• Journal of the Korean Statistical Society
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• v.27 no.4
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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.

• The Korean Journal of Applied Statistics
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• v.14 no.1
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• pp.1-11
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• 2001

In PPS sampling, measure of size(MOS) is used to determine the probability of selection of sampling unit. However, some large scale surveys conducted in NSO(National Statistical Office) showed that the sampling units have the similar MOS. In such case, simple cluster sampling method instead of PPS sampling is recommended to give the interviewers a similar work load. In this paper, MSE and CV of the above two sampling methods applied to the 1997 Economically Active Population Survey sample design are compared.

• The Korean Journal of Applied Statistics
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• v.25 no.3
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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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• v.23 no.1
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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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• v.34 no.3
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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.

• The Korean Journal of Applied Statistics
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• v.11 no.1
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• pp.141-150
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• 1998

In this paper, we developed one-stage cluster randomized response model for obtaining quantitative data by using the Greenberg et al. model(1971) when the population was made up of sensitive quantitative clusters. We obtained the minimum variance by calculating the cluster's size and the optimum number of sample clusters under the some given constant cost. We compared the efficiency of our model with the Greenberg et al. model by simple random sampling.

• Journal of Nutrition and Health
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• v.31 no.3
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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).

• The Korean Journal of Applied Statistics
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• v.20 no.3
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• pp.605-618
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• 2007

Adaptive sampling design is the selection procedure which depends on observed values of the variable of interest. It is the method which could be applied to the rare and unapproachable population. Adaptive cluster sampling strategies are more efficient than simple random sampling on equivalent sample size. Adaptive sampling with new estimators through the Rao-blackwell method have lower variance than Horvitz-Thompson (HT) and Hansen-Hurwitz (HH). Also, to determine suitable sample size, it was used expected sample and the method finding appropriate sample size by changing initial sample size were studied.

• Journal of Veterinary Clinics
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• v.28 no.4
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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.

• Survey Research
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• v.9 no.1
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• pp.69-85
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• 2008

Stratification is a feature of the majority of field sample design. This paper considers the multivariate stratification strategy for multipurpose sample survey with several auxiliary variables. In a multipurpose survey, stratification procedure is very complicated because we have to simultaneously consider the efficiencies of stratification for several variables of interest. We propose stratification strategy based on factor analysis and cluster analysis using several stratification variables. To improve the efficiency of stratification, we first select the stratification variables by factor analysis, and then apply the K-means clustering algorithm to the formation of strata. An application of the stratification strategy in the sampling design for the Fisher Production Survey is discussed, and it turns out that the variances of estimators are significantly less than those obtained by simple random sampling.