• Journal of Korean Society of Industrial and Systems Engineering
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• v.13 no.21
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• pp.81-90
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• 1990

In stratified random sampling a simple random sample must be taken in each stratum to reduce the maximum gain in precision given the minimum cost. The purpose of this paper is to deal with the propertics of the estimates and variances and obtain the economic design of stratified random sampling through the optimum allocation of the sample sizes. In addition, the between stratum variation and the within stratum variation is stratifying the population are described.

• Survey Research
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• v.10 no.1
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• pp.155-168
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• 2009

As an efficient sampling design, stratified random sampling is often used when auxiliary information is available at the designing stage. Although one - per - stratum design is an efficient design that can be used when many auxiliary variables are available, it does not provide any unbiased variance estimator. With a two - per - stratum sample in which two elements are selected from each stratum, it is possible to obtain an unbiased variance estimator. However the loss of efficiency could be significant if any important stratification variable is missed. In this study, we investigated a sampling design that uses the all given auxiliary information and also permits an unbiased variance estimator suggested by Park and Fuller(2008). Through a simulation study, we compared several stratified random sampling and systematic sampling design. We also applied the proposed stratified sampling designs to 2007 youth panel data.

• Journal of the Korean Data and Information Science Society
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• v.28 no.2
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• pp.443-449
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• 2017

Estimation of population proportion like the distribution rate of LED TV and the prevalence of a disease are often estimated based on survey sample data. Population proportion is generally considered as a special form of population mean. In complex sampling like stratified multistage sampling with unequal probability sampling, the denominator of mean may be random variable and it is estimated like ratio estimator. In this research, we examined the estimation of distribution rate based on stratified multistage sampling, and determined some numerical outcomes using stratified random sample data with about 25% of missing observations. In the data used for this research, the survey weight was determined by deterministic way. So, the weights are not random variable, and the population distribution rate and its variance estimator can be estimated like population mean estimation. When the weights are not random variable, if one estimates the variance of proportion estimator using ratio method, then the variances may be inflated. Therefore, in estimating variance for population proportion, we need to examine the structure of data and survey design before making any decision for estimation methods.

• Journal of Korea Technology Innovation Society
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• v.5 no.2
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• pp.228-244
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• 2002

The survey of research and development (R&D) in science and technology (S&T) covers the current status of R&D activities in S&T in Korea, and provides a basis for decision making regarding S&T policy. Continuous improvement of the survey is widely needed to present reliable national basic statistics. Therefore, the purpose of the study is two-fold: to introduce sampling survey method in industrial sector and to make statistical technique to deal with non-response data from industrial sector. To these ends, first, case studies of the United States and Japan are illustrated. A new sampling design for the R&D survey is proposed and implementing stratified random sampling scheme is suggested. Moreover, statistical analysis of the non-response data is dealt with. Based on several screening criteria, we develop a new imputation method suitable for the R&D survey and also provide more detailed implementation plan. Various solutions to a problem arising from non-response item are also presented. Finally, some implications of the results are discussed.

• Communications for Statistical Applications and Methods
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• v.8 no.1
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• pp.73-85
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• 2001

The spatial population existing in a plane ares, such as an animal or aerial population, have certain relationships among regions which are located within a fixed distance from one selected region. We consider with the adaptive searching estimation in spatial sampling for a spatial population. The adaptive searching estimation depends on values of sample points during the survey and on the nature of the surfaces under investigation. In this paper we study the estimation by the adaptive searching in a spatial sampling for the purpose of estimating the area possessing a particular characteristic in a spatial population. From the viewpoint of adaptive searching, we empirically compare systematic sampling with stratified sampling in spatial sampling through the simulation data.

• Journal of Preventive Medicine and Public Health
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• v.42 no.2
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• pp.135-142
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• 2009

Objectives : An appropriate sampling strategy for estimating an epidemiologic volume of diabetes has been evaluated through a simulation. Methods : We analyzed about 250 million medical insurance claims data submitted to the Health Insurance Review & Assessment Service with diabetes as principal or subsequent diagnoses, more than or equal to once per year, in 2003. The database was re-constructed to a 'patient-hospital profile' that had 3,676,164 cases, and then to a 'patient profile' that consisted of 2,412,082 observations. The patient profile data was then used to test the validity of a proposed sampling frame and methods of sampling to develop diabetic-related epidemiologic indices. Results : Simulation study showed that a use of a stratified two-stage cluster sampling design with a total sample size of 4,000 will provide an estimate of 57.04%(95% prediction range, 49.83 - 64.24%) for a treatment prescription rate of diabetes. The proposed sampling design consists, at first, stratifying the area of the nation into "metropolitan/city/county" and the types of hospital into "tertiary/secondary/primary/clinic" with a proportion of 5:10:10:75. Hospitals were then randomly selected within the strata as a primary sampling unit, followed by a random selection of patients within the hospitals as a secondly sampling unit. The difference between the estimate and the parameter value was projected to be less than 0.3%. Conclusions : The sampling scheme proposed will be applied to a subsequent nationwide field survey not only for estimating the epidemiologic volume of diabetes but also for assessing the present status of nationwide diabetes control.

• The Korean Journal of Applied Statistics
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• v.28 no.6
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• pp.1217-1225
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• 2015

Survey research, data is commonly collected through a sample design with complex design features that allow the relative efficiency on the precision of an estimator to be measured using the concept of the design effect compared to simple random sampling as a reference design. This concept is most useful when the design effect can be expressed as a function of various design features. We propose a design effect formula suitable under a stratified multistage sampling by generalizing Gabler et al. (1999, 2006)'s approaches for multistage sampling. Its use can either guide improvement in the design efficiency when in design stage or enable the evaluation of the adopted design features afterwards.

• 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.

• Journal of Nutrition and Health
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• v.17 no.3
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• pp.236-241
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• 1984

In order to make clear the relationship between sample design and sample survey in community, it was conducted research on sample design for National Nutrition Survey in 1983. In this paper it was tried to analize the data based on The Report of a Settled Population, 1981 conducted by National Bureau of Statistics Economic Planning Board. The sample was basically using stratified two-stage sampling with systematic sampling of Ban or Li as administrative unit. The population represents the whole nation excluding Jeju-do because of budget. The selection of sampling unit and sampling procedure was as follows. 1) Stratify the nation-wide area in 20 sections according to administrative districts. 2) Determine the sample size in each section according to equal proportional rate (1 / 8040) and to about 1,000 households in the sample. 3) Select the 25 sampling units by section according to households proportion. 4) Select the 10 households at random from each Ban or Li according to equal probability proportion as the final sampling unit. Using the procedure, it was sampled 1,000 households for National Nutrition Survey in 1983.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.9
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• pp.4661-4664
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• 2012

Mammographic breast density is a known risk factor for breast cancer. To conduct a survey to estimate the distribution of mammographic breast density in Korean women, appropriate sampling strategies for representative and efficient sampling design were evaluated through simulation. Using the target population from the National Cancer Screening Programme (NCSP) for breast cancer in 2009, we verified the distribution estimate by repeating the simulation 1,000 times using stratified random sampling to investigate the distribution of breast density of 1,340,362 women. According to the simulation results, using a sampling design stratifying the nation into three groups (metropolitan, urban, and rural), with a total sample size of 4,000, we estimated the distribution of breast density in Korean women at a level of 0.01% tolerance. Based on the results of our study, a nationwide survey for estimating the distribution of mammographic breast density among Korean women can be conducted efficiently.