Probability Sampling
A
probability sample is one in which each member of
the population has an equal chance of being selected
- there are four main types of probability sample.
The decision as to which sample to use is dependent
upon the nature of the research aim, the desired
level of accuracy in the sample and the availability
of a good sampling frame, money and time.
- Simple Random Sampling
- Systematic Sampling
- Stratified Sampling
- Multi-Stage Cluster Sampling
1) Simple Random Sampling
Put
simply, this method is where we select a group of
people for a study from a larger group i.e. from a
population. Each individual is chosen randomly by
chance, and therefore each person has the same
chance as any other of being selected. The easiest
way of selecting a sample using this method is to
first obtain a complete sampling frame. Once this
has been achieved, each person within the frame
should be allocated a unique reference number
starting at one. The size of the sample must be
decided and then that many numbers should be
selected, from the table of random numbers. If the
sampling frame consists of 500 people, three digit
numbers must be selected from the random number
table, similarly if the highest identifying number
on the sampling frame is a two digit number e.g. 50
you must select two digit numbers from the random
number table. If, as in the example below, the
numbers are five digits, simply decide on any two
digits (e.g. first two or last two) and stick to
this for the rest of the procedure.
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Example
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Random Numbers; |
Select numbers from
every third column and every row.
If a number comes up twice or is larger
than the population number, discard it.
Be sure to stick to the pattern of
movement through the table.
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|
87456 |
34098 |
88900 |
11128 |
|
87456 |
34098 |
88900 |
64554 |
|
45666 |
77789 |
82276 |
12555 |
|
22333 |
45767 |
87900 |
99989 |
|
2) Systematic Sampling
Systematic sampling is very similar to simple random
sampling, except instead of selecting random numbers
from tables, you move through the sample frame
picking every nth name.
In
order to do this, it is necessary to work out the
sampling fraction. This is done by dividing the
population by the desired sample. |
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Example
For a population of 100,000 and a desired sample of 2,000, the
sampling fraction is 2/100 or 1/50. This means that you would select
one person out of every fifty in the population. With this method, with
the sampling fraction of 1/50, the starting point must be within the first
50 people in your list. |
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This method does bring about a problem worth highlighting.
If you used a sampling frame which is arranged by
gender or marital status, problems could occur i.e.
if the list was arranged; Husband/Wife/Husband/Wife
etc. and if every tenth person was to be
interviewed, there would be an increased chance of
males being selected. This is known as periodicity
– if this exists in the frame it is necessary to
either mix up the cases or use Simple Random
Sampling.
Ordering a sampling frame before starting selections
can however be very useful - see
Selecting from a
List or Database. 3) Stratified Sampling
Stratified sampling is a modification of Simple Random
Sampling and Systematic Sampling and is designed to
produce a more representative and thus more accurate
sample. A stratified sample is obtained by taking
samples from each sub-group of a population. These
could be, for example, age, gender or marital status.
The rationale here is to choose 'stratification
variables' that have a major influence on the survey
results.
For example, in a lifestyle survey 'age' is likely
to have a key effect on 'lifestyle' and you might
want to ensure your sample contains the correct
proportion of residents from each age group.
Remember, stratification in this way will only be
possible when selecting the sample if the (in this
case) age of the resident is known on the sampling
frame.
Having selected the variable, such as age or gender, you need to
order the sampling frames into groups according to
the category, and then use systematic sampling to
select the appropriate proportion of people within
each variable - see Selecting from a List or
Database for illustration.
4) Multistage Cluster Sampling
This technique is perhaps the most economical of
those looked at so far, particularly if face-to-face
interviewing is to be used. As its name suggests, it is
a combination of several different samples. The
entire population is divided into groups, or
clusters, and a random sample of these clusters are
selected. Following that, smaller 'clusters'
are chosen from within the selected clusters.
Multistage cluster sampling is often used when a
random sample would produce a list of subjects so
widely scattered geographically that surveying them would prove to
be far too expensive. It should, however, be
noted that sampling errors are larger when
using cluster sampling. |
Example
- Stage 1: Define population - (say) adults
16+ living in the South East of England.
- Stage 2: Select (say) 100 electoral wards
from the SE at random
- Stage 3: Select a member of smaller areas
(e.g. EDS) from within each selected ward.
- Stage 4: Interview all residents
within the smaller areas (alternatively, select
a sample from the each smaller area.
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| Two practical examples of sampling are given by
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