• Asian Pacific Journal of Cancer Prevention
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• 제13권12호
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• pp.6181-6185
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• 2012

Background: The aim of the study was to evaluate the available breast nomograms (MSKCC, Stanford, Tenon) to predict non-sentinel lymph node metastasis (NSLNM) and to determine variables for NSLNM in SLN positive breast cancer patients in our population. Materials and Methods: We retrospectively reviewed 170 patients who underwent completion axillary lymph node dissection between Jul 2008 and Aug 2010 in our hospital. We validated three nomograms (MSKCC, Stanford, Tenon). The likelihood of having positive NSLNM based on various factors was evaluated by use of univariate analysis. Stepwise multivariate analysis was applied to estimate a predictive model for NSLNM. Four factors were found to contribute significantly to the logistic regression model, allowing design of a new formula to predict non-sentinel lymph node metastasis. The AUCs of the ROCs were used to describe the performance of the diagnostic value of MSKCC, Stanford, Tenon nomograms and our new nomogram. Results: After stepwise multiple logistic regression analysis, multifocality, proportion of positive SLN to total SLN, LVI, SLN extracapsular extention were found to be statistically significant. AUC results were MSKCC: 0.713/Tenon: 0.671/Stanford: 0.534/DEU: 0.814. Conclusions: The MSKCC nomogram proved to be a good discriminator of NSLN metastasis in SLN positive BC patients for our population. Stanford and Tenon nomograms were not as predictive of NSLN metastasis. Our newly created formula was the best prediction tool for discriminate of NSLN metastasis in SLN positive BC patients for our population. We recommend that nomograms be validated before use in specific populations, and more than one validated nomogram may be used together while consulting patients.

• 대한간호학회지
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• 제33권8호
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• pp.1093-1110
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• 2003

Purpose. The number of elders in institutions has increased as family supporting systems have changed in Korea. The purpose of this study were to understand the life satisfaction among elders in institutions and to identify the factors influencing on life satisfaction. Methods. The instruments used were Yun(1982)'s scale modified Memorial University of Newfoundland Scale for Happiness(MUNSH) in life satisfaction, ADL and IADL in activity level, Self-rating Depression Scale(SDS) in depression and Norbeck Social Support Questionnaire(NSSQ) scale in social support. Also, Perceived health status was measured by Visual Graphic Rating Scale. The subject of this study is 107 cognitively intact and ambulatory elders in 7 institutions in Daegu city and Kyungpook province. The data have been collected from May 1 to June 30, 2001. For the analysis of collected data, frequency analysis, mean, standard deviation, Pearson's correlation and stepwise multiple regression analysis were used for statistical analysis by SPSS win(version 9.0) program. Results. Life satisfaction for the elders in institutions showed negative correlation with SDS, and positive correlation with activity level. The regression form of the stepwise multiple regression analysis to investigate the influencing factors of life satisfaction for the elders in institutions was expressed by y =90.988-0. 733x1-0.188x2-0.069x3-0.565x4 (xl: SDS x2: Social support x3: Activity level x4: Monthly pocket Money) and 57.9% of varience in life satisfaction was explained by the model. Conclusion. The factors influencing on life satisfaction among the elders in institutions were SDS, social support, activity level and monthly pocket money. According to the results of this study, depression, social support and activity level are considered the prime causal factors for life satisfaction.

• 한국콘텐츠학회논문지
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• 제14권7호
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• pp.324-333
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• 2014

본 연구는 간호대학생의 문제해결능력과 사회불안 정도 및 이들 간의 관계를 파악하고 사회불안에 영향을 미치는 요인을 규명하는 서술적 조사연구이다. 연구대상자는 A시와 Y시에 소재한 2개 대학의 간호대학생 227명이었다. 구조화된 자가 보고식 설문지를 이용하여 자료를 수집하였으며, 자료 분석은 SPSS WIN 프로그램을 사용하여 빈도와 백분율, 평균과 표준편차, t-test, ANOVA, Pearson correlation coefficient, multiple stepwise regression analysis를 실시하였다. 연구결과, 사회불안과 문제해결능력 수준은 중간정도의 수준이었으며, 문제해결능력과 사회불안은 부적 상관관계를 보였다. 사회불안에 영향을 미치는 요인으로는 문제해결능력의 하위요인인 인지적 반응과 일반적 특성 중 지각된 대인관계가 유의한 것으로 나타났으며 이들 변수의 사회불안에 대한 설명력은 23.6%이었다. 결론적으로, 친숙하지 않은 사회적 상황에서의 인지적 왜곡을 탐색하고 이를 수정하는 교육과 상담 및 대인관계능력을 향상시키는 프로그램 적용은 간호대학생의 사회불안을 감소시키는데 효과적인 전략이 될 수 있다.

• 한국산학기술학회논문지
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• 제15권3호
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• pp.1615-1622
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• 2014

본 연구는 간호대학생의 학업탄력성에 미치는 영향요인을 파악하기 위해 시도된 연구로 2013년 3월 4일부터 8일까지 일개 광역시에 소재한 간호대학생 2학년 280명의 자료를 이용하여 빈도와 백분율, 평균과 표준편차, ANOVA, Scheffe's test, Pearson's correlation coefficients 및 Stepwise multiple regression으로 분석하였다. 연구 결과, 간호대학생의 학업탄력성 정도는 3.67점, 문제해결능력 정도는 3.53점, 의사소통능력 정도는 3.50점이었고, 문제해결능력과 의사소통능력이 높을수록 학업탄력성이 높은 것으로 나타났다. 학업탄력성을 설명하는 요인은 계획실행, 해석능력, 목표설정능력, 원인분석, 수행평가로 학업탄력성을 50.7% 설명해 주었다. 본 연구결과에 의하면 간호대학생의 학업탄력성의 설명요인을 고려한 효율적인 교육프로그램 개발이 요구된다.

• 한국산학기술학회논문지
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• 제20권12호
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• pp.157-166
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• 2019

본 연구는 간호사의 감성지능과 전문직 자아개념이 재직의도에 미치는 영향을 파악하기 위해 시도되었다. 자료수집은 B광역시 소재 종합병원 3곳에 근무하는 간호사 170명을 대상으로 하였다. 자료수집 기간은 2019년 5월 7일부터 5월 30일까지 구조화된 설문지를 이용하여 시행되었다. 수집된 자료는 SPSS/WIN 24.0 프로그램을 이용하여 분석하였으며 t-test, ANOVA, Pearson correlation coefficients 와 stepwise multiple regression analysis를 이용하였다. 본 연구결과, 대상자의 감성지능 정도는 4.82점(7점만점), 전문직 자아개념 정도는 2.61점(4점만점), 재직의도 정도는 5.47점(8점만점)이었다. 대상자의 재직의도는 감성지능, 전문직 자아개념과 모두 유의한 양의 상관관계가 있는 것으로 나타났다. 대상자의 재직의도에 영향을 미치는 요인으로는 전문직자아개념(β=.456, p<.001), 연령(β=-.228, p<.001), 휴가(β=.197, p=.002)순으로 나타났다. 이들 요인들에 의한 재직의도 설명력은 총 35.9%(F=32.60, p<.001)로 나타났다. 따라서, 본 연구결과를 바탕으로 재직의도를 감소시키기 위한 프로그램 개발이 필요할 것으로 사료된다.

• 대한교통학회지
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• 제13권1호
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• pp.35-62
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• 1995

Traffic has a very important function but has caused such social problems as traffic congestion parking and traffic accidents in metropolitan areas. It is difficult to examine the causes of traffic accidents related to human life, which occur by human, vehicle and environmental factors. But human factor is the only measure requlating these factors together an analyzing factors influencing establishment of counterplan of traffic accidents. Consequently , this study employs the principal component analysis and stepwise multiple regression analysis to estimate the characteristics and influential factors of traffic accidents and defines the typical patterns of happening lots of traffic accidents. Accordingly, this study establishes an acknowledgement model of the causes and preference model of the counterplan of traffic accidents using Multi-Dimension Preference(MDPREF) method.

• Structural Engineering and Mechanics
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• 제66권6호
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• pp.677-691
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• 2018

Reliability assessment of complex structures using simulation methods is time-consuming. Thus, surrogate models are usually employed to reduce computational cost. AK-MCS is a surrogate-based Active learning method combining Kriging and Monte-Carlo Simulation for structural reliability analysis. This paper proposes three modifications of the AK-MCS method to reduce the number of calls to the performance function. The first modification is related to the definition of an initial Design of Experiments (DoE). In the original AK-MCS method, an initial DoE is created by a random selection of samples among the Monte Carlo population. Therefore, samples in the failure region have fewer chances to be selected, because a small number of samples are usually located in the failure region compared to the safe region. The proposed method in this paper is based on a uniform selection of samples in the predefined domain, so more samples may be selected from the failure region. Another important parameter in the AK-MCS method is the size of the initial DoE. The algorithm may not predict the exact limit state surface with an insufficient number of initial samples. Thus, the second modification of the AK-MCS method is proposed to overcome this problem. The third modification is relevant to the type of regression trend in the AK-MCS method. The original AK-MCS method uses an ordinary Kriging model, so the regression part of Kriging model is an unknown constant value. In this paper, the effect of regression trend in the AK-MCS method is investigated for a benchmark problem, and it is shown that the appropriate choice of regression type could reduce the number of calls to the performance function. A stepwise approach is also presented to select a suitable trend of the Kriging model. The numerical results show the effectiveness of the proposed modifications.

• 대한인간공학회지
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• 제27권2호
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• pp.1-7
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• 2008

This study quantified 7 trunk muscles' physiological cross-sectional areas (PCSAs) and developed prediction equations for the physiological cross-sectional area as a function of anthropometic variables for Korean people. Nine females and nine males were participated in the magnetic resonance imaging (MRI) scans approximately from S1 through T8. Muscle fiber angle corrected cross-sectional areas (anatomical cross sectional areas: ACSAs) were recorded at each vertebral level and maximum value of ACSAs were determined as physiological cross sectional area (PCSA). There was a significant gender difference in PCSAs of all muscles (p<0.05). Stepwise linear regression techniques using anthropometric measures (e.g., height, weight, trunk depths and widths) as independent variables were conducted to develop prediction equations for the PCSA for each muscle. For males, six muscles' significant prediction equations (p<0.05) were developed except quadratus lumborum. For females, three prediction equations were developed for psoas, quadratus lumborum, and erector spinae muscles (p<0.05).

• Journal of the Korean Data and Information Science Society
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• 제22권4호
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• pp.691-699
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• 2011

현재까지 우리나라뿐만 아니라 세계적으로 급경사지 붕괴는 대표적인 자연재해로 알려져 있다. 급경사지 붕괴 피해를 방지하기 위해 행해진 많은 선행 연구를 바탕으로 일부 국내기관에서는 급경사지 평가표를 만들어 붕괴 예측에 활용하고 있다. 하지만, 대부분의 기존 연구는 비통계전문가들에 의해 행해졌기 때문에 평가표 구성의 통계적 타당성을 제시하지 못했다. 본 연구는 전국 지역을 대상으로 급경사지 (암반사면, 토사사면) 붕괴에 영향을 미칠 것으로 예상되는 인자들의 자료를 수집하고 그 인자들의 가중치를 판정하기 위하여 로지스틱 회귀분석 방법을 사용하였다. 선행연구들 중에 로지스틱 회귀분석을 이용한 기존의 연구들이 있었지만 다중공선성을 전혀 고려하지 않았기 때문에 결과가 신뢰할 만하지 못하다. 본 연구에서는 다중공선성을 제거된 급경사지 붕괴 예측모형을 제시하였다.

• 국제학술발표논문집
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• The 4th International Conference on Construction Engineering and Project Management Organized by the University of New South Wales
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• pp.432-438
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• 2011

The purpose of the present thesis is to develop bankruptcy prediction models capable of being applied to the Korean construction industry and to deduce an optimal model through comparative evaluation of final developed models. A study population was selected as general contractors in the Korean construction industry. In order to ease the sample securing and reliability of data, it was limited to general contractors receiving external audit from the government. The study samples are divided into a bankrupt company group and a non-bankrupt company group. The bankruptcy, insolvency, declaration of insolvency, workout and corporate reorganization were used as selection criteria of a bankrupt company. A company that is not included in the selection criteria of the bankrupt company group was selected as a non-bankrupt company. Accordingly, the study sample is composed of a total of 112 samples and is composed of 48 bankrupt companies and 64 non-bankrupt companies. A financial ratio was used as early predictors for development of an estimation model. A total of 90 financial ratios were used and were divided into growth, profitability, productivity and added value. The MDA (Multivariate Discriminant Analysis) model and BLRA (Binary Logistic Regression Analysis) model were used for development of bankruptcy prediction models. The MDA model is an analysis method often used in the past bankruptcy prediction literature, and the BLRA is an analysis method capable of avoiding equal variance assumption. The stepwise (MDA) and forward stepwise method (BLRA) were used for selection of predictor variables in case of model construction. Twenty two variables were finally used in MDA and BLRA models according to timing of bankruptcy. The ROC-Curve Analysis and Classification Analysis were used for analysis of prediction performance of estimation models. The correct classification rate of an individual bankruptcy prediction model is as follows: 1) one year ago before the event of bankruptcy (MDA: 83.04%, BLRA: 93.75%); 2) two years ago before the event of bankruptcy (MDA: 77.68%, BLRA: 78.57%); 3) 3 years ago before the event of bankruptcy (MDA: 84.82%, BLRA: 91.96%). The AUC (Area Under Curve) of an individual bankruptcy prediction model is as follows. : 1) one year ago before the event of bankruptcy (MDA: 0.933, BLRA: 0.978); 2) two years ago before the event of bankruptcy (MDA: 0.852, BLRA: 0.875); 3) 3 years ago before the event of bankruptcy (MDA: 0.938, BLRA: 0.975). As a result of the present research, accuracy of the BLRA model is higher than the MDA model and its prediction performance is improved.