• The Korean Journal of Applied Statistics
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• v.28 no.1
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• pp.61-74
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• 2015

Structural equation modeling (SEM) is a widely used in social sciences such as education, business administration, and psychology. In SEM, the latent variable score is the estimate of the latent variable which cannot be observed directly. This study uses stagewise structural equation modeling(stagewise SEM; SSEM) by partitioning the whole model into several stages. The traditional estimation method minimizes the discrepancy function using the variance-covariance of all observed variables. This method can lead to inappropriate situations where exogenous latent variables may be affected by endogenous latent variables. The SSEM approach can avoid such situations and reduce the complexity of the whole SEM in estimating parameters.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.2
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• pp.121-131
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• 2022

The goal of the study was to see how quality management (QMA), strategy (STR), and innovative capability (INC) influence the competitive advantage of a Thai information communication technology (ICT) firm (COA). The researchers collected 431 surveys from Thailand's owners and managers employed in ICT enterprises from the beginning of June 2021 to the end of September 2021using diverse sample strategies. A questionnaire with an index of item-objective congruence (IOC) value of 0.60-1.00 and a reliability value of 0.92-0.96 was used as the research tool. Participants in the survey were requested to fill out a seven-level opinion survey posted on Google Forms. A latent variable structural equation model (SEM) path analysis using LISREL 9.1 was used for the four latent variables, 31 manifest variables, and the five hypotheses testing. The analysis showed that all three causal variables positively affected COA, which had a total effect (TE) R² value = 80% when combined with the other latent variables. Moreover, the values for the latent variables when ranked by total effect (TE) were STR, QMA, and INC with TE values of 0.95, 0.89, and 0.25, respectively. Finally, there were very strong influences from COA to STR (0.95), INC to QMA (0.86), and STR to QMA (0.71).

• 한국데이터정보과학회:학술대회논문집
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• 2005.10a
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• pp.177-188
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• 2005

Association rule mining searches for interesting relationships among items in a given large data set. Association rules are frequently used by retail stores to assist in marketing, advertising, floor placement, and inventory control. There are three primary threshold measures in association rule; support and confidence and lift. In the case of appling real world to association rules, we have some difficulties in data interpretation because we obtain many rules. In this paper, we develop the model of association rules using latent variables for environmental survey data.

• Journal of the Korean Data and Information Science Society
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• v.17 no.1
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• pp.149-160
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• 2006

Association rule mining searches for interesting relationships among items in a given large data set. Association rules are frequently used by retail stores to assist in marketing, advertising, floor placement, and inventory control. There are three primary threshold measures in association rule; support and confidence and lift. In the case of appling real world to association rules, we have some difficulties in data interpretation because we obtain many rules. In this paper, we develop the model of association rules using latent variables for environmental survey data.

• Nuclear Engineering and Technology
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• v.55 no.10
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• pp.3628-3638
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• 2023

This paper uses 135 Licensed Operator Event Reports (LOER) from Chinese nuclear plants to analyze how safety culture affects unsafe behaviors in nuclear power plants. On the basis of a modified human factors analysis and classification system (HFACS) framework, structural equation model (SEM) is used to explore the relationship between latent variables at various levels. Correlation tests such as chi-square test are used to analyze the path from safety culture to unsafe behaviors. The role of latent error is clarified. The results show that the ratio of latent errors to active errors is 3.4:1. The key path linking safety culture weaknesses to unsafe behaviors is Organizational Processes → Inadequate Supervision → Physical/Technical Environment → Skill-based Errors. The most influential factors on the latent variables at each level in the HFACS framework are Organizational Processes, Inadequate Supervision, Physical Environment, and Skill-based Errors.

• Journal of the Korean Statistical Society
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• v.23 no.2
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• pp.463-482
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• 1994

In Bayesian analysis of randomized response models, the likelihood function does not combine tractably with typical priors for the parameters of interest, causing computational difficulties in posterior analysis of the parameters of interest. In this article, the difficulties are solved by introducing appropriate latent variables to the model and using the Gibbs sampling algorithm.

• International Journal of Quality Innovation
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• v.6 no.2
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• pp.147-166
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• 2005

The purpose of this study is to analyze the causal relationship, in the perspective of Total Quality Management, among the education quality factors, which were suggested in the previous researches. Lee et al. [16] had tried to analyze the relationship among education factors, but they did not estimate the education factor using latent variable concept, which is very reasonable and efficient to represent the constructed concepts. So this study attempts to analyze the causal relationship among education quality factors, represented as latent variables used in structural equation modeling (SEM), and compared with each other. In this study, education quality factors were measured by several measures, constructed as several latent variables, and then processed with AMOS, the most efficient statistical package in the SEM area. In order to analyze the causal relationship among the education quality factors constructed as latent variables, this study designed the structural equation model with suggested factors and established several research hypotheses. This study discovered a prominent causality among the education quality factors, such as education leadership, student scholastic performance and satisfaction of education quality, which is similar to that of previous research. This outcome is really a unique Korean syndrome manifest within our educational career orientation.

• Environmental and Resource Economics Review
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• v.16 no.3
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• pp.701-721
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• 2007

A key issue in the development and application of stated preference nonmarket valuation is the incorporation of unobserved heterogeneity in utility models. Two approaches to this task have dominated. The first is to include individual-specific characteristics into the estimated indirect utility functions. These characteristics are usually socioeconomic or demographic variables. The second employs generalized models such as random parameter logit or probit models to allow model parameters to vary across individuals. This paper examines a third approach: the inclusion of psychological or 'latent' variables such as general attitudes and behaviour-specific attitudes to account for heterogeneity in models of stated preferences. Attitudinal indicators are used as explanatory variables and as segmentation criteria in a choice modelling application. Results show that both the model significance and parameter estimates are influenced by the inclusion of the latent variables, and that attitudinal variables are significant factors for WTP estimates.

• IE interfaces
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• v.12 no.4
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• pp.506-513
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• 1999

We use a path analysis to identify influential latent variables on employees' anticipated commitment to a merged bank. Survey samples are taken from Hanvit bank which is a merged form of Hanil and Sangup. Latent variables used in the path analysis are perceptions of organizational support, contact conditions, organizational unity, employee threat and organizational commitment. We find an interesting pre-merger group membership effect as well as merger pattern perceived by employees on the path for the anticipated commitment to the merged bank.

• Communications for Statistical Applications and Methods
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• v.8 no.3
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• pp.777-786
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• 2001

The autoregressive models have been used to describe a wade variety of time series. Then the problem of determining the order in the times series model is very important in data analysis. We consider the Bayesian approach for finding the order of autoregressive(AR) error models using the latent variable which is motivated by Tanner and Wong(1987). The latent variables are combined with the coefficient parameters and the sequential steps are proposed to set up the prior of the latent variables. Markov chain Monte Carlo method(Gibbs sampler and Metropolis-Hasting algorithm) is used in order to overcome the difficulties of Bayesian computations. Three examples including AR(3) error model are presented to illustrate our proposed methodology.