• Restorative Dentistry and Endodontics
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• 제49권2호
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• pp.21.1-21.8
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• 2024

Objectives: This paper aims to serve as a useful guide for sample size determination for various correlation analyses that are based on effect sizes and confidence interval width. Materials and Methods: Sample size determinations are calculated for Pearson's correlation, Spearman's rank correlation, and Kendall's Tau-b correlation. Examples of sample size statements and their justification are also included. Results: Using the same effect sizes, there are differences between the sample size determination of the 3 statistical tests. Based on an empirical calculation, a minimum sample size of 149 is usually adequate for performing both parametric and non-parametric correlation analysis to determine at least a moderate to an excellent degree of correlation with acceptable confidence interval width. Conclusions: Determining data assumption(s) is one of the challenges to offering a valid technique to estimate the required sample size for correlation analyses. Sample size tables are provided and these will help researchers to estimate a minimum sample size requirement based on correlation analyses.

• 품질경영학회지
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• 제28권2호
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• pp.17-38
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• 2000

In application, sample size determination is one of the important problems in designing an experiment. A large amount of literature has been published on the problem of determining sample size and power for various statistical models. In practice, however, it is not easy to calculate sample size and/or power because the formula and other results derived from statistical model are scattered in various textbooks and journal articles. This paper describes some previously published theories that have practical relevance for sample size and power determination in various statistical problems, including life-testing problems with censored cases and introduces a statistical package which calculates sample size and power according to the results described. The screens and numerical results made by the package are demonstrated.

• 여성건강간호학회지
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• 제4권3호
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• pp.375-387
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• 1998

In clinical trials of nursing research, the sample size determination is one of the most important factor. Although sample size must be considered at the design stage, it has been disregarded in most clinical trials. The power analysis is usually performed before study begins to compute sample size and the power can also be calculated at the end of study in order to justify study result. The power analysis is essential especially when the clinical trials can not show significant differences. In this paper, we review the statistical methods for power analysis and sample size formulae in nursing research. Sample size formulae and the corresponding examples are discussed according to the six types of studies ; mean for one sample, proportion for one sample, means in two samples, proportions in two samples, correlation coefficient and ANOVA.

• 응용통계연구
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• 제11권2호
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• pp.403-413
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• 1998

표본크기 결정 문제는 추정의 정밀도 및 조사비용에 영향을 주는 중요한 사안이다. 정규 모집단의 모평균 추정시 필요한 표본크기는 일반적으로 2단계 추출을 이용한 결정방법들에 의하여 이루어지는데, 이들에 대한 평가를 통하여 올바른 표본크기 결정을 행할 수 있는 토대를 마련하였다

• Journal of the Korean Data and Information Science Society
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• 제18권2호
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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.

• 대한교통학회지
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• 제24권3호
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• pp.7-15
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• 2006

O/D 추정을 위한 표본조사시 최소표본율의 결정은 조사 전체 및 구축된 O/D의 신뢰성과 직결되는 중요한 문제이다. 현재 대부분의 O/D 추정을 위한 교통조사시 정해진 기준 없이 단순히 전체 모집단에 대하여 정률로 표본율을 결정하거나, 모집단의 크기에 따라 약간씩 표본수를 가감하는 표본율을 사용하고 있으나, 적용시 신뢰성 문제가 존재하므로 이에 대한 보완이 필요하다. 본 연구에서는 이러한 문제점의 해결방안으로 최악의 경우에도 zero cell을 없애도록 고안된 교통조사지침의 표본수결정식을 이용하되, 이 방법의 문제점인 과도한 표본율을 줄이기 위하여 카테고리수를 조사여건에 따라 차등 적용하여 예산제약의 문제를 해결하는 방안에 대하여 검토하였다. 전국 지역간 여객 O/D자료를 대상으로 기존 O/D자료에서 zero cell을 제외하는 경우(1안), 대권역으로 적용하는 경우(2안) 인접죤으로 통행하는 경우(3안), 다음 인접죤까지 통행하는 경우(4안) 등 4개 안을 제안하여 분석하였고. 그 결과 각 대안들은 신뢰성과 표본율 측면에서 대체관계(trade-off)로 각각 장단점을 내포하고 있는 것으로 나타났으므로, 각 조사기관은 조사의 신뢰성과 예산 등의 문제를 포괄적으로 고려하여 최적의 방법을 선택하여 적용하여야 할 것이다.

• 한국임상수의학회지
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• 제32권1호
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• pp.73-77
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• 2015

There has been increasing attention on sample size requirements in peer reviewed medical literatures. Accordingly, a statistically-valid sample size determination has been described for a variety of medical situations including diagnostic test accuracy studies. If the sample is too small, the estimate is too inaccurate to be useful. On the other hand, a very large sample size would yield the estimate with more accurate than required but may be costly and inefficient. Choosing the optimal sample size depends on statistical considerations, such as the desired precision, statistical power, confidence level and prevalence of disease, and non-statistical considerations, such as resources, cost and sample availability. In a previous paper (J Vet Clin 2012; 29: 68-77) we briefly described the statistical theory behind sample size calculations and provided practical methods of calculating sample size in different situations for different research purposes. This review describes how to calculate sample sizes when assessing diagnostic test performance such as sensitivity and specificity alone. Also included in this paper are tables and formulae to help researchers for designing diagnostic test studies and calculating sample size in studies evaluating test performance. For complex studies clinicians are encouraged to consult a statistician to help in the design and analysis for an accurate determination of the sample size.

• 대한치과의사협회지
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• 제52권9호
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• pp.558-569
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• 2014

Sample size determination is critical, but not easy to do. Sample size defined as the number of observations in a sample to be studied should be big enough to have a high likelihood of detecting a true difference between groups. Practical procedure for determining sample size, using $G^*$power and previous dental articles, was shown in this study. Examples involving independent t-test, paired t-test, one-way analysis of variance(ANOVA), and one-way repeated-measures(RM) ANOVA were used. The purpose of this study is to enable researchers with non-statistical backgrounds to use in practice freely available statistical software G*power to determine sample size and power.

• 응용통계연구
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• 제11권2호
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• pp.269-285
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• 1998

임상시험연구의 설계에서 적절한 표본수의 결정은 매우 중요한 문제 중의 하나이다. 본 논문에서는 생존분포를 비교하기 위한 여러 가지 방법들을 소개하고, 각 방법에서의 가정들을 고찰하였다. 또한 다양한 상황에서의 표본수와 검정력 등을 비교제시하고 모의실험을 통해 각 방법들의 이론상의 검정력과 실제 검정력을 알아보았다 그 결과로서 의학연구자들이 처한 여러 상황에 적합한 표본수의 결정방법을 제시하였다.

• Communications for Statistical Applications and Methods
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• 제31권1호
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• pp.55-64
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• 2024

In this study, we consider the sample size determination problem for clustered count data with many zeros. In general, zero-inflated Poisson and binomial models are commonly used for zero-inflated data; however, in real data the assumptions that should be satisfied when using each model might be violated. We calculate the required sample size based on a discrete Weibull regression model that can handle both underdispersed and overdispersed data types. We use the Monte Carlo simulation to compute the required sample size. With our proposed method, a unified model with a low failure risk can be used to cope with the dispersed data type and handle data with many zeros, which appear in groups or clusters sharing a common variation source. A simulation study shows that our proposed method provides accurate results, revealing that the sample size is affected by the distribution skewness, covariance structure of covariates, and amount of zeros. We apply our method to the pancreas disorder length of the stay data collected from Western Australia.