• 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.

• Journal of the Ergonomics Society of Korea
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• v.30 no.1
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• pp.137-150
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• 2011

Nationally and internationally reported statistics on marine accidents show that 80% or more of all marine accidents are caused fully or in part by human error. According to the statistics of marine accident causes from Korean Maritime Safety Tribunal(KMST), operating errors are implicated in 78.7% of all marine accidents that occurred from 2002 to 2006. In the case of the collision accidents, about 95% of all collision accidents are caused by operating errors, and those human error related collision accidents are mostly caused by failure of maintaining proper lookout and breach of the regulations for preventing collision. One way of reducing the probability of occurrence of the human error related marine accidents effectively is by investigating and understanding the role of the human elements in accident causation. In this paper, causal factors/root causes classification systems for marine accident investigation were reviewed and some typical human error analysis methods used in shipping industry were described in detail. This paper also proposed a human error analysis method that contains a cognitive process model, a human error analysis technique(Maritime HFACS) and a marine accident causal chains, and then its application to the actual marine accident was provided as a case study in order to demonstrate the framework of the method.

• Journal of the Ergonomics Society of Korea
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• v.20 no.3
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• pp.47-60
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• 2001

The purpose of this paper is propose an analytical method for evaluating user satisfaction of Internet website and identifying casual relationships between user satisfaction of Internet website and performance measures as like revisit intention and complaints using the structural equation model (SEM). This paper is intended to identify critical evaluation factors of user satisfaction for Internet website to determine criteria for evaluating the website. and use the criteria to develop a SEM model for quantitatively evaluation of each factors effects of user preference. The SEM model used 5 latent variables for the evaluation factors of website user satisfaction and 2 latent variables for performance evaluation. 2 portal sites were evaluated to construct the SEM model. and 74 subjects participated the website evaluation using the walk-through and face-to face survey method. Analysis results showed that the SEM model was statistically significant for all the 2 websites evaluated.

• Journal of Korea Society of Industrial Information Systems
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• v.8 no.2
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• pp.72-83
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• 2003

The main purpose of this study was to analyze the causal relationship, in the perspective of Total Quality Management, among the education quality factors, which were suggested in the previous researches, including education leadership, process improvement, educational environment, regional commitment, student performance and satisfaction of education quality. In this study, education quality factors were measured by several measures, and were processed with the most efficient statistical package in the SEM area, AMOS. In order to analyze the causal relationship among the education quality, this study designed the structural equation model with suggested factors and established several the research hypotheses. Also this study found that there was a prominent causality among the education quality factors, such as education leadership, education environment, student performance and satisfaction of education quality.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.2
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• pp.169-174
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• 2018

In general, the methods of the analysis of variance(ANOVA) for the continuous data and the chi-square test for the discrete data are used for statistical analysis of the effect and the association. In multidimensional data, analysis of hierarchical structure is required and statistical linear model is adopted. The structure of the linear model requires the normality of the data. A multidimensional categorical data analysis methods are used for causal relations, interactions, and correlation analysis. In this paper, Bayesian network model using probability distribution is proposed to reduce analysis procedure and analyze interactions and causal relationships in categorical data analysis.

• Journal of the Korean earth science society
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• v.30 no.1
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• pp.111-126
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• 2009

The purpose of this study was to derive the criterions of each type of mental models on the plate boundaries and to investigate high school students' mental models on these concepts. The 11th grade student participants were requested to draw the collisional, convergent, and divergent boundaries and were interviewed individually. The drawings and the data gathered through the interviews were analyzed qualitatively. The mental models on the plate boundaries were classified as 'naive model', 'unstable model', 'causal model', and 'conceptual model'. The criterions for analyzing the mental models were the differentiations of the lithospheric plates and the mantle, the explanations of the motion of the plates and lower mantle, the demonstrations of topographical features of the plate boundaries and the causal relationships between the mantle convection and the topographical features. The findings revealed that the students holding 'the naive model' and 'the unstable model' were unable to relate the mantle convection and the three boundaries. In contrast, the students holding 'the causal model' and 'the conceptual model' were able to explain that the mantle convection causes the three boundaries. Also, the types of epistemological belief were different depending on their mental models. Students holding the naive model and the unstable model tended to rely upon the external authorities.

• The Journal of Korean Academic Society of Nursing Education
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• v.10 no.1
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• pp.54-63
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• 2004

The purpose of this study is to identify the factors that influence stress experienced by nursing students and to provide a perceived causal structure model among these variables. The ultimate goal of this study is to develop efficient guidance to clinical nursing education in this population. This study intends to apply perceived causal structure: network analysis method which was developed by Kelly(1983), and has been applied in nursing research. This method is selected to show dynamic relationship of stressor using network method. Data was collected from convenient sample of 186 junior college nursing students who had the clinical practice experience during 10 weeks. Data collection and analysis was conducted in 2 steps from December, 9, 2002 to February, 8, 2003. Step 1.: Data was collected using literature review(10 articles) to identify the causes of stress. Nine causes of stress were extracted. Step 2.: As perceived casual structure network study, data was collected using questionnaires which included 9 extracted cause and stress. The questionnaire contained a 10 X 10 grid table with 10 causes and effects printed. In network analysis, 'Yes' was scored as 1, 'No' was scored as 0, and the mean(maximum 1, minimum 0) was calculated. Construction of the network under inductive eliminative analysis which stopped the construction of the network when the consensual agreement level dropped near 50% was proceeded by adding causes in order of the mean rating level. In this study, construction of the final network was stopped by consensual agreement level of 52% of the total subjects. The results are summarized as follows : Step 1: Investigation of the causes of stress ; The extracted causes of stress from quality data was identified 9 categories ; negative nurse, lack of clinical practice opportunity, ambiguous role, negative patient, lack of nursing knowledge and skill, difficult of personal relations, inefficient clinical practice guidance, gap of theory and practice, lack of support. Step 2 : Construction of the perceived causal structure model ; 1) The most central cause of stress is ambiguous role in the systems of causation. 2) The distal cause of stress is inefficient clinical practice guidance 3) The causes that have a number of outgoing link are negative nurse, ambiguous role. 4) The causes that have a number of incoming link are ambiguous role, gap of theory- practice, lack of clinical practice opportunity, lack of nursing knowledge- skill. 5) There is a mutual relationship between stress and difficult of personal relations, stress and ambiguous role, ambiguous role and negative nurse, ambiguous role and lack of clinical practice opportunity, ambiguous role and lack of nursing knowledge-skill, lack of nursing knowledge-skill and gap of theory- practice. In conclusion, the network suggests that the first centre cause is related on ambiguous role and the second on negative nurse, inefficient clinical practice guidance in the systems of causation

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.35 no.12
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• pp.181-188
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• 2019

The maintenance of school building is pivotal issue. However, it is difficult to obtain basic analysis data for LCC(Lifecycle Cost) analysis and maintenance planning of school building. Therefore, this study proposed System Dynamics(SD) techniques to make maintenance decisions for school building. The interaction between the major parameters related to the aging of a building, maintenance activities, and cost were expressed in Causal Loop Diagram. Based on this, the formula for the relationship between causal maps was defined and converted to Stock and Flow Diagram. Through the completed SD model the 50-year plan of 214 educational building were tested by considered in account budget, maintainability, and budget allocation opinions. As a result, the integrated SD model demonstrated that it can support strategic decision making by identifying the status class and LCC behavior of school buildings by scenario. According to the scenario analysis, the rehabilitation action of preventive maintenance that primarily repairs the buildings in condition grade C showed the best performance improvement effect relative to the cost. Therefore, if the proposed SD model is expanded to consider the effects of other educational policies, the crucial performance improvement budget can be estimated in the long-term perspective.

• Journal of Korea Society of Industrial Information Systems
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• v.22 no.6
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• pp.71-84
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• 2017

Over the past decade, we have witnessed Serious Debates in MISQ and Other Journals Between Two Camps that have Differing Views on the use of Causal Indicators to Measure Constructs. There is the Camp that advocates Causal Indicators (ADVOCATE) and the Camp that opposes Their Usage (OPPONENT). The Debates have been primarily centered on the OPPONENT's Argument that the Meaning of a Latent Variable is determined by its Outcome Variables. However, Little Effort has been made to Validate the ADVOCATE's Dispute (Against the OPPONENT's Arguments) that the Meaning of a Latent Variable is decided by its Causal Indicators if there is no Misspecification. Our Study precisely examines the Integrity of the Argument. For this, we empirically examine how the two Primary Psychometric Properties-Comprehensiveness and Interrelationship-of Causal Indicators Influence Theory Testing between Latent Variables through Three Different Tests (i.e., Comprehensive Test, Interrelationship Test, and Mixed Test). Conducted on Two Different Datasets, Our Analysis Consistently Reveals that Structural Path Coefficients are Hardly Sensitive to the Changes (i.e., Misspecification) in the Properties of Causal Indicators. The Discovery offers Important Evidence that the Sound Theoretical Logic of a Causal Model is not in Sync with the Empirical Mechanism of Parameter Estimation. This Underscores that a Latent Variable Formed by Causal Indicators is empirically an elusive notion that is Difficult to Operationalize. As Our Results have Significant Implications on the Integrity of Numerous IS studies which have conducted Theory or Hypothesis Testing Using Causal Indicators, we strongly advocate Open Discussions among Methodologists regarding Our Findings and Their Implications for Both Published IS Research and Future Practices.

• Journal of Information Technology Services
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• v.6 no.1
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• pp.47-63
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• 2007

A lot of researchers have been interested in the factors influencing the use of mobile phone services. Most of the previous studies, however, verified only the research models developed by the authors. They did not consider which model agrees better with the real situation. This study presents six alternative models based on various previous studies, and compares the data fitness of the models. A Web survey of mobile phone users collected 2,217 cases. Statistical analyses, using SEM (Structural Equation Modeling), show that the fitness of the simplest alternative model is better than that of any other model. The simplest model has no causal relationship among exogenous factors, and proposes that all of exogenous factors have direct impacts on the customer satisfaction. In addition, the analyses say that corporate and brand image and additional service are more important than the other exogenous factors such as communication quality.