• Journal of Wellbeing Management and Applied Psychology
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• v.4 no.4
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• pp.1-14
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• 2021

Purpose: This study aims to measure the level of satisfaction with the overall education of current students, who are major consumers of education by department at the Health Care Convergence University, and to establish development strategies such as improving education quality and satisfaction by deriving problems and improvements. Research design, data and methodology: This study drew conclusions through secondary data analysis based on data surveyed by the Educational Performance Management Center of a university in the academic year of 2020. The target of the education consumer satisfaction survey was 470 students of the College in the academic year 2020. Frequency analysis and correlation analysis were applied to the data using the statistical package program SPSS 25 version. Results: In the major curriculum satisfaction survey, the department with the highest score was optics and optics, 4.11, and the department with the lowest score was medical IT, 3.29. Conclusions: The department with the highest correlation in the correlation analysis result of the demand survey by department was the Department of Medical Management and the Department of Medical IT, with a correlation coefficient r=0.984, and the lowest correlation in the correlation analysis result of the demand survey by department was the Department of Medical IT and Dental Hygiene, with a correlation coefficient r= -0.085.

• Asian Journal for Public Opinion Research
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• v.3 no.4
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• pp.156-175
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• 2016

This paper considers whether random sampling always produces more accurate survey results in the case of South Korea. We compare information from the 2010 census to the demographic variables of three public opinion surveys from South Korea: Gallup Korea's Omnibus Survey (Survey A) is conducted every two months by Gallup Korea; the annual Social Survey (Survey B) is conducted by Statistics Korea (KOSTAT); the Korean General Social Survey (KGSS or Survey C) is conducted annually by the Survey Research Center (SRC) at Sungkyunkwan University (SKKU). Survey A uses quota sampling after randomly selecting the neighborhood and initial addresses; Survey B uses random sampling, but allows replacements in some situations; Survey C uses simple random sampling. Data from more than one year was used for each survey. Our analysis suggests that Survey B is the most representative in most respects, and, in some respects, Survey A may be more representative than Survey C. Data from Survey C was the least stable in terms of representativeness by geographical area and age. Single-person households were underrepresented in both Surveys A and C, but the problem was more severe in Survey A. Four-person households and married persons were both over-represented in Survey A. Less educated people were under-represented in both Survey A and Survey C. There were differences in income level between Survey A and Survey C, but income data was not available for Survey B or the census, so it is difficult to ascertain which survey was more representative in this case.

• Survey Research
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• v.9 no.1
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• pp.69-85
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• 2008

Stratification is a feature of the majority of field sample design. This paper considers the multivariate stratification strategy for multipurpose sample survey with several auxiliary variables. In a multipurpose survey, stratification procedure is very complicated because we have to simultaneously consider the efficiencies of stratification for several variables of interest. We propose stratification strategy based on factor analysis and cluster analysis using several stratification variables. To improve the efficiency of stratification, we first select the stratification variables by factor analysis, and then apply the K-means clustering algorithm to the formation of strata. An application of the stratification strategy in the sampling design for the Fisher Production Survey is discussed, and it turns out that the variances of estimators are significantly less than those obtained by simple random sampling.

• Proceedings of the Korean Geotechical Society Conference
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• 2005.10a
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• pp.639-648
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• 2005

Up to now, practical engineers applying simplicity value of rockfall block weight suggested in design manual without considering in-situ rockfall block weight which reflect joint characteristics. However, the size of rockfall block varies with joint spacing of discontinuities and influences over rockfall analysis results. In this paper, we estimate realistic rockfall block weight distribution using statistical invariances of joint spacing derived from scan-line survey result. And, we study whether this distribution is applicable in practical rockfall analysis directly. As the results of this study, rockfall analysis results that using rockfall block weight distribution estimated from scan-line survey show resonable and realistic outcomes.

• Journal of Distribution Science
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• v.10 no.12
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• pp.5-11
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• 2012

Purpose - This paper proposes measurement models to evaluate the management performance of small and medium-sized retail businesses on the basis of a trading area analysis that compares their proposed revenue to actual revenue in the trading area. Research design, data, methodology - The study proposes measurement models consisting of five stages, namely: (1) district background survey, (2) customer survey, (3) competitor survey, (4) business district survey, and (5) business performance analysis. Results - To identify business districts easily, this study preferred a minor-adjusted method based on the Euclidean distance, as it is simple to employ for the small and medium-sized businesses. This model was applied to select coffee shops in Daejeon. Results indicated that although the targeted shop was not located in an appropriate location, actual sales were higher than expected. Conclusions - Small- or medium-sized retail businesses face difficulties regarding the economies of scale and brand recognition and must choose an appropriate location to ensure management stability. However, such businesses will find it difficult to evaluate their competitive edge accurately using a trading area analysis.

• Korean Journal of Community Nutrition
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• v.23 no.5
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• pp.444-455
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• 2018

Objectives: The current survey environment is changing and participation rates in national nutrition surveys are decreasing. Therefore, the purpose of this study was to develop strategies for improving the nutrition survey system in the Korea National Health and Nutrition Examination Survey (KNHANES). Methods: To develop an alternative system for conducting the KNHANES nutritional survey, we conducted focus group interviews with stakeholders of the survey, SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis, and expert reviews. In addition, spatial analysis of potential sites for conducting surveys instead of relying on household visits was performed, and the perception of nutritional surveys in the population eligible for KNHANES was evaluated. Results: Based on the results of the focus group interviews, SWOT analysis, and expert reviews, we propose two options for survey sites: vehicles specifically prepared for nutritional surveys and public facilities such as community service centers or public health centers. Among public facilities, community service centers were found to be more appropriate sites than public health centers because they were considered more accessible. About 90% of respondents would participate in the survey in public facilities and about 74% would in vehicles. Conclusions: Conducting national nutrition surveys in specially designed vehicles and public facilities could be a viable alternative to home visits. Next, the validity of these newly proposed nutrition survey methods needs to be compared to the results of the current national nutrition survey.

• Journal of Preventive Medicine and Public Health
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• v.46 no.2
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• pp.96-104
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• 2013

Objectives: The inherent nature of the Korean National Health and Nutrition Examination Survey (KNHANES) design requires special analysis by incorporating sample weights, stratification, and clustering not used in ordinary statistical procedures. Methods: This study investigated the proportion of research papers that have used an appropriate statistical methodology out of the research papers analyzing the KNHANES cited in the PubMed online system from 2007 to 2012. We also compared differences in mean and regression estimates between the ordinary statistical data analyses without sampling weight and design-based data analyses using the KNHANES 2008 to 2010. Results: Of the 247 research articles cited in PubMed, only 19.8% of all articles used survey design analysis, compared with 80.2% of articles that used ordinary statistical analysis, treating KNHANES data as if it were collected using a simple random sampling method. Means and standard errors differed between the ordinary statistical data analyses and design-based analyses, and the standard errors in the design-based analyses tended to be larger than those in the ordinary statistical data analyses. Conclusions: Ignoring complex survey design can result in biased estimates and overstated significance levels. Sample weights, stratification, and clustering of the design must be incorporated into analyses to ensure the development of appropriate estimates and standard errors of these estimates.

• Proceedings of the Korean Association for Survey Research Conference
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• 2002.11a
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• pp.5-28
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• 2002

• Journal of Engineering Education Research
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• v.9 no.4
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• pp.71-82
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• 2006

To accomplish the educational objectives of a department, a system for CQI(Continuous Quality Improvement) is necessary. Improving the educational system by survey analysis is one of the most important factors for accomplishing the educational objectives. In general, survey analysis is carried out by using statistical distribution on an attribute or correlation analysis between two attributes. However, these analysis schemes have a limitation that they cannot find relations among various attributes. In this paper, an in-depth survey analysis method applying data mining techniques is presented. Data mining is a technique for extracting interesting knowledges from a large set of data. Survey from undergraduate students in the School of Computing of Soongsil University is analyzed in this paper by using a data mining tool, called Clementine. Results of Clementine analysis show the relationship between 'grade', and other attributes hierarchically, and provide useful information that can be applied in student consulting and program improvement.

• Proceedings of the KIEE Conference
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• 2003.07a
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• pp.142-144
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• 2003

Recently, the bidding between generating company and transmission company have been accomplished according to restructure of power system industry. And the bidding will develop to TWBP (Two-Way Bidding Pool)type. In this way, it will be important issue on optimum reliability maintenance of the power supply. Thus, in this paper illustrate to customer survey result for the interruption cost analysis. Actually the domestic customer survey is performed on 300 samples. Then the points at customer survey and direction are presented in this paper. Customer is divided into 5 types, total response of customer survey was 22 percent. Furthermore domestic customer survey result characteristics will be use for the basis data of the large scale customer survey.