The Internet Usage Rate in Turkey: A Machine Learning Approach
Year 2024,
Volume: 24 Issue: 2, 165 - 178, 25.05.2024
Mustafa İncekara
,
Cemal Öztürk
Abstract
Most studies on measuring coverage bias in internet surveys use internet access as a critical measurement variable. However, access to the internet does not mean that individuals are using it. Therefore, using the internet usage rate as a key variable is crucial to get an accurate overview of the internet coverage of a population. This study closes these gaps by using a better indicator for measuring the internet usage rate. It is the first study measuring the internet usage rate in Turkey by using the real internet usage rate of the population and applying a machine learning algorithm. The results exposed significant differences in socio-demographic characteristics when internet users were compared with non-users. Furthermore, the coverage bias associated with internet users remained different for several demographic categories. The results of web-based surveys based on the actual internet usage rate are crucial for the scientific community and marketers.
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Year 2024,
Volume: 24 Issue: 2, 165 - 178, 25.05.2024
Mustafa İncekara
,
Cemal Öztürk
References
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- Couper, Mick P. 2000. "Review: Web Surveys: A Review of Issues and Approaches." Public Opinion Quarterly 64 (4): 464–94. https://academic.oup.com/poq/article-pdf/64/4/464/5414708/640464.pdf.
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- Pew Research Center. 2015. "Coverage Error in Internet Surveys: Who Web-Only Surveys Miss and How That Affects Results." Pew Research Center. 20150922. http://www.pewresearch.org/2015/09/22/coverage-error-in-internet-surveys/ (July 31, 2018).
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- Selwyn, Neil. 2004. "Reconsidering Political and Popular Understandings of the Digital Divide." New Media & Society 6 (3): 341–62.
- SPSS Statistics, version 25 (IBM Corp., Armonk, NY, USA)
- Stern, Michael J., Alison E. Adams, and Shaun Elsasser. 2009. "Digital Inequality and Place: The Effects of Technological Diffusion on Internet Proficiency and Usage across Rural, Suburban, and Urban Counties." Sociological Inquiry 79 (4): 391–417.
- Stern, Michael J., Ipek Bilgen, and Don A. Dillman. 2014. "The State of Survey Methodology." Field Methods 26 (3): 284–301.
- Sterrett, David, Dan Malato, Jennifer Benz, Trevor Tompson, and Ned English. 2017. "Assessing Changes in Coverage Bias of Web Surveys in the United States." Public Opinion Quarterly 81 (S1): 338–56.
- Tourangeau, Roger, Frederick G. Conrad, and Mick Couper. 2013. The science of web surveys [eng]. Oxford: Oxford Univ. Press.
- TUIK: Turkish Statistical Institute. Information and Communication Technology (ICT) Usage Survey on Households and Individuals, 2017.
- van Deursen, A.J.A.M., and J.A.G.M. van Dijk. 2009. "Improving digital skills for the use of online public information and services." Government Information Quarterly 26 (2): 333–40.
- Vicente, Paula, and Elizabeth Reis. 2012. "Coverage Error in Internet Surveys: Can Fixed Phones Fix It?" International Journal of Market Research 54 (3): 323–45.
- Worldbank. 2018. "Individuals using the Internet (% of population) - World Bank Open Data." https://data.worldbank.org/indicator/IT.NET.USER.ZS (August 7, 2018).
- Yeager, David S., Jon A. Krosnick, LinChiat Chang, Harold S. Javitz, Matthew S. Levendusky, Alberto Simpser, and Rui Wang. 2011. "Comparing the Accuracy of RDD Telephone Surveys and Internet Surveys Conducted with Probability and Non-Probability Samples." Public Opinion Quarterly 75 (4): 709–47.
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- Zhang, Y. (2021). An interactive machine learning approach to integrating physician expertise into delirium prediction model development (Order No. 28770345). Available from ProQuest Dissertations and Theses Global. (2607315462). Retrieved from https://www.proquest.com/dissertations-theses/interactive-machine-learning-approach-integrating/docview/2607315462/se-2