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Model-based estimation using the sample design we describe provides a transparent method for estimating biomass for particular spatial domains.
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In both the hypothetical drug examples (cross-over and parallel study design) and in the dihydroartemisinin case study, similar or better precisions on all parameter estimates were obtained using the dense sampling design compared to the sparse sampling design.
Sampling weights included adjustment for non-response, unequal probability of selection used in the sampling design.
Mplus can accommodate sample weights in performing latent class analyses, but not the strata used in the sampling design.
Survey analysis procedures were used to adjust for the sample design.
For all analyses conducted, probability weights provided in the NPHS microdata files documentation were used to account for the sample design.
We adjusted all analyses for sampling weights and strata included in the sample design using the statistical package Stata version 8.0 (Stata Corp ,College Station, TX).
The difference can be explained by the sample design used in each study, especially population characteristics and sample size.
The sample design used is of the conglomerate type in three selection stages: schools, classes of year attended and students.
First, the sample design used is not representative, and therefore we need caution before generalizing our results.
Soil seed bank spatial distribution was also studied for one year in the same two habitats, using the same sampling design.
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