• Journal of the Korean Statistical Society
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• 제27권4호
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• pp.459-469
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• 1998

Balanced half sample method is a simple variance estimation method for complex sampling designs. Since it is simple and flexible, it has been widely used in large scale sample surveys. However, the usual BHS method overestimate the true variance in without replacement sampling and two-stage cluster sampling. Focusing on this point , we proposed an unbiased BHS variance estimator in a stratified two-stage cluster sampling and then described an implementation method of the proposed estimator. Finally, partially BHS design is explained as a tool of reducing the number of replications of the proposed estimator.

• 응용통계연구
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• 제14권1호
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• pp.1-11
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• 2001

확률비례추출법(pps sampling)에서 표본단위의 추출확률을 결정하기 위하여 크기측도(measure of size:MOS)를 사용한다. 통계청에서 실시하는 일부 대규모조사의 경우 각 표본단위들의 크기측도를 살펴보면 서로 매우 유사한 편임을 알 수 있다. 이런 경우 조사원 업무량을 고려하여 확률비례추출법 대신 단순집락추출법의 사용 필요성이 제시되었다. 본 논문에서는 1997년 통계청의 경제활동인구조사 표본설계법에 두 가지 추출법을 적용하여 평균제곱오차(MSE)와 변이계수(CV)를 비교하였다.

• 응용통계연구
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• 제25권3호
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• pp.447-456
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• 2012

For a sensitive survey in which the population is comprised of several clusters with a quantitative attribute, we present an additive quantitative randomized response model by cluster sampling that adapts a two-stage cluster sampling instead of a simple random sample based on Himmelfarb-Edgell's additive quantitative attribute model and Gjestvang-Singh's one. We also derive optimum values for the number of 1st stage clusters and the optimum values of observation units in a 2nd stage cluster under the condition of minimizing the variance given constant cost. We can see that Himmelfarb-Edgell's model is more efficient than Gjestvang-Singh's model under the condition of cluster sampling.

• Communications for Statistical Applications and Methods
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• 제23권1호
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• pp.47-55
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• 2016

Stratified cluster sampling has been widely used for effective parameter estimations due to reductions in time and cost. The probability proportional to size (PPS) sampling method is used when the number of cluster element are significantly different. However, simple random sampling (SRS) is commonly used for simplicity if the number of cluster elements are almost the same. Also it is known that the ratio estimator produces a good performance when the total number of population elements is known. However, the two stage cluster estimator should be used if the total number of elements in population is neither known nor accurate. In this study we suggest a composite estimator by combining the ratio estimator and the two stage cluster estimator to obtain a better estimate under a certain population circumstance. Simulation studies are conducted to compare the superiority of the suggested estimator with two other estimators.

• Journal of the Korean Statistical Society
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• 제34권3호
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• pp.185-195
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• 2005

This article is concerned with the intraclass correlation in survey sampling. From a design-based viewpoint the intraclass correlation is generalized to a finite population with unequal sized clusters. Under simple random cluster sampling the intraclass correlation is given in an explicit form, which is a generalization of the usual one. The range of it is found and the design effect is expressed by means of it. An example is given to compare the intraclass correlation with the homogeneity measure numerically, which shows that two measures are not the same except some limited cases.

• 응용통계연구
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• 제11권1호
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• pp.141-150
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• 1998

본 논문에서는 매우 민감한 조사에서 모집단이 양적속성을 갖는 여러 개의 집락으로 구성되어 있을 때, 집락을 추출단위로 하는 단순집락추출법에 양적속성의 무관질문모형을 적용하였다. 그리고, 일정한 비용하에서 분산을 최소로 하는 집락의 크기와 표본집락의 수의 최적값을 구하여 최소분산의 형태를 도출하였다. 또한, 제안한 단순집략추출법에 의한 무관질문모형과 단순임의 추출법에 의한 무관질문모형과의 효율성을 비교해 보았다.

• Journal of Nutrition and Health
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• 제31권3호
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• pp.354-362
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• 1998

The purposes of this study were to develop standardized indices of staffing needs in each school, foodservice system through work sampling methodology . Conventional school foodservices were classified into 5 groups depending on size of meals served. Commissary school foodservices were also classified into 5 groups by cluster analysis using number of meals served, number of satellite schools, and time for transportation of food. Work measurement through work sampling methodology was conducted in 15 conventional and 21 commissary foodservices during 3 consecutive days from September to October in 1995. Statistical data analysis was completed using the SAS programs for descriptive analysis, cluster analysis, and simple linear regression. The results were as follows : Average points of leveling factors of conventional and commissary foodservices were 1.066 and 1.061 , respectively. Mean labor hours per work force was 328 minutes and 366 minutes in conventional and commissary foodservice , respectively. Standardized work time was calculated using leveling factor, ILO allowance rate (175) , and observational work time. The model for standardized indices of staffing needs was developed based on simple linear regression in each school foodservice system. In conventional school foodservice systems(for 100-1,900 meals per day) standardized staffing needs=3.2497 +0.005267$\times$number of meals served (F=273.1, R-square 0.9750, p<0.001). In commissary school foodservice systems (for 200-1,600 meals per day ) Standardized staffing needs=3.393384 +0.0063$\times$number of meals served (F=30.78, R-square 0.6580, p<0.001).

• 응용통계연구
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• 제20권3호
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• pp.605-618
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• 2007

모집단 단위들이 희박하게 존재하고 접근하기 어려운 경우에 적용하는 적응추출설계에서의 추출과정은 관심변수의 관측값에 의존한다. 동일한 표본크기에서 적응집락추출의 추정량은 단순임의추출의 추정량에 비해 효율이 더 좋다 적응추출에서 Rao-blackwell의 정리를 적용하여 Murthy의 추정량의 형태로 수정한 한센-휴비_(HH) 추정량과 호르비_-톰슨 (HT) 추정량은 기존의 추정량에 비해 작은 분산을 가진다. 본 연구는 초기표본을 바꿔가면서 기대표본크기와 적응추출의 표본크기 하의 단순임의추출의 추정량과 적응추출의 추정량의 효율을 비교하였다.

• 한국임상수의학회지
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• 제28권4호
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• pp.422-426
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• 2011

For a national survey in which wide geographic region or an entire country is targeted, multi-stage sampling approach is widely used to overcome the problem of simple random sampling, to consider both herd- and animallevel factors associated with disease occurrence, and to adjust clustering effect of disease in the population in the calculation of sample size. The aim of this study was to establish sample size for estimating bovine tuberculosis (TB) in Korea using stratified two-stage sampling design. The sample size was determined by taking into account the possible clustering of TB-infected animals on individual herds to increase the reliability of survey results. In this study, the country was stratified into nine provinces (administrative unit) and herd, the primary sampling unit, was considered as a cluster. For all analyses, design effect of 2, between-cluster prevalence of 50% to yield maximum sample size, and mean herd size of 65 were assumed due to lack of information available. Using a two-stage sampling scheme, the number of cattle sampled per herd was 65 cattle, regardless of confidence level, prevalence, and mean herd size examined. Number of clusters to be sampled at a 95% level of confidence was estimated to be 296, 74, 33, 19, 12, and 9 for desired precision of 0.01, 0.02, 0.03, 0.04, 0.05, and 0.06, respectively. Therefore, the total sample size with a 95% confidence level was 172,872, 43,218, 19,224, 10,818, 6,930, and 4,806 for desired precision ranging from 0.01 to 0.06. The sample size was increased with desired precision and design effect. In a situation where the number of cattle sampled per herd is fixed ranging from 5 to 40 with a 5-head interval, total sample size with a 95% confidence level was estimated to be 6,480, 10,080, 13,770, 17,280, 20.925, 24,570, 28,350, and 31,680, respectively. The percent increase in total sample size resulting from the use of intra-cluster correlation coefficient of 0.3 was 22.2, 32.1, 36.3, 39.6, 41.9, 42.9, 42,2, and 44.3%, respectively in comparison to the use of coefficient of 0.2.

• 한국조사연구학회지:조사연구
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• 제9권1호
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• pp.69-85
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• 2008

층화는 표본설계 단계에서 예비정보를 활용하는 대표적인 방법으로 대부분의 전국 단위의 표본설계에서 널리 활용된다. 층화의 효율을 극대화시키기 위해서는 조사목적에 부합되는 적절한 층화변수를 선택하는 것이 매우 중요하다. 하나의 표본을 통해 여러 개의 관심변수를 동시에 조사하는 다목적조사에서 다변량 층화변수가 있을 때 층화 전략을 세우는 것은 매우 복잡한 양상을 띤다. 본 연구에서는 관심변수의 수가 매우 많은 다목적조사를 위한 층화전략을 다룬다. 층화를 위해 구체적으로 사용하는 통계적 도구는 요인분석과 군집분석 등의 다변량 통계기법인데, 먼저 요인분석을 통해 적절한 층화변수들을 선정한 후 그 변수들을 이용하여 군집분석을 통해 층화를 하는 전략을 소개한다. 본 연구에서는 구체적으로 해양수산부의 어업비계통생산량조사를 위한 표본설계에서의 층화과정을 다룬다.