• Proceedings of the Korean Information Science Society Conference
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• 2006.06a
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• pp.4-6
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• 2006

Tree-dependent component analysis (TCA) is a generalization of independent component analysis (ICA), the goal of which is to model the multivariate data by a linear transformation of latent variables, while latent variables fit by a tree-structured graphical model. In contrast to ICA, TCA allows dependent structure of latent variables and also consider non-spanning trees (forests). In this paper, we present a TCA-based method of clustering gene expression data. Empirical study with yeast cell cycle-related data, yeast metaboiic shift data, and yeast sporulation data, shows that TCA is more suitable for gene clustering, compared to principal component analysis (PCA) as well as ICA.

• 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.

• Industrial Engineering and Management Systems
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• v.11 no.4
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• pp.371-384
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• 2012

This study proposes a multi-level data analysis approach to identify both superficial and latent relationships among variables in the data set obtained from a vocational rehabilitation (VR) services program of people with significant disabilities. At the first layer, data mining and statistical predictive models are used to extract the superficial relationships between dependent and independent variables. To supplement the findings and relationships from the analysis at the first layer, association rule mining algorithms at the second layer are employed to extract additional sets of interesting associative relationships among variables. Finally, nonlinear nonparametric canonical correlation analysis (NLCCA) along with clustering algorithm is employed to identify latent nonlinear relationships. Experimental outputs validate the usefulness of the proposed approach. In particular, the identified latent relationship indicates that disability types (i.e., physical and mental) and severity (i.e., severe, most severe, not severe) have a significant impact on the levels of self-esteem and self-confidence of people with disabilities. The identified superficial and latent relationships can be used to train education program designers and policy developers to maximize the outcomes of VR training programs.

• 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.

• 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.

• Journal of the Korean Society of Safety
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• v.36 no.3
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• pp.60-65
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• 2021

Human error is often in part in the cause of accidents and the result of various factors in an organization. Accidents should be investigated to elucidate all causes. Therefore, to reduce accidents, it is necessary to identify which factors affect human error within the organization. In this study, five groups of influencing factors on human error were selected using previousresearch, and operational definitions were made based on them. In addition, a questionnaire for measuring latent variables by operational definition was developed as an observation variable, and responses were received from employees of chemical companies in Ulsan. Based on SEM (structural equation modeling) analysis, 1) confirmatory factor analysis of variables in the human error model, 2) reliability and validity of latent variables, 3) correlations among latent variables, 4) influencing coefficients among influence factors, and 5) the verification results of the paths that these influencing factors have on human error are introduced in this study.

• International Journal of Highway Engineering
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• v.14 no.6
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• pp.121-129
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• 2012

PURPOSES : Traffic accidents at intersections have been increased annually so that it is required to examine the causations to reduce the accidents. However, the current existing accident models were developed mainly with non-linear regression models such as Poisson methods. These non-linear regression methods lack to reveal complicated causations for traffic accidents, though they are right choices to study randomness and non-linearity of accidents. Therefore, to reveal the complicated causations of traffic accidents, this study used structural equation methods(SEM). METHODS : SEM used in this study is a statistical technique for estimating causal relations using a combination of statistical data and qualitative causal assumptions. SEM allow exploratory modeling, meaning they are suited to theory development. The method is tested against the obtained measurement data to determine how well the model fits the data. Among the strengths of SEM is the ability to construct latent variables: variables which are not measured directly, but are estimated in the model from several measured variables. This allows the modeler to explicitly capture the unreliability of measurement in the model, which allows the structural relations between latent variables to be accurately estimated. RESULTS : The study results showed that causal factors could be grouped into 3. Factor 1 includes traffic variables, and Factor 2 contains turning traffic variables. Factor 3 consists of other road element variables such as speed limits or signal cycles. CONCLUSIONS : Non-linear regression models can be used to develop accident predictions models. However, they lack to estimate causal factors, because they select only few significant variables to raise the accuracy of the model performance. Compared to the regressions, SEM has merits to estimate causal factors affecting accidents, because it allows the structural relations between latent variables. Therefore, this study used SEM to estimate causal factors affecting accident at urban signalized intersections.

• Journal of Families and Better Life
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• v.33 no.1
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• pp.1-17
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• 2015

This study investigated how variables of internalizing and externalizing problematic behaviors change according to gender and as time passes by and how the variables influence adolescent problematic behaviors. The variables selected for the analysis are personal variables, parent variables, peer and school variables, and community variables. longitudinal data collected for 4 years from the Korea Youth Panel Survey(KYPS) were utilized for the analysis. Data were collected initially from 2,707 fifth grade elementary students in 2005 and they were traced until 2008. The final respondents were 2,448 students. The findings are as follows. Frist, the statistical significance was found in changes of problematic behavioral variables in terms of the gender difference. Second, variables, such as self-esteem and self-control are negatively correlated to the problematic behaviors and stress level is strongly positively correlated to the behaviors. Third, the study pressure and peer attachment level are correlated to the initial value of internalizing problematic behaviors. In conclusion, given that more statistical significances were found at initial values than the change rates among variables, early intervention is important in addressing adolescent problematic behaviors.

• The Korean Journal of Applied Statistics
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• v.27 no.7
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• pp.1077-1095
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• 2014

Unlike randomized trial, statistical strategies for inferring the unbiased causal relationship are required in the observational studies. Recently, new methods for the causal inference in the observational studies have been proposed such as the matching with the propensity score or the inverse probability treatment weighting. They have focused on how to control the confounders and how to evaluate the effect of the treatment on the result variable. However, these conventional methods are valid only when the treatment variable is categorical and both of the treatment and the result variables are directly observable. Research on the causal inference can be challenging in part because it may not be possible to directly observe the treatment and/or the result variable. To address this difficulty, we propose a method for estimating the average causal effect when both of the treatment and the result variables are latent. The latent class analysis has been applied to calculate the propensity score for the latent treatment variable in order to estimate the causal effect on the latent result variable. In this work, we investigate the causal effect of adolescents delinquency on their substance use using data from the 'National Longitudinal Study of Adolescent Health'.

• International journal of advanced smart convergence
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• v.10 no.4
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• pp.124-133
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• 2021

The purpose of this study is to analyze the types of latent profiles of high school students' fire safety awareness and to identify the characteristics of related variables. For this purpose, a survey was conducted from March 22 to May 25, 2021 for 1054 high school students (male; 569, female; 485) in 3 cities, in Jeollabuk-do. The latent profile was analyzed using a scale consisting of 4 sub-factors: 'fire prevention', 'fire preparedness', 'indirect fire response', and 'direct fire response'. It was checked whether there were differences according to the inter-individual differences of the latent group. As a result of the analysis, fire safety awareness of high school students was classified into three latent profiles. The three groups were named 'High Perception Type', 'Moderate Perception Type', and 'Low Perception Type' according to their types. In fire safety awareness, there is a significant difference in the individual differences according to the gender and academic achievement of the latent profile. These results are meaningful as the first study to analyze the latent profile of high school students' fire safety awareness, and it is also meaningful to provide a useful basis for the contents and methods of customized fire safety education by identifying the tendencies of spontaneous groups and their fire safety awareness.