• Journal of Home Health Care Nursing
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• v.27 no.3
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• pp.271-283
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• 2020

Purpose: This study aimed to develop a scale measuring the Tae-Wom in nurses. Methods: The initial items were based on an extensive literature review and in-depth interviews with seven nurses with varied careers. Thirty-five items were derived from a pilot survey. Data were collected from 196 hospital nurses, and analyzed to identify items of the scale as well as to verify the exploratory factor analysis, reliability, and validity of the scale. Results: The results of the exploratory factor analysis showed that six factors contained 26 items and 65.2% of the total explained variance. These six factors comprised negative behavior exhibited by the Tae-Wom recipient, personal character of the Tae-Wom giver, personal character of nursing unit managers, work attitude of the Tae-Wom giver, overwhelming workload, and unskilled relationships. Furthermore, convergent and discriminant validity testing verified these items. The internal consistency reliability was acceptable (Cronbach's α= .93), and Cronbach's α for each factor ranged from .62 to .90. Conclusion: The developed Tae-Wom scale will serve as a tool for verifying the Tae-Wom state in nursing organizations. Therefore, this scale is expected to mitigate the negative effects of the Tae-Wom.

• The Korean Journal of Applied Statistics
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• v.25 no.3
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• pp.415-422
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• 2012

For statistical microarray data analysis, clustering analysis is a useful exploratory technique and offers the promise of simultaneously studying the variation of many genes. However, most of the proposed clustering methods are not rigorously solved for a time-course microarray data cluster and for a fitting time covariate; therefore, a statistical method is needed to form a cluster and represent a linear trend of each cluster for each gene. In this research, we developed a modified hierarchical mixture of an experts model to suggest clustering data and characterize each cluster using a linear mixed effect model. The feasibility of the proposed method is illustrated by an application to the human fibroblast data suggested by Iyer et al. (1999).

• Nuclear Engineering and Technology
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• v.49 no.2
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• pp.349-359
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• 2017

During the past 25 years, in the context of probabilistic safety assessment, efforts have been directed towards establishment of comprehensive pipe failure event databases as a foundation for exploratory research to better understand how to effectively organize a piping reliability analysis task. The focused pipe failure database development efforts have progressed well with the development of piping reliability analysis frameworks that utilize the full body of service experience data, fracture mechanics analysis insights, expert elicitation results that are rolled into an integrated and risk-informed approach to the estimation of piping reliability parameters with full recognition of the embedded uncertainties. The discussion in this paper builds on a major collection of operating experience data (more than 11,000 pipe failure records) and the associated lessons learned from data analysis and data applications spanning three decades. The piping reliability analysis lessons learned have been obtained from the derivation of pipe leak and rupture frequencies for corrosion resistant piping in a raw water environment, loss-of-coolant-accident frequencies given degradation mitigation, high-energy pipe break analysis, moderate-energy pipe break analysis, and numerous plant-specific applications of a statistical piping reliability model framework. Conclusions are presented regarding the feasibility of determining and incorporating aging effects into probabilistic safety assessment models.

• Journal of the Korean Society of Clothing and Textiles
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• v.28 no.910
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• pp.1253-1264
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• 2004

The purpose of this study is to develop clothing shopping orientation[CSO] scale reflecting the conceptual structure of CSO. For this purpose, a questionnaire composed of comprehensive 85 CSO items was developed through 2-step preliminary tests. Data was collected from two samples. One sample(n=559) was for scale development and the other sample(n=235) was for cross validity test. Descriptive analysis, correlation analysis, exploratory factor analysis, regression analysis, ANOVA, and confirmatory factor analysis were used for data analysis. For each lower-dimension within the CSO conceptual structure model, 1-2 items were selected based on the quantitative and the qualitative standards. As a result, a CSO scale composed of 31 items was developed, and reliability, construct validity, cross validity, convergent validity, discriminant validity, and criterion validity of the scale were verified. This study has significance in offering the standardized scale to both the academic and the practical fields.

• Journal of the Korean Geographical Society
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• v.43 no.1
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• pp.134-153
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• 2008

The main purpose of the research is to illustrate the value of the spatial statistical approach to residential differentiation by providing a framework for exploratory spatial data analysis (ESDA) using a local spatial separation measure. ESDA aims, by utilizing a variety of statistical and cartographic visualization techniques, at seeking to detect patterns, to formulate hypotheses, and to assess statistical models for spatial data. The research is driven by a realization that ESDA based on local statistics has a great potential for substantive research. The main results are as follows. First, a local spatial separation measure is correspondingly derived from its global counterpart. Second, a set of significance testing methods based on both total and conditional randomization assumptions is provided for the local measure. Third, two mapping techniques, a 'spatial separation scatterplot map' and a 'spatial separation anomaly map', are devised for ESDA utilizing the local measure and the related significance tests. Fourth, a case study of residential differentiation between the highly educated and the least educated in major Korean metropolitan cities shows that the proposed ESDA techniques are beneficial in identifying bivariate spatial clusters and spatial outliers.

• The Journal of the Korea Contents Association
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• v.12 no.2
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• pp.461-471
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• 2012

The purpose of this paper is to visualize the housing market dynamics with ESDA (Exploratory Spatial Data Analysis) using MATLAB toolbox, in terms of the modeling housing market dynamics in the Busan Metropolitan City. The data are used the real housing price transaction records in Busan from the first quarter of 2006 to the second quarter of 2009. Hedonic house price model, which is not reflecting spatial autocorrelation, has been a powerful tool in understanding housing market dynamics in urban housing economics. This study considers spatial autocorrelation in order to improve the traditional hedonic model which is based on OLS(Ordinary Least Squares) method. The study is, also, investigated the comparison in terms of $R^2$, Sigma Square(${\sigma}^2$), Likelihood(LR) among spatial econometrics models such as SAR(Spatial Autoregressive Models), SEM(Spatial Errors Models), and SAC(General Spatial Models). The major finding of the study is that the SAR, SEM, SAC are far better than the traditional OLS model, considering the various indicators. In addition, the SEM and the SAC are superior to the SAR.

• Journal of the Korean Institute of Landscape Architecture
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• v.20 no.3
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• pp.103-109
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• 1992

This research suggest a causal model to investigate factors and variables which significant effects on user satisfaction with city park recreational facilities in Duryu city park in Taegu, and thereby identifying implications for planning and development of urban parks and open space. For this study the data were gathered by self-adminstered questionnares from 993 households selected by the multi-stage probability sampling method. The analysis of the data consists of two phases. The first involves exploratory factor analysis to draw meaningful factors from the data. Three factors were indentified. The second phase test the causal model of this research employing LISREL methodology. On the base of the analysis results, important implications for planning of city park and open space are recommended.

• The Journal of Economics, Marketing and Management
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• v.10 no.2
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• pp.15-22
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• 2022

Purpose: This study explores the effect of employee's growth needs strength on counterproductive work behaviors. Perceived job insecurity was also examined as a moderating variable on the relationship between the two variables. Research Design, data and methodology: This study collected 108 data samples from working individuals from South Korea. The Exploratory Factor Analysis (EFA) and the hierarchical regression analysis were used to analyze the data. Hierarchical regression analysis was performed using SPSS 24.0. Results: Our research results indicated that employee's growth needs strength has a negative effect on counterproductive work behaviors. Perceived job insecurity moderates the relationship between the two variables. Conclusions: Organizations should focus on creating growth opportunities for employees, since facilitating employee's growth need strength will counteract the desire to engage in behaviors that can be detrimental to the organization. and its members.

• International Journal of Advanced Culture Technology
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• v.11 no.4
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• pp.378-384
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• 2023

In this study, we explore social awareness, interest, and acceptance of generative AI, including chatGPT, which has revolutionized web search, 30 years after web search was released. For this purpose, we performed a machine learning-based topic modeling analysis based on Korean news big data collected from November 30, 2022, when chatGPT was released, to August 31, 2023. As a result of our research, we have identified seven topics related to chatGPT and generative AI; (1)growth of the high-performance hardware market, (2)service contents using generative AI, (3)technology development competition, (4)human resource development, (5)instructions for use, (6)revitalizing the domestic ecosystem, (7)expectations and concerns. We also explored monthly frequency changes in topics to explore social interest related to chatGPT and Generative AI. Based on our exploration results, we discussed the high social interest and issues regarding generative AI. We expect that the results of this study can be used as a precursor to research that analyzes and predicts the diffusion of innovation in generative AI.

• Culinary science and hospitality research
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• v.23 no.4
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• pp.22-32
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• 2017

The purpose of this study was to explore awareness of food tourism using big data analysis. For this, this study collected data containing 'food tourism' keywords from google web search, google news, and google scholar during one year from January 1 to December 31, 2016. Data were collected by using SCTM (Smart Crawling & Text Mining), a data collecting and processing program. From those data, degree centrality and eigenvector centrality were analyzed by utilizing packaged NetDraw along with UCINET 6. The result showed that the web visibility of 'core service' and 'social marketing' was high. In addition, the web visibility was also high for destination, such as rural, place, ireland and heritage; 'socioeconomic circumstance' related words, such as economy, region, public, policy, and industry. Convergence of iterated correlations showed 4 clustered named 'core service', 'social marketing', 'destinations' and 'social environment'. It is expected that this diagnosis on food tourism according to changes in international business environment by using these web information will be a foundation of baseline data useful for establishing food tourism marketing strategies.