• Journal of the Korea Society of Computer and Information
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• v.25 no.10
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• pp.193-201
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• 2020

This study is to evaluate the effectiveness of problem-solving ability by applying a team-based learning model to the classes of humanities and social science students, and to conduct a structural model analysis on the relationship between sub-factors. Team-based learning was conducted six times in six teams with 30 students in the second and third grades of the humanities and social sciences. The problem solving ability score of the target students was significantly higher after team-based learning and was statistically significant. There was no problem in normality with the latent variables, which are the sub-factors of problem solving ability, and the factor load value was statistically significant at the .001 level in the confirmatory factor analysis of the observed variables for the latent variables, which was a valid model. A good level of fitness was also shown in the verification of the fitness of the research model. As a result, it was analyzed that latent variables of cause analysis, problem clarification, planning execution, performance evaluation, and alternative development had an indirect or direct influence on each other.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.184-184
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• 2020

수자원 계획 및 관리 시 증발산량의 정량적 분석은 필수적으로 고려되는 사항 중 하나이다. 일단위 이하의 잠재증발산량 산정은 세계식량기구(FAO)가 Penman-Monteith 방법을 기반으로 개발한 FAO56 PM 방법을 주로 활용하며, 이는 다른 방법에 비하여 높은 정확성과 적용성이 뛰어나다. 그러나 FAO56 PM 방법의 입력 매개변수는 다양한 기상자료이며, 장기간의 신뢰성 높은 자료를 구축하는 것은 어려운 실정이다. 이에 본 연구에서는 증발산량 공식인 Hargreaves 공식을 활용하여 FAO56 PM 방법으로 산정된 잠재증발산량과 기온차 사이의 시계열 관계를 재구성한 회귀분석 기법을 개발하였다. 개발된 모형에 유역면적을 적용하여 유역면적별 잠재증발산량을 산정하였으며, 이를 기존의 잠재증발산량과의 비교를 통해 모형의 적합성을 평가하였다. 결과적으로, 복잡한 잠재증발산량식을 단순한 대체모형(surrogate model)으로 제시함으로써 효율적인 증발산량 정량적 평가와 제한적인 기상자료 조건에 보편적 활용이 가능하다. 향후 연구에서는 회귀분석방법에 Bayesian 추론기법을 활용하여 구성함으로 잠재증발산량의 불확실성을 정량적으로 표현하고자 한다.

• Survey Research
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• v.12 no.2
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• pp.113-144
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• 2011

The purposes of the present study were (1) to identify the latent classes depending on youth employees' trajectories in job satisfaction and turnover intention and (2) to test the effects of person-job fit(major fit, education level fit, skill level fit) on job satisfaction and turnover intention using Youth Panel 2001. In order to estimate latent classes of job satisfaction and turnover intention changes simultaneously and study probabilities linking latent class membership in trajectory across the two variables, we applied dual trajectory model, an extension of semi-parametric group-based approach, Results showed that four latent classes were identified for job satisfaction, which were defined, based on the trajectory patterns, as increasing group, decreasing group, medium-level group, and high-level group. And, three latent classes estimated for turnover intention were defined as low-level group, maintaining group, and rapidly decreasing group. To test the effects of person-job fit variables, we added the variables as time-dependant variables to the unconditional latent class model. The effect of education level fit and skill level fit were found significant in the groups which are low in job satisfaction and have high in turnover intention. Findings from this study suggest the need to consider trajectory heterogeneity in the study of youth employees' job satisfaction and turnover intention to capture the dynamic dimension of overlap between the two constructs.

• The Korean Journal of Applied Statistics
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• v.13 no.1
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• pp.189-196
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• 2000

Conjugate prior density families were motivated by considerations of tractability in implementing the Bayesian paradigm. But we consider problem that the conjugate prior p($\Theta$) cannot be used in restriction of the parameter $\Theta$. This article considers the nonconjugate prior problem of hierarchical Poisson model. We demonstrate the use of latent variables for sampling non-standard densities which arise in the context of the Bayesian analysis of non-conjugate by using a Gibbs sampler.

• Journal of Global Scholars of Marketing Science
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• v.15 no.2
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• pp.141-159
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• 2005

In this research, we develope an estimation method for the estimation of the market potential in the new service (or product) diffusion model. The developed method is based on the ordered response model which can effectively incorporate the survey result of the multi-point scale intention for subscription as well as the responder's characteristics, the characteristics & attitudes of the related service. We also apply the developed method to an estimation of the market potential of the digital multimedia broadcasting (DMB) service. As a result, an optimistic and a pessimistic estimates of DMB market potential are 41.10% and 14.83% of the cellular subscribers respectively.

• Communications for Statistical Applications and Methods
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• v.15 no.4
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• pp.633-642
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• 2008

Variable selection algorithm for partial least square regression using penalty function is proposed. We use the fact that usual partial least square regression problem can be expressed as a maximization problem with appropriate constraints and we will add penalty function to this maximization problem. Then simulated annealing algorithm can be used in searching for optimal solutions of above maximization problem with penalty functions added. The HARD penalty function would be suggested as the best in several aspects. Illustrations with real and simulated examples are provided.

• The Korean Journal of Applied Statistics
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• v.30 no.5
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• pp.615-632
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• 2017

Unlike randomized trial, statistical strategies for inferring the unbiased causal relationship are required in the observational studies. The matching with the propensity score is one of the most popular methods to control the confounders in order to evaluate the effect of the treatment on the outcome variable. Recently, new methods for the causal inference in latent class analysis (LCA) have been proposed to estimate the average causal effect (ACE) of the treatment on the latent discrete variable. They have focused on the application study for the real dataset to estimate the ACE in LCA. In practice, however, the true values of the ACE are not known, and it is difficult to evaluate the performance of the estimated the ACE. In this study, we propose a method to generate a synthetic data using the propensity score in the framework of LCA, where treatment and outcome variables are latent. We then propose a new method for estimating the ACE in LCA and evaluate its performance via simulation studies. Furthermore we present an empirical analysis based on data form the 'National Longitudinal Study of Adolescents Health,' where puberty as a latent treatment and substance use as a latent outcome variable.

• The Korean Journal of Applied Statistics
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• v.33 no.1
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• pp.25-46
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• 2020

This paper presents ordinal probit semiparametric regression models using Bayesian Spectral Analysis Regression (BSAR) method. Ordinal probit regression is a way of modeling ordinal responses - usually more than two categories - by connecting the probability of falling into each category explained by a combination of available covariates using a probit (an inverse function of normal cumulative distribution function) link. The Bayesian probit model facilitates posterior sampling by bringing a latent variable following normal distribution, therefore, the responses are categorized by the cut-off points according to values of latent variables. In this paper, we extend the latent variable approach to a semiparametric model for the Bayesian ordinal probit regression with nonparametric functions using a spectral representation of Gaussian processes based BSAR method. The latent variable is decomposed into a parametric component and a nonparametric component with or without a shape constraint for modeling ordinal responses and predicting outcomes more flexibly. We illustrate the proposed methods with simulation studies in comparison with existing methods and real data analysis applied to a Korean National Health and Nutrition Examination Survey (KNHANES) 2016 for investigating nonparametric relationship between smoking behavior and coffee intake.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.6
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• pp.49-65
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• 2023

In response to the growing global concern for the environment, the international community has recently committed to achieving 'carbon neutrality.' As a result, numerous studies have been conducted on mode choice models that include carbon emissions as a variable. However, few studies have established a correlation between individual preferences and carbon emissions. In this study, a new mode of transportation named sustainable public transit (SPT), incorporating carbon-reducing transport options like electric scooters, is proposed. Analyzing the individual preferences of commuters on carbon emissions through factor analysis, a stated preference (SP) survey was conducted. A mode choice model for SPT was constructed using multinomial logit models. The results of the analysis showed that gender, income, and specific preferences, such as a passion for exploring new routes, a preference for intermodal transfers, knowledge of carbon reduction, and carbon reduction practices, significantly influence latent preferences for SPT. Therefore, this study is significant as it considers carbon emissions as an attribute variable during the construction of mode choice models and reflects the individual preference variables associated with carbon reduction.

• Journal of the Korean Society for Library and Information Science
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• v.56 no.4
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• pp.455-472
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• 2022

The purpose of this study is to analyze variables affecting the number of circulated books which is one of the indicators representing the library use behavior. For the analysis, 2015-2019 data for public libraries was acquired from the National Library Statistics System. The Latent Growth Model estimating a latent intercept and a latent slop based on the individual library trajectories was applied. The results are as followed; first, the circulation rate tends to be decreased. Second, the most affecting factor on the library circulation decrease was the collection budget. This study suggests increasing a collection budget in order to prevent the library circulation decrease while the library is operating in a daily routine.