• Korean Journal of Adult Nursing
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• v.18 no.2
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• pp.213-222
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• 2006

Purpose: This study was designed to construct a structural model for explaining model health-related quality of life in acquired people with physical disabilities. Method: The hypothetical model of this study was consisted of 6 latent variables and 14 observed variables. Exogenous variables included in this model were physical status and economical level. Endogenous variables were social attitudes, family function, self-esteem, and health-related quality of life. Data were collected from 226 acquired people with physical disabilities residing in Seoul and Kyunggi-do from January to February, 2005. The collected data were analyzed using SAS 8.2 version and LISREL 8.32 version program. Results: The results of the fitness test of the modified model were follow as; ${\chi}^2=67.479$ (df=50, p=.05), GFI=.959, AGFI=.914, SRMR=.049, NFI=.961, NNFI=.979, CN=249.244. Health-related quality of life was influenced directly by physical status, economic level, and social attitudes and accounted for 88.8% of the variance by these factors. Conclusion: These results suggest that physical status is the most significant effect on health-related quality of life, and social attitudes and economic level are important factors having influences on health- related quality of life. Therefore improving physical status and economic level, and modifying negative attitudes are necessary to increase health-related quality of life of acquired people with acquired physical disabilities.

• Korea Journal of Hospital Management
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• v.6 no.2
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• pp.22-36
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• 2001

The hospital bankruptcy rate is increasing, therefore it is very important to predict the bankruptcy using the existing hospital management information. The hospital bankruptcy is often measured in year intervals, called grouped duration data, not by the continuous time elapsed to the bankruptcy. This study introduces a hierarchical generalized linear model(HGLM) for analysis of hospital bankruptcy data. The hazard function for each hospital may be influenced by unobservable latent variables, and these unknown variables are usually termed as random effects or frailties which explain correlations among repeated measures of the same hospital and describe individual heterogeneities of hospitals. Practically, the data of twenty bankrupt and sixty profitable hospitals were collected for five years, and were fitted to HGLM. The results were compared with those of the logit model. While the logit model resulted only in the effects of explanatory variables on the bankruptcy status at specific period, the HGLM showed variables with significant effects over all observed years. It is concluded that the HGLM with a fixed ratio and a period of total asset turnrounds was justified, and could find significant within and between hospital variations.

• Communications for Statistical Applications and Methods
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• v.24 no.2
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• pp.143-154
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• 2017

A variable selection method based on probabilistic principal component analysis (PCA) using penalized likelihood method is proposed. The proposed method is a two-step variable reduction method. The first step is based on the probabilistic principal component idea to identify principle components. The penalty function is used to identify important variables in each component. We then build a model on the original data space instead of building on the rotated data space through latent variables (principal components) because the proposed method achieves the goal of dimension reduction through identifying important observed variables. Consequently, the proposed method is of more practical use. The proposed estimators perform as the oracle procedure and are root-n consistent with a proper choice of regularization parameters. The proposed method can be successfully applied to high-dimensional PCA problems with a relatively large portion of irrelevant variables included in the data set. It is straightforward to extend our likelihood method in handling problems with missing observations using EM algorithms. Further, it could be effectively applied in cases where some data vectors exhibit one or more missing values at random.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.2
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• pp.44-57
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• 2019

It is crucial to understand how people make decisions on mode choice and to accurately predict their behaviors in transportation planning. One of avenues for advancing modeling is, in particular, taking into account for taste heterogeneity in modeling that can incorporate different decision-making processes across group. In this study, we hypothesize that how people make decisions on mode choice would differ by destination in that land use characteristics are heterogeneous by zone even if zones are all in the same area. To this end, we apply Latent Class Modeling (LCM) to commute trips in Seoul by using 2010 household travel diary survey, investigate types of latent classes with the aid of characteristics of destination, and analyze how those classes differently response to factors. The LCM identifies two classes: in the first one, modal split of auto and public transit (bus and metro) is almost half-and-half and the trip destinations are characterized by relatively more residence facilities and less business/commercial facilities; in the second one, public transit has a notably high share and trip destinations are characterized by relatively more business/commercial facilities. In addition, it turns out that demographic and socio-economic variables affect mode choice differently by class.

• 한국노년학
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• v.31 no.3
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• pp.733-749
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• 2011

The purpose of this research are as follows. The first is to investigate the mediating effect of interpersonal skill on the relationship between the ability to use Internet and social activity among the 50's and 60's. The second is to compare 60's latent mean of core variables with 50's ones in terms of information gap. The data was Seoul welfare panel data made by Seoul welfare foundation in 2008 and the total subject was 941(50's=644, 60's=297). When it comes to the research methods, structured equation analysis for verifying the mediating effect and latent mean analysis for comparing the two groups were practiced. The results of this research are as follows. First of all, interpersonal skill did function as partial mediator. Second, according to the latent mean analysis, the group of 50's showed a more higher level of the ability to use Internet. On the other hand, the group of 60's revealed a more active social participation. In conclusion, Information educational programs should focus on Internet communication skill for enhancing social activities of the elderly and consider the differentiations among the elder generations.

• Korean Journal of Culture and Social Issue
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• v.25 no.4
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• pp.325-351
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• 2019

The present study examined patterns of co-occurrence between DSM-5 posttraumatic stress disorder(PTSD) symptoms and posttraumatic growth(PTG) among Korean populations(n= 860). Latent profile analysis was used to identify subclasses and suggested that the 3-class model fit best: (1) Low PTSD/Mild PTG group (2) Low PTSD/High PTG group; (3) High PTSD/High PTG group. Class membership was predicted by demographic variables, social isolation, and frequency of traumatic experiences. Classes also differed with respect to self-destructive behaviors(binge eating, non-suicidal self-injury, and problem drinking). These findings contribute to future research about the coexisting patterns of PTSD and PTG, and to identify high-risk individuals who suffer from trauma-related problems in clinical practice.

• The Mathematical Education
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• v.63 no.1
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• pp.19-33
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• 2024

This study investigates longitudinal patterns in middle school students' mathematics interest and achievement using panel data from the 4th to 6th year of the Gyeonggi Education Panel Study. Results from the multivariate growth mixture model confirmed the existence of heterogeneous characteristics in the longitudinal trajectory of students' mathematics interest and achievement. Students were classified into four latent classes: a low-level class with weak interest and achievement, a high-level class with strong interest and achievement, a middlelevel-increasing class where interest and achievement rise with grade, and a middle-level-decreasing class where interest and achievement decline with grade. Each class exhibited distinct patterns in the change of interest and achievement. Moreover, an examination of the correlation between intercepts and slopes in the multivariate growth mixture model reveals a positive association between interest and achievement with respect to their initial values and growth rates. We further explore predictive variables influencing latent class assignment. The results indicated that students' educational ambition and time spent on private education positively affect mathematics interest and achievement, and the influence of prior learning varies based on its intensity. The perceived instruction method significantly impacts latent class assignment: teacher-centered instruction increases the likelihood of belonging to higher-level classes, while learner-centered instruction increases the likelihood of belonging to lower-level classes. This study has significant implications as it presents a new method for analyzing the longitudinal patterns of students' characteristics in mathematics education through the application of the multivariate growth mixture model.

• Journal of Labour Economics
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• v.38 no.1
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• pp.1-30
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• 2015

The aggregate matching function is estimated to explain dynamics among job seekers, vacancies and new hires in Korea. Due to measurement errors inherent in vacancies data, I introduce a latent variable for job openings and use the instrumental variables to correct its endogeneity. Matching efficiency is also estimated using some explanatory variables like job seekers' characteristics and public employment services. The result shows that Korea's matching function also exhibits a constant returns to scale.

• Journal of Information Technology Applications and Management
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• v.23 no.3
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• pp.25-34
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• 2016

Often we receive misled conclusion in the research if properly variables are not analyzed. In different functional issues of management it is very essential that all the latent and observed variable are properly understood so management decisions will be relevant and effective. The objective of this paper is to investigate the use of different multivariate tools for analyzing in the management research : applied or basic. The sources of data is primary as well as secondary. The primary includes the observation of different research articles of the proceedings of different conferences. And the secondary includes different publications related to multivariate analysis. The study has revealed the reasons of not using such tools of research. The preliminary finding reveals that most of the researches do not use such analytical tools in a comprehensive manner. Carelessness in design while fixing the design aspect is the main reasons of not using appropriate design.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1998.03a
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• pp.179-184
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• 1998

Semi-solid forging can be applied in industry only with enough knowledge of the effects of the forming parameters related with the process and their exact control which can be obtained by empirical or numerical methods. In the current study, the effects of process variables on semi-solid forging are discussed based on mainly numerical results. Die preheating temperature, initial solid fraction of the workpiece, and die velocity were selected as process variables, and numerical analyses using a rigid-thermoviscoplastic finite element approach that considered the release of latent heat due to phase change were carried out. In the analyses, a proposed flow stress material characterization and a solid fraction updating algorithm were employed. The obtained results from numerical analysis are discussed and are compared with some experimental observations.