods of data collection and processing until the available solutions are reached.
Conclusion
Although my research question is considered simple because it looks at ways to combat pollution resulting from construction sites, the scarcity, difficulty, and high cost of solutions make the challenges in implementing these solutions ineffective theories, and this is what I will try hard to solve in the research.
References
Cramer, D. (2003). Advanced quantitative data analysis. McGraw-Hill Education (UK).
Merlano, M., Denaro, N., Benasso, M., Licitra, L., Bossi, P., Locati, L., ... & Bruzzi, P. (2019). Difficulties in conducting pure academic research, data collection obstacles, and information quality: The example of the INTERCEPTOR study. Oral Oncology, 97, 99-104.
McKubre, M. C., Macdonald, D. D., Sayers, B., & Macdonald, J. R. (2018). Measuring techniques and data analysis. Impedance spectroscopy: theory, experiment, and applications, 107-174.
Phillips, P. P., & Stawarski, C. A. (2008). Data collection: Planning for and collecting all types of data. John Wiley & Son
Sayago, S. (2015). Constructing qualitative and quantitative data using discourse analysis as a research technique. Quality & Quantity, 49(2), 727-737.
Sgier, L. (2012). Qualitative data analysis. An Initiat. Gebert Ruf Stift, 19, 19-21.