• 제목/요약/키워드: intracluster correlation coefficient

검색결과 3건 처리시간 0.014초

Determination of Sampling Unit Size for Cultivation Area Survey using Remote Sensing Technology

  • Park, Jin-Woo;Shin, Gi-Eun;Lee, Suk-Hoon;Byun, Jong-Seok
    • 응용통계연구
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    • 제25권5호
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    • pp.733-741
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    • 2012
  • The successful launch of Arirang satellites allow the acquisition of high resolution satellite imagery of Korean territory and enables the transition from the conventional cultivation area survey method to new image based methods adopted in advanced nations. In this study, we suggested reasonable sizes of the primary sampling unit and the secondary sampling unit for the satellite imagery based sampling design in 8 provinces preselected for this research. The PSU size was determined mainly in consideration of intracorrelation that shows the degree of homogeneity within each cluster and the efficiency of the image process. For the SSU size, we considered the relative standard error and the differences between the land cover maps produced by the Ministry of Environment and the satellite imagery processed by the National Statistical Office.

설계효과모형을 통한 설계요소의 유용성 이해 (Understanding Complex Design Features via Design Effect Models)

  • 박인호
    • 응용통계연구
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    • 제28권6호
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    • pp.1217-1225
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    • 2015
  • 조사자료분석에 있어서 표본추정량에 대해 설계요소가 갖는 효율성은 단순확률추출과 비교한 복잡표본설계의 의한 표본추출이 주는 분산의 상대적 크기인 설계효과를 통해 평가할 수 있다. 설계효과의 유용성은 복잡설계요소의 함수형태로 표현될 수 있을때 극대화될 수 있다. 본 연구에서는 층화다단추출의 표본설계에서 적용될 수 있는 설계효과모형을 제시하였다. 제시된 설계효과모형은 기존 다단추출을 위한 Gabler 등 (1999, 2006)의 모형을 일반화한 것으로 층구조, 표본할당, 집락추출 및 불균등가중치 등의 설계요소들이 정도수준에 갖는 영향력을 함수식으로 명확히 나타내주고 있다. 이를 활용하면 사전에 기술된 추정정도를 얻기 위해 설정한 표본크기가 줄 수 있는 설계효과를 예측하는데 활용할 수 있다. 또한 사후적으로 표본설계의 개별 설계요소들이 표본추정량에 대해 갖는 효율성을 평가하는데 활용될 수 있다.

다층모형을 이용한 국내 양돈농가의 돼지생식기호흡기증후군 위험요인 분석 (The Use of Multilevel Model to Evaluate the Risk Factors for Porcine Reproductive and Respiratory Syndrome in Swine Herds)

  • 김으뜸;이경기;김성희;박선일
    • 한국임상수의학회지
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    • 제34권2호
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    • pp.140-145
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    • 2017
  • The goal of this study was to investigate risk factors associated with porcine reproductive and respiratory syndrome (PRRS) in pig farms in the Republic of Korea using logistic regression and a multilevel model. A cross-sectional study was applied to 305 pig farms with a questionnaire-based interview by veterinarians between March 2014 and February 2015. The questionnaire comprised eight categories: proximity to neighbors, disinfection, visitors, vehicles, insecticides, wild animals, gilts, and feeding. In total, 61 questions in eight categories related to pig farm biosecurity were investigated. Farms were classified as PRRS stable or unstable based on the results of an antibody test and PCR. For univariate analysis, keeping production records with computers (OR = 0.283, 95% CI = 0.056 - 1.425), accredited farm with no use of antibiotics (OR = 0.412, 95% CI = 0.134 - 1.269), reviewing health record of semen prior to purchasing (OR = 0.492, 95% CI = 0.152 - 1.589), complete isolation of runt pigs (OR = 0.264, 95% CI = 0.084 - 0.829), compulsory registering for visitors (OR = 0.424, 95% CI = 0.111 - 1.612), keeping records of insecticide history (OR = 0.406, 95% CI = 0.089 - 1.846), routine on-farm monitoring by veterinarians (OR = 0.314, 95% CI = 0.069 - 1.423), and use of on-farm checklist for biosecurity monitoring (OR = 0.313, 95% CI = 0.063 - 1.553) were found to decrease the probability of PRRS infection. Multivariate and multilevel analysis revealed only two factors, complete isolation of runt pigs (OR = 0.165, 95% CI = 0.045 - 0.602 and OR = 0.208, 95% CI = 0.055 - 0.782) and compulsory registering for visitors (OR = 0.106, 95% CI = 0.017 - 0.655 and OR = 0.119, 95% CI = 0.017 - 0.809) were found to decrease the probability of PRRS infection. The intracluster correlation coefficient of a province for multilevel model was 0.05. The results of this study might facilitate biosecurity measures for individual farms to reduce the probability of PRRS infection.