Earticle

몽골은 2011년 경제성장률이 무려 17.3% 증가한 것으로 중국의 9.1%나 인도의 6.9%를 훨씬 상회하 는 급속한 경제발전의 시작에 있다는 점과 향후 10년간 연평균 12%이상 성장하여 2020년에는 현재 GDP의 2배가 될 것이며, 이에 따른 새로운 신흥시장으로서 발전가능성에 초점이 맞춰지고 있다. 몽골 의 이와 같은 눈부신 경제발전의 토대에는 몽골이 갖고 있는 풍부한 천연자원이 주요 영향을 미치고 있으며, 우리나라의 입장에서 몽골의 풍부한 천연자원은 우리나라 경제의 지속적인 발전을 위해 최우 선적으로 필요하다는 자원 외교적 시각에서도 점증적으로 몽골에 대한 해외직접투자를 적극 촉진시킬 것으로 기대된다. 그러나, 최근 들어 우리나라의 몽골에 대한 해외직접투자들 중에는 몽골에 대한 정확한 지식없이 투 기식으로 진행되는 경우가 늘어나 이에 대한 우려가 커지고 있는 상황이다. 따라서 본 논문은 몽골의 경제적 상황 변화 및 추이에 대한 흐름에 대해 우선적으로 살펴보고, 우리나라의 경제자유구역을 중심 으로 투자한 외국인 직접투자기업들을 중심으로 해외직접투자 시 고려하는 투자결정요인을 분석하여, 이들 요인들이 해외직접투자 의사결정시 어떠한 역할을 하는지에 대해 실태 및 실증 조사를 하였다. 분석 결과, 전략적 동기는 비 시장 요인과 시장 요인의 2개 요인으로 설문항목이 구성되며, 형태적 요인은 정주환경요인 1개, 경제적 동기는 직접수익요인과 투자수익요인의 2개 요인 그리고 재무적 동 기는 투자환경 요인 1개 등 6개의 요인이 해외직접 투자시 각각 영향을 미치고 있는 것으로 분석되었 다.

In 2011, Mongolia has the economic growth rate which was increased over 17.3 percent, at the start of rapid economic development far exceed the 6.9% of China and 9.1% of India. The next 10 years, Mongolia has grown more than 12% of year on average and is going to be more than twice the present GDP in 2020. Therefore, most countries have focused on the possibility of the development of Mongolia as a new emerging market. Foundation of development like this is very closely with the Mongolian natural resources and Korea have been promoting FDI to Mongolia. However, some investments of Korean FDI were invested without precise knowledge of Mongolian. So at first, this study consider for change in economic conditions and trends of Mongolia, and we derive the determinant factors form foreign investment companies in Korea Free Economic Zone. The results of study conduct 6 factors from four types of investment motivation, and these factors effects differently to FDI decision making.

This study investigated the factor structure of RSACQ(Revised Student Adaption to College Questionnaire) using SPSS 11.5 and AMOS 4.5 program that supply estimation method for data. The participants consisted of 277 tourism major's students aged 18-51. The results showed that student's adaption accessed by RSACQ has for factors. individual and emotional adaption, scholastic adaption, adaption of university's general environment and social adaption. This strengths of this study include the application of more rigorous questionnaire methodology than has been used in previous studies. Also, this result that tourism major's students accessed by RSACQ has four factors is possible to used in clinical settings as well as future researches.

Purpose The purpose of this paper is to examine differences between two factor analytical methods and their relevance for symptom cluster research: common factor analysis (CFA) versus principal component analysis (PCA). Methods Literature was critically reviewed to elucidate the differences between CFA and PCA. A secondary analysis (N= 84) was utilized to show the actual result differences from the two methods. Results CFA analyzes only the reliable common variance of data, while PCA analyzes all the variance of data. An underlying hypothetical process or construct is involved in CFA but not in PCA. PCA tends to increase factor loadings especially in a study with a small number of variables and/or low estimated communality. Thus, PCA is not appropriate for examining the structure of data. Conclusion If the study purpose is to explain correlations among variables and to examine the structure of the data (this is usual for most cases in symptom cluster research), CFA provides a more accurate result. If the purpose of a study is to summarize data with a smaller number of variables, PCA is the choice. PCA can also be used as an initial step in CFA because it provides information regarding the maximum number and nature of factors. In using factor analysis for symptom cluster research, several issues need to be considered, including subjectivity of solution, sample size, symptom selection, and level of measure. [Asian Nursing Research 2008;2(1):17?24]

Altmetrics measure the frequency of references about an article on social media platforms, like Twitter. This paper studies a variety of factors that affect the popularity of articles (i.e., the number of article mentions) in the field of psychology on Twitter. Firstly, in this study, we classify Twitter users mentioning research articles as academic versus non-academic users and experts versus non-experts, using a machine learning approach. Then we build a negative binomial regression model with the number of Twitter mentions of an article as a dependant variable, and nine Twitter related factors (the number of followers, number of friends, number of status, number of lists, number of favourites, number of retweets, number of likes, ratio of academic users, and ratio of expert users) and seven article related factors (the number of authors, title length, abstract length, abstract readability, number of institutions, citation count, and availability of research funding) as independent variables. From our findings, if a research article is mentioned by Twitter users with a greater number of friends, status, favourites, and lists, by tweets with a large number of retweets and likes, and largely by Twitter users with academic and expertise knowledge on the field of psychology, the article gains more Twitter mentions. In addition, articles with a greater number of authors, title length, abstract length, and citation count, and articles with research funding get more attention from Twitter users.

Purpose. The present study was intended to compare difference in research variables between delinquent adolescents and student adolescents, and to analyze discriminative factors of delinquent behaviors among Korean adolescents. Methods. The research design of this study was a questionnaire survey. Questionnaires were administered to 2,167 adolescents (1,196 students and 971 delinquents), sampled from 8 middle and high school and 6 juvenile corrective institutions, using the proportional stratified random sampling method. Statistical methods employed were Chi-square, t-test, and logistic regression analysis. Results. The discriminative factors of delinquent behaviors were smoking, alcohol use, other drug use, being sexually abused, viewing time of media violence and pornography. Among these discriminative factors, the factor most strongly associated with delinquency was smoking (odds ratio: 32.32). That is, smoking adolescent has a 32-fold higher possibility of becoming a delinquent adolescent than a non-smoking adolescent. Conclusions. Our findings, that smoking was the strongest discriminative factor of delinquent behavior, suggest that educational strategies to prevent adolescent smoking may reduce the rate of juvenile delinquency. Antismoking educational efforts are therefore urgently needed in South Korea.

This research attempts to identify how members of an online community collaboratively engaged with particular social information behaviors and accomplished a defined collective action. While responding to the Sewol Ferry tragedy, MissyUSA members quickly communicated and mobilized a collective action, a full-page ad campaign in The New York Times. As a follow up study, this secondary analysis quantitatively analyzes the primary data from a previous study to explore potential relationships or underlying factors among the various identified information behaviors. In this study, nineteen of the previously identified information behaviors were analyzed using exploratory factor analysis, yielding a total of eight factors. The two major factors of shared representation/collective identification and mobilizing resources verified the findings of the previous study and are in line with the findings typical of political science. The three factors of collaborative decision-making, reaction to tension, and brainstorming were factors that maximized communication and mobilization online, without any face-to-face communication or physical organization. Three emergent factors of outburst of dissent, boycott, and planning explained how members used negative emotions of anger, referential information for boycott, and incubated next collective actions. Through exploratory factor analysis, this study verifies and expands on the findings of the previous study by identifying several emergent factors that relate to the collaborative information behaviors of an online community engaged in a collective action.

Objective To investigate the prevalence and risk factors of axillary web syndrome (AWS) in Korean patients. Methods This retrospective study included a total of 189 women who underwent breast cancer surgery and received physical therapy between September 2019 and August 2020. We analyzed AWS and the correlation between the patients’ demographics, underlying disease, type of surgery and chemotherapy or radiation therapy, and lymphedema. Results The prevalence of AWS was found to be 30.6%. In the univariable analysis, age, chemotherapy, and hypertension were related to AWS. Finally, the multivariable logistic regression revealed that chemotherapy (odds ratio [OR]=2.84; 95% confidence interval [CI], 1.46?5.53) and HTN (OR=2.72; 95% CI, 1.18?6.30) were the strongest risk factors of AWS. Conclusion To the best of our knowledge, this was the first study that explored the risk factors of AWS in a Korean population after breast cancer surgery. As almost one-third of patients suffer from AWS after breast cancer surgery, it is essential to closely monitor the development of AWS in patients with hypertension or undergoing chemotherapy.

Purpose: This study aimed to investigate sub-health status (SHS) of people living in China during the Coronavirus disease 2019 (COVID-19) COVID-19 pandemic. COVID-19 is a severe acute respiratory syndrome coronavirus (SARS-CoV) infection-induced acute infectious disease, which is featured by universal susceptibility and strong infectivity, and SHS (a status of low quality health) refers to a status of low-quality health. COVID-19 has gradually developed into a global pandemic, making the public in a high stress situation in physiological, psychological and social states in the short term. Methods: From March 6 to 11, 2020, a large-scale cross-sectional survey was conducted by convenient sampling, and SHS assessment scale was used in the questionnaire. The ordinal logistic regression analysis was used to identify the factors affecting SHS. Results: In this study, 17,078 questionnaires were delivered with 16,820 effective questionnaires collected, and 10,715 subjects (63.7%) were found with SHS, with moderate SHS primarily. Physiological sub-scale scored the highest, followed by psychological and social sub-scales. Ordinal logistic regression analysis indicated that man, only-child, workers and farmers were risk factors of SHS. Protective factors of SHS included living in rural areas and townships, laid-off retirees and education degree. Conclusion: It shows many people in China place in a poor health status during COVID-19 pandemic. It is necessary that relevant departments pay more attention to people with poor health such as men, only-child, urban people, workers and farmers, and groups with high education degree during and after pandemic stabilization.

There have been extensive studies done on webometrics, particularly on the impact of websites and the web impact factor. The present study analyzed the websites of ICAR organizations, according to the webometrics indicator. It examines and explores the 92 ICAR organizational websites in India and identifies a number of web pages and link pages, and calculates the Overall Web Impact Factor (WIF) and Absolute Web Impact Factor (WIF). In this study, all websites were analyzed and data extracted using Google search engine. It suggests that Web Impact Factors can be calculated as a way of comparing the attractiveness of web sites or domains on the Web.

Objective: Mild cognitive impairment (MCI) is a condition in which cognitive and executive functions are reduced, and older adults with MCI are ten times more likely to develop dementia than healthy older adults. Expression of brain-derived neurotrophic factor (BDNF) through aerobic exercise is associated with increased cognitive and executive functions. in this review, randomized controlled trials (RCTs) on the effects of aerobic exercise on BDNF in individuals with mild cognitive impairment are summarized and qualitatively and quantitatively analyzed to suggest the necessity of aerobic exercise. Design: a systematic review and meta-analysis. Methods: RCTs were searched for changes in BDNF through aerobic exercise using four international databases. Quality assessment and quantitative analysis were performed using RevMan 5.4. Quantitative analysis was quantified with a standardized mean difference (SMD) and presented as a random effect model. Results: Three RCTs evaluated BDNF in 123 patients with MCI. There was a significant improvement in the experimental group that performed aerobic exercise compared to the control group. The results analyzed using the random effects model were SMD = 0.48. Conclusions: In this review, we reported the effects and mechanisms of aerobic exercise in individuals with MCI. As a result of synthesizing RCTs that performed aerobic exercise, a significant increase in BDNF was confirmed.

Background/Objectives: This study was designed to compare the Varimax analysis of Principal Component Analysis(PCA) with the Exploratory Factor Analysis(EFA) method using Maximum Likelihood Estimation(MLE) and oblique rotation(Direct Oblimin) for the multicultural experience of university students. multicultural experience variables of college students were analyzed as two factors through Maximum Likelihood Estimation(MLE). Methods/Statistical analysis Q1, Q2, Q3 are multicultural direct experiences, and Q4, Q5, Q6, Q7 are multicultural indirect experiences. Findings: As a result of this study, it is suggested to use Maximum Likelihood Estimation(MLE) and oblique rotation(Direct Oblimin). As a prerequisite for applying the Maximum Likelihood Estimation(MLE), it is thought that Improvements/Applications it is necessary to increase the accuracy of the response rate by using the face-toface interview method when conducting a survey.

본 연구는 K-POP의 킬러컨텐츠가 아시아를 넘어 전 세계에서 주목받고 있는 현상에 중점을 두고 있으며 다른 문화 속에서 K-POP이 성공할 수 있는 요인을 분석하고 그 중 소셜미디어를 비롯한 스마트미디어 환경의 변화가 글로 벌시대에 미치는 영향력을 탐색하여 K-POP의 지속가능 방안을 분석하였다. 또한 국내외 실감컨텐츠 관련 제작사례를 탐구하여 K-POP의 잠재적인 지표 및 그에 따른 시장을 분석하고 컨텐츠 제작자, 플랫폼사업자, 네트워크 사업자, 가 전사 등 효율적인 생태계 구축과 비즈니스 모델을 통해 K-POP의 지속확대 방안을 제시하였다. 이를 통해 지속가능한 K-POP한류의 가능성을 확인하고 지속 발전할 수 있는 대안을 제시하였다.

This study focuses on the phenomenon that K-POP has become spotlight as killer contents which can be spread over the world beyond Asia. In order to identify critical factors of successful impact of K-POP on other cultures, the influence of the change in media on the globalization of popular culture was explored and the related requirements were analyzed. Furthermore, the potential markers for K-POP, a core field of the Korean Wave, were also analyzed and specific factors such as effective infrastructure of among producer of contents, platform subscriber, network provider, terminal enterprise and retailers for building new businesses model were investigated in detail.

The purpose of this study is two-fold: (1) to find context-specific motivational factors in Korean college contexts within the framework of Gardner’s integrativeness and Dörnyei’s (2005, 2009) L2 Motivational Self System; (2) to examine how those motivational factors explain motivated behavior. For this purpose, motivation data by 787 students was analyzed. The factor analysis revealed eight factors in Korean context: promotional instrumentality, bad learning experience, ought-to L2 self, integrativeness, elusive ideal L2 self, ethnocentrism, intercultural avoidance, and international posture. The regression analysis showed that four motivational factors, bad learning experience, ought-to L2 self, elusive ideal L2 self, and promotional instrumentality, significantly explained the participants’ motivated behavior. The findings indicate that there existed context-specific motivational factors which could better explain success of L2 in Korean EFL college contexts. The theoretical and pedagogical implications were provided at the conclusion.

의사결정나무 알고리즘은 데이터마이닝 기법중 하나인데 관심이 되는 데이터들에 대하여 분류 및 예측을 가능하게 해준다. 이 기법은 데이터 형태의 특성을 분석할 수 있고 산업재해 형태의 차이점을 찾아내는데 사용될 수 있다. 본 연구에서는 산업재해 데이터의 특성을 파악하고자 C4.5 알고리즘을 사용하였다. 본 연구에서 분석을 위하여 사용된 데이터는 강원도에서 발생한 2년 동안의 산업재해 관련 데이터로서 연구에 적용된 데이터의 수는 19,909개로 구성되어 있다. 본 연구의 목적을 위하여 한 개의 목표변수와 여덟 개의 독립변수가 산업재해 형태에 따라 세분화 되었다. 분석 후 데이터는 222개의 전체 나뭇가지와 151개의 줄기가지로 분류되었다. 또한 본 연구에서는 재해자들의 위험도 관리와 감소를 위하여 이익도표를 제공하였다.