RESEARCH METHOD COHEN ok

112 SAMPLING
terms of sex, a random selection of subjects
from group A and group B must be taken. If
needed, the exact proportion of males to females
in the whole population can be reflected
in the sample. The researcher will have to identify
those characteristics of the wider population
which must be included in the sample, i.e. to
identify the parameters of the wider population.
This is the essence of establishing the sampling
frame (see http://www.routledge.com/textbooks/
9780415368780 – Chapter 4, file 4.9.ppt).
To organize a stratified random sample is a
simple two-stage process. First, identify those
characteristics that appear in the wider population
that must also appear in the sample, i.e. divide
the wider population into homogenous and, if
possible, discrete groups (strata), for example
males and females. Second, randomly sample
within these groups, the size of each group
being determined either by the judgement of
the researcher or by reference to Boxes 4.1
or 4.2.
The decision on which characteristics to include
should strive for simplicity as far as possible, as
the more factors there are, not only the more
complicated the sampling becomes, but often the
larger the sample will have to be to include
representatives of all strata of the wider population.
A stratified random sample is, therefore, a
useful blend of randomization and categorization,
thereby enabling both a quantitative and
qualitative piece of research to be undertaken.
A quantitative piece of research will be able
to use analytical and inferential statistics, while
a qualitative piece of research will be able to
target those groups in institutions or clusters of
participants who will be able to be approached to
participate in the research.
Cluster sampling
When the population is large and widely dispersed,
gathering a simple random sample poses
administrative problems. Suppose we want to survey
students’ fitness levels in a particularly large
community or across a country. It would be completely
impractical to select students randomly
and spend an inordinate amount of time travelling
about in order to test them. By cluster sampling,
the researcher can select a specific number of
schools and test all the students in those selected
schools, i.e. a geographically close cluster is sampled
(see http://www.routledge.com/textbooks/
9780415368780 – Chapter 4, file 4.10.ppt).
One would have to be careful to ensure that
cluster sampling does not build in bias. For
example, let us imagine that we take a cluster
sample of a city in an area of heavy industry or
great poverty; this may not represent all kinds of
cities or socio-economic groups, i.e. there may be
similarities within the sample that do not catch
the variability of the wider population. The issue
here is one of representativeness; hence it might be
safer to take several clusters and to sample lightly
within each cluster, rather to take fewer clusters
and sample heavily within each.
Cluster samples are widely used in small-scale
research. In a cluster sample the parameters of the
wider population are often drawn very sharply; a
researcher, therefore, would have to comment on
the generalizability of the findings. The researcher
may also need to stratify within this cluster sample
if useful data, i.e. those which are focused and
which demonstrate discriminability, are to be
acquired.
Stage sampling
Stage sampling is an extension of cluster sampling.
It involves selecting the sample in stages, that
is, taking samples from samples. Using the large
community example in cluster sampling, one type
of stage sampling might be to select a number of
schools at random, and from within each of these
schools, select a number of classes at random,
and from within those classes select a number of
students.
Morrison (1993: 121–2) provides an example
of how to address stage sampling in practice. Let
us say that a researcher wants to administer a
questionnaire to all 16-year-old pupils in each
of eleven secondary schools in one region. By
contacting the eleven schools she finds that there
are 2,000 16-year-olds on roll. Because of questions