• Asia pacific journal of information systems
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• v.14 no.2
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• pp.151-172
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• 2004

본 연구는 가상커뮤니티의 상업화에 관한 연구이다. 가상커뮤니티의 상업화에 미치는 영향요인을 문헌조사에 의하여 정리하고 가상커뮤니티 사이트를 실증 분석하였다. 공변량 구조방정식을 이용하여 연구모형을 제시하였으며 215개의 설문내용에 대해서 통계분석을 하였다. 본 연구결과는 현재 가상커뮤니티를 구축하거나, 운영중인 기업의 의사결정과 운영 전략 수립에 기여할 수 있을 뿐 아니라 가상커뮤니티의 비즈니스 모델을 제시하는데 유용한 자료가 될 것이다.

• Journal of the Korean Data and Information Science Society
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• v.7 no.1
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• pp.145-159
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• 1996

다변량 발병시간자료는 각 개개 환자에게 있어 합병증이 발생되거나 혹은 유사 환자군(집락) 내의 발병시간이 상관되어진 생의학자료에서 흔히 볼 수 있다. HMULCOX는 그런 자료를 분석하기 위한 한글 통계 패키지 가운데 하나이다. 이 프로그램은 관련된 발병시간들이 독립이 아닐때에도 COX 비례 위험 모형의 주변확률분포를 계산해 준다. 주어진 조건으로는 주변확률모형의 기본위험율은 일정한 상수, 흑은 변수라도 관계없다. 또한 치료실패율의 치료변수들(공변량)의 효과에 대해 다양한 통계적 추론이 가능하다. 기본적으로 주변확률분포접근법으로 설계되었지만 HMULCOX는 여러 가지 추론 방법을 선택하는 데 일반적으로 충분하다. 이 프로그램으로 2개의 예를 들어 실행하겠다.

• Journal of agriculture & life science
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• v.45 no.6
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• pp.109-114
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• 2011

The objective of this study was to investigate the effects of environments (farms born, testing groups, age at the tests, date at slaughter, ages at slaughter) on body weights at 6, 12, 18 and 24 months of ages, body type measurements at 18 months of age and carcass characteristics in Hanwoo steer populations that were collected from commercial farms and reared in a progeny testing station. Performances of a total of 1,838 steer calves set for tests from 2004 to 2008 were recorded. Carcass characteristics were the carcass grading results evaluated and data collected slaughter scores at 24 months of age. For growth traits of all age classes and body type traits measured at 18 months of age, farms born, test group and linear covariate of age at test were fit in the models. For carcass traits, date at slaughter and linear covariate of ages at slaughter were fit in the models. Effect of farm at birth was not significant for body weight at 24 months of age. Carcass weight, eye muscle area, yield score and back fat thickness were affected by dates at slaughter but not by the ages at slaughter. Marbling score, however, was affected by these two effects. Farms at birth did not seem to affect body type measures greatly. This study will be utilized for Hanwoo Steers genetic evaluation.

• The Korean Journal of Applied Statistics
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• v.36 no.4
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• pp.323-335
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• 2023

In causal analysis of high dimensional data, it is important to reduce the dimension of covariates and transform them appropriately to control confounders that affect treatment and potential outcomes. The augmented inverse probability weighting (AIPW) method is mainly used for estimation of average treatment effect (ATE). AIPW estimator can be obtained by using estimated propensity score and outcome model. ATE estimator can be inconsistent or have large asymptotic variance when using estimated propensity score and outcome model obtained by parametric methods that includes all covariates, especially for high dimensional data. For this reason, an ATE estimation using an appropriate dimension reduction method and semiparametric model for high dimensional data is attracting attention. Semiparametric method or sparse sufficient dimensionality reduction method can be uesd for dimension reduction for the estimation of propensity score and outcome model. Recently, another method has been proposed that does not use propensity score and outcome regression. After reducing dimension of covariates, ATE estimation can be performed using matching. Among the studies on ATE estimation methods for high dimensional data, four recently proposed studies will be introduced, and how to interpret the estimated ATE will be discussed.

• Journal of the Korean Data and Information Science Society
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• v.28 no.3
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• pp.547-557
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• 2017

The health-related quality of life data are commonly skewed and bounded with spike at the perfect health status, and the variance tended to be heteroscedastic. In this study, we have developed a prediction model for EQ-5D using linear regression model, beta regression model, and extended beta regression model with mean and precision submodel, and also compared the predictive accuracy. The extended beta regression model allows to model skewness and differences in dispersion related to covariates. Although the extended beta regression model has higher prediction accuracy than the linear regression model, the overlapped confidence intervals suggested that the extended beta regression model was superior to the linear regression model. However, the expended beta regression model could explain the heteroscedasticity and predict within the bounded range. Therefore, the expended beta regression model are appropriate for fitting the health-related quality of life data such as EQ-5D.

• The Journal of the Korea Contents Association
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• v.14 no.9
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• pp.350-361
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• 2014

The purpose of this study was to find general trends in dental fear among adolescences at 15-17 years of age, differences in levels of dental fear according to relevant variables, and the degrees to which those variables influence fear of dental treatment and their causal relationships. The researcher made use of a questionnaire including tools of questionnaire survey DFS, DBS and questions regarding characteristics of adolescences, and then analyzed covariate structure modeling by using LISREL 8.12 after conducting univariate analysis by employing SPSS. Cronbach's reliability coefficients showed higher in DFS(0.957), DBS(0.916), and GFS(0.910). The more recent experience in pain in the oral cavity and the stronger pain when treating dental disease and the more frequent experience in pain when treating dental disease and also the more broken dental appointments, the higher levels of dental fear showed with statistical significance. The linear structure equation model was statistically appropriate and well fit. By the model, severity and frequency of pain during treatment, experience of breaking dental visit appointment, distrust for dentists and general fear were directly influenced on dental fear.

• Journal of Korean Home Economics Education Association
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• v.23 no.1
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• pp.113-141
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• 2011

The purpose of this study was to examine the effects of home economics instruction in food labeling using a motivational(ARCS-Attention, Relevance, Confidence, and Satisfaction) strategy to increase middle school students' learning motivation, recognition and use of food labels. To achieve this purpose, teaching-learning plans of food label instruction using a motivation(ARCS) strategy were developed over four class periods using a pretest-posttest experimental design. The experiment was conducted across two groups as follows: 4 experimental groups that received the motivation(ARCS) strategy instruction, and 3 comparative groups that received lecture type instruction. The pretest-posttest scores of the experimental and comparative groups were compared. The 203 data of questionnaires for the experiment were analyzed and evaluated by Analysis of Covariance(ANCOVA) using SPSS Win 12,0. The results of this study were as follows: First, teaching-learning plans, learning materials, and teacher reference materials for the home economics food label instruction that applied the motivation(ARCS) strategy were developed in five subject areas: nutrition labels, food additives, genetically modified food, irradiated food, and food quality verification labels. Second, students' learning motivation of the two groups showed statistically meaningful differences. Home economics instruction using a motivation(ARCS) strategy was more effective in increasing students' learning motivation than lecture type instruction. Third, as a result of ANCOVA which regulated the recognition of food labels in the pre-experimental design, the recognition of food labels in the post-experimental design showed the meaningful differences depending on the instruction style(motivation strategy and lecture type instruction). In addition, comprehensibility, practical use and educational necessity of food label details showed statistically meaningful differences. Home economics instruction using motivation(ARCS) strategy was more effective than lecture type instruction in improving students' recognition of food labeling. Fourth, as a result of ANCOVA which regulated the use of food labels in the pre-experimental stage, the use of food labels in the post-experimental stage showed meaningful differences between experimental and comparative groups depending on the instruction style. Therefore, home economics instruction in food labeling using motivation(ARCS) strategy was more effective than lecture type instruction in increasing students' use of food labels.

• The Korean Journal of Applied Statistics
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• v.34 no.6
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• pp.957-968
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• 2021

Inverse censoring probability weighting (ICPW) is a popular technique in survival data analysis. In applications of the ICPW technique such as the censored regression, it is crucial to accurately estimate the censoring probability. A simulation study is undertaken in this article to see how censoring probability estimate influences model performance in censored regression using the ICPW scheme. We compare three censoring probability estimators, including Kaplan-Meier (KM) estimator, Cox proportional hazard model estimator, and local KM estimator. For the local KM estimator, we propose to reduce the predictor dimension to avoid the curse of dimensionality and consider two popular dimension reduction tools: principal component analysis and sliced inverse regression. Finally, we found that the Cox proportional hazard model estimator shows the best performance as a censoring probability estimator in both mean and median censored regressions.

• 한국경영정보학회:학술대회논문집
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• 2007.06a
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• pp.627-633
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• 2007

본 연구는 인터넷 비즈니스 모델 중에서 가장 성공한 비즈니스 모델이며, 고객의 참여가 가장 능동적이고 적극적이라고 할 수 있는 인터넷 경매를 대상으로 개인 특성, 정보기술 특성, 그리고 인터넷 경매 특성 차원에서 어떠한 요인들이 인터넷 경매 과정에서 플로우에 영향을 미치는지 탐색하고, 이러한 플로우가 브랜드 자산에 유의한 영향을 미치는지를 실증분석 하였다. 인터넷 경매 이용자 350명을 대상으로 온라인 설문 조사를 실시하였으며, 수집된 자료를 기초로 LlSREL 8.50을 이용한 공변량 구조 분석을 실시하였다. 연구 결과, 인터넷 경매 사이트의 브랜드 자산 구축을 위해서는 고객에게 최적의 경험을 제공하는 것이 중요하고, 이를 위해서는 사이트 이용자의 개인 특성, 정보기술 특성, 인터넷 경매 특성을 향상시켜야 함을 확인할 수 있었다. 다시 말해, 이용자 개인의 도전감, 사이트의 정보 품질과 시스템 품질, 인터넷 경매의 상호작용성을 증대시킬 수 있는 다양한 노력을 함으로써 고객에게 최적의 경험을 제공할 수 있을 것이고 이를 통해 브랜드 자산을 구축할 수 있을 것이다.

• The Korean Journal of Applied Statistics
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• v.17 no.1
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• pp.1-12
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• 2004

In this paper, we consider choosing the appropriate covariance structure for analyzing repeated measures data with three repeat factors from a study of blood pressure data, which is collected from the local residents of Yangpyeong, Gyeonggi-do (2001) and fitted linear mixed models to find the significant covariates on outcome variable(Blood Pressure)