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CLUSTER SAMPLING AND COMPLEX SAMPLING DESIGNS

Cluster sampling is based on the fact that people act out their lives in more or less natural groups, or ‘‘clusters,’’ like geographic areas (counties, precincts, states), and institutions (like schools, churches, brotherhoods, credit unions, and so on). By sampling from these clusters, we narrow the sampling field from large, heterogeneous chunks to small, homogeneous ones that are relatively easy to find. This minimizes travel time in reaching scattered units of data collection. It also lets you sample populations for which there are no convenient lists or frames.

For example, there are no lists of schoolchildren in large cities, but children cluster in schools. There are lists of schools, so you can take a sample of them, and then sample children within each school selected.

Laurent et al. (2003) wanted to assess the rate of sexually transmitted diseases among unregistered female sex workers in Dakar, Senegal. By definition, unregistered means no list, so the researchers used a two-stage cluster sample. They created a sampling frame of all registered and all clandestine bars in Dakar, plus all the unregistered brothels, and all the nightclubs. They did this over a period of several months with the help of some women prostitutes, some local physicians who had treated those women, and two social workers, each of whom had worked with female sex workers for over 25 years. Laurent et al. calculated that they needed 94 establishments, so they chose a simple random of places from the list of 183. Then they went in teams to each of the 94 places and interviewed all the unregistered prostitutes who were there at the time of the visit.

Anthony and Suely Anderson (1983) wanted to compare people in Bacabal County, Brazil, who exploited the babassu palm with those who didn’t. There was no list of households, but they managed to get a list of the 344 named hamlets in the county. They divided the hamlets into those that supplied whole babassu fruits to new industries in the area and those that did not. Only 10.5% of the 344 hamlets supplied fruits to the industries, so the Andersons selected 10 hamlets randomly from each group for their survey. In other words, in the first stage of the process they stratified the clusters and took a disproportionate random sample from one of the clusters.

Next, they did a census of the 20 hamlets, collecting information on every household and particularly whether the household had land or was landless. At this stage, then, they created a sampling frame (the census) and stratified the frame into landowning and landless households. Finally, they selected 89 landless households randomly for interviewing. This was 25% of the stratum of landless peasants. Since there were only 61 landowners, they decided to interview the entire population of this stratum.

 
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