• Korean Journal of Child Studies
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• v.32 no.2
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• pp.87-104
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• 2011

The purpose of this study was to develop a basic life habit scale for infant early childhood. The participants of this study were composed of 1,000 parents of children aged from two to five years old in the Seoul, Gyeong-gi, and In-cheon areas. For the purposes of data analysis, the study made use of the following methods : descriptive statistics for SES variables, item-analysis, factor analysis for validity, and Cronbach's a for reliability. Most items were acceptable in terms of item response rates, and item discrimination. The results of factor analysis uncovered six factors, and 46 items were selected from a total of 69 items in the original scale. The six factors were (1) safety and rules (2) neatness (3) manners (4) self-help (5) eating habits (6) cleanliness. Cronbach's a value for the reliability of the factors ranged from .76 to .94.of Cooperative Learning. Methods. Westport, CT : Greenwood Press.

• Journal of the Korean Data and Information Science Society
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• v.25 no.4
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• pp.727-736
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• 2014

The fundamental concerns of this paper are to analyze the effects of some characteristics on the employment of new college graduated students in viewpoint of data visualization. We use individual and department characteristic data of K-university graduated students in 2010. We apply multiple correspondence analysis, decision tree analysis, association rules and social network analysis for data visualization. The results of the analysis are summarized as follows. First, an analysis of the determinants of employment shows that GPA, department category, age and number of majors, recruiting time affect the employment rate. Second, higher GPA and natural category of department positively affect the employment rate. Finally, low age, single major and early recruiting time also positively affect the employment rate.

• The Journal of the Korea Contents Association
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• v.12 no.4
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• pp.76-85
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• 2012

Through opinion mining, we can analyze the degree of positive or negative sentiments that customers feel about important entities or attributes in online customer reviews. But, the limit of the opinion mining techniques is to provide only simple functions in analyzing the reviews. In this paper, we proposed novel techniques that can analyze the online customer reviews multi-dimensionally. The novel technique is to modify the existing OLAP techniques so that they can be applied to text data. The novel technique, that is, multi-dimensional analytic model consists of noun, adjective and document axes which are converted into four relational tables in relational database. The multi-dimensional analysis model would be new framework which can converge the existing opinion mining, information summarization and clustering algorithms. In this paper, we implemented the multi-dimensional analysis model and algorithms. we recognized that the system would enable us to analyze the online customer reviews more complexly.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.1281-1281
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• 2024

About 70% of the data generated on building construction sites consists of unstructured data, such as text, photos, videos, etc. However, the text data, which constitutes the largest proportion of unstructured data, has been restrictively utilized. When using standardized data to evaluate safety performance, there are a few difficulties in addressing issues such as lack of data, omissions, and errors. This copes with limitations on the practical evaluation of safety performance on building construction sites. Despite generating extensive text-centric documents, the previous researches on evaluating safety performance levels using unstructured data are still in its infancy. This study proposes a framework for evaluating the safety performance by preprocessing and refining text-based construction supervision documents. In this framework, relevant keywords related to safety performance are extracted from supervision documents, tokenized, and analyzed for association rules among keywords. Based on the results of the association rule analysis, keywords are selected, and the unsatisfactory or satisfactory level of safety performance is quantified using logistic regression analysis, considering the frequency of their occurrence. While the proposed framework focuses on quantifying the safety performance levels of construction sites, it can be expanded to implement integrated performance diagnostics on-site by linking with tools that evaluate diverse performance levels. This extension will allow for a comprehensive assessment of on-site performance. Furthermore, the framework can serve as a tool supporting practical and proactive inspections and responses of safety managers by utilizing unstructured data alongside the traditional approach focused on standardized data for safety performance assessment.

• The Research Journal of the Costume Culture
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• v.32 no.2
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• pp.199-214
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• 2024

Until now, research on consumers' purchasing behavior has primarily focused on psychological aspects or depended on consumer surveys. However, there may be a gap between consumers' self-reported perceptions and their observable actions. In response, this study aimed to investigate consumer purchasing behavior utilizing a big data approach. To this end, this study investigated the purchasing patterns of fashion items, both online and in retail stores, from a data-driven perspective. We also investigated whether individual consumers switched between online websites and retail establishments for making purchases. Data on 516,474 purchases were obtained from fashion companies. We used association rule analysis and K-means clustering to identify purchase patterns that were influenced by customer loyalty. Furthermore, sequential pattern analysis was applied to investigate the usage patterns of online and offline channels by consumers. The results showed that high-loyalty consumers mainly purchased infrequently bought items in the brand line, as well as high-priced items, and that these purchase patterns were similar both online and in stores. In contrast, the low-loyalty group showed different purchasing behaviors for online versus in-store purchases. In physical environments, the low-loyalty consumers tended to purchase less popular or more expensive items from the brand line, whereas in online environments, their purchases centered around items with relatively high sales volumes. Finally, we found that both high and low loyalty groups exclusively used a single preferred channel, either online or in-store. The findings help companies better understand consumer purchase patterns and build future marketing strategies around items with high brand centrality.

• Journal of the Korean Data and Information Science Society
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• v.22 no.6
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• pp.1113-1121
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• 2011

Recently, a variety of data mining techniques has been applied in various fields like healthcare, insurance, and internet shopping mall. Association rule mining is a popular and well researched method for discovering interesting relations among large set of data items. Association rule mining is the method to quantify the relationship between each set of items in very huge database based on the association thresholds. There are three primary quality measures for association rules; support and confidence and lift. In this paper we consider some similarity measures with negative co-occurrence frequencies which is widely used in cluster analysis or multi-dimensional analysis as association thresholds. The comparative studies with support, confidence and some similarity measures are shown by numerical example.

• The Journal of the Korea Contents Association
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• v.19 no.7
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• pp.99-109
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• 2019

Data Envelopment Analysis (DEA), a non-parametric productivity analysis tool, has become an accepted approach for assessing efficiency in a wide range of fields. Despite of its extensive applications, some features of DEA remain bothersome. For example DEA is good at estimating "relative" efficiency of a DMU(Decision Making Unit), it only tells us how well we are doing compared with our peers but not compared with a "theoretical maximum." Thus, in order to measure efficiency of a new DMU, we have to develop entirely new DEA with the data of previously used DMUs. Also we cannot predict the efficiency level of the new DMU without another DEA analysis. We aim to show that DEA can be used to evaluate the efficiency of ports and suggest the methodology which overcomes the limitation of DEA through hybrid analysis utilizing DEA along with C5.0. We can generate classification rules C5.0 in order to classify any new Port without perturbing previously existing evaluation structures by proposed methodology.

• Lingua Humanitatis
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• v.5
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• pp.127-163
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• 2003

This thesis analyzes the Chapter "Hoyeonjigi(浩然之氣)" of Mencius(孟子), using text linguistics theory and reading theory of Korean. In this process the model of macro-structure #1∼5 are presented, according to Vandijk′s rules of macro-structure; Auslassen, Selektierne, Generalisieren, Konstruieren odor Integrieren. As a result, this certifies; First, macro-structure could make arbitrarily a several steps of macro-structure by types of text or purpose of analysis. Second, macro-structure applies various cognitive mechanisms of outer world as well as inner world. Third, a text with profound symbolism could be figured as a two-or threefolded symbolic structure. At the same time, macro-structure enables the clearer analysis of the content of the Chapter to verify the following; first, Hoyeinjigi itself is the best measure of developing "Imperturbable Mind(不動心)" Second, benevolence-righteousness(仁義) and wisdom(智) would be reached by cultivating Hoyeonjigi. Third, Mencius′ own view of language is well expressed in "Jieon(知言)", which is not only a condition for Imperturbable Mind, but also the Oriental view of language focused especially on listening in terms of language usage, not language analysis. This Mencius′ view of language has a thread connection with that of Oriental′s.

• East Asian Journal of Business Economics (EAJBE)
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• v.10 no.2
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• pp.71-80
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• 2022

Purpose - It is chaotic when doing a business without legal patterns and rules; individuals who make legal decisions without legal consideration are often protecting their interests and forgetting others. This study aims to suggest key solutions how companies can make better decision based on legal considerations through investigation comprehensive literature analysis. Research design, Data, and methodology - This research conducted qualitative textual method and a technique called 'Qualitative Comparative Analysis' (QCA) can be used to understand better why certain things change while others do not. In tough situations, QCA is a strategy for comparing several occurrences. Result - Total six considerations were founded by the QCA for better corporate decision. Based on these considerations, all stakeholders, shareholders, and every employee should nominate and vote on one person to be their leader in the organization; fair practices in choosing the governor of the organization through legal binding will bring peace and order to the company. Conclusion - It was time consuming to go through every detailed material that entails legal consideration in making corporate decision. The concept of same profile in the research is critical whereby many authors are using same concept to write their articles and books. Using pure concept from one source limits the research and gives inadequate information.

• English Language & Literature Teaching
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• v.12 no.3
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• pp.1-24
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• 2006

This study investigated differences in beliefs about English learning of 286 EFL college students and 52 English teachers in Korea. Data was collected using Horwitz's Beliefs About Language Learning Inventory and compared between students and teachers in beliefs. To address the research questions, the data were analyzed through descriptive statistics including frequencies, factor analysis, MANOVA, ANOVA, t-test, and reliability coefficients. The results showed four factors in student beliefs: Difficulty of learning English, nature of learning English, importance of correctness in learning English, and motivation and perceived importance of learning English. Clear differences were found in students and teachers' beliefs in English learning aptitude and importance of translation, error correction, and grammar rules. A few belief differences were also identified between Koreans and native-speaking English teachers related to the importance of vocabulary learning, pronunciation, and cultural knowledge. The findings of the study indicated that background variables such as gender and major field of study have an effect on student beliefs about L2 learning. The present study also provided pedagogical considerations to reduce mismatch between students and teachers beliefs and to improve the L2 planning and instruction.