Sorry to have been vague and I had no intention to mislead. I was aware that that post was long.
To clarify ...
In our field we are really talking about adaptations / derivations of the WHO Expanded Programme on Immunisation (EPI) coverage survey method when we talk about "SMART" or "30-by-30" or "PPS".
The EPI method uses a two-stage cluster sampling approach which begins by dividing a population into clusters for which population estimates are available. A subset of clusters is selected in the first sampling stage. The probability of a particular cluster being selected is proportional to the size of the population in that cluster.
Clusters with large populations are more likely to be selected than clusters with small populations. This sampling procedure, called probability proportional to size (PPS), helps to ensure that individuals in the program area have an equal chance of being sampled when a quota sample is taken in the second stage of the survey.
In recognition of the difficulties of drawing a random sample in many developing countries, the EPI method (and derivatives such as SMART) uses a non-random sampling method in the second stage. The most commonly used second stage sampling method is a proximity technique. The first household to be sampled is chosen by selecting a random direction from the centre of the cluster, counting the houses along that route, and picking one at random. Subsequent households are sampled by their physical proximity to the previously sampled household. Sampling continues until a fixed sample size as been collected. Sampling is simple and requires neither mapping nor enumeration of households. It is, consequently, both quicker and cheaper than using simple random sampling in the second stage of the survey.
All this supports the use of the EPI method. It does, however, have problems:
(1) The PPS process should result in a self-weighted sample but it cannot be relied upon to do so if estimates of cluster population sizes are inaccurate. Lack of accurate population data may frequently be the case in emergencies. Also, when estimating (e.g.) coverage of a selective feeding program such as CMAM the population data that we should use is the number of cases in each potential PSU (not the population at each PSU). Prevalence of a condition such as SAM which is strongly influences by infectious phenomena which cluster spatially will cluster spatially. Unless we know the pattern of prevalence and total population in advance we will not be able to take a properly supported PPS sample.
(2) PPS locates the bulk of data-collection in the most populous communities. Woody seems to suggest that this a wholly good thing. When investigating coverage (e.g.) PPS may leave areas of low population density unsampled (i.e. those areas consisting of communities likely to be distant from health facilities, feeding centres, and distribution points). This may cause surveys to evaluate coverage as being adequate even when coverage is poor or non-existent in areas outside of urban centres.
(3) With the exception of the first child, none of the observations in the within-cluster sample are selected using an equal probability selection method (a quick "experiment" with geometry will show that under all but restrictive condition the method is not EPSeM even for the first child). This, together with the fact that the within-cluster sample size is usually too small to estimate or classify in any cluster with reasonable accuracy and precision, means that the EPI method can return only a single estimate of coverage or prevalence, even when coverage or prevalence is spatially inhomogeneous. This is an important limitation since identifying areas with poor coverage (or high prevalence) is an essential step towards improving program coverage and, hence, program impact.
(4) The within-PSU sampling method described above is know to produce biased estimates for a wide range of indicators that we might be interested in. Published evidence suggests that this is not a problem for GAM.
(5) The within-PSU sampling method results in large loss of sampling variation. This is large design effects and small effective sample sizes. This is only a real issue when measurement costs are large relative to sampling costs or when indicators relating to subsamples (e.g. as in some IYCF indicator sets) are used.
(6) PPS does not attempt to take a spatially representative sample. PPS (unlike CSAS, S3M, or list-based spatially stratified samples) does not guarantee an even spatial sample. In fact, it does the opposite in that it will tend to select larger communities which will tend to be clustered along roads, rails, rivers, natural harbours, &c. The emphasis is on size of village not location of village. This means that PPS cannot be used to map phenomena in any detail.
All of these problems are there even when everything is done correctly.
Proponents of PPS often criticise alternative schemes as not being representative of a population (I don't think Woody is doing this). These criticisms are not well founded as a spatial representative sample can be made population representative by the use of posterior weighting (the opposite transformation is not possible). The number of different statistical weights used by S3M and PPS is the same. The difference is when the weights are used (i.e. before or after sampling). In some cases the weights used in S3M will be more accurate as they can be collected or confirmed as a survey process. In many cases the weights used will be identical. The overall analysis of an S3M sample produces a population representative result. BTW ... the same approach is used in the RAM method.
If you look at the list of problems above you will see that S3M (and similar methods) are designed to address these problems:
(A) No population data are needed in advance. Population weights can use (e.g.) hut counts collected during the survey.
(B) The spatial sample is agnostic with regard to population so small and large communities are sampled. If there are more small than large communities in the survey area then more small communities will be sampled.
(C) The map-segment-sample (MSS) technique more closely approximates EPSEM than the proximity technique. MSS is an innovation that could be adopted by SMART today.
(D) MSS does not produced biassed estimates.
(E) as (C) above.
(F) S3M is designed as a mapping method.
Here is an example from an S3M survey.
I hope this is less vague and not misleading.