• East Asian Economic Review
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• v.27 no.3
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• pp.243-269
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• 2023

The multidimensional poverty index is an indicator system established for defining and evaluating poverty, to understand poverty in dimensions beyond just monetary scarcity. Based on income, education, health, living standards, and social dimensions, this article measures and analyzes the level of multidimensional poverty in Xinjiang using the AlkireFoster method, with cross-sectional data obtained from a 2022 survey. Probit model is constructed for regression analysis, further considering the impact of education on enhancing feasible capabilities and alleviating multidimensional poverty at the post-poverty alleviation era. The data shows that many people still face significant challenges from the perspective of multidimensional poverty; the decomposition results of each dimension show that education contributes more to the multidimensional poverty; the regression analysis results show that the higher the education level, the lower the multidimensional poverty; heterogeneity analysis revealed that the inhibitory effect of education on multidimensional poverty is greater for females than males, and the poverty reduction effect of education mainly concentrates on middle-aged and older individuals. This article is meaningful for exploring strategies to alleviate multidimensional poverty in ethnic minority regions in frontier areas in the new era, accelerating regional economic development, and achieving shared prosperity.

• Journal of Fisheries and Marine Sciences Education
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• v.24 no.3
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• pp.436-445
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• 2012

The study aims to analyze of factors influencing on the mentoring participation of college student for low-income children using Cooper's multiple lense. The multidimensional policy analysis model is composed of the normative dimension, structural dimension, constructive dimension, technological dimension. The results of the research are as follows. First, the education difference solution shows the meaningful positive relationship in the category of normative dimension. Second, the budget and support setup shows the meaningful positive relationship in the category of technological dimension. But other factors do not show the meaningful influence.

• The Journal of Information Technology and Database
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• v.6 no.2
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• pp.115-130
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• 1999

The price structure of housing market varies depending upon market price policy rather than low or high price policy because of IMF. The object of this study is to develop an analysis system for analyzing housing market and its demand. The analysis system consists of four major categories: macro index analysis, market decision analysis, housing market analysis, and consumer analysis. We model each category by using a variety of techniques such as generalized linear model, categorical analysis, bubble analysis, drill-down analysis, price sensitivity meter analysis, optimum price index analysis, profit index measurement analysis, correspondence analysis, conjoint analysis, and multidimensional scaling analysis. Seoul apartment data is analyzed to demonstrate the practical usefulness of the system.

• Health Policy and Management
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• v.21 no.2
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• pp.213-248
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• 2011

Past studies on organizational accountability have had similar limitations. First, empirical evidence of organizational accountability is rare as the majority of research takes a conceptual approach of the topic. Only a few of these studies are applicable to health care organizations (HCOs). To fill these gaps, we attempted to develop a model for analysis of organizational accountability for HCOs. Accountability for HCOs was conceptualized by two axes: answerability(X, horizontal) and value-creation(Y, vertical). Our concept building could relieve competing accountability mechanism which past studies stressed. Four elements of accountability(legal, economical, social, and clinical) were applied to specify each of the two features of organizational accountability. And then four types of accountability behavior were coordinated by this x-y axis : high A/high VC, high A/low VC, low A/high VC, low A/low VC. Finally, a multidimensional model of HCOs' accountability, enabling an empirically testable multi-level analysis, was proposed.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.1
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• pp.135-144
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• 2024

In this study, we propose an automatic classification model for quantitative multidimensional analysis based on facet theory to understand public opinions and demands on major issues through big data analysis. Civil complaints, as a form of public feedback, are generated by various individuals on multiple topics repeatedly and continuously in real-time, which can be challenging for officials to read and analyze efficiently. Specifically, our research introduces a new classification framework that utilizes facet theory and political analysis models to analyze the characteristics of citizen complaints and apply them to the policy-making process. Furthermore, to reduce administrative tasks related to complaint analysis and processing and to facilitate citizen policy participation, we employ deep learning to automatically extract and classify attributes based on the facet analysis framework. The results of this study are expected to provide important insights into understanding and analyzing the characteristics of big data related to citizen complaints, which can pave the way for future research in various fields beyond the public sector, such as education, industry, and healthcare, for quantifying unstructured data and utilizing multidimensional analysis. In practical terms, improving the processing system for large-scale electronic complaints and automation through deep learning can enhance the efficiency and responsiveness of complaint handling, and this approach can also be applied to text data processing in other fields.

• The Journal of the Korea Contents Association
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• v.21 no.6
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• pp.415-424
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• 2021

The life of studying abroad includes not only school life, but also various areas such as economy, social relationship, and culture, so the level of satisfaction in each area could be differently shown in each individual. Based on this critical mind, this study aims to analyze the satisfaction with life of studying abroad in the multidimensional perspective. To analyze this, a latent class analysis was applied to identify subgroups, and a multinomial logistic regression model was applied to verify factors influencing group classification. The results of the analysis could be summarized into two. First, there were sub-groups showing different satisfaction with life of studying abroad. The sub-groups showed different levels of satisfaction in five areas such as housing, economy, social relationship, study, and culture, which were not discerned in single dimension. Second, the classification of group was complexly influenced by academic factor, psychological/emotional factor, and environmental factor. Especially, the predictive factor had different influences on each sub-factor. Based on such results of this study, this study aims to seek for the practical and policy-level suggestions for improving foreign students' satisfaction with life of studying abroad.

• Health Policy and Management
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• v.15 no.4
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• pp.46-64
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• 2005

This study was conducted to verify the current criteria and classification system used to determine specialized general hospitals status. In this study, we proposed a new classification system which Is simpler and more convenient than the current one. In the new classification system clinical procedure was chosen as the unit of analysis in order to reflect all the resource consumption and the complexities and degree of medical technologies in determining specialized general hospitals. We developed a statistical model and applied this model to 117 general hospitals which claim their national insurance through electronic data interchange(EDI). Analysis based on 984 clinical procedures and medical facilities' characteristic variable discriminated specialized general hospital in present without misclassification. It means that we can determine specialized general hospital's permission In new way without using the current complicated criteria. This study discriminated specialized general hospital by the new proposed model based on clinical procedures provided by each hospital. For clustering the same types of medical facilities using 984 clinical procedures, we executed multidimensional scale analysis and divided 117 hospitals into 4 groups by two axises : a variety of procedure and the Proportion of high technology Procedure. Therefore, we divided 117 hospitals into 4 groups and one of them was considered as specialized general hospital. In discriminating analysis, we abstracted proportion of 16 clinical procedures which effect on discriminating the specialized general hospital in statistical system also we identify discriminating function which include these variables. As a result, we identify 2 discriminating functions, one is for current discriminating system and the other two is for new discriminating system of specialized general hospital.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.10B
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• pp.946-961
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• 2008

This study aims to provide guidelines on future policy for restructuring the scheme of aid programs associated with If small and medium-sized enterprises (i.e. SME) in Korea. For this purpose, we investigate an empirical dataset of recent aid programs deployed by Ministry of Information and Communication (i.e. MIC) for the last four years First, it is examined that the programs are practiced in accordance with their own policy objective by comparing matching samples between two groups such as program beneficiary and non-beneficiary companies. Second, positioning transition of programs within a same category is visualized in terms of two business portfolio analysis matrices. Third, an affiliation network matrix of (he programs is newly developed and then we attempt to analyze the programs relationship by the application of multidimensional scaling method to the affiliation network matrix. The empirical dataset is composed of two different kinds of corporate datasets. One is a corporate dataset of 8,994 beneficiary companies that are aided by MIC during the year of '03-'06. The other is also a corporate dataset of 18,354 non-beneficiary companies that have no records of the program supports during the years at all. Particularly, the matching samples of non-beneficiary companies are prepared in order to have comparable corporate age years (i.e. CAY) against beneficiary companies' CAY. Results show that; 1) up-to-date, the programs are properly assigned to IT SME conforming to their own policy objective; 2) however, as the year goes on, the following two distinct positioning transitions are revealed such as (1) both CAY and corporate sales (i.e. SAL) are increased simultaneously, (2) ratio of intangible assets (i.e. RIA) is decreased and ratio of operating gain to revenue (i.e. ROR) is increased. Hence, the role of the programs gets weakened with regard to providing seed money to technology innovation-typed IT SME so that a managerial adjustment of the programs is required consequently; 3) even though the model adequacy is not satisfactory through the analysis of multidimensional scaling method, the relationship of indirect-typed programs can relatively be stronger than that of direct-typed programs.

• The Journal of Bigdata
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• v.5 no.1
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• pp.11-27
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• 2020

In this paper, we developed a big data platform to store, process, and analyze effectively on such education longitudinal study data. And it was applied to the Seoul Education Longitudinal Study(SELS) to confirm its usefulness. The developed platform consists of data preprocessing unit and data analysis unit. The data preprocessing unit 1) masking, 2) converts each item into a factor 3) normalizes / creates dummy variables 4) data derivation, and 5) data warehousing. The data analysis unit consists of OLAP and data mining(DM). In the multidimensional analysis, OLAP is performed after selecting a measure and designing a schema. The DM process involves variable selection, research model selection, data modification, parameter tuning, model training, model evaluation, and interpretation of the results. The data warehouse created through the preprocessing process on this platform can be shared by various researchers, and the continuous accumulation of data sets makes further analysis easier for subsequent researchers. In addition, policy-makers can access the SELS data warehouse directly and analyze it online through multi-dimensional analysis, enabling scientific decision making. To prove the usefulness of the developed platform, SELS data was built on the platform and OLAP and DM were performed by selecting the mathematics academic achievement as a measure, and various factors affecting the measurements were analyzed using DM techniques. This enabled us to quickly and effectively derive implications for data-based education policies.

• Journal of Digital Convergence
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• v.16 no.6
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• pp.251-258
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• 2018

We study convergent factors to depression(CES-D: Center for Epidemiologic Studies Depression scale) among administrative staff in general hospital. The questionnaire was used using an unregistered self-administered questionnaire for 201 staff from 9 general hospitals located in J area from Jul. 3rd, 2017 to Jul. 29th, 2017. The hierarchical multiple regression analysis shows the following results. The depression of respondents turned out to be significantly higher in following groups: a group in which Rosenberg Self-Esteem Scale(RES) is lower, a group in which Multidimensional Fatigue Scale(MFS) is higher, a group in which Psychosocial Well-being Index Short Form(PWI-SF) are higher. The results show explanatory power of 32.5%. The results of the study indicate that the efforts, to increase RES, and to decrease MFS and PWI, are required to improve the depression among administrative staff in general hospital. These results could be used in organizing human resource management and industrial health education to lower the level of depression in general hospital administration staff. Following studies need to analyze the structural equation model that effects the depression levels of administrative staff in general hospital.