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The second-order Akaike information criteria (AICc), which are appropriate for small sample sizes, to select models predicting variation in the spring arrival date was used.
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The second stage consisted of the selection of households, and a 30% increase in the probabilistic sample size to select the houses was used to compensate for non-responses.
Thus, only 56% (0.70 [missingness] × 0.80 [stage B+]) of the registered CRC cases will qualify for the study, and we will therefore inflate the sample size to select N cases so that 0.56*N = 719, i.e. around 1,300 cases.
We used the Akaike's Information Criterion corrected by sample size AICc to select the most parsimonious model (27).
We then used the probability proportional-to-size sampling to select the required number of MSM from each commune.
The cDNA was size-fractionated to select for cDNA strands longer than 400 bp prior to construction of the cDNA library.
One of the first steps in computing a sample size is to select a power analysis method that adequately aligns with the data analysis method [ 1].
Know what size load to select.
Stratified sampling technique with proportional to size was used to select the study participants.
Multi-stage sampling using probability in proportion to size was used to select a nationally representative sample.
Probability proportional to size was used to select suburbs, and a revised random walking method with random start points was used to select children in each suburb.
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