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Because these estimates of species richness per habitat are biased by unequal sample sizes (point counts per habitat), we also carried out rarefaction analysis to interpolate estimated species richness to equal sample sizes.
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The largest sample consisted of 18,316 tested people in Guangdong Province, followed by the study in Hubei Province (5,224 tested people) and in Shandong Province (5,217 tested people) and the study conducted by Chinese National Consortium on Eperythrozoonosis Research CNCERrankedked 4th from this sample size point of view (4,033 tested people).
For designs with two and three stages at overall significance levels of.05 and.0001 and a power of.8, we calculated necessary sample sizes, time points, and critical boundaries for interim and final analyses.
The available longitudinal data included variable lengths of follow-up and forcibly smaller sample sizes at later time points that would not reflect typical study attrition.
See text for details Cut points, group sample sizes, and accuracy are shown only for the best models considered significant (p < 0.05).
Note that the sample sizes at the different time points may deviate somewhat from previous ABC publications due to small changes in the latest quality-assured data files released for research.
Note that the sample sizes at the different time points may deviate somewhat from previous AHUS publications due to small changes in the latest quality-assured data file released for research.
Previous estimate of sample size (1 point) 5.
A comparison of results of the main basic OLS specification (column (1), Table 4) for the multi-child sample with the full sample results suggests that, in spite of the coefficients in the siblings sample being less precisely estimated due to the smaller sample size, the point estimates are similar (results available upon request).
*The representativeness criterion was met when ≥80% of women eligible were invited and 80% agreed to participate, or when sample size >300 (1 point).
These results showed that the expected number of different dendrograms increases with sample size, as pointed out by MacCuish [21] and that the ties problem is not only a problem of large data sets, since 5%% of the samples (having only 77 descriptors) had a high likelihood (95 %) of yielding 10 different dendrograms.
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