| ¡@¡@In contrast to conventional sampling designs, adaptive sampling designs are sampling procedures that may depend on observed values of the population variable of interest. The class of adaptive sampling designs gives us more flexibility in practice to select sampling units that can offer more plausible results. In the past decade, different adaptive sampling designs have been constructed. Although no optimal design-based sampling strategy exists, adaptive design-based sampling strategies can have advantages for certain population types. As a new development, the family of adaptive cluster sampling designs has been widely applied to ecological and social network populations. It has certain advantages compared to the comparable conventional designs in the sense of giving lower mean square error and increasing sample yield, especially for hidden, rare or patchy populations. The basic principle and recent development of adaptive cluster sampling will be introduced in this talk. From the perspective of model-based approach, on the other hand, it can be shown that the optimal sampling strategy under a given population model is in general an adaptive one. A model-based optimal two-phase sampling strategy, which gives lower mean square error than the optimal conventional strategy, will be discussed as well. |