• Journal of the Ergonomics Society of Korea
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• v.32 no.6
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• pp.535-540
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• 2013

Objective: The aim of this study is to evaluate the effectiveness and efficiency of causal links between various error causes in human error analysis. Background: As finding root causes of human error in safety-critical systems is often a cognitively demanding and time-consuming task, it is particularly necessary to develop a method for improving both the quality and efficiency of the task. Although a few methods such as CREAM have suggested causal linking between error causes as a means to enhance the quality and efficiency of human error analysis, no published research to date has evaluated the performance of the causal links. Method: The performance of the CREAM links between error causes were evaluated with 80 railway accident investigation reports from the UK. From each report, errorneous actions of operators were derived, and for each error, candidate causes were found by following the predefined links. Two measures, coverage and selectivity, were used to evaluate the effectiveness and efficiency of the links, respectively. Results: On average, 96% of error causes actually included in the accident reports were found by following the causal links, and among the total of 121 possible error causes, the number of error causes to be examined further was reduced to one-tenth on average. As an additional result of this work, frequent error causes and frequently used links are provided. Conclusion: This result implies that the predefined causal links between error causes can significantly reduce the time and effort required to find the multiple levels of error causes and their causal relations without losing the quality of the results. Application: The CREAM links can be applied to human error analysis in any industry with minor modifications.

• Journal of Korean Academy of Nursing
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• v.24 no.3
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• pp.364-376
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• 1994

This study was initiated to find the characteristic awareness of disease in Korean culture and then, with its applying to psychological nursing, to help cancer victims cope with their disease. Research period was from Dec. 1, 1989 to Aug.3, 1992. The research method, while the method of face-to-face interview with 33 cancer victims were mainly adopted, was to identify the causal perception through analyses of literature and traditional sayings deeply rooted in Korean culture. The causal perceptions were differentiated into 4 sections, which apply to 32 cancer victims with Q-sorting. Be-ing coded into grades from 1 to 9, the data were analyzed with the aid of Quanal program on PC ; in analyzing Q-factor principal component analysis method was used. The results were revealed as follows : 1. Subject victims owe their disease to 1) the omnipotent and animating powers in Shamanism rooted in Korean culture, 2) their intimate persons, i.e. their husband, wife, children, or other fellows ameng their groups. 3) victims themselves, and 4) nowhere, for they thought the disease is the struggle with their own self. 2. In Q-methodology analysis, cancer victims are categorized into 5 types. The first type, self-mastery type, consisting of 11 subjects, has the characteristic of overcoming their disease with their own strong will or by the help of the Omnipotent God, which is estimated to be the ideal type to cope with the disease. The second type, omnipotent & animating powers-dependent type, consisted of 7 subjects, who have the causal perception of traditional shamanism. The third type, intimate person-dependent type, consisted of 4, all of whom are women and whose causal perception has the characteristic of the their complains about each member of their family, espectially about their husband. The fourth type, fate-recipient type, was the com-plex form of the first and the second types. It consisted of 6 subjects, to whom cancer had meant bad fate coming on them but had to be overcome by their strong will. The fifth type, personal type, consisted of 4, whose causal perception is toward themselves personality It is hoped that the study provide the chance of developing nursing intervention to help cancer victims accept and overcome their disease as their own reality instead of attributing to anyone or any-thing else.

• Communications for Statistical Applications and Methods
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• v.23 no.1
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• pp.1-20
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• 2016

Causal inference methodologies have been developed for the past decade to estimate the unconfounded effect of an exposure under several key assumptions. These assumptions include, but are not limited to, the stable unit treatment value assumption, the strong ignorability of treatment assignment assumption, and the assumption that propensity scores be bounded away from zero and one (the positivity assumption). Of these assumptions, the first two have received much attention in the literature. Yet the positivity assumption has been recently discussed in only a few papers. Propensity scores of zero or one are indicative of deterministic exposure so that causal effects cannot be defined for these subjects. Therefore, these subjects need to be removed because no comparable comparison groups can be found for such subjects. In this paper, using currently available causal inference methods, we evaluate the effect of arbitrary cutoffs in the distribution of propensity scores and the impact of those decisions on bias and efficiency. We propose a tree-based method that performs well in terms of bias reduction when the definition of positivity is based on a single confounder. This tree-based method can be easily implemented using the statistical software program, R. R code for the studies is available online.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.247-250
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• 2003

Due to the increasing use of very large databases, mining useful information and implicit knowledge from databases is evolving. However, most conventional data mining algorithms identify the relationship among features using binary values (TRUE/FALSE or 0/1) and find simple If-THEN rules at a single concept level. Therefore, implicit knowledge and causal relationships among features are commonly seen in real-world database and applications. In this paper, we thus introduce the mechanism of mining fuzzy association rules and constructing causal knowledge base form database. Acausal knowledge base construction algorithm based on Fuzzy Cognitive Map(FCM) and Srikant and Agrawal's association rule extraction method were proposed for extracting implicit causal knowledge from database. Fuzzy association rules are well suited for the thinking of human subjects and will help to increase the flexibility for supporting users in making decisions or designing the fuzzy systems. It integrates fuzzy set concept and causal knowledge-based data mining technologies to achieve this purpose. The proposed mechanism consists of three phases: First, adaptation of the fuzzy membership function to the database. Second, extraction of the fuzzy association rules using fuzzy input values. Third, building the causal knowledge base. A credit example is presented to illustrate a detailed process for finding the fuzzy association rules from a specified database, demonstration the effectiveness of the proposed algorithm.

• Journal of the Korean Society of Safety
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• v.37 no.5
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• pp.42-55
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• 2022

In this study, an accident at an in-house maintenance subcontractor of a manufacturing company was analyzed using representative systemic analysis methods, and the results were compared to determine the socio-technical and organizational structure causal factors. Systemic accident analyses were performed using AcciMap, STAMP-CAST, and a method that utilizes work processing procedures. The causal factors derived from the three methods were classified according to HFACS classification criteria. AcciMap and STAMP-CAST analyses were able to derive legal problems and defects in organizational structure between the company and the subcontractors. The method that utilized the work processing procedures drew the most causal factors of the three methods but showed some limitations in deriving legal and facility-related problems. Most of the causal factors identified through the systemic methods could be classified according to the HFACS classification criteria, except for the legal and organizational structure matters. Socio-technical and organizational problems with a holistic perspective of the company and subcontractors could be found using systemic analysis methods. However, it is necessary to conduct analysis using various methods in order to derive more comprehensive measures to prevent accidents because each analysis method showed some limitations in the derivation or expression of some causal factors. The results of this study can be helpful in selecting and using an appropriate method for accident analysis.

• 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'.

• Asian Oncology Nursing
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• v.3 no.1
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• pp.66-74
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• 2003

Purpose: This study is designed to identify clusters according to the causal attribution that people make about the cancer and to determine influences of coping behavior and depression as output of causal attribution. Method: The subjects were 192 patients who had been diagnosed cancer one year ago and attended an outpatient clinic. For cancer patients to be classified homogenious groups according to causal attribution, cluster analysis of subjects' ratings on the Causal Dimension Scale was been made. Results: Cluster 1(n=71) had patients with having external, stable and uncontrollable attribution. Cluster 2(n =70) had patients with having unstable and external controllable attribution regarding cause of cancer. They were not important whether cause of cancer was self or other. Cluster 3(n=51) had patients with having internal, unstable and internal controllable attribution. Coping behaviors between cluster 1 and 3 were significant difference. However, depression was not significant difference among clusters. Conclusion: Based upon these results, it is recommended that the developing training program to be changed to the more positive attribution is necessary.

• Journal of muscle and joint health
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• v.2 no.2
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• pp.168-184
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• 1995

This deductive-survey study was undertaken in order to examine if there were relationship between causal perceptions, expectation for the cure and compliance. The sampling method was a non-probability, purposive sampling technique. The participants of this study was 195 volunteers 1) who have been diagnosed as having chronic arthritis and 2) who were at the rheumatis center of one university hospital in Seoul between September 18th to September 25th 1989. This instruments used for this study were the compliance scale developed by Choi and causal perception scale developed by the researcher. Analysis of data was done using pass analysis, Pearson correlation coefficient. The result of study were as follow : Hypothesis 1 : "It's correlated that causal perception, expectation for cure and compliance" was accepted. (F=4.85, p< .05) Hypothesis 2 : "It's correlated that causal perception, expectation for cure and with depression" was partially accepted. Total age group-worry and anxiety (r=.1580, p<.001) After 40-function of immunity (r=.1731, p<.05) warry and anxiety (r=.1730, p<.001). In the relationship between general characteristics and the variables, age group correlated with compliance and causal perception.

• 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.

• 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.