Sampling Method 
Definition 
Uses 
Limitations 
Cluster Sampling)

Units in the population can
often be found in certain geographic groups or "clusters"
(e.g. primary school children in Derbyshire. A random sample of
clusters is taken, then all units within the cluster are examined 
Quick & easy; does not
require complete population information; good for facetoface surveys 
Expensive if the clusters are
large; greater risk of sampling error 
Convenience Sampling 
Uses those who are willing to
volunteer 
Readily available; large amount
of information can be gathered quickly 
Cannot extrapolate from sample
to infer about the population; prone to volunteer bias 
Judgement Sampling 
A deliberate choice of a sample
 the opposite of random 
Good for providing illustrative
examples or case studies 
Very prone to bias; samples
often small; cannot extrapolate from sample 
Quota Sampling 
Aim is to obtain a sample that
is "representative" of the overall population; the
population is divided ("stratified") by the most important
variables (e.g. income,. age, location) and a required quota sample is
drawn from each stratum 
Quick & easy way of
obtaining a sample 
Not random, so still some risk
of bias; need to undertand the population to be able to identify the
basis of stratification 
Simply Random Sampling 
Ensures that every member of
the population has an equal chance of slection 
Simply to design and interpret;
can calculate estimate of the population and the sampling error 
Need a complete and accurate
population listing; may not be practical if the sample requires lots
of small visits all over the country 
Systematic Sampling 
After randomly selecting a
starting point from the population, between 1 and "n",
every nth unit is selected, where n equals the
population size divided by the sample size 
Easier to extract the sample
than via simple random; ensures sample is spread across the population 
Can be costly and
timeconsuming if the sample is not conveniently located 