• Restorative Dentistry and Endodontics
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• v.49 no.2
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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.

• Journal of Korean Society for Quality Management
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• v.28 no.2
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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.

• Women's Health Nursing
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• v.4 no.3
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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.

• The Korean Journal of Applied Statistics
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• v.11 no.2
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• pp.403-413
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• 1998

Sample size determination is a crucial part of sampling design. This paper gives a assessment for sample size determination using two-stage sampling scheme for estimating the population mean with a given precision.

• 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 Korean Society of Transportation
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• v.24 no.3 s.89
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• pp.7-15
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• 2006

A large sample can Provide more information about the Population. As the sample size Increases, analysts will be more confident about the survey results. On the other hand, the costs for survey will increase in time and manpower. Therefore, determination of the sample size is a trade-off between the required accuracy and the cost. In addition, permitted error and significance level should be considered. Sample size determination in surveys for O/D estimation is also connected with confidence of survey result. However, the past methods were usually too simple to consider confidence. Therefore, a new method for O/D surveys was Proposed and it was accurate enough, but it has too large sample size when we have current budget constraint. In this research, several minimum sample size determination methods for origin-destination survey under budget constraint were proposed. Each method decreased sample size, but has its own advantages. Selection of the sample size will depend on the study Purpose and budget constraint.

• Journal of Veterinary Clinics
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• v.32 no.1
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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.

• The Journal of the Korean dental association
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• v.52 no.9
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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.

• The Korean Journal of Applied Statistics
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• v.11 no.2
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• pp.269-285
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• 1998

One of the most important issues in the area of clinical trial research is the determination of the sample size required to insure a specified power in detecting a real or clinically relevant difference of a stated magnitude. Increasingly, medical journals are requiring authors to provide information on the sample size needed to detect a given difference. We restrict our attention to the designs far comparirng two survival distributions. These are concerned with the survival time which is defined as the interval from a baseline(e.g. randomization) to failure (e.g. death, recurrence of disease). Survival times axe right censored when patients have not foiled by the time of analysis or have been loss to follow-up during the trial. For different types of clinical trials for comparing survival distributions, there have been marry research in sample size determination. We review the existing literature concerning commonly used sample size formulae in the design of randomized clinical trials, and compare the assumption, the power and the sample size calculation of these methods. We also compare by simulation the expected power and observed power of each method under various circumstances. As a result, guidelines in terms of practical usage are provided.

• Communications for Statistical Applications and Methods
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• v.31 no.1
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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.