On the other hand, if your survey includes a question about access to water, that is likely to be highly dependent on where you are asking. For example, if your survey includes a question about the respondent's sex, in most contexts you wouldn't expect sex to be clustered together in villages: each village is likely to have more or less the same distribution between male and females. An ICC value of 1.0 means that all responses within a cluster are the same, while an ICC of 0.0 means that people within clusters are just as diverse as the general population.Įach variable that you're measuring will have its own ICC that depends on your context. Whether this effects the precision of your sample depends on something called the Intra-cluster correlation coefficient (ICC), which is a measure of how similar people are to each in other in the cluster, at least with regard to what you're trying to measure. If the villages are the same size, each respondent still has the same chance of being selected, but the chances are no longer independent: if one person is selected in a village, their neighbor has a greater chance of being selected. Selecting 1,000 individuals completely at random might mean that travelling to 1,000 different villages, which may be too costly or impossible within a certain time frame.Īs an alternative, you might consider two stages of random selection: first, randomly choose 25 villages, and then randomly choose 40 individuals to interview in each village. In many cases, particularly in humanitarian and development contexts, this may not be feasible.įor example, if you are surveying a population, in person, across a large geographic area, a SRS may be completely impractical. The other calculators in this library are based on a simple random sample (SRS), a kind of survey where every one has an equal and independent chance of being selected for the survey. Calculate design effect from cluster surveys Clustered sampling
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