• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.5
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• pp.2251-2258
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• 2012

This study compared the unweighted sample design, frequency weighted sample design and complex sample design to using 2009 Korea National Health and Nutrition Examination Survey in an effort to identify whether or not there is any difference in potential risk factors. Pearson chi-square test and Rao-scott chi-square test were applied to the analytic methods. As a result of analyses, all the variables were overestimated as significant risk factors in case of the unweighted sample design to which only the frequency weights were applied. In addition, there were differences in the confidence levels and results from the simple random sampling analysis and complex sample design to which no weight was applied. It is necessary to carry out the complex sample design rather than the analysis to which the frequency weights are applied, in order to ensure the findings to represent the whole population when our national statistics data is used.

• Journal of the Korean Statistical Society
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• v.23 no.2
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• pp.403-420
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• 1994

An economic design of the $\bar{X}-R$ chart using variable sample size (VSS) scheme is proposed in this paper. In this design the sample size at each sampling time changes according to the values of the previous two sample statistics, sample mean and range. The VSS scheme uses large sample if the sample statistics appear near inside the control limits and smaller sample otherwise. The set of process parameters, such as the sampling interval, control limits and the sample sizes, are chosen to minimize the expected cost per hour. The efficiency of the VSS scheme is compared to the fixed sample size one for cases where there is multiple of assignable causes. Percent reductions of the expected cost in the VSS design are calculated for some given sets of cost parameters. It is shown that the VSS scheme improves the confidence of the procedure and performs statistically better in terms of the number of false alarms and the average time to signal, respectively.

• Journal of the Korean Statistical Society
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• v.14 no.2
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• pp.100-117
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• 1985

This paper describes an outline of the sampling design for crop area survey and product survey in Korea. The design attempts to from a double statification, to obtain an efficient allocation of the sample and to reduce the sampling error by establishign crop concentrated strata. The optimum numbers of sample field and sample plot are investigated. The design is made it possible to reduce the sampling errors as well as to reduce the sample size further than the present survey.

• Journal of the Korean Data and Information Science Society
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• v.18 no.2
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• pp.365-373
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• 2007

Sample size determination plays an important role in designing a bioequivalence trial. Formulae of sample sizes based on Schuirmann's two one-sided tests procedures are given for bioequivalence studies with the $2{\times}2$ crossover design and two-sample parallel design. A practical discussion for the relationship among these formulae is given.

• Journal of Veterinary Clinics
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• v.31 no.4
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• pp.288-292
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• 2014

A critical assumption of the standard sample size calculation is that the response (outcome) for an individual patient is completely independent to that for any other patient. However, this assumption no longer holds when there is a lack of statistical independence across subjects seen in cluster randomized designs. In this setting, patients within a cluster are more likely to respond in a similar manner; patient outcomes may correlate strongly within clusters. Thus, direct use of standard sample size formulae for cluster design, ignoring the clustering effect, may result in sample size that are too small, resulting in a study that is under-powered for detecting the desired level of difference between groups. This paper revisit worked examples for sample size calculation provided in a previous paper using nomogram to easy to access. Then we present the concept of cluster design illustrated with worked examples, and introduce design effect that is a factor to inflate the standard sample size estimates.

• Proceedings of the Korean Statistical Society Conference
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• 2003.05a
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• pp.137-142
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• 2003

We discuss why sample design by mathematical programming can be beneficial to practical surveys. We illustrate some developments of software for sample design using mathematical programming in several statistical organizations. Also, we present certain restrictions on the use of mathematical programming.

• Proceedings of the Korean Society of Applied Pharmacology
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• 1995.10a
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• pp.73-78
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• 1995

표본수를 결정하는 방법에는 크게 sequential design과 fixed sample size design이 있다. Fixed sample size design은 연구를 시행하기 전에 표본수를 합리적으로 결정하고 정해진 표본내에서 연구를 진행하는 방법이며, sequential design은 연구를 진행하면서 결과의 차이가 있는가 또는 없는가에 대해 미리 정해진 한계영역을 기준으로 계속적으로 연구대상을 추출하여 연구를 진행하는 방법이다. 여기서는 많이 사용되는 fixed sample size design에 대해서만 생각하기로 한다.

• Journal of Korean Society for Quality Management
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• v.46 no.3
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• pp.651-658
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• 2018

Purpose: The algorithm described in Lim(1998) is available to determine the sample size directly given specified significance level, power and signal-to-noise ratio. We research on the efficient determination of the sample size by visual methods. Methods: We propose three graphs for investigating the mutual relationship between the sample size r, power $1-{\beta}$ and the detectable signal-to-noise ratio ${\Delta}$. First graph shows the relationship between ${\Delta}$ and $1-{\beta}$ for the given r and it can be checked whether the power is sufficient enough. Second graph shows the relationship between r and ${\Delta}$ for the given power $1-{\beta}$. Third graph shows the relationship between r and $1-{\beta}$ for the given ${\Delta}$. It can be checked that which effects are sensitive to the efficient sample size by investigating those graphs. Results: In factorial design, randomized block design and the split plot design how to determine the sample size directly given specified significance level, power and signal-to-noise ratio is programmed by using R. A experiment to study the split plot design in Hicks(1982) is used as an example. We compare the sample sizes calculated by randomized block design with those by split plot design. By using graphs, we can check the possibility of reducing the sample size efficiently. Conclusion: The proposed visual methods can help an engineer to make a proper plan to reduce the sample size.

• 한국데이터정보과학회:학술대회논문집
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• 2003.10a
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• pp.185-195
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• 2003

Recently, the importance of livestock statistics is increasing because of the food consumption pattern in Korea is changing. We compare the old sample design based on the 1995 National Agriculture Census with the new sample design based on the 2000 National Agriculture Census. We present some considerations to improve the efficiency of the sample design in livestock sector survey.

• Journal of Nutrition and Health
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• v.45 no.6
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• pp.600-612
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• 2012

Nutritional researchers world-wide are using large-scale sample survey methods to study nutritional health epidemiology and services utilization in general, non-clinical populations. This article provides a review of important statistical methods and software that apply to descriptive and multivariate analysis of data collected in sample surveys, such as national health and nutrition examination survey. A comparative data analysis of the Korea National Health and Nutrition Examination Survey (KNHANES) was used to illustrate analytical procedures and design effects for survey estimates of population statistics, model parameters, and test statistics. This article focused on the following points, method of approach to analyze of the sample survey data, right software tools available to perform these analyses, and correct survey analysis methods important to interpretation of survey data. It addresses the question of approaches to analysis of complex sample survey data. The latest developments in software tools for analysis of complex sample survey data are covered, and empirical examples are presented that illustrate the impact of survey sample design effects on the parameter estimates, test statistics, and significance probabilities (p values) for univariate and multivariate analyses.