• The Korean Journal of Applied Statistics
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• v.27 no.1
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• pp.89-102
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• 2014

In this study, we analyze the effect on the smoking status in the Seoul Metropolitan area using a multilevel logistic model with Community Health Survey data from the Korea Centers for Disease Control and Prevention. Intraclass correlation coefficient (ICC), profiling analysis and two types of predicted value were used to determine the appropriate multilevel analysis level. Sensitivity, specificity, percentage of correctly classified observations (PCC) and ROC curve evaluated model performance. We showed the applicability for multilevel analysis allowed for the possibility that different factors contribute to within group and between group variability using survey data.

• Journal of Korean Society of Transportation
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• v.20 no.7
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• pp.79-85
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• 2002

In this paper, multilevel models are adopted to identify interactions among household members in trip making behaviors. The multilevel approach is a proper methodology to handle samples, which are extracted from a hierarchical structure universe. PSTP dataset is used in developing models and understand proportion of variations among individuals and household. The results of this study show that for activity participation and travel behavior household level variance is more than 1/4 of person level variance and therefore not negligible. The results confirm the importance of multilevel model in travel behavior analysis.

• Journal of Korean Society of Transportation
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• v.21 no.4
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• pp.145-154
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• 2003

Multilevel and Multivariate Structural Equation Mpdel is applied to handle the hierarchical nature of the data and explain complex relationship among socioeconomic factors of individuals and household, activity participation, and travel behavior using Puset Sound Transportation Panel data. From analysis, variations of individual activity participation and travel behavior can be divided into two categories : Within-household variation and Between-households variation. Empirical results show that the interdependency index(p) of variables for household members within a household is between 0.13 and 0.33 indicating high interdependency. These results suggest that Multilevel and Multivariate SEM approach is an appropriate modeling methodology and gives additional information for activity participation and travel behavior. Also most of personal and household characteristics influence on activity participation and travel behavior within a household as well as between households.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.2
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• pp.561-571
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• 2014

This study analyzes traffic accident severity of old age drivers in fourteen cities and counties of Jeonbuk Province. It is assumed that traffic accident effecting factors have two staged structure by personal and driving environment and urban characteristics. Multilevel Analysis Model is used under the assumption of hierarchical characteristics to analyze factors effecting severity. As the driver's age increases after sixty-five years old, accident damages become severe. The drunk driving is likely to make traffic accident damage more severer. The number of fatal accident by old age drivers is about three time more than by no old age drivers. Old age drivers have higher number of night traffic accidents but severer ones in daytime. Old age drivers show the higher number of traffic accidents but severer ones in fine weather. Wet road surface also influences damage severity and especially old age drivers show higher serious damage and fatal than no old drivers.

• Journal of the Korean Data and Information Science Society
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• v.27 no.2
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• pp.499-510
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• 2016

It is very important to select relevant variables in regression models for survival analysis. In this paper, we introduce a penalized variable-selection procedure in multi-level frailty models based on the "frailtyHL" R package (Ha et al., 2012). Here, the estimation procedure of models is based on the penalized hierarchical likelihood, and three penalty functions (LASSO, SCAD and HL) are considered. The proposed methods are illustrated with multi-country/multi-center bladder cancer survival data from the EORTC in Belgium. We compare the results of three variable-selection methods and discuss their advantages and disadvantages. In particular, the results of data analysis showed that the SCAD and HL methods select well important variables than in the LASSO method.

• Journal of the Korean Regional Science Association
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• v.36 no.1
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• pp.69-88
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• 2020

There has been an increasing attention to social diversity within communities. Previous studies suggest that social diversity is positively related to social trust and social capital that are intimately related to quality of life. Because of the controversy on social segregation that is an opposite concept of social diversity and is a factor negatively affecting quality of life, more studies on the association between social diversity and quality of life should be undertaken. This study employs a multilevel model to examine the association between social capital and quality of life. We analyzed the association using both the pooled sample and samples splitted based on social class. The empirical result shows that the occupational diversity positively affects quality of life while the income diversity negatively affects quality of life. These results provide useful policy implications for setting urban policy directions.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.4
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• pp.571-579
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• 2022

This research empirically analyzed the factors associated with the energy consumption of tourism hotels in Busan, Ulsan, and the Kyoungnam region of Korea based on their individual and locational characteristics. The study adopted a comprehensive modeling approach involving multi-level regression analyses that allowed for improved accuracy by considering the hierarchical structures of the hotels and their locational characteristics. The results indicated that the majority of energy consumption can be explained by the hotels'individual characteristics, including the size of building structure and the services, while their effects vary by region with statistical significance. Furthermore, the proximity to central commercial districts and hotel clusters had a significant influence on the variability in their energy consumption, indicating that locational factors are also important determinants. The findings here suggest the need for regional energy policies and solutions at various urban scales along with conventional energy policies at the building level and highlight regional responsibilities when attempting to create sustainable tourism industries.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1169-1182
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• 2016

Cancer is a typical cause of death in Korea that becomes a major issue in health care. According to Cause of Death Statistics (2014) by National Statistical Office, SMRs (standardized mortality rates) in Busan were counted as the highest among all cities. In this paper, we used data of Busan Regional Cancer Center to estimate the extent of the cancer incidence rate and cancer mortality rate. The data are considered in small areas of administrative units such as Gu/Dong from years 2003 to 2009. All cancer including four major cancers (stomach cancer, colorectal cancer, lung cancer, liver cancer) have been analyzed. We carried out model selection and parameter estimation using spatial multi-level model incorporating a spatial correlation. For the spatial effects, CAR (conditional autoregressive model) has been assumed.

• Korean Journal of Labor Studies
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• v.19 no.1
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• pp.35-64
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• 2013

This paper explores whether or not a minimum wage increase can do much to alleviate working poor. For this purpose, I analyze transitions from working poor to working non-poor and to unemployment or non-economically active states, using KLIPS (Korea Labor and Income Panel Study). This study uses the multilevel multinomial logit model to control unobserved individual heterogenous characteristics. It finds that a minimum wage increase tends to cause a higher probability of transitions from working poor to working non-poor. It is also discovered that a minimum wage increase is not negatively related with the persistence of the working state. It is concluded that minimum wage increases are likely to be effective in improving the living standards of the 'working poor'.

• Korean Journal of Social Welfare Studies
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• v.43 no.1
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• pp.389-411
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• 2012

A new era of research has focused on examining the growth of change in drinking problems among the elderly. Thus, the purpose of the present study was two fold: (1) to investigate trajectories of drinking problems(CAGE) among the Korean elderly(age$${\geq_-}65$$); and (2) to identify the predicting factors for the intercept and the slope of alcohol problems using multi-level growth curve model. Data come from three waves(1st wave(2006)~3rd wave(2008) of the Korea Welfare Panel(KWP) study. The results indicated that the levels of drinking problems decreased over time and that age, gender, marital status, religion, poverty, self-rated health, and social relationship satisfaction were associated with the baseline CAGE. Further analysis showed that social relationship satisfaction affected the declining slope of drinking problems over time. Specifically, among those who satisfied social relationship, there was a sharp decline of CAGE over time. Overall findings highlight the importance of developing and implementing effective alcohol prevention programs for the elderly in the community settings to mitigate the harmful effects of various psycho-social stressors. Especially, programs to maintain and form healthy social support network are suggested as critical interventions for prevention as well as recovery of alcohol problems in late life.