Optimum sample size determination in stratified case-control studies with cost considerations

1992 ◽  
Vol 11 (4) ◽  
pp. 547-556 ◽  
Author(s):  
Jun-Mo Nam ◽  
Thomas R. Fears
Keyword(s):  
Sample Size ◽  
Case Control ◽  
Sample Size Determination ◽  
Size Determination ◽  
Case Control Studies ◽  
Optimum Sample Size ◽  
Optimum Sample

scholarly journals Sample Size Determination in Survey Research

2020 ◽  
pp. 90-97 ◽  
Author(s):  
Anokye M. Adam
Keyword(s):  
Sample Size ◽  
Survey Research ◽  
Sample Size Determination ◽  
Categorical Variables ◽  
Size Determination ◽  
Population Variance ◽  
Optimum Sample Size ◽  
Margin Of Error ◽  
Optimum Sample ◽  
Sample Estimate

Obtaining a representative sample size remains critical to survey researchers because of its implication for cost, time and precision of the sample estimate. However, the difficulty of obtaining a good estimate of population variance coupled with insufficient skills in sampling theory impede the researchers’ ability to obtain an optimum sample in survey research. This paper proposes an adjustment to the margin of error in Yamane’s (1967) formula to make it applicable for use in determining optimum sample size for both continuous and categorical variables at all levels of confidence. A minimum sample size determination table is developed for use by researchers based on the adjusted formula developed in this paper.

scholarly journals Optimum sample size to estimate mean parasite abundance in fish parasite surveys

2018 ◽  
Vol 55 (1) ◽  
pp. 52-59 ◽  
Author(s):  
S. Shvydka ◽  
V. Sarabeev ◽  
V. D. Estruch ◽  
C. Cadarso-Suárez
Keyword(s):  
Sample Size ◽  
Sample Size Determination ◽  
Superior Performance ◽  
Size Determination ◽  
Optimum Sample Size ◽  
True Value ◽  
Wide Range ◽  
Precision Level ◽  
The Mean ◽  
Optimum Sample

Summary To reach ethically and scientifically valid mean abundance values in parasitological and epidemiological studies this paper considers analytic and simulation approaches for sample size determination. The sample size estimation was carried out by applying mathematical formula with predetermined precision level and parameter of the negative binomial distribution estimated from the empirical data. A simulation approach to optimum sample size determination aimed at the estimation of true value of the mean abundance and its confidence interval (CI) was based on the Bag of Little Bootstraps (BLB). The abundance of two species of monogenean parasites Ligophorus cephali and L. mediterraneus from Mugil cephalus across the Azov-Black Seas localities were subjected to the analysis. The dispersion pattern of both helminth species could be characterized as a highly aggregated distribution with the variance being substantially larger than the mean abundance. The holistic approach applied here offers a wide range of appropriate methods in searching for the optimum sample size and the understanding about the expected precision level of the mean. Given the superior performance of the BLB relative to formulae with its few assumptions, the bootstrap procedure is the preferred method. Two important assessments were performed in the present study: i) based on CIs width a reasonable precision level for the mean abundance in parasitological surveys of Ligophorus spp. could be chosen between 0.8 and 0.5 with 1.6 and 1x mean of the CIs width, and ii) the sample size equal 80 or more host individuals allows accurate and precise estimation of mean abundance. Meanwhile for the host sample size in range between 25 and 40 individuals, the median estimates showed minimal bias but the sampling distribution skewed to the low values; a sample size of 10 host individuals yielded to unreliable estimates.

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