Hamilton, John R., Maxwell, Stephen, and Tee, Singwhat (2024) Useful Excel Data Validation of Qualtrics Respondent Data Prior to Analysis. In: Proceedings of the International Conference on Electronic Business (24) pp. 566-575. From: ICEB 2024: 24th International Conference on Electronic Business, 24-28 October 2024, Zhuhai, China.

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Abstract

This study reports on useful tracking components of the Qualtrics survey instrument, and their application to higher degree research and survey validity. Qualtrics captures the unique survey responses from an individual as coming from a specific device or computer with its own unique internet protocol (IP) address. This IP address is expressed as a numeric in 4 blocks of 3 digits. This numeric provides information that separately locates each individual respondent’s online survey completion as created from a specific, unique, Internet-connected digital device or computer. Thus, a Qualtrics uniqueness-of-position survey-location-check is achieved for each individual respondent. Qualtrics adds a day, date, and time of survey response stamp. Applied in conjunction with digital device or computer IPv4 address one can detect any multiple survey data sets created from one digital device or computer. Qualtrics longitude and latitude data can precision-map each survey respondent’s digital device or computer geographical location. This specific geographical data can location-compares each respondent’s survey digital device or computer position. Hence, representativeness to an overall targeted population is possible – and sometimes even an area-by-area comparison. Used collectively, these three digital device or computer survey checks help measure (or gauge) and authenticate uniqueness, and so further validate survey representativeness against a targeted population. Also, higher degree researchers, and supervisors, can check validity, uniqueness, and representativeness of each individual respondent’s survey, and can readily eliminate any respondent survey duplication(s) – thereby improving final data set quality. One benefit of qualified data sets is facilitation of AI systems to make real time operational recommendations. However, in some countries, this raises governance concerns regarding the privacy dilemma such as university ethics respondent anonymity requirements.

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