• Journal of the Korea Safety Management & Science
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• v.25 no.3
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• pp.63-71
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• 2023

The construction industry stands out for its higher incidence of accidents in comparison to other sectors. A causal analysis of the accidents is necessary for effective prevention. In this study, we propose a data-driven causal analysis to find significant factors of fatal construction accidents. We collected 14,318 cases of structured and text data of construction accidents from the Construction Safety Management Integrated Information (CSI). For the variables in the collected dataset, we first analyze their patterns and correlations with fatal construction accidents by statistical analysis. In addition, machine learning algorithms are employed to develop a classification model for fatal accidents. The integration of SHAP (SHapley Additive exPlanations) allows for the identification of root causes driving fatal incidents. As a result, the outcome reveals the significant factors and keywords wielding notable influence over fatal accidents within construction contexts.

• Journal of the Korean Home Economics Association
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• v.37 no.10
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• pp.107-122
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• 1999

Based on the theoretical framework of the Double ABCX model of family stress and adaptation, this study was to analysis the causal relationships of stressors (psychological sexual, and physical wife abuse), personal resources (self-efficacy and self-esteem) and social support(emotional and informational support), appraisal(positive appraisal and downward comparisons) with adaptation (psychological well-being and somatic symptoms). Self-administered questionnaire method was used to collect data from 264 wives residing in Chonbuk-do and Kyonggi-do area. The causal model was tested and modified by the maximum likelihood method using UISREL 7 program. Results showed that wife abuse had effect on adaptation indirectly through the latent variables of personal resource and appraisal, which influenced the adaptation directly. In addition, social support indirectly affect the adaptation through personal resource and appraisal. It was also found that all these variables explained 27.6% of the total variance of wives'adaptation, and that personal resources was the most powerful variable in predicting the adaptation of the wives.

• International Commerce and Information Review
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• v.10 no.1
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• pp.329-347
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• 2008

The purpose of this study is to examine the causal relationship between the exchange rate and economic growth, and to induce policy implications. In order to test whether time series data is stationary and the model is fitness or not, we put in operation unit root test, cointegration test. And we apply Granger causality based on an error correction model. The results indicate that uni-dierctional causality between exchange rate and economic growth is detected. Exchange rate impacts on economic growth, but economic growth don't impact on exchange rate. The analysis of impulse reaction function shows that the impulse of exchange rate impacts on Korean economic growth in negative direction. We can infer policy suggestion as follows: The fluctuation of exchange rate much affects economic growth, thus we must make a stable policy of exchange rate to continue economic growth.

• Communications for Statistical Applications and Methods
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• v.27 no.6
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• pp.675-688
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• 2020

In survival analysis of observational data, the inverse probability weighting method and the Cox proportional hazards model are widely used when estimating the causal effects of multiple-valued treatment. In this paper, the two kinds of weights have been examined in the inverse probability weighting method. We explain the reason why the stabilized weight is more appropriate when an inverse probability weighting method using the generalized propensity score is applied. We also emphasize that a marginal hazard ratio and the conditional hazard ratio should be distinguished when defining the hazard ratio as a treatment effect under the Cox proportional hazards model. A simulation study based on real data is conducted to provide concrete numerical evidence.

• Journal of Korean Academy of Nursing
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• v.30 no.2
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• pp.366-379
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• 2000

The purpose of this study was to identify causal relationships among variables of transtheoretical model for exercise in the elderly A predictivel model explaining the stage of change was constructed based on a transtheoretical model. Empirical data for testing the hypothetical model was collected from 198 old adults over 60 years old in a community setting in Seoul, Korea in April and May,1999. Data were analyzed by descriptive statistics and correlational analysis using pc-SAS program. The Linear Structural Modeling (LISREL) 8.0 program was used to find the best fit model which predicts causal relationship of variables. The fit of the hypothetical model to the data was X2=132.85. (df=22, p=.000). GFI=.88, NNFI=.35, NFI=.77, AGFI=.59 which was not favorable but the fit of modified model to the data was X2=46.90. (df=27, p=.01).GFI= .95, NNFI=.91, NFI=.92, AGFI=.87) which was more than moderate. The predictable variables of stage of change for exercise of the Korean elderly were helping relationship, self cognitive determination, conversion of negative condition in process of change and efficacy for exercise. These variables explained 68% of stage of change for exercise of the Korean elderly.

• Journal of the Korean Society of Food Culture
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• v.23 no.3
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• pp.301-307
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• 2008

The purpose of this study was to assess the causal relationships among belief, subjective norm, moral feeling, attitude, and the intention to consume organic beef. A total of 326 questionnaires were completed by adult in Jeonnam area. Structural equation modeling was used to measure the causal relationships among the constructs. The structural analysis results indicated an excellent model-data fit. The covariance effects of belief and subjective norm, and belief and moral feeling were statistically significant. The effects of belief and moral feeling on attitude and intention to consume were also statistically significant. As expected, attitude had significant effects on intention to consume. Moreover, attitude played a mediating roles in the relationship between belief and intention to consume. Finally, based on structural analysis, a model was proposed for the interrelations among belief, subjective norm and moral feeling towards organic food, attitude and intention to consume. It should be noted that the original organic food consumption model was modified, and should preferably be validated by future research. Other constructs, such as perceived behavior control and consumption, may be incorporated to form models that consist of new antecedent and consequence pairs.

• The Journal of Information Systems
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• v.28 no.3
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• pp.203-225
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• 2019

Purpose The purpose of this study is to empirically verify an ePortfolio success model which shows the structural causal relationships among the basic modeling constructs. We developed the success model based on DeLone and Mclean(2003) from IS perspective and on Balaban et al(2013) from ePortfolio perspective. And we are focused on the success of ePortfolio system designed for college students and professors. Design/methodology/approach Structural equation modeling techniques were applied to data collected 375 users of the ePortfolio system, K-Folio, at least one semester during one academic year. The structural model was analyzed using a Lisrel 8.54 statistical program. Findings The followings are the findings of the study. First, the constructs of IS success model are well applied to ePortfolio for college education. Second, the proposed model which has direct causal relationships from ePortfolio's quality to performance is more significant than the D&M and Balaban's model(2013). Third, the information quality among ePortfolio quality constructs does not have effect on system usability and performance. It means that information contents' quality issued by professor or system manager is not satisfied with the students yet. Finally, by using indirect analysis of structural equation model, we found that the ePortfolio operation manager should focus on ensuring system maintenance activities and providing fast and accurate services for improving ePortfolio performance.

• Journal of the Korean Home Economics Association
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• v.50 no.7
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• pp.59-66
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• 2012

The purpose of this study was to identify a causal relationship in the career preparation behavior, parental social support, career decision making self-efficacy and the career maturity of the pre-service elementary school teachers. A total of 374 questionnaires were used for data analysis, excluding the 23 copies deemed insincere in response. To ensure the reliability and validity of the questions, technical statistics of the frequency, ratio, average, standard deviation, skewness, and kurtosis via PASW 18.0, item-total correlation, the totality, and the reliability analysis. The structural analysis via AMOS 7.0 in the bootstrapping method was undertaken to perform the path analysis among the variables and to assess the suitability of the model. The findings of the study led to the following conclusions: First, the causal model for the career preparation behavior, parental social support, career decision making self-efficacy, and the career maturity of the pre-service elementary school teachers is suitable to empirical analysis on research variables. Second, the career decision making self-efficacy of pre-service elementary teachers has direct effect on career preparation behavior positively. Third, parental social support of the pre-service elementary teachers has indirect effects on the career preparation behavior positively.

• Journal of the Korean Home Economics Association
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• v.51 no.1
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• pp.89-95
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• 2013

The purpose of this study is to identify a causal relationship among green consumer behavior, environmental knowledge and environmental attitudes of students at the university of education. A total of 366 copies of questionnaires were used for the data analysis; 31 copies were excluded due to lack of response to any given question. To ensure the reliability and validity of the questions, technical statistics were performed, such as frequency, ratio, average, standard deviation, skewness, and kurtosis via SPSS 15.0, item-total correlation and the totality, and reliability analysis. A structural analysis was undertaken via AMOS 7.0 in a bootstrapping method in order to perform a path analysis among variables as well as to assess the suitability of the model. The findings of the study were led to the following conclusions: First, the causal model among green consumer behavior, environmental knowledge and environmental attitudes of students at the university of education is suitable to the empirical analysis on research variables. Second, the environmental attitudes of students at the university of education has a direct, positive effect on green consumer behavior. Third, the environmental knowledge of students at the university of education has an indirect, positive effect on green consumer behavior.

• Journal of the Korean Society of Safety
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• v.36 no.3
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• pp.50-59
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• 2021

The main purpose of accident analysis is to identify the causal factors and the mechanisms of those factors leading to the accident. However, current accident analysis techniques focus only on finding the factors related to the accident without providing more insightful results, such as structures or mechanisms. For this reason, preventive actions for safety management are concentrated on the elimination of causal factors rather than blocking the connection or chain of accident processes. This greatly reduces the effectiveness of safety management in practice. In the present study, a technique to model the correlational structure of accident risk factors is proposed by using the co-occurrence keyword network analysis technique. To investigate the effectiveness of the proposed technique, a case study involving a portable ladder fall accident is conducted. The results indicate that the proposed technique can construct the correlational structure model of the risk factors of a portable ladder fall accident. This proves the effectiveness of the proposed technique in modeling the correlational structure of accident risk factors.