3.5 Collecting
Samples
(Text Section 2.3)
DEFINITIONS/FORMULAS

Random – occurring by chance

population size
Sampling Interval =
sample size
TYPES OF SAMPLING
Simple Random Sampling
A simple random sample requires that all
selections must be equally likely and all
combinations of selections must be equally likely.
(Example 1 on page 95-96)
Systematic Random Sampling
A systematic random sample is used when you
are sampling a fixed percent of the population.
A random starting point (i.e. individual, household,
or object) is chosen and then you select every kth
individual for your study, where ‘k’ is the sampling
interval.
(Example 2 on page 96)
Stratified Random Sampling
When using a stratified random sample, the
population is divided into groups called strata
(such as geographic areas, age groups, places of
work, etc.)
A simple random sample of the members in each
stratum is then taken.
Note: The size of the sample for each stratum is
proportionate to the stratum’s size.
(Example 3 on page 96-97)
Cluster Random Sampling
Cluster samples require that the population be
organized into groups (such as schools,
companies, neighbourhoods, etc.) A random
sample of groups would then be chosen. All the
members of the chosen groups would then be
surveyed.
(Example 4 on page 97)
Multi-Stage Random Sampling
Multi-stage samples require that the population
be organized into groups. A random sample of
groups is taken, followed by a random sample of
members of the chosen groups.
(Example 5 on page 97-98)
Voluntary-Response Sampling
In voluntary-response sampling, the researcher
simply invites any member of the population to
participate in the survey. The results can be skewed
because the people who choose to respond are
often not representative of the population.
Example: Call-in shows, Mail-in surveys
Convenience Sampling
A sample is selected because it is easily accessible.
While obviously not as random as other techniques,
such convenience samples can sometimes yield
helpful information.
Example: survey everyone in your class
Destructive Sampling
Samples from which the selected elements cannot
be reintroduced into the population are called
destructive samples.
Example: crash testing
Homework:
 Read textbook pages 94-98
 Consider what you think to be the advantages and
disadvantages of each sampling method
 Work through homework questions listed on Unit
Outline for Day 5