• 한국데이터정보과학회:학술대회논문집
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• 2005.04a
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• pp.200-201
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• 2005

• Genomics & Informatics
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• v.20 no.3
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• pp.34.1-34.11
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• 2022

Multilevel analysis is an appropriate and powerful tool for analyzing hierarchical structure data widely applied from public health to genomic data. In practice, however, we may lose the information on multiple nesting levels in the multilevel analysis since data may fail to capture all levels of hierarchy, or the top or intermediate levels of hierarchy are ignored in the analysis. In this study, we consider a multilevel linear mixed effect model (LMM) with single imputation that can involve all data hierarchy levels in the presence of missing top or intermediate-level clusters. We evaluate and compare the performance of a multilevel LMM with single imputation with other models ignoring the data hierarchy or missing intermediate-level clusters. To this end, we applied a multilevel LMM with single imputation and other models to hierarchically structured cohort data with some intermediate levels missing and to simulated data with various cluster sizes and missing rates of intermediate-level clusters. A thorough simulation study demonstrated that an LMM with single imputation estimates fixed coefficients and variance components of a multilevel model more accurately than other models ignoring data hierarchy or missing clusters in terms of mean squared error and coverage probability. In particular, when models ignoring data hierarchy or missing clusters were applied, the variance components of random effects were overestimated. We observed similar results from the analysis of hierarchically structured cohort data.

• Nuclear Engineering and Technology
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• v.53 no.12
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• pp.4042-4051
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• 2021

In this work, we applied a multilevel modeling technique to estimate the diametral creep in the pressure tubes of Korean Canada Deuterium Uranium (CANDU) units. Data accumulated from in-service inspections were used to develop the model. To confirm the strength of the multilevel models, a 2-level multilevel model considering the relationship between channels for a CANDU unit was compared with existing linear models. The multilevel model exhibited a very robust prediction accuracy compared to the linear models with different data pooling methods. A 3-level multilevel model, which considered individual bundles, channels, and units, was also implemented. The influence of the channel installation direction was incorporated into the three-stage multilevel model. For channels that were previously measured, the developed 3-level multilevel model exhibited a very good predictive power, and the prediction interval was very narrow. However, for channels that had never been measured before, the prediction interval widened considerably. This model can be sufficiently improved by the accumulation of more data and can be applied to other CANDU units.

• Nuclear Engineering and Technology
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• v.50 no.4
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• pp.532-541
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• 2018

Decision support systems are a key focus of research on developing control rooms to aid operators in making reliable decisions and reducing incidents caused by human errors. For this purpose, models of complex systems can be developed to diagnose causes or consequences for specific alarms. Models applied in safety systems of complex and safety-critical systems require rigorous and reliable model building and testing. Multilevel flow modeling is a qualitative and discrete method for diagnosing faults and has previously only been validated by subjective and qualitative means. To ensure reliability during operation, this work aims to synthesize a procedure to measure model performance according to diagnostic requirements. A simple procedure is proposed for validating and evaluating the concept of multilevel flow modeling. For this purpose, expert statements, dynamic process simulations, and pilot plant experiments are used for validation of simple multilevel flow modeling models of a hydrocyclone unit for oil removal from produced water.

• Journal of Periodontal and Implant Science
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• v.45 no.1
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• pp.2-7
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• 2015

A fundamental problem in analyzing complex multilevel-structured periodontal data is the violation of independency among the observations, which is an assumption in traditional statistical models (e.g., analysis of variance and ordinary least squares regression). In many cases, aggregation (i.e., mean or sum scores) has been employed to overcome this problem. However, the aggregation approach still exhibits certain limitations, such as a loss of power and detailed information, no cross-level relationship analysis, and the potential for creating an ecological fallacy. In order to handle multilevel-structured data appropriately, mixed effects models have been introduced and employed in dental research using periodontal data. The use of mixed effects models might account for the potential bias due to the violation of the independency assumption as well as provide accurate estimates.

• Journal of Agricultural Extension & Community Development
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• v.16 no.1
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• pp.69-98
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• 2009

This study empirically studies the effects of Agricultural Technology Service Center's educational program for farmers on their incomes. The educational program for farmers has widely been managed in discourse and policy in Korea. In 2008, Agricultural Technology Service Center managed 88 educational program for farmers, where 6,409 farmers received a certificate. While there are important studies, most of them have concentrated on qualitative analysis and noneconomic effects to an educational program for farmers. This study tried to analyze whether or not there is an economic effect of an educational program for farmers, focusing on the relationship between the educational program for farmers and their income status. Multilevel models (or hierarchical linear models) were applied to this study. Multilevel model is a quantitative model of parameters that vary at more than one level and show hierarchical structures between levels. This study particularly accentuates that an educational program for farmers is more meaningful when it can raise farmers' incomes by region and by educational program for farmers.

• 대한예방의학회:학술대회논문집
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• 2005.07b
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• pp.69-86
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• 2005

• Korean Journal of Health Education and Promotion
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• v.27 no.4
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• pp.177-188
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• 2010

Objectives: The aim of this study was to examine factors related to smoking behavior, and to develop multilevel communication strategies for smoking cessation. Methods: This paper reviewed theories and empirical findings with currents ecological models to develop communication strategies. Theory comparison was also performed to identify important mediators in the process of smoking cessation. Results: Factors that have been identified to influence smoking behavior ranges from individual perception, attitudes and self efficacy toward smoking to organizational norms, regulations, community capacity, media advocacy and public smoking regulation policy. In order to address these multi-level determinants of smoking behavior, objectives and strategies for smoking cessation intervention were developed utilizing ecological perspectives to cover intrapersonal, interpersonal(mainly family member and peers), organizational and community/public policy level factors. Conclusion: Multilevel approaches have advanced the existing knowledge on determinants of health behaviors. New direction of research focusing on testing multilevel intervention approaches should be expanded to inform the efficacy of applying social ecological models to health behavior change process.

• Journal of Korean Society of Transportation
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• v.20 no.7
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• pp.79-85
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• 2002

In this paper, multilevel models are adopted to identify interactions among household members in trip making behaviors. The multilevel approach is a proper methodology to handle samples, which are extracted from a hierarchical structure universe. PSTP dataset is used in developing models and understand proportion of variations among individuals and household. The results of this study show that for activity participation and travel behavior household level variance is more than 1/4 of person level variance and therefore not negligible. The results confirm the importance of multilevel model in travel behavior analysis.

• The Journal of Economics, Marketing and Management
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• v.10 no.5
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• pp.1-6
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• 2022

Purpose: To overcome the question that depends too much on expert's subjective judgment in traditional risk identification, this paper structure the multilevel generalized fuzzy comprehensive evaluation mathematics model of the risk identification of project, to research the risk identification of the project. Research design, data and methodology: This paper constructs the multilevel generalized fuzzy comprehensive evaluation mathematics model. Through iterative algorithm of AHP analysis, make sure the important degree of the sub project in risk analysis, then combine expert's subjective judgment with objective quantitative analysis, and distinguish the risk through identification models. Meanwhile, the concrete method of multilevel generalized fuzzy comprehensive evaluation is probed. Using the index weights to analyse project risks is discussed in detail. Results: The improved fuzzy comprehensive evaluation algorithm is proposed in the paper, at first the method of fuzzy sets core is used to optimize the fuzzy relation matrix. It improves the capability of the algorithm. Then, the method of entropy weight is used to establish weight vectors. This makes the computation process fair and open. And thereby, the uncertainty of the evaluation result brought by the subjectivity can be avoided effectively and the evaluation result becomes more objective and more reasonable. Conclusions: In this paper, we use an improved fuzzy comprehensive evaluation method to evaluate a railroad engineering project risk. It can give a more reliable result for a reference of decision making.