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As case study data are not statistically representative but aim to strengthen understanding of social processes, sample size is of less concern than the depth of understanding generated[ 18, 19].
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Variability in individual level outcomes may reflect higher level processes; sample sizes may need to be larger to take account of the extra variability.
Based on level of knowledge acquired during PPQ and the level of knowledge further required to comprehend process, sample size for CPV was calculated using Bayesian statistics to accomplish reduced sampling design for CPV.
The inclusion process, sample size estimation and the randomization process is described in detail elsewhere [ 14].
While this study represents one of the few multi-site surveys of RACFs, and the first to provide a detailed examination of information exchange processes, the sample size was modest.
However, the process of sample size estimation is often confusing.
A SAS macro is programmed to simulate the process of sample size adjustment and randomization test.
To optimise imaging quality the samples were processed, reducing sample size and approaching original tooth size.
It is generated by heterogeneity in the SNP discovery process, variable sample size and composition, and may cause underestimation or overestimation of globally distributed SNP frequencies [ 20].
The power of the study will be monitored during the model-building process, using sample size calculators specifically developed for multilevel models (PinT).
We also illustrate the process of sample size selection by working through an example with repeated measurements of pain memory, using the web-based power and sample size program GLIMMPSE.
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