• Journal of the Korea Society of Computer and Information
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• v.1 no.1
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• pp.139-146
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• 1996

The estimation technique of psychophysical magnitude Is useful tool which measures to subjective feeling or opinion of human. This paper Introduces properties of the measured data, scales(nominal. ordinal. Interval. and ratio scale). and right analyzing methods of the measured data.

• Proceedings of the ESK Conference
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• 1993.10a
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• pp.133-144
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• 1993

Psychophysical data, in general, belong to one of the four scale categories : Nominal, Ordinal, Interval, and Ratio Scale. This paper introduces properties of the four scale categories and describes some psychophysical scales that attempt to measure subjective feeling or opinion of human. In addition, guidelines of analyzing and interpreting measured data are suggested. Some examples of analyzing and interpreting paychophysical data inappropriately are presented especially with category scales which have been used most widely in measuring subjective information.

• Asia-Pacific Journal of Business
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• v.14 no.1
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• pp.281-294
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• 2023

Purpose - The purpose of this study is to explore the determinants of the organizational level for the advancement of smart factory. We suggested three determinants of the organizational level such as CEO's entrepreneurship, high-involvement human resource management, and cooperative industrial relations. Design/methodology/approach - The population of our survey was manufacturing SMEs, and we took a sample and conducted a survey of 232 companies. Since the level of smart factory advancement, which is a dependent variable, was measured on an ordinal scale, ordinal logistic regression analysis was used to test the hypothesis. Findings - The higher the level of high-involvement human resource management, the higher the level of smart factory advancement. As the level of high-involvement human resource management increases by one unit, the probability of smart factory advancement increases by 22.8%. On the other hand, the CEO's entrepreneurship did not significantly affect the level of smart factory advancement. Interestingly, the cooperative industrial relations negatively affected to the level of smart factory advancement, contrary to the hypothesis prediction. Research implications or Originality - This study explored determinants at the organizational level that affect the advancement of smart factories. Through this, various implications are presented for related research and policy fields.

• Journal of Distribution Science
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• v.20 no.10
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• pp.11-17
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• 2022

Purpose: This research aims to find the link between public service Distribution and individual job performance in the provincial municipality. Research design, data, and methodology: This is a quantitative approach study with a non-experimental and correlational design. The sample consisted of 140 employees appointed and hired by the provincial municipality of Huaraz. For data collection, Two questionnaires with an ordinal Likert-type scale and the Rho Spearman correlation coefficient were used to assess the link between the research variables., For Analysis: two questionnaires with an ordinal Likert-type scale and the Rho Spearman correlation coefficient were used to determine the connection between the research variables. Results: It was determined that both variables have a high degree of correlation (0.725), indicating a direct and significant relationship between the Distribution of public service and skill performance in the provincial municipality (0.614). Conclusion: Finally, this allows us to conclude that the institutional context is essential; that is, there is a significant correlation between the PSM and contextual performance in the provincial municipality of Huaraz, which has a Rho Spearman value of 0.723.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.4
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• pp.375-383
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• 1999

사용자가 최소의 이해와 수정 노력으로 적합한 컴포넌트를 선택할수 있는 방안이 요구된다, 따라서 본 논문에서는 컴포넌트의 재사용도 측정을 위한 혼합적 척도를 제안한다. 현업에서의 연구와 경험을 통해서 증명된 객관성 있는 척도들을 측정인자로 설정한다. 러프집합을 이용하여 각측정인자들이 컴포넌트 재상요에 미치는 영향의 정도를 평가하고 각 측정인자들의 상대적 중요도를 구한다, Sugeno의 퍼지적분을 이용하여 측정인자들의 중요도와 측정값들을 종합함으로써 컴포넌트들의 재사용도를 측정한다. 마지막으로 제안된 ordinal scale과 ratio scale에 따름을 보여준다.

• Journal of the Korean Data and Information Science Society
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• v.27 no.2
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• pp.373-380
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• 2016

Even though Likert type data is ordinal scale, many researchers who regard Likert type data as interval scale adapt as parametric methods. In this research, simulations have been used to find out a proper analysis of Likert type data. The locations and response distributions of five point Likert type data samples having diverse distribution have been evaluated. In estimating samples' locations, we considered parametric method and non-parametric method, which are t-test and Mann-Whitney test respectively. In addition, to test response distribution, we employed Chi-squared test and Kolmogorov-Smirnov test. In this study, we assessed the performance of the four aforementioned methods by comparing Type I error ratio and statistical power.

• The Korean Journal of Ecology
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• v.20 no.5
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• pp.303-313
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• 1997

A new evaluation technique, i.e. multicriterion matrix technique, on the vegetation assessment was proposed and compared with several techniques having been previously used in the environmental impact assessment. Four criterias and 10 subcriterias were selected for two evaluation indices such as vegetation naturalness value and vegetation class. These criterias were characterized by syntaxonomical informations of hemeroby concept and potential vegetation, hierarchical system between criterias, and ordinal scale of vegetation naturalness valuse. Vegetation naturalness values were classified into 11 ordinal levels and condensed to five vegetation classes for facilitating practical use. In the example study two sites were compared by using two indices. This technique could have useful applications for ssessment of regional vegetation. A vegetation map of naturalness described by combination of two indices was proposed in order to illustrate regional vegetation naturalness.

• Journal of Korean Academy of Nursing
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• v.51 no.1
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• pp.5-14
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• 2021

Purpose: This study aimed to investigate sub-health status (SHS) of people living in China during the Coronavirus disease 2019 (COVID-19) COVID-19 pandemic. COVID-19 is a severe acute respiratory syndrome coronavirus (SARS-CoV) infection-induced acute infectious disease, which is featured by universal susceptibility and strong infectivity, and SHS (a status of low quality health) refers to a status of low-quality health. COVID-19 has gradually developed into a global pandemic, making the public in a high stress situation in physiological, psychological and social states in the short term. Methods: From March 6 to 11, 2020, a large-scale cross-sectional survey was conducted by convenient sampling, and SHS assessment scale was used in the questionnaire. The ordinal logistic regression analysis was used to identify the factors affecting SHS. Results: In this study, 17,078 questionnaires were delivered with 16,820 effective questionnaires collected, and 10,715 subjects (63.7%) were found with SHS, with moderate SHS primarily. Physiological sub-scale scored the highest, followed by psychological and social sub-scales. Ordinal logistic regression analysis indicated that man, only-child, workers and farmers were risk factors of SHS. Protective factors of SHS included living in rural areas and townships, laid-off retirees and education degree. Conclusion: It shows many people in China place in a poor health status during COVID-19 pandemic. It is necessary that relevant departments pay more attention to people with poor health such as men, only-child, urban people, workers and farmers, and groups with high education degree during and after pandemic stabilization.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2008.10a
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• pp.444-447
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• 2008

This study is intended to develop a improved version of Cross Impact Analysis Model based on Scaling Theory. In developing the model, we applied the scale transformation technique and regression technique to existing CIA model. Improved CIA model is composed of two sub-models: 'model for impact value measurement,' and 'model for impact value conversion'. We applied a technique which measures data by ordinal scale and then transforms them into interval scale and ratio scale data to CIA model. The accuracy of forecasting and the usability of CIA application have been improved.

• Journal of Electrical Engineering and Technology
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• v.10 no.5
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• pp.1930-1939
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• 2015

Wind power planning aims to locate and size wind farms optimally. Traditionally, wind power planners tend to choose the wind farms with the richest wind resources to maximize the energy benefit. However, the capacity benefit of wind power should also be considered in large-scale clustered wind farm planning because the correlation among the wind farms exerts an obvious influence on the capacity benefit brought about by the combined wind power. This paper proposes a planning model considering both the energy and the capacity benefit of the wind farms. The capacity benefit is evaluated by the wind power capacity credit. The Ordinal Optimization (OO) Theory, capable of handling problems with non-analytical forms, is applied to address the model. To verify the feasibility and advantages of the model, the proposed model is compared with a widely used genetic algorithm (GA) via a modified IEEE RTS-79 system and the real world case of Ningxia, China. The results show that the diversity of the wind farm enhances the capacity credit of wind power.