Method

  

Do Elderly Individuals Living In Nursing Homes Expire More Rapidly Than Elderly Living At Home With Assistance? 

Table 6.1 Overview of Sampling Techniques

Technique Characteristics Example Advantages Disadvantages

Simple
random

Each member of the
population has an equal
probability of being
selected using random
sampling.

Students are chosen
randomly from a list of all
students at a university.

Reduces sampling error
by choosing from all
members of the
population to best
represent the
population.

Difficult to ensure that
each member of a
large population can
be chosen in a
sample.

Cluster Clusters of individuals are
identified and then a
subset of clusters is
randomly chosen to
sample from.

Doctors who work at
hospitals are chosen for
a sample by identifying
all hospitals in different
areas of the United
States and then
randomly choosing 10
hospitals in each area of
the United States to
sample from.

Makes it easier to
choose members
randomly from smaller
clusters to better
represent the
population.

Can ignore segments
of the population that
are not in the clusters
chosen for the
sample.

Stratified
random

Members of a population
are selected such that the
proportion of a group in
the sample is equal to the
proportion of that group in
the population using
random sampling.

Registered voters are
randomly selected from
lists of Democrats and
Republicans to equal the
proportion of registered
Democrats and
Republicans in the
United States.

Reduces bias due to an
identified characteristic
of the population by
equating proportions in
the sample and the
population for that
characteristic to better
represent the
population.

Similar to simple
random—can be
difficult to ensure
equal probability of
being chosen from a
large population.

Convenience Members of population
are chosen based on
convenience and on who
volunteers.

Sample is chosen from
students who volunteer
to complete an extra
credit assignment in their
psychology course.

Easier to obtain than
probability samples.

May not represent the
population properly
due to selection bias
because random
sampling is not used.

PROBABILITY SAMPLES
Probability samples can reduce the amount of sampling error that exists in a study. Thus, it is important to use a probability
sample when sampling error is likely to be large. Sampling error will increase whenever observations differ greatly from
participant to participant in a sample or when a sample is chosen such that a segment of the population is not represented in
the sample. For example, in small samples, it is more likely that data will differ from participant to participant. In this section
we will consider three different types of probability samples (see Table 6.1) that researchers can consider when sampling
error is likely to be high: simple random samples, cluster samples, and stratified random samples. The following section
will describe different types of convenience samples.

Simple Random Samples

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