Cluster Sampling

The basics

What are we trying to achieve in a survey?

A sample that is representative of the larger population

EPI Method

Samples groups of person rather than individuals

30 Clusters with 7 persons per cluster = 210 persons

Based on smallpox immunization surveys in West Africa in ‘68 and ’69

Is this as precise as a simple random sample (SRS)?

Design Effect

Cluster sampling is commonly used, rather than simple random sampling, mainly as a means of saving

money when, for example, the population is spread out, and the researcher cannot sample from

everywhere. However, “respondents in the same cluster are likely to be somewhat similar to one

another” . As a result, in a clustered sample “Selecting an additional member from the same cluster

adds less new information than would a completely independent selection”. Thus, for example, in

single stage cluster samples, the sample is not as varied as it would be in a random sample, so that the

effective sample size is reduced. The loss of effectiveness by the use of cluster sampling, instead of

simple random sampling, is the design effect. The design effect is basically the ratio of the actual

variance, under the sampling method actually used, to the variance computed under the assumption of

simple random sampling

Comparison of 2 cluster sample designs

Some problems with EPI cluster sampling Communities selected by PPS with inaccurate

data Households not selected from a sampling

frame (selection bias) Possibility of non-response bias

Compact cluster sampling

Still select clusters based on PPS from census data

Clusters then divided into segments with equal number of households (HHs)

One segment randomly chosen and all HHs in that segment surveyed

Partially addresses selection and non-response bias