Sampling Techniques
If the population is small, you may be able to sample the entire group. However, in most cases
you need to determine a preferred sampling method.
Simple Random Sample: every member of the population has an equal chance of being selected.
(ex. drawing names randomly; random number generator)
Systematic Random Sample: select a random starting point and then go through the population
sequentially and select members at regular intervals
Stratified Random Sample: sometimes a population includes common groups called strata (ex.
gender, age, education level, etc.). A stratified random sample has the same proportion of
members from each stratum as the population does. A simple random sample is taken within
each stratum.
Cluster Random Sample: if groups will be representative of an entire population, then randomly
select entire groups.
(ex. a fast food chain surveys all of its employees at randomly selected locations versus
randomly sampling all of the employees throughout the chain)
Multi-Stage Random Sample: organize the population into groups (several levels) and then a
simple random sample is taken from randomly selected groups.
(ex. a population study in Ontario in which townships and cities are randomly sampled,
then subdivisions and blocks within those townships and cities are randomly sampled,
and then households within the subdivisions and blocks are randomly sampled)
Destructive Random Sample: samples are taken and destroyed in the process of testing
(ex. cars used in crash tests)
Generally speaking, the larger the sample, the better. But, be aware that some factors can affect
the reliability of the data collected (i.e. the sampling method chosen)
*** PLEASE SEE OTHER GREAT EXAMPLES OF EACH SAMPLING TECHNIQUE
ON PAGES 94-98. ***