Reliability studies are commonly used in questionnaire development studies and questionnaire validation studies. This study reviews the sample size guideline for Cronbach's alpha test. Manual sample size calculation using Microsoft Excel software and sample size tables were tabulated based on a single coefficient alpha and the comparison of two coefficients alpha. For a single coefficient alpha test, the approach by assuming the Cronbach's alpha coefficient equals to zero in the null hypothesis will yield a smaller sample size of less than 30 to achieve a minimum desired effect size of 0.7. However, setting the coefficient of Cronbach's alpha larger than zero in the null hypothesis could be necessary and this will yield larger sample size. For comparison of two coefficients of Cronbach's alpha, a larger sample size is needed when testing for smaller effect sizes. In the assessment of the internal consistency of an instrument, the present study proposed the Cronbach's alpha's coefficient to be set at 0.5 in the null hypothesis and hence larger sample size is needed. For comparison of two coefficients' of Cronbach's alpha, justification is needed whether testing for extremely low and extremely large effect sizes are scientifically necessary.

Different study designs and population size may require different sample size for logistic regression. This study aims to propose sample size guidelines for logistic regression based on observational studies with large population. We estimated the minimum sample size required based on evaluation from real clinical data to evaluate the accuracy between statistics derived and the actual parameters. Nagelkerke r-squared and coefficients derived were compared with their respective parameters. With a minimum sample size of 500, results showed that the differences between the sample estimates and the population was sufficiently small. Based on an audit from a medium size of population, the differences were within ± 0.5 for coefficients and ± 0.02 for Nagelkerke r-squared. Meanwhile for large population, the differences are within ± 1.0 for coefficients and ± 0.02 for Nagelkerke r-squared. For observational studies with large population size that involve logistic regression in the analysis, taking a minimum sample size of 500 is necessary to derive the statistics that represent the parameters. The other recommended rules of thumb are EPV of 50 and formula; n = 100 + 50i where i refers to number of independent variables in the final model.

Sensitivity and specificity analysis is commonly used for screening and diagnostic tests. The main issue researchers face is to determine the sufficient sample sizes that are related with screening and diagnostic studies. Although the formula for sample size calculation is available but concerning majority of the researchers are not mathematicians or statisticians, hence, sample size calculation might not be easy for them. This review paper provides sample size tables with regards to sensitivity and specificity analysis. These tables were derived from formulation of sensitivity and specificity test using Power Analysis and Sample Size (PASS) software based on desired type I error, power and effect size. The approaches on how to use the tables were also discussed.

Bujang MA, et al. Restor Dent Endod. 2024 Jan;49:e3. https://doi.org/10.5395/rde.2024.49.e3

Determination of a minimum sample size required for a study is a major consideration which all researchers are confronted with at the early stage of developing a research protocol. This is because the researcher will need to have a sound prerequisite knowledge of inferential statistics in order to enable him/her to acquire a thorough understanding of the overall concept of a minimum sample size requirement and its estimation. Besides type I error and power of the study, some estimates for effect sizes will also need to be determined in the process to calculate or estimate the sample size. The appropriateness in calculating or estimating the sample size will enable the researchers to better plan their study especially pertaining to recruitment of subjects. To facilitate a researcher in estimating the appropriate sample size for their study, this article provides some recommendations for researchers on how to determine the appropriate sample size for their studies. In addition, several issues related to sample size determination were also discussed.

Background and aims: To estimate sample size for Cohen’s kappa agreement test can be challenging especially when it is expected that the true marginal rating frequencies are not the same. This study aims to present the tables that display a minimum sample size determination for an agreement test when certain assumptions are hold. Method: We adopted the sample size formula provided by Flack and colleagues (1988) to calculate the minimum sample sizes required using PASS software. The power is pre-specified to be at least 80% and the alpha to be less than 0.05. The effect sizes were derived from several pre-specified estimates such as the pattern of the true marginal rating frequencies and the difference between the two kappa coefficients in the hypothesis testing. Results: When the true marginal rating frequencies are the same, the minimum sample size determination can range from 2 to 698 depending on the actual value of the effect size. When the true marginal rating frequencies are not the same, then the majority of the minimum sample size required for this condition is more than double than that required sample size when the true marginal rating frequencies are the same. Conclusion: Concerning that the sample size formula could produce a very extreme small sample size, therefore, the determination of K1 and K2 should be based on reasonable estimates. We recommend for all sample size determinations for Cohen’s kappa agreement test, the true marginal rating frequencies can be assumed the same. Otherwise, it will be necessary to multiple the estimated minimum sample size by two to accommodate if the true marginal rating frequencies is not the same.