• Journal of the Korean Data and Information Science Society
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• v.27 no.4
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• pp.935-946
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• 2016

In this paper, we analyzed wage determinants of the vocational high school graduates utilizing both individual-level and work region-level variables. We formulate the models in the way wage determination has multi-level structure in the sense that individual wage is influenced by individual-level variables (level-1) and work region-level (level-2) variables. To incorporate dependency between individual wages into the model, we utilize hierarchical linear model (HLM). The major results are as follows. First, it is shown that the HLM model is better than the OLS regression models which do not take level-1 and level-2 variables simultaneously into account. Second, random effects on sex, maester dummy and engineering dummy variables are statistically significant. Third, the fixed effects on business hours and mean wage of regular job for level-2 variables are statistically significant effect individual-level wages. Finally, parental education level, parental income, number of licenses and high school grade are statistically significant for higher individual-level wages.

• Journal of the Korean Data and Information Science Society
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• v.26 no.1
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• pp.65-75
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• 2015

This paper analyzes the determinants of wage for the college and university graduates utilizing both individual-level and industry-level variables. We note that wage determination has multi-level structure in the sense that individual wage is influenced by individual-level variables (level-1) and industry-level (level-2) variables. Then, the assumption that individual wage is independent in the classical regression is violated. Therefore, this paper utilizes the hierarchical linear model (HLM). The major results are the followings. First, the multiple correspondence analysis including level-1 and 2 variables reveals that both level 1 and level 2 variables affects individual wages judging from the fact that the values of level 1 and level 2 variables differ across the different level of individual wage groups. Second, the decision tree analysis including level-1 and 2 variables shows that the most influential variable in wage determination is industry-level wage and the next is industry-level working hour, ages and sex in the decling order in. This suggests that the utilization of the HLM is appropriate since the characteristics of industry is important in determining the individual wage. Third, it is shown that the HLM model is the best compared to the other models which do not take level-1 and level-2 variables simultaneously into account.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1285-1296
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• 2013

The fundamental concerns of this paper are to analyze the effects of student course evaluation using subject characteristic and student characteristic variables. We use a 2-level hierarchical linear model since the data structure of subject characteristic and student characteristic variables is multilevel. Four models we consider are as follows; (1) null model, (2) random coefficient model, (3) mean as outcomes model, (4) intercepts and slopes as outcomes model. The results of the analysis were given as follows. First, the result of null model was that subject characteristics effects on course evaluation had much larger than student characteristics. Second, the result of conditional model specifying subject and student level predictors revealed that class size, grade, tenure, mean GPA of the class, native class for level-1, and sex, department category, admission method, mean GPA of the student for level-2 had statistically significant effects on course evaluation. The explained variance was 13% in subject level, 13% in student level.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.14 no.2
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• pp.39-53
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• 2015

The paper analyzes factors to affect pedestrian volumes by land-use type using 2012 Seoul Pedestrian Survey. First of all, five groups were classified based on land-use types around survey points such as residential, commercial, industrial and green uses, using k-average cluster analysis. Then, differences in average pedestrian volumes by group were compared for a day and time of day. In addition, multiple regression analysis was employed to identify factors to affect pedestrian volumes, considering physical features, land use types, public transportation accessibility, and socio-economic indices as independent variables by spatial hierarchy. Model results show that the walkway width positively influenced on pedestrian volumes for all groups, whereas other variables differently affected by group. Our results can be used as basic data for establishing polices with respect to pedestrian road design and improvement as well as estimating pedestrian demand by land-use type.

• Journal of Environmental Impact Assessment
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• v.27 no.2
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• pp.152-169
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• 2018

The purpose of this study was to analyze the effect of adjacentroads on the concentration of negative air ions in urban neighborhood park. The measured factors were negative air ion concentration, positive airion concentration, airtemperature,relative humidity, wind speed, green formation volume factor (GVZ) and distance from highway and general road. The mean negative air ion concentration was $206ea/cm^3$ and the positive air ion concentration was $416ea/cm^3$ in the influence zone of roads. On the other hand, the mean negative air ion was $339ea/cm^3$ and the positive air ion was $229ea/cm^3$ in the unaffected zone of roads, which are inner areas of the park. The difference of the negative air ion concentration according to the influence of the road was statistically significant. The negative air ion concentration model was presented by integrating the correlation analysis with the influence factors, and the explanatory power of the model was increased by adding the influence factor of the road.

• Journal of the Korean Institute of Landscape Architecture
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• v.42 no.4
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• pp.21-29
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• 2014

In terms of quality of life, leisure and health have become important issues with increasing incomes and decreasing working hours in Korea. This study empirically investigates the effects of urban open space on outdoor leisure activities, emphasizing that parks, river banks, and physical activity sites can provide opportunities such as walking, jogging, stretching, and cycling, free of charge to all residents. Based on 2010 sample survey data on leisure activities, multiple regression model as well as hierarchical linear model are estimated, taking account of both individual characteristics on demand and environmental/areal factors on supply side, including open space. Major findings include: first, urban open space significantly increases residents' outdoor leisure activities, second, the effect is more significant for the elderly and third, the effect is more valid for those with relatively low incomes and less education. These results imply that urban open space could be available as a local public good to cope with population aging and to realize health city and social welfare, since this space is not only a leisure place but also public health and welfare facilities.