Surveys are one of the most common research methods that allow directly getting information of a different nature from the respondent. The survey belongs to the group of quantitative methods, which makes it possible to apply various mathematical methods to analyze the results obtained (Glastonbury & MacKean, 2020). The instrument for collecting information is a formalized questionnaire filled in by the respondent either independently or by the interviewer according to the respondent. Respondents in the survey may be representatives of various social and age groups with different income levels, selected following pre-agreed characteristics. This process can inform by obtaining many data, but specific control methods must accompany the decision-making process.
The apparent advantage of surveys is the relatively low cost of the method with high information content and reliability of the data obtained. In addition, in the modern world, the possibility of conducting mass research using a unified technology is quite affordable for almost everyone (Guo et al., 2020). The use of complex mathematical methods for analyzing the data obtained can provide information about correlations that are not obvious at first glance, which, for example, is extremely useful in marketing research. Finally, the ability to use visual materials can provide a deeper and more reliable assessment in surveys.
However, this method has certain limitations. First, surveys always have an error, even during fieldwork: any assessment methods, in this case, require some control, which, with limited resources, is not always effective and profitable (Sun et al., 2019). In addition, the formalization of the questionnaire does not give scope and opportunity to discover hidden motives. This factor can be neutralized by the survey duration, which will affect the time resources, and the quality, which will affect the mass character.
References
Glastonbury, B., & MacKean, J. (2020). Survey methods. In Handbook for research students in the social sciences (pp. 225-247). Routledge.
Guo, R., Cheng, L., Li, J., Hahn, P. R., & Liu, H. (2020). A survey of learning causality with data: Problems and methods. ACM Computing Surveys (CSUR), 53(4), 1-37.
Sun, S., Cao, Z., Zhu, H., & Zhao, J. (2019). A survey of optimization methods from a machine learning perspective. IEEE Transactions on Cybernetics, 50(8), 3668-3681.