2. NB! Students studying the Cambridge syllabus will only be
asked questions about random, stratified and quota
sampling – the other methods are included for comparison
purposes and to show the range of possible alternatives.
3. Probability
Sampling
This involves the selection of a sample from a
population based on the principle of random chance. It is
more complex, more time-consuming and usually more
costly than non-probability sampling. However, because
the sample is selected randomly and the probability of
each unit’s inclusion in the sample can be calculated,
very reliable estimates can be made about both the
whole target market (the ‘statistical population’) and
about the chances of errors occurring.
Following are the most common probability sampling
methods.
4. 1. Simple
Random
Sampling
This makes sure that every member of the population has an
equal chance of selection. Each member of the
target population has an equal chance of being included in
the sample.To select a random sample the following
are needed:
• a list of all of the people in the target population
• sequential numbers given to each member of
this population
• a list of random numbers generated by computer.
If a sample of 100 is required, then the first 100 numbers
on the random number list are taken and the people who
had these numbers allocated to them will form the sample.
Advantages: Simple to design and interpret; can calculate
both estimate of the population and sampling error.
Disadvantages: Need a complete and accurate population
listing; may not be practical if the sample requires lots of
small visits over the country.
5. 2.
Systematic
Sampling
After randomly selecting a starting point from the population
between 1 and *n, every nth unit is selected.
*n equals the population size divided by the sample size.
In this method, the sample is selected by taking every nth
item from the target population until the desired size of
sample is reached. For example, suppose a supermarket
wants to study the buying habits of its customers.The
sample could be chosen by asking every tenth customer
entering the supermarket until the required sample size had
been reached.The researcher must ensure that the chosen
sample does not hide a regular pattern, and a random
starting point must be selected.
Advantages: Easier to extract the sample than via simple
random; ensures sample is spread across the population.
Disadvantages: Can be costly and time-consuming if the
sample is not conveniently located.
6. 3. Stratified
Sampling
This method recognises that the target population may be made up
of many different groups with many different opinions.These
groups are called strata or layers of the population and for a sample
to be accurate it should contain members of all of these strata –
hence the term, stratified sampling.
For example, if you were asked to sample 100 fellow students in
your school about soft drink preferences for the school shop, it
would be more accurate if, instead of asking 100 friends, you split
the school up into certain strata, such as class groups, ages or
gender. So if the whole school contains 1,000 students of whom 50
are girls inYear 8, an accurate sample of 100 would contain five
girls fromYear 8 (50/1,000 × 100).This process would be
repeated with all year groups until the total required sample of
100 was reached.The people to be surveyed in each stratum should
be selected randomly.
Stratified sampling may also be used when a product is designed to
appeal to just one segment of the market. So if a computer game is
aimed at 16−24-year-olds, only people from this stratum of
the population will be included in the market research sample.
7. 4. Quota
Sampling
The aim is to obtain a sample that is "representative" of the overall population. The
population is divided ("stratified") by the most important variables such as income, age
and location.The required quota sample is then drawn from each stratum.
This is similar to stratified sampling. By this method, interviewees are selected according
to the different proportions that certain consumer groups make up of the whole target
population. For instance, if it is already known that out of all consumers of denim jeans:
• 65% are male
• 35% are female
• 35% are aged 14–20
• 35% are aged 21–30
• 20% are aged 31–40
• 10% are aged over 41
then the sample selected would conform to the same proportions.Therefore, if there
were a sample of 200 people, 130 would be male, 70 female, 70 between 14 and 20 years
old and so on.The interviewer could then choose the quotas by questioning the right
number of people in the high street. However, the interviewer might be biased in their
selection of people in each quota – preferring to ask only very attractive people, for
example.This makes quota sampling less probability-based than the other methods and
more open to individual bias.
Advantages: Quick and easy way of obtaining a sample.
Disadvantages: Not random, so some risk of bias; need to understand the population to
be able to identify the basis of stratification.
8. 5. Cluster
Sampling
Units in the population can often be found in certain
geographic groups or "clusters" for example, primary school
children in Derbyshire. A random sample of clusters is taken,
then all units within the cluster are examined.
When a full sampling frame list is not available or the target
population is too geographically dispersed, then cluster
sampling will take a sample from just one or a few groups –
not the whole population.This
might be just one town or region and this will help to
reduce costs – but it may not be fully representative of the
whole population. Random methods can then be used to
select the sample from this group. A multinational
company wanting to research global attitudes towards its
product would save time and money by concentrating on just
a few areas for its research.
Advantages: Quick and easy; doesn't need complete
population information; good for face-to-face surveys.
Disadvantages: Expensive if the clusters are large; greater
risk of sampling error.
9. Non-
Probability
Sampling
This approach to sampling cannot be used to calculate the
probability of any particular sample being selected. Non-
probability sample results cannot be used to
make inferences or judgements about the total
population.Any general statements that are made as a
result of this method of sampling must be analysed very
carefully and filtered by the researcher knowledge of the
topic being researched.
Following are the most common methods of non-
probability sampling.
10. 1.
Convenienc
e Sampling
Uses those who are willing to volunteer and easiest to
involve in the study. Members of the population
are chosen based on their relative ease of access.
Sampling friends, fellow workers or shoppers in just one
location are all examples of convenience sampling.
Advantages: Subjects are readily available; large
amounts of information can be gathered quickly
Disadvantages: The sample is not representative of the
entire population, so results can't speak for them -
inferences are limited; prone to volunteer bias.
11. 2. Snowball
Sampling
The first respondent refers a friend who then refers
another friend… and so the process continues.This is a
cheap method of sampling and is often used by
companies in the financial services sector, such as health-
and motor-insurance companies.
It is likely to lead to a biased sample, as each respondent’s
friends are likely to have a similar lifestyle and opinions.
12. 3.
Judgement
Sampling
A deliberate choice of a sample - the opposite of random.
The researcher chooses the sample based on who they
think would be appropriate to study.This could be used by
an experienced researcher who may be short of time as
they have been asked to produce a report quickly.
Advantages: Good for providing illustrative examples or
case studies.
Disadvantages: Very prone to bias; samples often small;
cannot extrapolate from sample.
13. 4. Ad Hoc
Quotas
A quota is established (say 55% women) and researchers
are told to choose any respondent they wish up to the
pre-set quota.
14. In
Conclusion
• All of these samples are likely to lead to less accurate
results – which are less representative of the whole
population – than probability sampling techniques.
• Each method of sampling has its own advantages and
limitations – so which is best?This depends on the size
and financial resources of the business and how
different consumers are in their tastes between
different age groups and so on.Cost-effectiveness is
important in all market research decisions.
15. Sources • Simpson, P. & Farquharson,A. 2014. Cambridge
International AS and A Level Business Coursebook with
CD-ROM (Cambridge International Examinations).
Cambridge University Press.
• Tutor2u. Market Research: Sampling.Available:
https://www.tutor2u.net/business/reference/marketing
-research-sampling
