• Title/Summary/Keyword: stratified sampling

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A Combined Randomized Response Technique Using Stratified Two-Phase Sampling (층화이중추출을 이용한 결합 확률화응답기법)

  • 홍기학
    • The Korean Journal of Applied Statistics
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    • v.17 no.2
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    • pp.303-310
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    • 2004
  • We suggest a method to procure information from the sensitive population which combine a direct survey method, BB and an indirect survey one, RRT, and a combined estimator that uses the stratified double sampling to estimate the sensitive parameter. We compare the efficiency of our estimator with that of Mangat and Singh model.

Development of a Forest Inventory System for the Sustainable Forest Management (지속가능한 산림경영에 적합한 표본조사 방법의 개발)

  • Shin, Man Yong;Han, Won Sung
    • Journal of Korean Society of Forest Science
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    • v.95 no.3
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    • pp.370-377
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    • 2006
  • This study was conducted to develop an efficient method of sampling design appropriate for the sustainable forest management. For this, data were collected in Yangpyung-Gun, Gyunggi Province based on three different sampling designs such as systematic design, systematic cluster design, and stratified cluster design. Based on evaluation statistics, the sampling designs were compared to select a sampling method fitted to sustainable forest management. It was found that the systematical cluster sampling is the most efficient sampling method in terms of feasibility for sustainable forest management. It was also recommended that the sample plots should be made as a cluster of triangle-shape. The clusters should be consisted of a main plot and three sub-plots. And the sub-plots should be arranged with a distance of 50m from the main plot in the center of cluster.

An Evaluation of Sampling Design for Estimating an Epidemiologic Volume of Diabetes and for Assessing Present Status of Its Control in Korea (우리나라 당뇨병의 역학적 규모와 당뇨병 관리현황 파악을 위한 표본설계의 평가)

  • Lee, Ji-Sung;Kim, Jai-Yong;Baik, Sei-Hyun;Park, Ie-Byung;Lee, June-Young
    • 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.

A Study on Sample Allocation for Stratified Sampling (층화표본에서의 표본 배분에 대한 연구)

  • Lee, Ingue;Park, Mingue
    • The Korean Journal of Applied Statistics
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    • v.28 no.6
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    • pp.1047-1061
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    • 2015
  • Stratified random sampling is a powerful sampling strategy to reduce variance of the estimators by incorporating useful auxiliary information to stratify the population. Sample allocation is the one of the important decisions in selecting a stratified random sample. There are two common methods, the proportional allocation and Neyman allocation if we could assume data collection cost for different observation units equal. Theoretically, Neyman allocation considering the size and standard deviation of each stratum, is known to be more effective than proportional allocation which incorporates only stratum size information. However, if the information on the standard deviation is inaccurate, the performance of Neyman allocation is in doubt. It has been pointed out that Neyman allocation is not suitable for multi-purpose sample survey that requires the estimation of several characteristics. In addition to sampling error, non-response error is another factor to evaluate sampling strategy that affects the statistical precision of the estimator. We propose new sample allocation methods using the available information about stratum response rates at the designing stage to improve stratified random sampling. The proposed methods are efficient when response rates differ considerably among strata. In particular, the method using population sizes and response rates improves the Neyman allocation in multi-purpose sample survey.

Two-stage Sampling for Estimation of Prevalence of Bovine Tuberculosis (이단계표본추출을 이용한 소결핵병 유병률 추정)

  • Pak, Son-Il
    • 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.

A Case Study on the Target Sampling Inspection for Improving Outgoing Quality (타겟 샘플링 검사를 통한 출하품질 향상에 관한 사례 연구)

  • Kim, Junse;Lee, Changki;Kim, Kyungnam;Kim, Changwoo;Song, Hyemi;Ahn, Seoungsu;Oh, Jaewon;Jo, Hyunsang;Han, Sangseop
    • Journal of Korean Society for Quality Management
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    • v.49 no.3
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    • pp.421-431
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    • 2021
  • Purpose: For improving outgoing quality, this study presents a novel sampling framework based on predictive analytics. Methods: The proposed framework is composed of three steps. The first step is the variable selection. The knowledge-based and data-driven approaches are employed to select important variables. The second step is the model learning. In this step, we consider the supervised classification methods, the anomaly detection methods, and the rule-based methods. The applying model is the third step. This step includes the all processes to be enabled on real-time prediction. Each prediction model classifies a product as a target sample or random sample. Thereafter intensive quality inspections are executed on the specified target samples. Results: The inspection data of three Samsung products (mobile, TV, refrigerator) are used to check functional defects in the product by utilizing the proposed method. The results demonstrate that using target sampling is more effective and efficient than random sampling. Conclusion: The results of this paper show that the proposed method can efficiently detect products that have the possibilities of user's defect in the lot. Additionally our study can guide practitioners on how to easily detect defective products using stratified sampling

A Study on the Survey of Vocational Training Teachers and Instructors through Institutional Panel Sampling Design (기관패널 표집설계를 통한 훈련 교·강사 실태조사 방안 연구)

  • Jung, Hye-kyung;Jung, Il-chan;Lee, Jin-gu
    • Journal of Practical Engineering Education
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    • v.13 no.2
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    • pp.393-403
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    • 2021
  • The purpose of this study is to propose a method of designing a systematic panel survey at the institutional level to lay the foundation for data-based decision-making using vocational training teachers and instructors as the population. In this study, the target population and sampling frame, which are the main elements necessary for planning a panel survey, are proposed. Also based on expert advice and empirical data analysis, the sampling unit and sampling method taking into account the outer and inner variables are presented, comprehensively considering the representativeness of data, the efficiency and sustainability of data collection. As a result of the study, with the unit of the panel as a vocational training institution, a two-stage stratified proportional sampling plan is proposed so that the institution selected as the panel and the vocational training teachers and instructors belonging to the institution can participate in the survey. Based on this, implications for the panel survey sample design are presented.

Measuring stratification effects for multistage sampling (다단추출 표본설계의 층효율성 연구)

  • Taehoon Kim;KeeJae Lee;Inho Park
    • The Korean Journal of Applied Statistics
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    • v.36 no.4
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    • pp.337-347
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    • 2023
  • Sampling designs often use stratified sampling, where elements or clusters of the study population are divided into strata and an independent sample is chosen from each stratum. The stratification strategy consists of stratification and sample allocation, which are important issues that are repeatedly considered in survey sampling. Although a stratified multistage sample design is often used in practice, the literature tends to discuss simple sampling in terms of stratum effects or stratum efficiency. This study examines an existing stratum efficiency measure for two-stage sampling and further proposes additional stratum efficiency measures using the design effect model. The proposed measures are used to evaluate the stratification strategy of the sample design for high school students of the 4th Korean National Environmental Health Survey (KoNEHS).

Maximizing the Overlay of Sample Units for Two Stratified Designs by Linear Programming

  • Ryu, Jea-Bok;Kim, Sun-Woong
    • Communications for Statistical Applications and Methods
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    • v.8 no.3
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    • pp.719-729
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    • 2001
  • Overlap Maximization is a sampling technique to reduce survey costs and costs associated with the survey. It was first studied by Keyfitz(1951). Ernst(1998) presented a remarkable procedure for maximizing the overlap when the sampling units can be selected for two identical stratified designs simultaneously, But the approach involves mimicking the behaviour of nonlinear function by linear function and so it is less direct, even though the stratification problem for the overlap corresponds directly to the linear programming problem. furthermore, it uses the controlled selection algorithm that repeatedly needs zero-restricted controlled roundings, which are solutions of capacitated transportation problems. In this paper we suggest a comparatively simple procedure to use linear programming in order to maximize the overlap. We show how this procedure can be implemented practically.

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A Stratified Randomized Response Technique (층화 확률화 응답 기법)

  • Ki Hak Hong;Jun Keun Yum;Hwa Young Lee
    • The Korean Journal of Applied Statistics
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    • v.7 no.1
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    • pp.141-147
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    • 1994
  • In the present paper an attempt has been made to develop a stratified ramdomized response technique when the respondents are selected using simple random sampling without replacement (SRSWOR) as well as simple random sampling with replacement (SRSWR). The conditions under which the proposed technique will be more efficient than the corresponding Warner's technique have been obtained.

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