• Journal of the Korean Data and Information Science Society
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• v.19 no.2
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• pp.451-461
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• 2008

In this study, factor and cluster analysis have been conducted to group the differences in Korean view on national economic policy in the sample of the 2006 Korean General Social Survey (KGSS). According to the 2006 KGSS, the 6 items with a 5-point Likert scale include the questions about whether or the extent to which each respondent supports the specific types of governmental economic policy. In our study, at first, the factor analysis has converted the original 6 items into the 3 composite variables that account for 81% in the total variability. As the second step of factor analysis, factor scores have been computed. Then, the K-means cluster analysis based on the factor scores has been conducted to group the survey respondents into the 3 clusters. In particular, the cross-tabulation analysis has shown that the distribution of the 3 clusters varies with the respondents' socio-demographic characteristics.

• Journal of the Korean Statistical Society
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• v.31 no.2
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• pp.163-175
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• 2002

An extended version of the minimax eccentricity factor estimation for multiple set case is proposed. In addition, two more simple methods for multiple set factor analysis exploiting the concept of generalized canonical correlation analysis is suggested. Finally, a certain connection between the generalized canonical correlation analysis and the multiple set factor analysis is derived which helps us clarify the relationship.

• The Korean Journal of Emergency Medical Services
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• v.26 no.3
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• pp.7-19
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• 2022

Purpose: The purpose of this study was to confirm the exploratory factor analysis and reliability analysis of the burden of 119 emergency medical technicians. Methods: The data collection period was from November 2, 2022 to November 6, 2022. This study had 316 subjects, and the collected data were analyzed using exploratory factor analysis and Cronbach's α coefficient using IBM SPSS statistics 27.0. Results: The reliability was .924. The exploratory factor analysis yielded the following information: the first factor was lack of violence policy, the second factor was conflict between the organization and the paramedics, the third factor was lack of psychological support, and the fourth factor was lack of education and communication. The explanation power of 4 factors was 54.31%. Conclusion: This study is significant as it performs exploratory factor analysis as a preliminary step in the development of a burden measurement tool.

• Proceedings of the Korean Geotechical Society Conference
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• 2008.03a
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• pp.784-791
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• 2008

In this study, we recognized about application of the load distribution factor for design of tunnel in 3D numerical analysis. Generally, load distribution factor of tunnel is applied to describe 3D arching effect that can not describe when 2D numerical analysis. Through result of 3D numerical analysis, we used to apply in numerical analysis for the load distribution factor that ratio of finally displacement to displacement of construction step. But 3D numerical analysis need to apply to load distribution factor for convenience of numerical analysis. Therefore, we proposed load distribution factor that reduce time and coast. It corrected variable of advanced length in load distribution factor of 3D numerical analysis.

• Journal of Korean Society on Water Environment
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• v.23 no.1
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• pp.161-168
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• 2007

The main aim of this work is focus on the Geum river water quality evaluation of pollution data obtained by monitoring measurement during the period 2001-2005. The complex data matrix 19 (entire monitoring stations)*13 (parameters), 60 (month)*13 (parameters) and 20 (season)*13 (parameters) were treated with different multivariate techniques such as factor analysis/principal component analysis (FA/PCA). FA/PCA identified two factor (19*13) classified pollutant Loading factor (BOD, COD, pH, Cond, T-N, T-P, $NH_3$-N, $NO_3$-N, $PO_4$-P, Chl-a), seasonal factor (water temp, SS) and three Factor (60*13, 20*13) classified pollutant Loading factor (BOD, COD, Cond, T-N, T-P, $NH_3$-N, $NO_3$-N, $PO_4$-P), seasonal factor (water temp, SS) and metabolic factor (Chl-a, pH). Loadings of pollutant factor is potent influence main factor in the Geum river which is explained by loadings of pollutant factor at whole sampling stations (71.16%), month (52.75%) and season (56.57%) of main water quality stations. Result of this study is that pollutant loading factor is affected at Gongju 1, 2, Buyeo 1, 2, Gangkyeong, Yeongi stations by entire stations and entire month (Gongju 1, Cheongwon stations), April, May, July and August (buyeo 1) by month. Also the pollutant Loading factor is season gives an influence in winter (Gongju 1, buyeo 1) from main sampling stations, but Cheongwon characteristic is non-seasonal influenced. This study presents necessity and usefulness of multivariate statistic techniques for evaluation and interpretation of large complex data set with a view to get better information data effective management of water sources.

• Journal of Korean Academy of Nursing
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• v.43 no.5
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• pp.587-594
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• 2013

Purpose: The purpose of this study is to provide researchers with a simplified approach to undertaking exploratory factor analysis for the assessment of construct validity. Methods: All articles published in 2010, 2011, and 2012 in Journal of Korean Academy of Nursing were reviewed and other relevant books and articles were chosen for the review. Results: In this paper, the following were discussed: preliminary analysis process of exploratory factor analysis to examine the sample size, distribution of measured variables, correlation coefficient, and results of KMO measure and Bartlett's test of sphericity. In addition, other areas to be considered in using factor analysis are discussed, including determination of the number of factors, the choice of rotation method or extraction method of the factor structure, and the interpretation of the factor loadings and explained variance. Conclusion: Content validity is the degree to which elements of an assessment instrument are relevant to and representative of the targeted construct for a particular assessment purpose. This measurement is difficult and challenging and takes a lot of time. Factor analysis is considered one of the strongest approaches to establishing construct validity and is the most commonly used method for establishing construct validity measured by an instrument.

• Korea Journal of Hospital Management
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• v.10 no.2
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• pp.26-44
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• 2005

The purpose of this study was to investigate these major factors on patient satisfaction, and to examine the affecting level of major factors in. The subjects in this study was 70 hospitals that were surveyed the hospital evaluation program containing the survey of patient satisfaction by KHIDI(Korea Health Industry Development Institute) from 1997 to 1999. The collected data was analysed SPSS for Windows(Ver 10.0). On basically, frequency analysis, t-test, and ANOVA was performed and, for more analysis, correlation analysis, factor analysis, multiple regression analysis, logistic regression analysis was utilized. According to this study, the major factors of inpatient satisfaction are divided 3 types facility factor, manpower factor, and service factor. And the major factors of outpatient satisfaction are analyzed 5 types; facility factor related direct medical service, facility factor related indirect medical services, manpower factor, pharmacy factor, and facility factor related utilization convenience. The importance of this study lies in the identification of major factors on hospital patient satisfaction.

• Journal of the Korean Statistical Society
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• v.25 no.1
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• pp.67-80
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• 1996

Factor analysis is a multivariate technique for describing the in-terrelationship among many variables in terms of a few underlying but unobservable random variables called factors. There are various approaches for this factor analysis. In particular, principal factor analysis is one of the most popular methods. This follows the mathematical algorithm of the principal component analysis based on the singular value decomposition. But it is known that the singular value decomposition is not resistant, i.e., it is very sensitive to small changes in the input data. In this article, using the resistant singular value decomposition of Choi and Huh (1994), we derive a resistant principal factor analysis relatively little influenced by notable observations.

• Journal of Korean Society on Water Environment
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• v.28 no.1
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• pp.84-93
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• 2012

The study was conducted to analyze the spatio-temporal changes in water quality of the major 36 sampling stations of Nakdong River, depending on each station, season using the 17 water quality variables from 2000 to 2010. The result was verified to interpret the characteristics of water quality variables in a more accurate manners. According to the Principal component analysis (PCA) and Exploratory factor analysis (EFA) results; the results of these analyses were identified 4 factors, Factor 1 (nutrients) included the concentrations of T-N, T-P, $NO_{3}-N$, $PO_{4}-P$, DTN, DTP for sampling station and season, Factor 2 (organic pollutants) included the concentrations of BOD, COD, Chl-a, Factor 3 (microbes) included the concentrations of F.Coli, T.Coli, and Factor 4 (others) included the concentrations of pH, DO. The results of a Cluster analysis indicated that Geumhogang 6 was the most contaminated site, while tributaries and most of the down stream sites of Nakdong River were mainly affected by each nutrients (Factor 1) and organic pollutants (Factor 2). The verification consequence of Confirmatory factor analysis (CFA) from Exploratory factor analysis (EFA) result can be summarized as follows: we could find additional relations between variables besides the structure from EFA, which we obtained through the second-order final modeling adopted in CFA. Nutrients had the biggest impact on water pollution for each sampling station and season. In particular, It was analyzed that P-series pollutant should be controlled during spring and winter and N-series pollutant should be controlled during summer and fall.

• International Journal of Highway Engineering
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• v.13 no.4
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• pp.211-220
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• 2011

TOD(Transit Oriented Development) has recently been active, which presents that TOD planning elements should be comprehensively taken into consideration in order to enhance domestic transit ridership by changing environments in rail station areas and an empirical analysis on the type of rail station areas and transportation demand should be a prerequisite for usage of future development planning. This study aims to grasp a variety of TOD of influence factors in Seoul rail station area and to perform analysis to identify relationship between public transportation demand and these TOD design factors. To make it come true, we gathered data with respect to Density, Diversity, and Accessibility as representative TOD planning elements and carried out factorial and regression analysis. Consequently, we drew 7 influence factors base on factorial analysis: Factor 1(Diversity/ -Use Mix(LUM)), Factor 2(Density/development density), Factor 3(Accessibility/public transportation facility supply), Factor 4(Design/street design), Factor 5(Green/access mode (pedestrian, bike), Factor 6(Design/subway size), Factor 7(Accessibility/Public transit operation) As the result of model development by using factorial and regression analysis, positive influence factors on passenger flow in rail station area are Factor 1(Diversity : Land-Use Mix), Factor 3(Accessibility : public transportation facility supply), Factor 2(Density : development density), Factor 5(Design/ access mode) and Factor 6(subway size) Next, negative influence factor on passenger flow in rail station area shows Factor 7(Accessibility/Public transit operation) as the most influential factor. This is because the growth of service interval of linked subway and bus leads to reduced demand.