The Internet Usage Rate in Turkey: A Machine Learning Approach

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.

Keywords

• Internet Usage Rate
• Socio-Economic Factors
• Machine Learning

References

1. Aggarwal, C.C. (2014) Data Classification: Algorithm and Applications, CRC Press, New York.
2. Boddin, Dominik. 2016. "The Role of Newly Industrialized Economies in Global Value Chains." IMF Working Papers 16 (207): 1. https://www.imf.org/external/pubs/ft/wp/2016/wp16207.pdf (Accessed August 15, 2018).
3. 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.
4. Couper, Mick P. 2000. "Web Surveys." Public Opinion Quarterly 64 (4): 464–94.
5. Couper, Mick P., Arie Kapteyn, Matthias Schonlau, and Joachim Winter. 2007. "Noncoverage and nonresponse in an Internet survey." Social science research 36 (1): 131–48.
6. de Leeuw, Edith, and Suzette Mathijsse. 2016. "Professional Respondents in Online Panels | Insights Association." https://www.insightsassociation.org/article/professional-respondents-online-panels (August 6, 2018).
7. Eckman, Stephanie. 2016. "Does the Inclusion of Non-Internet Households in a Web Panel Reduce Coverage Bias?" Social Science Computer Review 34 (1): 41–58.
8. Eurobarometer. 2017. "E-Communications and Digital Single Market: Report.: Special Eurobarometer 432.” [en]. Publications Office of the European Union. July 27. https://publications.europa.eu/en/publication-detail/-/publication/57889a55-8fb6-11e8-8bc1-01aa75ed71a1/language-en (September 26, 2018).

9. Fricker, Ronald D. 2008. "Sampling Methods for Web and E-mail Surveys." In The SAGE handbook of online research methods, eds. Nigel Fielding, Raymond M. Lee and Grant Blank. 1 Oliver's Yard, 55 City Road, London England EC1Y 1SP United Kingdom: Sage pub, 195–216.
10. Groves, R. M., and L. Lyberg. 2010. "Total Survey Error: Past, Present, and Future." Public Opinion Quarterly 74 (5): 849–79.
11. Groves, Robert M. 2006. "Nonresponse Rates and Nonresponse Bias in Household Surveys." Public Opinion Quarterly 70 (5): 646–75.
12. Hair, Joseph F., William C. Black, Barry J. Babin, and Rolph E. Anderson. 2014. Multivariate data analysis. Always learning. Harlow, Essex: Pearson.
13. Hastie, T., Tibshirani, R. and Friedman, J. (2013) The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, New York.
14. Hargittai, Eszter, and Yuli P. Hsieh. 2012. "Succinct Survey Measures of Web-Use Skills." Social Science Computer Review 30 (1): 95–107.
15. Hays, Ron D., Honghu Liu, and Arie Kapteyn. 2015. "Use of Internet Panels to Conduct Surveys." [eng]. Behavior research methods 47 (3): 685–90.
16. Heerwegh, D., and G. Loosveldt. 2008. "Face-to-Face versus Web Surveying in a High-Internet-Coverage Population: Differences in Response Quality." Public Opinion Quarterly 72 (5): 836–46.
17. Hosmer, David W., Stanley Lemeshow, and Rodney X. Sturdivant. 2013. Applied logistic regression [eng]. 3rd ed. Wiley series in probability and statistics. Hoboken, NJ: Wiley.
18. Hwang, Yeong-Hyeon, and Daniel R. Fesenmaier. 2004. "Coverage Error Embedded in Self-Selected Internet-Based Samples: A Case Study of Northern Indiana." Journal of Travel Research 42 (3): 297–304.
19. Mitchell, T., Buchanan, B., DeJong, G., Dietterich, T., Rosenbloom, P., & Waibel, A. (1990) 'Machine learning', Annual Review of Computer Science, Vol. 4, No. 1, pp.417–433.
20. Mohorko, Anja, Edith de Leeuw, and Joop Hox. 2013. "Internet Coverage and Coverage Bias in Europe: Developments Across Countries and Over Time." Journal of Official Statistics 29 (4): 609–22.
21. Mossberger, Karen, Caroline J. Tolbert, and Ramona S. McNeal. 2010. Digital citizenship: The internet, society, and participation. 2nd ed. Cambridge, Mass.: MIT Press.
22. 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).
23. Peytchev, Andy, Lisa R. Carley-Baxter, and Michele C. Black. 2011. "Multiple Sources of Nonobservation Error in Telephone Surveys: Coverage and Nonresponse." Sociological Methods & Research 40 (1): 138–68.
24. Robinson, Laura, Shelia R. Cotten, Hiroshi Ono, Anabel Quan-Haase, Gustavo Mesch, Wenhong Chen, Jeremy Schulz, Timothy M. Hale, and Michael J. Stern. 2015. "Digital inequalities and why they matter." Information, Communication & Society 18 (5): 569–82.
25. Schaefer, David R., and Don A. Dillman. 1998. "Development of a Standard E-Mail Methodology: Results of an Experiment." Public Opinion Quarterly 62 (3): 378.
26. Selwyn, Neil. 2004. "Reconsidering Political and Popular Understandings of the Digital Divide." New Media & Society 6 (3): 341–62.
27. SPSS Statistics, version 25 (IBM Corp., Armonk, NY, USA)
28. 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.
29. Stern, Michael J., Ipek Bilgen, and Don A. Dillman. 2014. "The State of Survey Methodology." Field Methods 26 (3): 284–301.
30. 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.
31. Tourangeau, Roger, Frederick G. Conrad, and Mick Couper. 2013. The science of web surveys [eng]. Oxford: Oxford Univ. Press.
32. TUIK: Turkish Statistical Institute. Information and Communication Technology (ICT) Usage Survey on Households and Individuals, 2017.
33. 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.
34. 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.
35. Worldbank. 2018. "Individuals using the Internet (% of population) - World Bank Open Data." https://data.worldbank.org/indicator/IT.NET.USER.ZS (August 7, 2018).
36. 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.
37. Yun, Gi W., and Craig W. Trumbo. 2000. "Comparative Response to a Survey Executed by Post, E-mail, & Web Form." Journal of Computer-Mediated Communication 6 (1): 0.
38. 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