Sometimes stratification has a dual role. If such thoughts worry you, then stratification will enable you to ensure that there is a sample of sufficient size to produce sensible estimates ie. having reasonable precision for each sub-group. We need a procedure which enables us to draw a stratified pps sample at any stage of the design. Any adjustment for certainty clusters can be applied as before. A distinction is often made between explicit and implicit stratification.
Explicit stratification implies that each cluster can be placed in a distinct sub-group eg.
However both cases are effective uses of stratification and increase the precision of the estimates from the study. We have noted that a multi-stage cluster sample will produce estimates with larger sampling errors than those using a simple random sample of the same size. Fieldwork considerations usually dictate that the number of population units sampled in each cluster is roughly equal because of the desire to equalise interviewer workloads in each cluster. However there are occasions eg. Thus the number of units sampled from each cluster will differ very little from the average number over all clusters.