• The Journal of Korean Association of Computer Education
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• v.13 no.4
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• pp.41-50
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• 2010

So far, the researches about the Internet addiction testing are diverse and enormous. However, since the testing factors have the same importance, there is no discrimination among the factors. In our paper, we analyze the problems of the existing researches by using Lisrel and data mining algorithms. Next, we propose an alternative for the Internet addiction testing. In order to accomplish these, we make an questionnaire consisted of Young's test and then, we asked to 440 primary and secondary school students. In addition, we analyzed the problems of Young's test with factor analysis and then, we distinguish more important items from others with J48 and PART. Finally, based on this analysis, we propose a method to diagnose addicted students and prescribe them.

• Asia Marketing Journal
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• v.3 no.2
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• pp.1-20
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• 2001

본 연구는 리즈렐을 이용한 자료 분석에서 실제적으로 발생할 수 있는 문제점과 유의해야 할 사항을 실증자료를 이용하여 고찰하기 위한 것이다. 분석자료는 수도권에 소재하고 있는 보험판매영업소에서 판매활동을 하고 있는 328명의 보험판매원의 조직시민 행동(organizational citizenship behaviors, OCB)에 영향을 미치는 선행변수를 파악하기 위해 수집된 자료를 이용하였다. LISREL 적용상의 유의사항으로는 구성 개념과 지표간의 관계 및 구성 개념간의 관계, 좋은 참조변수 지정의 중요성, χ² 검정 적용상의 유의사항, 식별되지 않은 모델과 관련한 유의사항, 초기설정치의 제공과 수용하기 어려운 해와 관련한 유의사항, 모델 탐색시의 유의사항, 공분산행렬로 분석하지 않고 상관 행렬로 분석하는 경우의 문제점, 표본크기 확인과 분석에 선택된 변수 지정의 중요성, 전반적인 적합도에 의한 모델의 검정, 결과 해석과 보고시의 유의사항을 다루고 있다. 이어서, LISREL에 있어서의 모수 유의성 검정상의 문제점에 대해 고찰하고 있으며, LISREL의 유용성과 한계점에 대해서도 다루고 있다.

• Journal of Digital Convergence
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• v.13 no.10
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• pp.121-133
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• 2015

This tutorial presents an approach to perform the covariance based structural equation modeling using the R. For this purpose, the tutorial defines the criteria for the covariance based structural equation modeling by reviewing previous studies, and shows how to analyze the research model with an example using the "lavaan" which is the R package supporting the covariance based structural equation modeling. In this tutorial, a covariance-based structural equation modeling technique using the R and the R scripts targeting the example model were proposed as the results. This tutorial will be useful to start the study of the covariance based structural equation modeling for the researchers who first encounter the covariance based structural equation modeling and will provide the knowledge base for in-depth analysis through the covariance based structural equation modeling technique using R which is the integrated statistical software operating environment for the researchers familiar with the covariance based structural equation modeling.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.2
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• pp.289-297
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• 2020

The purpose of this study is to verify the effect of university students's innovativeness on adoption intention of academic convergence. For this objectives, the Structural Equation Model(SEM) was designed and analysed by using Lisrel. This study developed a research model, based upon TAM, to analyse the factors: personal innovativeness, social expectation, perceived usefulness and adoption intention of academic convergence. The main results are follows as: first, personal innovativeness showed a significant positive direct effect on adoption intention of academic convergence, and indirect effect through perceived usefulness on it. Second, through perceived usefulness, there was indirect effect between social expectation and adoption intention of academic convergence. The SEM in this study showed the statistical significance, which the model fit was χ²=22.638(df=29, p=.793). Based upon the result of analyses, the study suggested to develop the subject and non-subject educational program through which students experience innovativeness and usefulness for academic convergence. Especially, the change of the role and the task of the curriculum of liberal arts was.

• Journal of Korean Society of Forest Science
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• v.97 no.1
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• pp.22-29
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• 2008

The objective of this study is to provide useful information for the establishment of efficient policy implementation strategies of forest road construction policy in South Korea. Data needed for the analysis was collected by a questionnaire survey. For the analysis, policy evaluation model was constructed based on theories of public policy. Evaluation model contains three independent variables (policy initiative factor, policy content, policy environment) and two dependent variables (policy result, policy impact). Since, these variables are unobservable latent variables, observable indicators are needed as proxy measures. LISREL (Linear Structural Relationships) was employed for the analysis since it is a useful measure for analysing linear structural model which consists of structural and measurement equations. It was confirmed that forest road construction is an effective policy mean for the development of rural region and activating forest resources management. The policy outcome, however, was not satisfactory. To improve the effectiveness of forest road construction policy some modification of policy contents are needed such as increased construction budget, allowing more flexibility and participation to the implementation personal and providing technical support.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.7
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• pp.978-984
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• 2018

Structural equation modeling is pointing to statistical procedures that simultaneously perform path analysis and confirmatory factor analysis. Today, this statistical procedure is an essential tool for researchers in the social sciences. There are as AMOS, LISREL and PLS representative tools that can perform structural equation modeling analysis. AMOS provides a convenient graphical user interface for beginners to use. PLS has the advantage of not having a constraint on normal distribution as well as a graphical user interface. Therefore, we compared and analyzed the three most commonly used tools (applications) in social sciences. Based on structural equation modeling, confirmatory factor analysis was performed using the IBM AMOS Ver. 23, the LISREL 8.70 and the SmartPLS 2.0. The comparative results show that LISREL has the highest explanatory power of dependent variables than other analytical tools. The path coefficients and T-values presented by the analysis results showed similar results for all three analysis tools. This study suggests practical and theoretical implications based on the results.