• Communications for Statistical Applications and Methods
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• 제8권1호
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• pp.39-49
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• 2001

When an appropriate model is fitted to explain a certain categorical data, outlying cell detection plays very important role to reduce the lack of fit. There exist many statistical methods to identify outlying cells in contingency table. In this paper, correspondence analysis is applied to identify one or two outlying cells. When corresponding relationships between categories of the row and columns are explored, we find that outlying cells could be identified via the correspondence analysis map.

• 한국의류학회지
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• 제31권8호
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• pp.1168-1179
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• 2007

The objective of this research is to validate the BRQ(Brand Relationship Quality) Construct perceived by fashion product consumers. In order to establish and verify the BRQ scale, qualitative survey and quantitative survey were conducted together. 1592 copies of questionnaire were distributed to women in their 20s to 40s living in Seoul and other metropolitan areas from Dec. 26, 2005 to Jan. 8, 2006, and 723 copies of them were used for statistical data. Samplel(n=482)was used for empirical analysis, and sample2(n=241) was used for cross validity test. The data was analyzed using Exploratory Factor Analysis, Confirmatory Factor Analysis, and Pearson's Correlation Analysis. BRQ emerged from exploratory factor analysis as the hierarchical construct composed of six facets including 'self-connective attachment', 'symbol/mystery', 'trust', 'nostalgia', 'intimacy', and 'knowledge'. As the fit of this structural model was not good as a result of Confirmatory Factor Analysis, it was revised to have better fitting. Finally, empirical survey results indicate the hierarchical construct consisting of eight distinct BRQ facets including 'love/commitment', 'self-connection', 'symbol', 'mystery', 'trust', 'nostalgia', 'intimacy', and 'knowledge' as best representing the final 39item BRQ Scale. Reliability, construct validity, and cross validity of the construct were verified.

• 소프트웨어공학소사이어티 논문지
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• 제29권2호
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• pp.13-20
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• 2020

탐색 테스트는 빠른 개발 주기라는 특징으로 바람직한 테스트 방법으로 소개되었으나 적용을 위하여 문서화 및 테스트 범위의 분석이 요구되어 적극적으로 채택하지 않고 있다. 한편 키워드 기반 테스트는 리소스 절약 및 유지 관리를 용이하게 하는 방법으로 소개되었으나 데이터, 설정, 상호 작용, 시퀀스 및 타이밍과 같은 변수가 많아 테스트를 미리 계획하는 것이 쉽지 않다. 하지만 키워드 기반 테스트에서 키워드를 작성하기 위한 명확한 기준과 방법을 제시하고 탐색 테스트 프로세스를 적용하여 키워드를 기반으로 테스트 사례를 만들 수 있다. 이 논문에서는 키워드 기반으로 탐색적 테스트를 자동화 하는 모델을 제안하고 실험한다. 효과를 검증하기 위해 일반 키워드 기반 테스트(KBT)와 탐색적 키워드 기반 테스트(KBET)와 비교하였고 탐색적 정상 테스트 사례(ETC) 및 탐색적 키워드 기반 테스트(KBET)와 비교하였다.

• 임상간호연구
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• 제29권2호
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• pp.186-197
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• 2023

Purpose: The purpose of this study was to develop a scale to measure professionalism of clinical nurses and evaluate the reliability and validity of the scale. Methods: DeVellis's scale-development eight steps were applied. The initial items were developed through a literature review and discussion with investigators, and the content validity was verified by seven experts. The data were collected from 250 hospital nurses for exploratory factor analysis and 217 hospital nurses for confirmatory factor analysis. Exploratory and confirmatory factor analyses were utilized to assess the construct validity. Cronbach's ⍺ was used to test the internal consistency reliability. Results: The results of the exploratory and confirmatory factor analyses showed that the scale comprised four factors: professional skill(eight items), sense of ethics (five items), knowledge-seeking activities (four items), and autonomy(three items). The four-factor structure was validated (x 2 =600.85 p<.001, GFI=.88, CFI=.84, RMSEA=.07), and Cronbach's ⍺ for the total scale was .84. Conclusion: The study results showed satisfactory reliability and validity of the professionalism measurement scale for clinical nurses. This scale has potential as an appropriate instrument for measuring clinical nurse professionalism.

• 인간식물환경학회지
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• 제22권6호
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• pp.621-628
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• 2019

The purpose of this study was to investigate the reliability and validity of the Korean version of the Connectedness to Nature Scale(K-CNS), which was translated from the Connectedness to Nature Scale(CNS) developed by Mayer and Frantz. For this study, questionnaires were conducted with a sample of 407 Korean citizens from various backgrounds and regions, with the ages ranging from 17 to 60. Exploratory factor analysis, internal consistency test and correlation checks were conducted on this questionnaires data. The results of exploratory factor analysis supported that the K-CNS has 10 items in a single factor. The internal consistency of the K-CNS was .880. The K-CNS was positively correlated to the NEP (New Environmental Paradigm), satisfaction with life, mental health, compassionate love, and negatively correlated to stress response. In addition, differences were found by group and age, but not by the gender. The K-CNS was higher in the natural-friendly group than in the general group. By age, the K-CNS scores was high in 40s and 50s than 10-39 years old.

• 응용통계연구
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• 제16권1호
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• pp.15-31
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• 2003

Lake Ontario에서 수년간 측정된 실제 생물학적 지표 자료의 각 변수에 대하여 관찰시점의 불규칙성과 의존성을 고려한 탐색적 분석모형의 수립과정에 대하여 연구하였다. 이 상점을 제거한 후 trend와 seasonal component를 수정 한 선형 모형으로부터 잔차를 계산하고 이로부터 variogram과 correlogram을 그려보았다.

• Fashion, Industry and Education
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• 제15권2호
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• pp.25-35
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• 2017

In this study, causal relationships of belief-attitude-purchase intention for unfamiliar-branded cosmetics (UBC) were tested based on the theory of reasoned action. Data of 249 consumers who have purchased UBC were submitted to the descriptive statistics, exploratory factor analysis, and regression analysis with SPSS. In results of exploratory analysis, three factors of beliefs on UBC were generated and titled as ingredients, publicity, and subsidiary features. In regression analysis results, ingredient factor affected attitude toward UBC while publicity and subsidiary features did not influence attitude toward UBC. It implies that consumers build their attitude toward brands focusing on ingredients that are core elements of cosmetic products. Attitude toward UBC solely influenced purchase intention while the effect of social acceptance on purchase intention was not found. In conclusion, consumers depend much on ingredients when they purchase UBC so brands newly launched or brands without reputations need to focus more on an excellence in ingredients and special functions of UBC to appeal to consumers when penetrating to the new market.

• 대한수학교육학회지:수학교육학연구
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• 제8권2호
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• pp.605-620
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• 1998

This paper suggested a geometry learning model which relates an exploratory learning model with GSP applications, Such a model adopts GSP's capability of visualizing dynamic geometric figures and exploratory learning method's advantages of discovering properties and relations of geometric problem proving and concepts associated with geometric inferencing of students. The research was conducted for 3 middle school students by applying the proposed model for 6times at computer laboratory. The overall procedure was videotaped so that the collected data was later analyzed by qualitative methodology. The analysis indicated that the students with less than van Hiele 4 level took advantages of adoption our proposed model to gain concrete understandings of geometric principles and concepts with GSP. One of the lessons learned from this study suggested that the roles of students and a teacher who want to employ the proposed model need to change their roles respectively.

• 실천공학교육논문지
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• 제14권1호
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• pp.49-59
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• 2022

본 연구는 A대학 교수학습센터(Center for Teaching & Learning)의 교수지원 프로그램 특성에 부합하는 교수역량 진단도구를 개발하고자 수행되었다. 이러한 연구목적을 달성하기 위해 선행연구 검토를 통해 3개 역량군(기본역량, 실천역량, 혁신역량)과 13개 교수역량, 26개 하위역량 요인으로 구성된 잠정적 교수역량 진단도구를 설정하였다. 잠정적 교수역량 진단도구의 신뢰도 및 타당도 검증을 위해 2020년 11월에 A대학교 교수자를 대상으로 온라인 설문조사를 실시하였으며, 설문 데이터를 정리하여 탐색적 요인분석과 확인적 요인분석을 수행하였다. 탐색적 요인분석 결과, 기본역량, 실천역량, 혁신역량 3개 역량군에 포함되는 26개 하위역량이 17개로 축소되었다. 확인적 요인분석을 실시한 결과, 모형의 적합도가 양호한 것으로 나타나 CTL 맞춤형 교수역량(하위역량) 17개 영역, 79개 문항을 최종 확정하였다. 또한 확정된 교수역량 요인의 개념신뢰도와 분산추출지수, 판별타당도를 분석한 결과 양호한 수준을 보였다. 본 연구를 통해 개발된 교수역량 진단도구는 CTL 교수지원 프로그램의 효과성 평가와 신규 프로그램 개발의 근거자료로 활용될 수 있다.

• 유통과학연구
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• 제13권9호
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• pp.103-112
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• 2015

Purpose - This paper aims to provide a step-by-step approach to factor analytic procedures, such as principal component analysis (PCA) and exploratory factor analysis (EFA), and to offer a guideline for factor analysis. Authors have argued that the results of PCA and EFA are substantially similar. Additionally, they assert that PCA is a more appropriate technique for factor analysis because PCA produces easily interpreted results that are likely to be the basis of better decisions. For these reasons, many researchers have used PCA as a technique instead of EFA. However, these techniques are clearly different. PCA should be used for data reduction. On the other hand, EFA has been tailored to identify any underlying factor structure, a set of measured variables that cause the manifest variables to covary. Thus, it is needed for a guideline and for procedures to use in factor analysis. To date, however, these two techniques have been indiscriminately misused. Research design, data, and methodology - This research conducted a literature review. For this, we summarized the meaningful and consistent arguments and drew up guidelines and suggested procedures for rigorous EFA. Results - PCA can be used instead of common factor analysis when all measured variables have high communality. However, common factor analysis is recommended for EFA. First, researchers should evaluate the sample size and check for sampling adequacy before conducting factor analysis. If these conditions are not satisfied, then the next steps cannot be followed. Sample size must be at least 100 with communality above 0.5 and a minimum subject to item ratio of at least 5:1, with a minimum of five items in EFA. Next, Bartlett's sphericity test and the Kaiser-Mayer-Olkin (KMO) measure should be assessed for sampling adequacy. The chi-square value for Bartlett's test should be significant. In addition, a KMO of more than 0.8 is recommended. The next step is to conduct a factor analysis. The analysis is composed of three stages. The first stage determines a rotation technique. Generally, ML or PAF will suggest to researchers the best results. Selection of one of the two techniques heavily hinges on data normality. ML requires normally distributed data; on the other hand, PAF does not. The second step is associated with determining the number of factors to retain in the EFA. The best way to determine the number of factors to retain is to apply three methods including eigenvalues greater than 1.0, the scree plot test, and the variance extracted. The last step is to select one of two rotation methods: orthogonal or oblique. If the research suggests some variables that are correlated to each other, then the oblique method should be selected for factor rotation because the method assumes all factors are correlated in the research. If not, the orthogonal method is possible for factor rotation. Conclusions - Recommendations are offered for the best factor analytic practice for empirical research.