• 제목/요약/키워드: Data analysis study

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상관행렬의 구조분석에서 집단평균차이의 효과: 요인분석기법을 중심으로 (The Effect of Group Mean Differences upon Factor Analysis)

  • 김청택;이소영
    • 한국조사연구학회지:조사연구
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    • 제2권2호
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    • pp.109-130
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    • 2001
  • 이 논문의 목적은 상관분석에서 집단차 변수를 무시하였을 때 자료에 대한 잘못된 해석을 유도 할 수 있음을 보여주고, 집단차를 고려한 분석의 중요성과 그 방법을 제시하는 것이었다. 연구 1은 시뮬레이션 연구로 상관구조에 대한 분석인 요인분석에서 집단차를 무시하면 자료가 지니고 있던 요인구조를 파악하지 못함을 보여주었다. 이에 대한 대비책으로 표준점수에 의한 자료의 변환방법과 공분산 구조모형의 집단분석을 이용하는 방법 등이 제시되었다. 연구 2는 사례연구로 실제 자료에서 집단의 평균차에 의한 효과가 발생하는지를 지능검사 자료를 이용하여 예증하고 이러한 문제점을 해결할 수 있는지를 보여주었다.

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소셜 빅데이터 특성을 활용한 ICT 정책 격발 메커니즘 분석방법 제안 (A Study on the Analysis Method of ICT Policy Triggering Mechanism Using Social Big Data)

  • 최홍규
    • 한국멀티미디어학회논문지
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    • 제24권8호
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    • pp.1192-1201
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    • 2021
  • This study focused on how to analyze the ICT policy formation process using social big data. Specifically, in this study, a method for quantifying variables that influenced policy formation using the concept of a policy triggering mechanism and elements necessary to present the analysis results were proposed. For the analysis of the ICT policy triggering mechanism, variables such as 'Scope', 'Duration', 'Interactivity', 'Diversity', 'Attention', 'Preference', 'Transmutability' were proposed. In addition, 'interpretation of results according to data level', 'presentation of differences between collection and analysis time points', and 'setting of garbage level' were suggested as elements necessary to present the analysis results.

Content Analysis of Online Resources Regarding Needs for Advance Care Planning

  • Minju Kim;Jieun Lee
    • Journal of Hospice and Palliative Care
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    • 제27권3호
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    • pp.87-98
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    • 2024
  • Purpose: This study aimed to investigate advance care planning needs expressed online. Methods: This study collected data from online community posts and healthcare news sites. The search keywords included "death," "euthanasia," "life-sustaining medical care," "life-sustaining treatment," "advance directives," "advance medical directives," and "advance care planning." Data collection spanned from February 2018 to February 14, 2020. Out of 2,288 posts, 1,190 were included in the final analysis. Data analysis was conducted using NVivo 12, a qualitative data analysis software program. Results: Content analysis categorized patients' advance care planning needs into eight themes, 11 theme clusters, and 33 meaningful statements. Similarly, care providers' advance care planning needs were categorized into eight themes, 14 theme clusters, and 42 meaningful statements. The identified themes of care needs included life-sustaining medical care, decision-making related to life-sustaining medical care, physical care, environmental care, supportive and spiritual care, respect, preparing for death, and family. Conclusion: This study identified care needs from the perspectives of patients and their families. The findings may serve as preliminary data for future research and clinical applications.

Street Fashion Information Analysis System Design Using Data Fusion

  • Park, Hee-Chang;Park, Hye-Won
    • 한국데이터정보과학회:학술대회논문집
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    • 한국데이터정보과학회 2005년도 추계학술대회
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    • pp.35-45
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    • 2005
  • Data fusion is method to combination data. The purpose of this study is to design and implementation for street fashion information analysis system using data fusion. It can offer variety and actually information because it can fuse image data and survey data for street fashion. Data fusion method exists exact matching method, judgemental matching method, probability matching method, statistical matching method, data linking method, etc. In this study, we use exact matching method. Our system can be visual information analysis of customer's viewpoint because it can analyze both each data and fused data for image data and survey data.

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도로 침수영역의 탐색을 위한 빅데이터 분석 시스템 연구 (A Study on the Big Data Analysis System for Searching of the Flooded Road Areas)

  • 송영미;김창수
    • 한국멀티미디어학회논문지
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    • 제18권8호
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    • pp.925-934
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    • 2015
  • The frequency of natural disasters because of global warming is gradually increasing, risks of flooding due to typhoon and torrential rain have also increased. Among these causes, the roads are flooded by suddenly torrential rain, and then vehicle and personal injury are happening. In this respect, because of the possibility that immersion of a road may occur in a second, it is necessary to study the rapid data collection and quick response system. Our research proposes a big data analysis system based on the collected information and a variety of system information collection methods for searching flooded road areas by torrential rains. The data related flooded roads are utilized the SNS data, meteorological data and the road link data, etc. And the big data analysis system is implemented the distributed processing system based on the Hadoop platform.

Sociodemographic and Health Related Factors Influencing Problem Drinking of the Echo Generation Using Data of the 2018 Korean National Health and Nutrition Examination Survey

  • Kwak, Minyeong
    • International Journal of Contents
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    • 제17권1호
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    • pp.54-60
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    • 2021
  • The aim of this study was to identify factors influencing problem drinking among the Echo Generation in South Korea and provide basic data for early intervention and mediation of problem drinking among the Echo Generation. This descriptive study performed a secondary analysis of raw data from the 2018 Korean National Health and Nutrition Examination Survey and used responses for problem drinking items from 999 Echo Generation participants born between 1979 and 1992. This study comprehensively investigated sociodemographic and health-related factors influencing problem drinking among the Echo Generation. SPSS WIN program (version 26.0) was used for data analysis. Gender (β=-.32, p<.001), education level (β=.10, p=.002), white-collar workers out of job (β=-.09, p=.041), and depression (β=.11, p<.001) were identified as factors that influenced problem drinking among the Echo Generation. Results of this study suggest that in order to prevent problem drinking among the Echo Generation, there should be user-customized prevention education and intervention programs.

비정형 텍스트 데이터 정제를 위한 불용어 코퍼스의 활용에 관한 연구 (A Study on the Use of Stopword Corpus for Cleansing Unstructured Text Data)

  • 이원조
    • 문화기술의 융합
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    • 제8권6호
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    • pp.891-897
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    • 2022
  • 빅데이터 분석에서 원시 텍스트 데이터는 대부분 다양한 비정형 데이터 형태로 존재하기 때문에 휴리스틱 전처리 정제와 컴퓨터를 이용한 후처리 정제과정을 거쳐야 분석이 가능한 정형 데이터 형태가 된다. 따라서 본 연구에서는 텍스트 데이터 분석 기법의 하나인 R 프로그램의 워드클라우드를 적용하기 위해서 수집된 원시 데이터 전처리를 통해 불필요한 요소들을 정제하고 후처리 과정에서 불용어를 제거한다. 그리고 단어들의 출현 빈도수를 계산하고 출현빈도가 높은 단어들을 핵심 이슈들로 표현해 주는 워드클라우드 분석의 사례 연구를 하였다. 이번 연구는 R의워드클라우드 기법으로 기존의 불용어 처리 방법인 "내포된 불용어 소스코드" 방법의 문제점을 개선하기 위하여 "일반적인 불용어 코퍼스"와 "사용자 정의 불용어 코퍼스"의 활용 방안을 제안하고 사례 분석을 통해서 제안된 "비정형 데이터 정제과정 모델"의 장단점을 비교 검증하여 제시하고 "제안된 외부 코퍼스 정제기법"을 이용한 워드클라우드 시각화 분석의 실무적용에 대한 효용성을 제시한다.

A Study on the General Public's Perceptions of Dental Fear Using Unstructured Big Data

  • Han-A Cho;Bo-Young Park
    • 치위생과학회지
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    • 제23권4호
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    • pp.255-263
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    • 2023
  • Background: This study used text mining techniques to determine public perceptions of dental fear, extracted keywords related to dental fear, identified the connection between the keywords, and categorized and visualized perceptions related to dental fear. Methods: Keywords in texts posted on Internet portal sites (NAVER and Google) between 1 January, 2000, and 31 December, 2022, were collected. The four stages of analysis were used to explore the keywords: frequency analysis, term frequency-inverse document frequency (TF-IDF), centrality analysis and co-occurrence analysis, and convergent correlations. Results: In the top ten keywords based on frequency analysis, the most frequently used keyword was 'treatment,' followed by 'fear,' 'dental implant,' 'conscious sedation,' 'pain,' 'dental fear,' 'comfort,' 'taking medication,' 'experience,' and 'tooth.' In the TF-IDF analysis, the top three keywords were dental implant, conscious sedation, and dental fear. The co-occurrence analysis was used to explore keywords that appear together and showed that 'fear and treatment' and 'treatment and pain' appeared the most frequently. Conclusion: Texts collected via unstructured big data were analyzed to identify general perceptions related to dental fear, and this study is valuable as a source data for understanding public perceptions of dental fear by grouping associated keywords. The results of this study will be helpful to understand dental fear and used as factors affecting oral health in the future.

센서 데이터를 이용한 전기 기관차의 이상 상태 요인분석 (Failure Analysis to Derive the Causes of Abnormal Condition of Electric Locomotive Subsystem)

  • 소민섭;전홍배;신종호
    • 산업경영시스템학회지
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    • 제41권2호
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    • pp.84-94
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    • 2018
  • In recent years, the diminishing of operation and maintenance cost using advanced maintenance technology is attracting many companies' attention. Especially, the heavy machinery industry regards it as a crucial problem since a failure of heavy machinery requires high cost and long downtime. To improve the current maintenance process, the heavy machinery industry tries to develop a methodology to predict failure in advance and to find its causes using usage data. A better analysis of failure causes requires more data so that various kinds of sensor are attached to machines and abundant amount of product usage data is collected through the sensor network. However, the systemic analysis of the collected product usage data is still in its infant stage. Many previous works have focused on failure occurrence as statistical data for reliability analysis. There have been less works to apply product usage data into root cause analysis of product failure. The product usage data collected while failures occur should be considered failure cause analysis. To do this, this study proposes a methodology to apply product usage data into failure cause analysis. The proposed methodology in this study is composed of several steps to transform product usage into failure causes. Various statistical analysis combined with product usage data such as multinomial logistic regression, T-test, and so on are used for the root cause analysis. The proposed methodology is applied to field data coming from operated locomotive and the analysis result shows its effectiveness.

ESG 사회적책임 제고를 위한 빅데이터 분석: 장애인 콜택시 운영 효율성 관점 (Big Data Analytics for Social Responsibility of ESG: The Perspective of the Transport for Person with Disabilities)

  • 서창갑;김종기;정대현
    • 한국정보시스템학회지:정보시스템연구
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    • 제32권2호
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    • pp.137-152
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    • 2023
  • Purpose The purpose of this study is to analyze big data related to DURIBAL from the operation of taxis reserved for the disabled to identify the issues and suggest solutions. ESG management should be translated into "environmental factors, social responsibilities, and transparent management." Therefore, the current study used Big Data analysis to analyze the factors affecting the standby of taxis reserved for the disabled and relevant problems for implications on convenience of social weak. Design/methodology/approach The analysis method used R, Excel, Power BI, QGIS, and SPSS. We proposed several suggestions included problems with managing cancellation data, minimization of dark data, needs to develop an integrated database for scattered data, and system upgrades for additional analysis. Findings The results showed that the total duration of standby was 34 minutes 29 seconds. The reasons for cancellation data were mostly use of other modes of transportation or delayed arrival. The study suggests development of an integrated database for scattered data. Finally, follow-up studies may discuss government-initiated big data analysis to comparatively analyze the use of taxis reserved for the disabled nationwide for new social value.