Earticle

The purpose of this study was to investigate the relationships among perceived role ambiguity, perceived role conflict and percieved job satisfaction of Taekwondo masters and instructors：Analysis by using Dummy variable between Masters and Instructors. Using a convenience sampling method, a total of 219 responses were collected from the Taekwondo Instructors who participated in the job training session hosted by Korea Taekwondo Association. Descriptive statistics, exploratory factor analysis, t-test, correlations analysis and dummy variable regression were executed using SPSS Window 21.0 Version With a series of data analysis, the researchers found that 1) the group of Taekwondo instructors showed the significantly higher role conflict. and 2) the group of Taekwondo instructors significantly affected perceived role conflict.

평균수명의 연장과 초고령사회 진입으로 인해 노후준비의 중요성이 더욱 부각되고 있으며, 특히 자영업자는 고용 불안정과 연금가입율이 저조하여 노후준비가 취약할 수 밖에 없다. 한국은 OECD 국가 중 자영업자 비율이 높은 편으 로 이들의 노후준비 실태에 대한 실증적 분석이 시급하다. 특히 중장년층 자영업자의 경제적 기반과 사회적 특성이 노 후준비에 어떤 영향을 미치는지에 대한 연구는 정책적 시사점이 클 것으로 예상된다. 따라서 본 연구에서는 국민노후보 장패널 9차 본조사 자료를 이용하여 전국의 50세 이상의 자영업를 대상으로 하였다. 조사 대상자의 인구사회학적 특성 이 노후준비에 미치는 영향을 분석한 결과 11.8%의 설명력을 나타냈다. 이에 따른 분석 결과는 다음과 같다. 먼저, 성 별은 노후준비에 부(-)적 영향을 미쳐서 여성에 비해 남성의 노후준비 수준이 낮았다. 이와 같은 결과는 여성이 세심하 고 계획적이며 준비성이 많기 때문일 것으로 추측이 된다. 교육수준은 노후준비에 정적(+)영향을 미치는 것으로 나타나 대졸이상의 집단에 비해 고졸, 중졸, 초졸이하가 노후준비가 잘 되어 있었다. 또한 배우자가 없을수록, 가구원수가 적을 수록, 주택을 소유하지 못할수록 노후준비 수준이 높게 나타났는데, 주택을 소유하지 못한 경우에는 주택연금 등을 받을 수 없기 때문에 좀 더 적극적으로 노후준비를 할 가능성이 있기 때문일 것이다. 가구총소득 및 가구총지출은 노후준비 에 부적(-)영향을 미쳐서 가구총소득이 낮을수록, 가구총지출이 적을수록 노후준비 수준이 높았다.

The importance of Preparing for Oldage is becoming more prominent due to the extension of life expectancy and entry into a super-aged society, and in particular, self-employed people are bound to be vulnerable due to employment instability and low pension subscription rates. Korea has a high proportion of self-employed people among OECD countries, so an empirical analysis of their presenting for Oldage status is urgently needed. In particular, research on how the economic foundation and social characteristics of middle-aged and elderly self-employed affect Preparing for Oldage is expected to have great policy implications. Therefore, this study used the 9th KReIS survey data and targeted self-employed people aged 50 or older nationwide. As a result of analyzing the effect of demographic and sociological characteristics of the survey subjects on Preparing for Oldage, 11.8% of explanatory power was shown. The analysis results are as follows. Gender had a negative (-) effect on Preparing for Oldage, so men had lower levels of Preparing for Oldage compared to women. These results are speculated to be due to the fact that women are meticulous, planned, and prepared. Education level was found to have a positive (+) effect on Preparing for Oldage, and high school, middle school, and elementary school graduates were better at Preparing for Oldage than the group with college graduates or higher. The level of Preparing for Oldage was higher when there was no spouse, the number of household members was small, and the number of people who did not own a home was high. If one does not own a house, they cannot receive a home pension or similar benefits, so they are more likely to actively prepare for old age. Household total income and household total expenditure had a negative (-) impact on preparing for old age. The lower the household total income and the lower the household total expenditure, the higher the level of preparing for old age.

This paper proposes a multiple time-series model with dummy variables for one-hour ahead load forecasting. We used 11 dummy variables that were classified by day characteristics such as day of the week, holiday, and special holiday. Also, model specification and selection of input variables including dummy variables were made by test statistics such as AIC(Akaike Information Criterion) and t-test statistics of each coefficient. OLS (Ordinary Least Squares) method was used for estimation and forecasting. We found out that model specifications for each hour are not identical usually at 30％ of optimal significance level, and dummy variables reduce the forecasting error if they are classified properly. The proposed model has much more accurate estimates in forecasting with less MAPE (Mean Absolute Percentage Error).