It is incumbent on the researcher to clearly define
the target population. There are no strict rules to
follow, and the researcher must rely on logic and
judgment. The population is defined in keeping with the
objectives of the study.
Sometimes, the entire population will be sufficiently
small, and the researcher can include the entire
population in the study. This type of research is called
a census study because data is gathered on every member
of the population.
Usually, the population is too large for the
researcher to attempt to survey all of its members. A
small, but carefully chosen sample can be used to
represent the population. The sample reflects the
characteristics of the population from which it is drawn.
Sampling methods are classified as either probability
or nonprobability. In probability samples, each
member of the population has a known nonzero probability
of being selected. Probability methods include random
sampling, systematic sampling, and stratified sampling.
In nonprobability sampling, members are selected from the
population in some nonrandom manner. These include
convenience sampling, judgment sampling, quota sampling,
and snowball sampling. The advantage of probability
sampling is that sampling error can be calculated.
Sampling error is the degree to which a sample might
differ from the population. When inferring to the
population, results are reported plus or minus the
sampling error. In nonprobability sampling, the degree to
which the sample differs from the population remains
unknown.
Random sampling is the purest form of
probability sampling. Each member of the population has
an equal and known chance of being selected. When there
are very large populations, it is often difficult or
impossible to identify every member of the population, so
the pool of available subjects becomes biased.
Systematic sampling is often used
instead of random sampling. It is also called an Nth name
selection technique. After the required sample size has
been calculated, every Nth record is selected from a list
of population members. As long as the list does not
contain any hidden order, this sampling method is as good
as the random sampling method. Its only advantage over
the random sampling technique is simplicity. Systematic
sampling is frequently used to select a specified number
of records from a computer file.
Stratified sampling is commonly used
probability method that is superior to random sampling
because it reduces sampling error. A stratum is a subset
of the population that share at least one common
characteristic. Examples of stratums might be males and females, or managers and nonmanagers. The researcher first identifies the
relevant stratums and their actual representation in the
population. Random sampling is then used to select
a sufficient number of subjects from each stratum. "Sufficient" refers to a sample size large enough for us to be reasonably confident that the stratum represents the
population. Stratified sampling is often used when one or
more of the stratums in the population have a low
incidence relative to the other stratums.
Convenience sampling is used in
exploratory research where the researcher is interested
in getting an inexpensive approximation of the truth. As
the name implies, the sample is selected because they are
convenient. This nonprobability method is often used
during preliminary research efforts to get a gross
estimate of the results, without incurring the cost or
time required to select a random sample.
Judgment sampling is a common
nonprobability method. The researcher selects the sample
based on judgment. This is usually and extension of
convenience sampling. For example, a researcher may
decide to draw the entire sample from one
"representative" city, even though the
population includes all cities. When using this method,
the researcher must be confident that the chosen sample
is truly representative of the entire population.
Quota sampling is the nonprobability
equivalent of stratified sampling. Like stratified
sampling, the researcher first identifies the stratums
and their proportions as they are represented in the
population. Then convenience or judgment sampling is used
to select the required number of subjects from each
stratum. This differs from stratified sampling, where the
stratums are filled by random sampling.
Snowball sampling is a special
nonprobability method used when the desired sample
characteristic is rare. It may be extremely difficult or
cost prohibitive to locate respondents in these
situations. Snowball sampling relies on referrals from
initial subjects to generate additional subjects. While
this technique can dramatically lower search costs, it
comes at the expense of introducing bias because the
technique itself reduces the likelihood that the sample
will represent a good cross section from the population.
