• The Journal of the Korea Contents Association
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• v.15 no.1
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• pp.233-241
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• 2015

The purpose of this study was to investigate the binge eating of university women and to discuss whether there was a mediating effect of job-stress. This study conducted a survey of 325 university women using the self-oriented perfectionism, job-stress and binge eating. Descriptive, correlation statistics with SPSS 18.0 and structural equation analysis with AMOS 20.0 was performed. The results of this study were as follows. First, the binge eating of female university students was lower than intermediate level. Second, the self-oriented perfectionism of female university students had a meaningful positive influence on the binge eating. Third, the job-stress had a partial mediating effect between the self-oriented perfectionism and the binge eating. As a result of this research, comprehensive implications were suggested for interventions.

• The Korean Journal of Applied Statistics
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• v.34 no.3
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• pp.309-327
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• 2021

Dynamic treatment regimes (DTRs) are decision-making rules designed to provide personalized treatment to individuals in multi-stage randomized trials. Unlike classical methods, in which all individuals are prescribed the same type of treatment, DTRs prescribe patient-tailored treatments which take into account individual characteristics that may change over time. The Q-learning method, one of regression-based algorithms to figure out optimal treatment rules, becomes more popular as it can be easily implemented. However, the performance of the Q-learning algorithm heavily relies on the correct specification of the Q-function for response, especially in observational studies. In this article, we examine a number of double-robust weighted least-squares estimating methods for Q-learning in high-dimensional settings, where treatment models for propensity score and penalization for sparse estimation are also investigated. We further consider flexible ensemble machine learning methods for the treatment model to achieve double-robustness, so that optimal decision rule can be correctly estimated as long as at least one of the outcome model or treatment model is correct. Extensive simulation studies show that the proposed methods work well with practical sample sizes. The practical utility of the proposed methods is proven with real data example.

• Korean Journal of Social Welfare
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• v.67 no.4
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• pp.279-299
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• 2015

This study examines the impact of Long-Term Care Insurance(LTCI) on family caregivers(especially focused on female household members) labor supply in South Korea. When public care and informal care are substitutes, LTCI will change allocation of time of family caregivers to spend more time to paid work. The impact of LTCI on labor supply depends on each country's institutional level of public care services. If public care can not substitute for informal care, labor supply of family caregivers will not rise significantly. The conclusions of vigorous empirical study from western countries' are incompatible and problem of endogeneity in terms of methodology has been raised consistently. The dataset of this study are used the third and ninth waves of Korea Welfare Panel. As a result, the introduction of LTCI had no effect on labor supply of household members. Robust findings suggest the positive effects of caregiving on labor market outcomes in simple comparison t-test, but not in fixed-effect regression. Compared with western countries, South Korea's public care services can be interpreted as a supplement to only part that remained at the level does not substitute informal care. These findings may suggest that if LTCI become much more prevalent in the future, senior citizens and family members will be able to choose the LTCI arrangement that best suits their needs.

• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1317-1325
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• 2015

There are two types of clinical research to figure out risk factor for disease using collected data. One is prospective study to approach the subjects from the present time and the other is retrospective study to find the risk factor using the subject's information in the past. Both approached and study design are different but the purpose of the two studies is to identify a significant difference between two groups and to find out what the variables to influence groups. Especially when comparing the two groups in clinical research, we have to look at the difference between the impact clinical variables by group while controlling the influence of the baseline characteristics variables such as age and sex. However, in the retrospective study, the difference of baseline characteristic variables can occur more frequently because the past records did not randomly assign subjects into two groups. In clinical data analysis use covariates to solve this problem. Typically, the analysis method using the analysis of covariance of variance, adjusted model, and propensity score matching method. This study is introduce the way of equality adjustment between groups data analysis using covariates in retrospective clinical studies and apply it to the recurrence of gastric cancer data.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.1
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• pp.225-236
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• 2023

Policy implications were derived by comparing/analyzing innovative SMEs and general SMEs that obtained innovation certification from 2015 to 2021 in terms of survival period. Work experience, scale (employment, capital and debt size, sales and operating profit) Korean standard industry classification (2 digit) was used to select general SMEs similar to innovative SMEs. Survival period was calculated by defining suspension, closure and overdue equivalent to default as events. As a result of the survival analysis, innovative SMEs showed a 9.8% reduction in the risk of delinquency compared to general SMEs, indicating that the survival period of innovative SMEs was significantly longer. In addition, it was found that the work experience and size (employment, capital) of SMEs had a positive effect on the survival period, but debt had a negative effect on the survival period. This means that the innovation certification system centered on innovation capabilities and future growth potential is a significant indicator in terms of survival period. As a result, it was concluded that the benefits and support policies provided by the innovation certification system need to be more systematic and sophisticated by reflecting the work experience and industry for the actual growth and survival of SMEs.