• Journal of Korean Public Health Nursing
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• v.33 no.3
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• pp.409-419
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• 2019

Purpose: Factors related to the major satisfaction of nursing students were systematically searched and quantitatively synthesized. Methods: Meta-analysis was conducted upon 47 articles in Korean master and doctorate degree dissertations and academic journals. Meta-analysis of major satisfaction-related variables was conducted using Comprehensive Meta-Analysis (CMA) 2.0 program. The effect size of the related variables was analyzed by converting the statistic r value to Fisher's Z. Results: The overall average effect size of major satisfaction was the largest effect size (ES=.49), followed by cognitive factors (ES=.58), affective factors (ES=.45), and psychomotor factors (ES=.31). The cognitive factors were in the order nursing professionalism(ES=.70), nurse's image (ES=.65), and critical thinking disposition (ES=.36). The affective factors were self-esteem (ES=.59), emotional intelligence (ES=.55), career identity (ES=.49), self-efficacy (ES=.48), college adjustment (ES=.45), practice satisfaction (ES=.45), resilience (ES=.42), (ES=.40), grit (ES=.34), and stress (ES=.26). The psychomotor factors were clinical performance ability (ES=.38) and career search behavior (ES=.31). Conclusion: The results of this study are valuable when giving consideration to the variables related to nursing students' major satisfaction, for developing a strategic model to enhance the satisfaction of nursing students.

• Journal of Biosystems Engineering
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• v.30 no.1 s.108
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• pp.45-55
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• 2005

Knowledge of the relationship between crop yield and yield-limiting factors is essential for precision farming. However, developing this knowledge is not easy because these yield-limiting factors are interrelated and affect crop yield in different ways. In this study, data for grain yield and yield-limiting factors, including crop chlorophyll content, soil chemical properties, and topography were collected for a small (0.3 ha) rice paddy field in Korea and a large (36 ha) upland corn field in the USA, and relationships were investigated with path analysis. Using this approach, the effects of limiting factors on crop yield could be separated into direct effects and indirect effects acting through other factors. Path analysis provided more insight into these complex relationships than did simple correlation or multiple linear regression analysis. Results of correlation analysis for the rice paddy field showed that EC, Ca, and $SiO_2$ had significant (P<0.1) correlations with rice yield, while pH, Ca, Mg, Na, $SiO_2,\;and\;P_2O_5$ had significant correlations with the SPAD chlorophyll reading. Path analysis provided additional information about the importance and contribution paths of soil variables to rice yield and growth. Ca had the highest direct effect (0.52) and indirect effect via Mg (-0.37) on rice yield. The indirect effect of Mg through Ca (0.51) was higher than the direct effect (-0.38). Path analysis also enabled more appropriate selection of important factors limiting crop yield by considering cause-and-effect relationships among predictor and response variables. For example, although pH showed a positive correlation (r=0.35) with SPAD readings, the correlation was mainly due to the indirect positive effects acting through Mg and $SiO_2$, while pH not only showed negative direct effects, but also negatively impacted indirect effects of other variables on SPAD readings. For the large upland Missouri corn field, two topographic factors, elevation and slope, had significant (P<0.1) direct effects on yield and highly significant (P<0.01) correlations with other limiting factors. Based on the correlation analysis alone, P and K were determined to be nutrients that would increase corn yield for this field. With the help of path analysis, however, increases in Mg could also be expected to increase corn yield in this case. In general, path analysis results were consistent with published optimum ranges of nutrients for rice and com production. We conclude that path analysis can be a useful tool to investigate interrelationships between crop yield and yield limiting factors on a site-specific basis.

• The Journal of Engineering Geology
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• v.33 no.4
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• pp.673-687
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• 2023

Exploratory data analysis is the process of observing and understanding data collected from various sources to identify their distributions and correlations through their structures and characterization. This process can be used to identify correlations among conditioning factors and select the most effective factors for analysis. This can help the assessment of landslide susceptibility, because landslides are usually triggered by multiple factors, and the impacts of these factors vary by region. This study compared two stages of exploratory data analysis to examine the impact of the data exploration procedure on the landslide prediction model's performance with respect to factor selection. Deep-learning-based landslide susceptibility analysis used either a combinations of selected factors or all 23 factors. During the data exploration phase, we used a Pearson correlation coefficient heat map and a histogram of random forest feature importance. We then assessed the accuracy of our deep-learning-based analysis of landslide susceptibility using a confusion matrix. Finally, a landslide susceptibility map was generated using the landslide susceptibility index derived from the proposed analysis. The analysis revealed that using all 23 factors resulted in low accuracy (55.90%), but using the 13 factors selected in one step of exploration improved the accuracy to 81.25%. This was further improved to 92.80% using only the nine conditioning factors selected during both steps of the data exploration. Therefore, exploratory data analysis selected the conditioning factors most suitable for landslide susceptibility analysis and thereby improving the performance of the analysis.

• The Research Journal of the Costume Culture
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• v.15 no.1 s.66
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• pp.179-192
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• 2007

The purpose of this study was to investigate the relationship among fashion retail-entertainment, store image and store loyalty. Two hundred eleven who female college students living near Seoul area participated in this study. Two hundred sample used for data analysis. For data analysis, frequency, factor analysis, correlation analysis and multiple regression analysis were used. The results were as follows: First, fashion retail-entertainment had 4 factors such as shopping environment, sales service, dining facilities, and others. Store image composed of psychological image, merchandise mix, customer service, and advertising exposure. Store loyalty had 3 factors; cognitive loyalty, intentional loyalty, emotional loyalty. Second, retail-entertainment factors had positive influences on store image and store loyalty. Third, store image had positively affected on store loyalty. Based on these results, successful strategies for fashion retail-entertainment business would provide.

• Fashion & Textile Research Journal
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• v.10 no.6
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• pp.936-946
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• 2008

The purpose of this study is to analyze whether transactional leadership and transformational leadership have an effect on team empowerment and performance. To this end, a survey was conducted from August 1 to 30 in 2008, among team members of fashion companies. The data was collected with 586 subjects, the statistical analysis methods were frequency analysis, reliability analysis, factor analysis and multiple regression analysis. The results of this study were as follows; First, contingent reward of transactional leadership had an effect on team empowerment factors and team performance factors. Second, charisma, individualized consideration and intellectual stimulation of transformational leadership had an effect on team empowerment factors and team performance factors. Third, team potency, team autonomy and team meaning of team empowerment had an effect on team performance factors.

• Journal of National Security and Military Science
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• s.5
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• pp.241-280
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• 2007

This study examines the relationship of the industry-specific factors that effect innovation of manufacturing technology and the market share within the defense industry. Since the establishment of the basic defense industry framework in 1973, there were numerous interactions of the industry-specific factors of the defense industry structure with the technological innovation and market organization of the defense industry. During last three decades, the domestic defense industry has achieved the considerable level but the framework of the basic system has not developed much in areas of the military science and the defense manufacturing technology. Industry-specific factors were formed in the process and appeared in a variety of behavioral characteristics as subsystems. Currently, there IS a growing trend where the management of defense industry is gradually deteriorating due to limitation of the domestic industry-specific factor (e.g. defense technologies, amount of demand, etc.). If there is a prominent imbalance of the industry-specific factors. it can trigger the potential problem of conflict, lack of cooperation and control, slowing the growth of the manufacturing technology thereby diminishing the market and deteriorating the defense supply/demand relationship. In a research conducted by Joe S. Bain, Bain analyzed the relationship of the traditional industrial organization where industry-specific factor(S) not only impacts the conductor(C). And, conductor(C) influences the shaping of the performance(P) of relationship of the traditional industrial organization. Consequently, the researcher has identified the demand monopoly, barriers to entry, and market competition with comparison of defense industry issues. These defense issues were three industry-specific factors identified, which are 1) The demand monopoly and The entry barriers to new market competition, 2) the industrial technical factor to a production technical competitiveness and a market sharing competitiveness, 3) the probability factor to revolution for military affairs(RMA) and a R&D production. According to baseline with these factors, the following research model is established from the special companies group(Group A), the systematization companies group(Group B), and the general companies group(Group 0. The hypothesis is that if there are more industry-specific factors, then there will be more relationships of defense industry relation statutes. This research is an empirical study on the relationship that the industry specific factors effects the innovation of manufacturing technology and the shaping of the market in the defense industry. Moreover, the existing models to evaluate the industry specific factors of the defense industry IS much to be desired with the controlled statistical analysis of the result. It is vital to study on current situation with suggesting alternative strategy to the efficient strategy. The descriptive analysis approach analysis is conducted with SPSSWIN to conduct reliability test, factor analysis, correlation analysis, cross-tabulation analysis, one-way ANOVA, and multiple regression analysis. However, there were some limitations of the survey such as the rigidity of concept about the technical factors and various market management factors. The wishes is that the decision-maker could be utilized these defence industrial factors to formulate efficient defence policy and strategy in the future.

• Journal of the Korea Society of Computer and Information
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• v.21 no.11
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• pp.127-134
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• 2016

In this paper, we propose factors that influence on the mobile commerce satisfaction conducted by data mining and a PLS regression analysis. We extracted the most frequent words from mobile application reviews in which there are a large number of user's requests. We employed the content analysis to condense the large number of texts. We took a survey with the categories by which data are condensed and specified as factors that influence on the mobile commerce satisfaction. To avoid multicollinearity, we employed a PLS regression analysis instead of using a multiple regression analysis. Discovered factors that are potential consequences of customer satisfaction from direct requests by customers, the result may be an appropriate indicator for the mobile commerce market to improve its services.

• Journal of The Korean Association For Science Education
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• v.22 no.5
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• pp.1010-1029
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• 2002

The purpose of this research was to survey middle school students' preference for science and its causal factors, so as to analyze the causal relationships between them. Preference for science and its causal factors were defined theoretically, and a theoretical model was constructed to measure them and analyze the causal relationship by structural equation modeling. According to the theoretical model and a pilot test, a questionnaire was developed with three parts; the background information of a respondent, the preference for science, and the causal factors of preference. The questionnaire was administered to one class per grade of randomly selected 8 middle schools from 4 areas across the country, and 819 students' data were collected. Preference for science was defined as a state of mind. It revealed to what extent, and how, one likes science. It consisted of 3 categories - 'emotional response', 'behavioral volition', 'valuational comprehension', and each category was divided into two subcategories. Causal factors affecting the preference for science consisted of three categories - personal, educational and social factors, and each was divided into 2 or 3 subcategories. Middle school students' preference for science was middling as a total. Curiosity about contents of science and valuation of science were high, comparatively, but behavioral volition about science was especially low. Students' responses to the causal factors were relatively high in every educational factor and sociocultural valuation of social factors, but relatively low in socioeconomic rewards of social factors, and especially low in personal factors. The causal relationship about the preference for science was investigated by multiple regression analysis and path analysis, using the structural equation model. Multiple regression analysis about the preference for science and its causal factors revealed important factors. The important factors were personal ability, the personal traits, rewards in school science, and contents of school science in order of magnitude of standardized regression coefficient ${\beta}$. Stepwise regression analysis with each of the subcategories of the preference for science as dependent variables showed what factors were important in each subcategory. According to the result of structural equation modeling, personal factors affected 'emotional response' and 'behavioral volition' directly, and social factors affected 'valuational comprehension' directly. Educational factors affected all categories of the preference for science by influencing not only 'emotional response' and 'valuational comprehension' directly, but also 'behavioral volition' indirectly. The way to promote middle school students' preference for science was suggested, based on the analysis result.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2003.09a
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• pp.231-238
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• 2003

The overstrength factor and the ductility factor are the two important factors that determines response modification factors used in current seismic codes. The objective of this paper is to obtain the overstrength and ductility factors of special concentric braced frames. For this purpose pushover analysis is performed with model structures until the maximum inter-story drift reaches 2.5% of story height. According to the analysis results, the overstrength factors increase as the height of structures decreases and the span length increases. Ductility factors for mid-story structures turns out to be higher than the other structures and span length does not contribute much to ductility factors.

• Journal of Distribution Science
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• v.16 no.5
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• pp.17-29
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• 2018

Purpose - This study analyzed the success strategy of Korean small & medium cosmetics exporting companies to enter the Southeast Asian market. Research design, data, and methodology - The independent factors are classified into firm capacity, financial factor, institutional factor, and operational factor. The results of the selection of distributor partners of cosmetics related export companies as a were classified as financial performance and non - financial performance. In order to analyze this, 65 Korean small and medium export companies were recruited through structured online questionnaire for 44 days from September 18, 2017 to October 31, 2017. These data were analyzed by frequency analysis, correlation analysis, factor analysis and regression analysis using SPSS. Results - The Cronbach's alpha coefficient was found to be 0.846. Factor analysis between variables revealed that the eigen value exceeded 1 and was considered valid. As a result of the correlation analysis between the variables, the financial factor and the corporate's competence showed the highest correlation with 0.774. Conclusions - Among the factors influencing the financial performance of the exporting firms, the factors influencing the financial performance of the exporting companies are the factors that influence the non - financial performance rather than the financial performance.