Cluster sampling

In this case the universe and the frame are defined and classified into homogeneous segments. Random samples are then chosen from each segment. The method offers a sharpening of sampling, virtually guaranteeing that the use of these cells of units - done in two dimensions or more - will provide a cross-section of each.

Cluster sampling is a suitable approach to sampling large consumer populations. Here the population is divided into mutually exclusive groups and the researcher draws a sample of the groups to interview. This time we are not interested in a person's social class but in where they live or some other characteristic. Assuming that residence is the key factor and that the objective is to interview household heads, then the first step is to divide up the locality under study into individual areas of housing. A random or stratified sample of the areas identified is then taken and interviews are held with every household head within each sampled area.

This is a 'single-stage' cluster sample since only a sample of the blocks or areas of housing is taken. A 'two-stage' cluster sample might involve undertaking the same number of interviews but making sure that a large number of blocks are covered but that only a sample of households in each block is interviewed. For example, if an area comprises three high-rise tower blocks of flats, we might randomly select one of the three blocks and interview all household heads within that block.

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