A more convenient method of selecting a random sample is to use
the identification number of each employee and a
table of
random numbers such as the
one in Appendix B.6.
260
Systematic Random Sampling
EXAMPLE
A population consists of 845 employees of Nitra Industries. A sample
of 52 employees is to be selected from that population.
First,
k
is calculated as the population size divided by the sample
size
. For Nitra Industries
, we would select every 16th (845/52)
employee list. If
k
is not a whole number
,
then round down.
Random sampling is used in the selection of the first name. Then,
select every 16
th
name on the list thereafter.
Systematic Random Sampling:
The items or individuals of the
population are arranged in some order.
A random starting point is
selected and then every
k
th member of the population is selected for
the sample.
262

Stratified Random Sampling
Stratified Random Sampling:
A population is first divided into
subgroups, called strata, and a sample is selected from each stratum.
Useful when a population can be clearly divided in groups based on
some characteristics
Suppose we want to study the advertising
expenditures for the 352 largest companies
in the United States to determine whether
firms with high returns on equity (a measure
of profitability) spent more of each sales
dollar on advertising than firms with a low
return or deficit.
To make sure that the sample is a fair
representation of the 352 companies, the
companies are grouped on percent return
on equity and a sample proportional to the
relative size of the group is randomly
selected.
262
Cluster Sampling
Cluster Sampling:
A population is divided into clusters using naturally
occurring geographic or other boundaries. Then, clusters are randomly
selected and a sample is collected by randomly selecting from each
cluster.
Suppose you want to determine the views
of residents in Oregon about state and
federal environmental protection policies.
Cluster sampling can be used by
subdividing the state into small units—
either counties or regions, select at random
say 4 regions, then take samples of the
residents in each of these regions and
interview them. (Note that this is a
combination of cluster sampling and simple
random sampling.)
263

Methods of Probability Sampling
z
The
sampling error
is the
difference between a
sample statistic and its corresponding population
parameter.

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