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Banda Aceh, Sumatra, Indonesia (Feb. 12, 2005)
Homogenous sampling involves selecting similar cases to further investigate a particular phenomenon or subgroup of interest. The logic of homogenous sampling is in contrast to the logic of maximum variation sampling.
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Photo by Scuola di Atene
A maximum variation sample contains cases that are purposefully as different from each other as possible. This type of sampling is useful for examining range in large national or global programs.
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Star of Post-its by riekhavoc
Intensity sampling uses the same logic as extreme case sampling – that much can be learned from the ends of the distribution range – but with less emphasis on the extremes. In composing an intensity sample, an evaluator would select cases that exhibited a particular phenomenon intensely. The sample would not contain extreme cases, however.  
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Humla, a basalt outlier Photo by George Brown
This type of sampling focuses on the extremes – the end-points of the normal distribution bell-curve. Outlier sampling studies cases that are unusual or special in some way, such as outstanding successes or notable failures. Many programs can have ‘best’ sites of implementation. Studying why these sites are different can provide insight into both what is unique to that case and also what is typical and shared with other sites.
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Menger Sponge depth 5 photo by fdecomite on Flickr
Multi-stage sampling represents a more complicated form of cluster sampling in which larger clusters are further subdivided into smaller, more targeted groupings for the purposes of surveying. Despite its name, multi-stage sampling can in fact be easier to implement and can create a more representative sample of the population than a single sampling technique. Particularly in cases where a general sampling frame requires preliminary construction, multi-stage sampling can help reduce costs of large-scale survey research and limit the aspects of a population which needs to be included within the frame for sampling.
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Inside Purple Flower by Frank Starmer
Sequential sampling is a non-probabilistic sampling technique, initially developed as a tool for product quality control.  The sample size, n, is not fixed in advanced, nor is the timeframe of data collection.  The process begins, first, with the sampling of a single observation or a group of observations.  These are then tested to see whether or not the null hypothesis can be rejected.  If the null is not rejected, then another observation or group of observations is sampled and the test is run again.  In this way the test continues until the researcher is confident in his or her results.