Vol. 14, No. 5, May 31, 2020
10.3837/tiis.2020.05.005,
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Abstract
Recent advancements in mobile device technology have enabled real-time positioning so that mobile patterns of people and favorable locations can be identified and related researches have become plentiful. One of the fields of research is the relationship between the object properties and the favored location to visit. The object properties of a person include personality, which is a major property jobs, income, gender, and age. In this study, we analyzed the relationship between the human personality and the preference of the location to visit. We used Spearman’s Rank correlation coefficient, one of the many methods that can be used to determine the correlation between two variables. Instead of using actual data values, Spearman‘s Rank correlation coefficient deals with the ranks of the two data sets. In our research, the personality and the location data sets are used. Our personality data is ranked in five ranks and the location data is ranked in 8 ranks. Spearman‘s Rank correlation coefficient showed better results compared to Pearson linear correlation coefficient and Kendall rank correlation coefficient. Using Spearman’s correlation coefficient, the degree of the relationship between the personality and the location preference is found to be 43%.
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Cite this article
[IEEE Style]
H. Y. Song and S. Park, "An Analysis of Correlation between Personality and Visiting Place using Spearman’s Rank Correlation Coefficient," KSII Transactions on Internet and Information Systems, vol. 14, no. 5, pp. 1951-1966, 2020. DOI: 10.3837/tiis.2020.05.005.
[ACM Style]
Ha Yoon Song and Seongjin Park. 2020. An Analysis of Correlation between Personality and Visiting Place using Spearman’s Rank Correlation Coefficient. KSII Transactions on Internet and Information Systems, 14, 5, (2020), 1951-1966. DOI: 10.3837/tiis.2020.05.005.
[BibTeX Style]
@article{tiis:23551, title="An Analysis of Correlation between Personality and Visiting Place using Spearman’s Rank Correlation Coefficient", author="Ha Yoon Song and Seongjin Park and ", journal="KSII Transactions on Internet and Information Systems", DOI={10.3837/tiis.2020.05.005}, volume={14}, number={5}, year="2020", month={May}, pages={1951-1966}}