5
Definitions
The sampling frame is the list of ultimate sampling units, which may be people,
households, organizations, or other units of analysis.
Random sampling is data collection in which every person in the population has a
chance of being selected which is known in advance. Normally this is an equal chance of
being selected. Random samples are always strongly preferred, as only random samples
permit statistical inference.
Probability proportion to size is a sampling procedure under which the probability of a
unit being selected is proportional to the size of the ultimate unit, giving larger clusters a
greater probability of selection and smaller clusters a lower probability. In order to ensure
that all units (ex. individuals) in the population have the same probability of selection
irrespective of the size of their cluster, each of the hierarchical levels prior to the ultimate
level has to be sampled according to the size of ultimate units it contains, but the same
number of units has to be sampled from each cluster at the last hierarchical level. This
method also facilitates planning for field work because a pre-determined number of
individuals is interviewed in each unit selected, and staff can be allocated accordingly
It is most useful when the sampling units vary considerably in size because it assures that
those in larger sites have the same probability of getting into the sample as those in
smaller sites, and vice verse.
The design effect (D) is a coefficient which reflects how sampling design affects the
computation of significance levels compared to simple random sampling (discussed
below). A design effect coefficient of 1.0 means the sampling design is equivalent to
simple random sampling. A design effect greater than 1.0 means the sampling design
reduces precision of estimate compared to simple random sampling (cluster sampling, for
instance, reduces precision). A design effect less than 1.0 means the sampling design
increases precision compared to simple random sampling (stratified sampling, for
instance, increases precision).