Logo PTI
Polish Information Processing Society
Logo FedCSIS

Annals of Computer Science and Information Systems, Volume 11

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

Survey as a source of low quality research data


DOI: http://dx.doi.org/10.15439/2017F266

Citation: Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 11, pages 939943 ()

Full text

Abstract. Survey is the most common way to gather information for research purposes. Gathering information is usually provided in the early stage of research procedure. The information create the basis for further activities which lead to scientific research results. The problem is that information gathered as a result of survey is exposed to the high risk of errors. Following factors generates mostly errors: the way of survey carrying out, the survey content and just by simple human factors. Researchers very rarely pay their attention to the data quality taking as an obvious that gathered information describes properly studied case. In the end of the process they get results, which are becoming basis for the conclusions. Taking that into account the questions is about the quality, of research results, which were based on the surveys. What is important, usually most publications do not contain any information about source data on the basis of which results came out. It disables the independent evaluation of the results. The goal was to investigate the scale of survey data errors, using the scientific experiment. To achieve it, authors used their own survey system, which helped them in instant verification of the respondents' answers.


  1. S. L. Pfleeger, B. A. Kitchenham, “Principles of Survey Research Part 1: Turning Lemons into Lemonade” in ACM SIGSOFT Software Engineering Notes, vol. 26, no. 6, 2001, pp. 16-18.
  2. R. Libby, P. C. Fishburn, “Behavioral models of risk taking in business decisions: A survey and evaluation”, in Journal of Accounting Research, 1977, pp. 272-292.
  3. G. Enderle, “A worldwide survey of business ethics in the 1990s”, in Journal of Business Ethics, vol. 16, no. 14, 1997, pp. 1475-1483.
  4. E. Quintelier, S. Vissers, “The effect of Internet use on political participation an analysis of survey results for 16-year-olds in Belgium”, in Social Science Computer Review, vol. 26, no. 4, 2008, pp.411-427.
  5. A. Pinsonneault, K. Kraemer, “Survey research methodology in management information systems: an assessment.”, in Journal of management information systems, vol. 10, no. 2, 1993, pp. 75-105.
  6. R.K. YIN, Case Study Research. Design and Methods, Sage Publications, Thousand Oaks - London - New Delhi 2003, s. 5-9.
  7. M. H. Hansen, W. N. Hurwitz, W. G. Madow, “Sample survey methods and theory” Vol. 1, p. 638. New York: Wiley, 1953
  8. R. M. Groves, “Research on survey data quality.” in The Public Opinion Quarterly, vol. 51, 1987, pp. S156-S172.
  9. P. P. Biemer, R. M. Groves, L. E. Lyberg, N. A. Mathiowetz, S. Sudman, “Measurement errors in surveys” Vol. 173, John Wiley & Sons, 2011.
  10. R. Y. Wang, D. M. Strong, “Beyond accuracy: What data quality means to data consumers”, in Journal of management information systems, vol. 12, no. 4, 1996, pp. 5-33.
  11. J. Bound, C. Brown, N. Mathiowetz, “Measurement error in survey data.” in Handbook of econometrics, vol. 5, 2001, pp. 3705-3843.
  12. F. M. Andrews, A. R. Herzog, “The quality of survey data as related to age of respondent”, in Journal of the American Statistical Association, vol. 81, no. 394, 1986, pp. 403-410.
  13. B. Hanscom, J. D. Lurie, K. Homa, J. N. Weinstein, “Computerized questionnaires and the quality of survey data.”, in Spine, vol. 27, no.16, 2002, pp. 1797-1801.
  14. C. Marta-Pedroso, H. Freitas, T. Domingos, “Testing for the survey mode effect on contingent valuation data quality: A case study of web based versus in-person interviews”, in Ecological economics, vol. 62, no.3, 2007, pp. 388-398.
  15. A. W. Meade, S. B. Craig, “Identifying careless responses in survey data.”, in Psychological methods, no. 17, vol. 3, 2012, p. 437.
  16. M. P. Couper, “Technology trends in survey data collection.”, in Social Science Computer Review, no. 23, vol. 4, 2005, pp. 486-501.
  17. C. L. Borgman, “The conundrum of sharing research data.”, in Journal of the American Society for Information Science and Technology, vol. 63, no. 6, 2012, pp. 1059-1078.
  18. J. M. Wicherts, M. Bakker, D. Molenaar, “Willingness to share research data is related to the strength of the evidence and the quality of reporting of statistical results”, in PloS one, vol. 6, no. 11, 2011, p. e26828.
  19. H. A. Piwowar, R. S. Day, D. B. Fridsma, “Sharing detailed research data is associated with increased citation rate”, in PloS one, vol. 2, no. 3, 2007, p. e308.
  20. P. Arzberger, P. Schroeder, A. Beaulieu, G. Bowker, K. Casey, L. Laaksonen, P. Wouters, “Promoting access to public research data for scientific, economic, and social development.”, in Data Science Journal, vol. 3, 2004, pp. 135-152.
  21. P. Langat, D. Pisartchik, D. Silva, C. Bernard, K. Olsen, M. Smith, R. Upshur, “Is there a duty to share? Ethics of sharing research data in the context of public health emergencies”, in Public Health Ethics, vol. 4, no. 1, 2011, pp. 4-11.
  22. A. L. McGuire, J. M. Oliver, M. J. Slashinski, J. L. Graves, T. Wang, P. A. Kelly, D. Treadwell-Deering, “To share or not to share: a randomized trial of consent for data sharing in genome research.”, in Genetics in Medicine, vol. 13, no. 11, 2011, pp. 948-955.
  23. J. C. Wallis, E. Rolando, C. L. Borgman, “If we share data, will anyone use them? Data sharing and reuse in the long tail of science and technology.”, in PloS one, vol. 8, no. 7, 2013, p. e67332.
  24. M. Walport, P. Brest, “Sharing research data to improve public health”, in The Lancet, vol. 377, no. 9765, 2011, pp. 537-539.
  25. T. Hyla, J. Pejaś, “A practical certificate and identity based encryption scheme and related security architecture.”, Proceedings of 12th IFIP TC8 International Conference CISIM 2013, Krakow, Poland, LNCS vol. 8104, 2013, pp. 190–205.
  26. D. Pons, R. VallėS, M. Abarca, F. Rubio, “QR codes in use: the experience at the UPV Library.” in Serials [on-line], vol. 24, no. 3, Retrieved from http://eprints.rclis.org/18047/1/QR%20codes%20in%20use.pdf, 2011
  27. W. Chmielarz, “Metody oceny werbalnych księgarni internetowych Komputerowo Zintegrowane Zarządzanie obszar: Gospodarka oparta na wiedzy”, 2010, in Poland
  28. W. Chmielarz, “Ocena użyteczności internetowych witryn sklepów komputerowych” Studia i Materiały Polskiego Stowarzyszenia Zarządzania Wiedzą Tom 13, 2008 s. 17-24, in Poland
  29. W. Chmielarz, „Przełączniki metodyczne w ocenie witryn internetowych sklepów komputerowych, Zarządzanie Wiedzą i Technologiami informatycznymi” red. C Ormowski, Z. Kowalczuk, E Szczerbinki, nr.4 seria: Automatyka i Informatyka, Pomorskie wydawnictwo Naukowo-Techniczne PWNT Gdańsk, v 43 2008, p. 361-368, in Poland
  30. M. Socorro Garcia-Cascales, M. Teresa Lamata, Solving a decision problem with linguistic information. Pattern Recognition Letters, October 2011, Volume 22, Issue 5, pp 779–788
  31. K.M.A. Al Harbi, Application of the AHP in project management. International Journal of Project Management 19(1): 19–27, 2001
  32. T.L Saaty, The Analytic Hierarchy and Analytic Network Processes for the Measurement of Intangible Criteria and for Decision-Making, w: Multiple criteria decision analysis: State of the art surveys, J. Figueira, S. Greco, M. Ehrgott, Springer, 2005, p. 345-405
  33. T.L Saaty, How to make a decision: the analytic hierarchy process. European Journal of Operational Research, 48, 1990, p. 9-26
  34. L. Thomas, T.L. Saaty. How to make a decision: The analytic hierarchy process. European Journal of Operational Research, 48:9-26, September, 1990
  35. K. Muszyńska, J. Swacha, A. Miluniec, Z. Drążek, „Evaluation of eGuides: a discussion of approaches.” in Information Management, 2014, pp. 45–54