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http://dx.doi.org/10.7837/kosomes.2020.26.2.121

A Study on Questionnaire Improvement using Text Mining  

Paek, Yun-Ji (Graduate School, Mokpo National Maritime University)
Jung, Chang-Hyun (Mokpo National Maritime University)
Publication Information
Journal of the Korean Society of Marine Environment & Safety / v.26, no.2, 2020 , pp. 121-128 More about this Journal
Abstract
The Marine Safety Culture Index (MSCI) was developed in the year 2018 for objectively assessing the public safety culture levels and for incorporating it as data to spread knowledge regarding the marine safety culture. The method for calculating the safety culture index should include issues that may affect the safety culture and should consist of appropriate attributes for estimating the current status. In addition, continuous verification and supplementation are required for addressing social and economic changes. In this study, to determine whether the questionnaire designed by marine experts reflects the people's interests and needs, we analyzed 915 marine safety proposals. Text mining was employed for analyzing the unstructured data of the marine safety proposals, and network analysis and topic modeling were subsequently performed. Analysis of the marine safety proposals was centered on attributes such as education, public relations, safety rules, awareness, skilled workers, and systems. Eighteen questions were modified and supplemented for reflecting the marine safety proposals, and reliability of the revised questions was analyzed. Furthermore, compared to the previous year, the questionnaire's internal consistency was improved upon and was rated at a high value of 0.895. It is expected that by employing the derived marine safety culture index and incorporating the improved questionnaire that reflects the requirements of marine experts and the people, the improved questionnaire will contribute to the establishment of policies for spreading knowledge regarding the marine safety culture.
Keywords
Marine Safety Culture Index (MSCI); Text mining; Network analysis; Topic modeling; Questionnaire;
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