• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.353-369
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• 2000

We systematized an stratified spatial sample design(SSSD) that uses the adequate stratification criteria such as the shapeness or the dispersion of an interesting region in a spatial population. And we proposed an adaptive searching estimation method in the SSSD to estimate the area of region of interest in two-dimensional surfaces. When wc adopt the proposed adaptive searching estimation method in SSSD, the observing sample size is more decreased than a classical sample design that all the designed sample size is observed. Nevertheless it has been shown that we can produce the moderate result but the efficiency is a slight reduced.

• The Korean Journal of Applied Statistics
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• v.21 no.3
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• pp.377-385
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• 2008

Most of the sample surveys conducted by several statistics preparation agencies are multipurpose surveys inquiring into several distinguishing items through a single sample. In a multipurpose sample design, the stratification tends to be very complex since the stratification variables which are both multivariate and heterogeneous must be considered collectively. In this paper we point out an outlier effect in a multivariate stratification to which the K-means clustering method is applied and propose to consider outliers prior to the stratification step. We also show an empirical stratification effect under consideration of outliers through a case study of sample design for The Rural Living Indicators.

• 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 4^th Korean National Environmental Health Survey (KoNEHS).

• The Korean Journal of Applied Statistics
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• v.14 no.1
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• pp.13-24
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• 2001

In this paper, we investigated mean estimation methods in two-phase sampling. Under the fixed expected cost we reviewed the optimal sample sizes, minimum variances and approximate unbiased variance estimators for usual ratio estimator, stratified sample mean with proportional allocation and Rao's allocation of the second phase sample. Also we proposed combined ratio estimator, which uses both ratio estimation and stratification and derived optimal sample size, minimum variance and unbiased variance estimator. Through a limited simulation study, we compared estimators by design effects and came to know that ratio estimator is more efficient than stratified sample mean in some cases and inefficient in the other cases, but combined ratio estimator is more efficient than others in most cases.

• Journal of the Korean Data and Information Science Society
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• v.25 no.3
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• pp.523-531
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• 2014

Design effect is often used in designing and planning sample surveys and/or in evaluating the efficiency of complex design features of the surveys. In this study, we applied Gabler et al. (2006)'s design effect model to 2013 Consumer behavior survey for food that was carried out by stratified two-stage sampling. Usability and adequacy of the design model to a real survey data are discussed and evaluated.

• The Korean Journal of Applied Statistics
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• v.32 no.4
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• pp.643-652
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• 2019

We propose a method for sample size determination using design effect formulas when a sample is resigned for a repeated survey. The proposed method enables the determination of the sample size by incorporating the impact of various design components to the sampling error through design effect formulas that are applicable under multistage sampling design and stratified multistage sampling designs.

• 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.

• Survey Research
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• v.11 no.1
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• pp.123-144
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• 2010

In new sample design for Korea Housing Survey to research about housing policy, total strata are forty five because individual results of sixteen regions are estimated. The sample size is determined by sample errors of several variables which are the living area, family income, householder income, and living expenses. The sample size of each region is determined by relative standard error of existing result, and the strata sample size is to use the square root proportion allocation. Enumeration districts are sampled by the probability proportion to size systematic sampling in proportion to the enumeration district size, and the systemic sampling to use assortment characteristics. We considered a new apartment complex because of variation reflections which are rebuilder and redevelopment of houses. To get estimators of mean and variance, we used the design weighting, non-response adjusting, and post-stratification. In order to consider estimation efficiency, we calculate the design effect using estimators of variance.

• The Korean Journal of Applied Statistics
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• v.7 no.1
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• pp.47-58
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• 1994

The farm population survey and the livestock survey are sample surveys related to agriculture. Two new sample designs for these surveys are considered. Shi-Gun(county) estimates in the farm population survey and Shi-Do(county) estimates in the livestock survey can be obtained. Also the sample sizes are reduced. To increase the precision of the estiamtes strarified simple random samples are used and particularly purposive samples are introduced in livestock survey. Lastly the method of management and replacement of samples are investigated for successive occasion survey.

• 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.