• 한국노년학
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• v.39 no.3
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• pp.467-484
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• 2019

The purpose of this study is to explore latent classes of longitudinal medical expenses of older people and to analyze its predictors and its effects on subjective health. Among participants of the Korean Health Panel, the sample of this study includes 1,119 people who is 65-year-old or older and reported their medical expenses for nine consecutive years. The analyses were conducted in three steps. First, Growth Mixture Model (GMM) was applied to find distinct subgroups showing similar patterns in medical expenses. The results showed four groups which were classified as high medical expenditure maintenance group, medical expenditure increase group, low medical expenditure maintenance group, and medical expenditure reduction group. Second, the multinominal logistic regression found that the presence of spouse, economic participation, the number of chronic diseases, and the type of health insurance were significant predictors of latent classes in medical expenses. In particular, the greater the number of chronic diseases, the higher the likelihood of belonging to the high medical expenditure maintenance group. In addition, medical benefit recipients are more likely to belong to the low medical cost maintenance and medical cost reduction groups. Third, multiple regression analysis revealed that the older people in the groups with low or reducing expenses reported better subjective health than people with higher expenses. This study has its meanings in exploring the heterogeneity in longitudinal medical expenses among older people and its predictors and its associations with health outcome. The results of this research provide background information in establishing public health policy for older people.

• Food Science and Preservation
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• v.17 no.3
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• pp.418-424
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• 2010

Although the wine industry continues to grow, little empirical research on consumer preferences has been conducted. Thus, our objective was to analyze consumer views on wine attributes. A choice experiment (CE) was designed to detect a marginal willingness to pay for particular characteristics of wine (balance, flavor, color, clarity, and value-for-money). A questionnaire was administered and 286 responses were received. A multinomial logit model was estimated using the maximum likelihood method. The results indicated that balance, flavor, color, clarity, and price were all important to consumers. The CE data revealed that estimates of marginal willingness to pay were 31,899 won/bottle for balance, 23,088 won/bottle for flavor, 3,230 won/bottle for color, and 25,936 won/bottle for clarity. The balance of a wine was most important, and the flavor, clarity, and color were also significant. The results of this work will be of assistance in promoting the domestic wine industry.

• Journal of Digital Convergence
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• v.12 no.8
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• pp.397-410
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• 2014

This study sought to identify types of health behavior clusters among Korean adults and their related factors. A secondary analysis of 1,441 subjects, aged 19 to 64, in the 2009 Korean National Health and Nutrition Examination Survey (KNHANES IV-3) was conducted. A cluster analysis was used to identify types of clusters related to physical activity, smoking, and alcohol drinking. A complex samples chi square test and multivariate logistic regression were performed to analyze the associations between types of health behavior clusters and sample's characteristics using SPSS WIN 21. Five clusters were identified: health promotion, smoking, alcohol drinking, passive attitude, and risky behavior. The passive attitude cluster had the most subjects, with 47.7% of subjects as members. Socio-demographic factors, hypertension, and depressive symptoms were associated with membership in the alcohol drinking, smoking, passive attitude, or risky behavior cluster rather than the health promotion cluster. The findings of this study suggest that integrated health promotion programs incorporating multiple strategies need to be investigated. In addition, further studies should explore psychosocial factors that affect health behavior clusters, such as stress, self-efficacy, social support, and social networks.

• Journal of Korean Society of Environmental Engineers
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• v.38 no.2
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• pp.87-95
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• 2016

Turbidity is a key indicator to the user that the 'Discolored Water' phenomenon known to be caused by corrosion of the pipeline in the water supply system. 'Discolored Water' is defined as a state with a turbidity of the degree to which the user visually be able to recognize water. Therefore, this study used data mining techniques in order to estimate turbidity changes in water supply system. Decision tree analysis was applied in data mining techniques to develop estimation models for turbidity changes in the water supply system. The pH and residual chlorine dataset was used as variables of the turbidity estimation model. As a result, the case of applying both variables(pH and residual chlorine) were shown more reasonable estimation results than models only using each variable. However, the estimation model developed in this study were shown to have underestimated predictions for the peak observed values. To overcome this disadvantage, a high-pass filter method was introduced as a pretreatment of estimation model. Modified model using high-pass filter method showed more exactly predictions for the peak observed values as well as improved prediction performance than the conventional model.

• Journal of Korean Home Economics Education Association
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• v.30 no.2
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• pp.103-116
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• 2018

The purpose of the present study is to categorize collaborative consumption intention in high school students based on providing and using collaborative consumption behaviors, to compare and analyze the factors that predict these. Data gathered from 418 high school students through an online survey were used to conduct a descriptive statistics analysis, cluster analysis, and multinomial logistic regression. Firstly, collaborative consumer intentions were classified into four groups, including a proactive group with high providing behaviors and using behaviors, an active providing group, an active using group, and a passive group with low providing and using behaviors. Secondly, mass media, social benefits, enjoyment, community effect, and reputation were revealed as factors that increased the potential for inclusion in the active groups, and home education, mass media, enjoyment, and reputation were factors that increased the potential for inclusion in the active providing group. Enjoyment was revealed as the factor that increased the potential for inclusion in the active using group. The results of the present study show that the active utilization of consumer education and a systematic approach are required to revitalize collaborative consumption and proper settlement. Furthermore, a systematic establishment of school consumer education is needed for the balanced development of collaborative consumption. Also, an environmental system in which actual expected benefits can be experienced and realized in a diverse manner should be created to encourage consumers collaborate more actively.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.3
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• pp.181-194
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• 2017

This study aims to develop a regression model for forest volume estimation using field-collected forest inventory information and airborne LiDAR data. The response variable of the model is forest stem volume, was measured by random sampling from each individual plot of the 30 circular sample plots collected in Bonghwa-gun, Gyeong sangbuk-do, while the predictor variables for the model are Height Percentiles(HP) and Height Bin(HB), which are metrics extracted from raw LiDAR data. In order to find the most appropriate model, the candidate models are constructed from simple linear regression, quadratic polynomial regression and multiple regression analysis and the cross-validation tests were conducted for verification purposes. As a result, $R^2$ of the multiple regression models of $HB_{5-10}$, $HB_{15-20}$, $HB_{20-25}$, and $HBgt_{25}$ among the estimated models was the highest at 0.509, and the PRESS statistic of the simple linear regression model of $HP_{25}$ was the lowest at 122.352. $HB_{5-10}$, $HB_{15-20}$, $HB_{20-25}$, and $HBgt_{25}-based$ models, thus, are comparatively considered more appropriate for Korean forests with complicated vertical structures.

• Journal of the Korean housing association
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• v.21 no.2
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• pp.123-132
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• 2010

The housing market requires customized housing to be supplied according to the various characteristics of households. Multinominal Logistic Regression was used to analyze the effects of variables of household characteristics according to the number of family members on the choice of housing size in the scope of the whole country's housing market. Analysis showed that the number of family members has its own characteristics. When a household has a smaller number of family members, there are more variables affecting choice of housing size. Living and housing expenses variables served as significant variables that affect all household types. Results showed that households with more living and housing expenses are more likely to choose a large sized house and where households have a greater number of family members, there is more influence on that choice. The age of the householder was only found to be a meaningful variable in 1-2 person households and 3-4 person households, particularly in the choice of a small or large sized house. This shows that the age of the householder does not play an important role in choosing medium sized houses for households of under 4 people, but affects the choice of small and large sized houses. The academic ability of household members also served as a significant variable. While 1-2 person households with high academic ability tend to select a large sized house, 3-4 person households with high academic ability tend to select a small sized house. It is observed that members of both 1-2 person households and 3-4 person households tend to select their house between a large sized house and a small sized house in order to own their own houses. The result of this research suggests that there are various and detailed variables on the choice of housing size. Especially, a notable result is that household characteristics more significantly affect the housing size choice of 1-2 person households, while the trend of an aging society will more significantly affect a 3-4 person households' choice of a large sized house. Therefore, a study on the choice of housing size according to characteristics of elderly households and 1-2 person households should be continually analyzed.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.20 no.5
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• pp.533-541
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• 2007

The Zienkiewicz-Zhu(Z/Z) error estimate is slightly modified for the hierarchical p-refinement, and is then applied to L-shaped plates subjected to bending to demonstrate its effectiveness. An adaptive procedure in finite element analysis is presented by p-refinement of meshes in conjunction with a posteriori error estimator that is based on the superconvergent patch recovery(SPR) technique. The modified Z/Z error estimate p-refinement is different from the conventional approach because the high order shape functions based on integrals of Legendre polynomials are used to interpolate displacements within an element, on the other hand, the same order of basis function based on Pascal's triangle tree is also used to interpolate recovered stresses. The least-square method is used to fit a polynomial to the stresses computed at the sampling points. The strategy of finding a nearly optimal distribution of polynomial degrees on a fixed finite element mesh is discussed such that a particular element has to be refined automatically to obtain an acceptable level of accuracy by increasing p-levels non-uniformly or selectively. It is noted that the error decreases rapidly with an increase in the number of degrees of freedom and the sequences of p-distributions obtained by the proposed error indicator closely follow the optimal trajectory.

• Korean Journal of Social Welfare
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• v.64 no.2
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• pp.273-298
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• 2012

This study is targeted to understand the giving of time and money among citizens in Seoul. It explores predictors of different combinations of giving behaviors: only volunteering, only donating, doing both, and neither. In exploring predictors, it also considers the effects of intensity in giving with differential measurement of general participation and regular participation. For the analysis, this study utilizes Seoul Welfare Panel Survey Data in 2008 and 2010 and employes a multi-nominal logistic regression model with selection of citizens' demographic factors, pro-social attitude factors, and psycho-social factors on these four type of giving behaviors. The findings show that previous giving experiences(+) and religious activities(+) have statistically significant effects on both selective and combined giving behavior. Gender(woman), income(+), education(+) have statistically significant effects on giving behaviors(only donating and doing both) related with donation. In the case of employment state, unemployment is significantly related to volunteering while employment is significantly related to donating. Finally, happiness(+) has significantly positive effect on intensive giving such as regular giving rather than general giving. This analysis has made a start in a new area of inquiry attempting to explain different giving behaviors with broadening and promoting understanding on giving behaviors. Moreover, it raises several implications for future research and strategic practice for resource mobilization of NPOs.

• Korean Journal of Social Welfare
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• v.64 no.2
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• pp.133-158
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• 2012

In the modern society, a growth in the non-profit sector and a decrease in the government budget on social welfare result in a greater need of individual giving and volunteering. Therefore, in order to manage non-profit organizations effectively, it is necessary to encourage citizen participation in giving and volunteering through identifying various factors affecting giving and volunteering. In order to identify what factors are affecting participation in giving and volunteering and whether the relationship between giving and volunteering is multiplicative or compensatory, this study uses multinomial logistic regression analysis by categorizing four groups based on the participation types of giving and volunteering. The research findings confirmed that common factors such as religion and satisfaction with leisure time were still significant, and specific factors were also found among factors affecting participation in giving and volunteering. Especially, this study identified that factors affecting giving and volunteering differ according to household income, education level, employment status, gender and social relationship satisfaction. The findings confirmed that giving and volunteering are compensatory behaviors. Finally, the implications of this study were discussed. A differential strategy for giving and volunteering is needed to encourage citizen participation in non-profit organizations.