SAMPLING
SIMPLE RANDOM SAMPLING – A sample in which all population members have the same
probability of being selected and the selection of each member is independent of the selection of
all other members.
SIMPLE RANDOM SAMPLING (RANDOM SAMPLING): Selecting a group of
subjects (a sample) for study from a larger group (population) so that each
individual (or other unit of analysis) is chosen entirely by chance. When used
without qualifications (such as stratified random sampling), random sampling
means “simple random sampling.” Also sometimes called “equal probability
sample,” because every member of the population has an equal probability
(chance) of being included in the sample. A random sample is not the same thing
as a haphazard or accidental sample. Using random sampling reduces the
likelihood of bias.
SYSTEMATIC SAMPLING – A procedure for selecting a probability sample in which every kth
member of the population is selected and in which 1/k is the sampling fraction.
SYSTEMATIC SAMPLING: A sample obtained by taking every ”nth” subject or
case from a list containing the total population (or sampling frame). The size of
the n is calculated by dividing the desired sample size into the population size.
For example, if you wanted to draw a systematic sample of 1,000 individuals
from a telephone directory containing 100,000 names, you would divide 1,000
into 100,000 to get 100; hence, you would select every 100th name from the
directory. You would start with a randomly selected number between 1 and 100,
say 47, and then select the 47th name, the 147th, the 247th, the 347th, and so on.
Is it always possible to do a simple random sample in cases where you can do a
systematic sample (both require a complete list of the population). Simple random
sampling is a more trustworthy method, but it is usually less convenient.
CLUSTER SAMPLING – The random selection of clusters (groups of population member)
rather than individual population members.
CLUSTER SAMPLING: A method for drawing a sample from a population in
two or more stages. It typically is used when researchers cannot get a complete
list of the members of a population they wish to study but can get a complete list
of groups or clusters in the population. It also is used when a random sample
would produce a list of subjects so widely scattered that surveying them would be
prohibitively expensive. Generally, the researcher wishes to use clusters
containing subjects as diverse as possible. By contrast, in stratified sampling, the
goal is often to find strata containing subjects as similar to one another as
possible.
The disadvantage of cluster sampling is that each stage of the process increases
sampling error. The margin of error therefore is larger in cluster sampling than in
simple or stratified random sampling, but because cluster sampling usually is
much easier (cheaper), this error can be compensated for by increasing the sample
size.
For example, suppose you wanted to survey undergraduates on social and political
issues. There is no complete list of all college students, but there are complete
lists of all 3,000+ colleges in the country. You could begin by getting such a list
of colleges (which are “clusters” of students). You could then select a probability
sample of, say, 100 colleges. Once the clusters (colleges) were identified, the
researchers could go to each school and get a list of its students; subjects to be
surveyed would be selected (perhaps by simple random sampling) from each of
these lists.
STRATIFIED RANDOM SAMPLING – The selection of a sample in which the population is
divided into subpopulations called strata, with all strata being represented.
There are different ways of allocating the sample: equal numbers could be selected from the
strata regardless of their sizes, proportional allocations can be used in which each stratum
contributes to the sample the number of members proportional to its size.
STRATIFIED RANDOM SAMPLING: A random or probability sample drawn
from particular categories (or strata) of the population being studied. The method
works best when the individuals within the strata are highly similar to one another
and different from individuals in other strata. Indeed, if the strata were not
different from one another, there would be no point in stratifying. Stratified
random sampling can be proportionate, so that the size of the strata corresponds to
the size of the groups in the population. It also can be disproportionate, as in the
following example.
Suppose that you wanted to compare the attitudes of Democrats, Republicans, and
Independents in a population in which those three groups were not present in
equal numbers. If you draw a simple random sample, you might not get enough
cases from one of the groups to make meaningful comparisons. To avoid this
problem, you could select random samples of equal sizes within each of the three
political groups (strata).
The first definitions are provided by: Hinkle, Wiersma, and Jurs (2003)
The underlined (and indented) definitions are provided by: Vogt, W. P. (1999). Dictionary of
Statistics and Methodology: A Non-technical Guide for the Social Sciences (2nd Ed.).
SAMPLING – CHAPTER 7