Sampling in the research method

1. SAMPLING IN RESEARCH
WAHEED SHAHZAD
ABDUL SHAKOOR

2. Sampling Definition
I. Refers to drawing a sample (a subset) from a population
(the full set).
II. A sample is “a smaller (but hopefully representative)
collection of units from a population used to
determine truths about that population” (Field, 2005).
Why we take sample?
I. Resources (time, money) and workload
II. Gives results with known accuracy that can be
calculated mathematically.

3. Terminology Used in Sampling
Population:
• The full set of elements or
people or whatever you are sampling.
Parameter:
• A numerical characteristic of
population.
population of interest:
• To whom do you want to generalize your
results?
– All doctors
– School children
Sampling
• A set of elements taken
from a larger population.
Statistic:
• Numerical characteristic of
a sample

4. Terminology Used in Sampling
The Response Rate:
• The percentage of people in
the sample selected for the
study who actually
participate in the study .
Sampling Error:
•
A Sampling Frame:
• Just a list of all the people
that are in the population
Refers to the difference between the
value of a sample statistic, such as the
sample mean, and the true value of
the population parameter, such as
the population mean
Note:
some error is always present in
sampling. With
random sampling methods, the
error is random rather than
systematic.

5. Representativeness
• The aim of any sample is to represent the
characteristics of the sample frame.
• There are a number of different methods
used to generate a sample.
• As a researcher you will have to select the
most appropriate method meet the
requirements of your research.

6. Types of Sampling
• Sampling methods can be split into two
distinct groups:
1. Probability samples
2. Non-probability samples

7. Probability Samples
Probability samples offer each respondent an
equal probability or chance at being included in
the sample.
They are considered to be:
• Objective
• Scientific
• Quantitative
• Representative
Sampling

8. Non Probability Samples
A non probability sample relies on the
researcher selecting the respondents.
They are considered to be:
• Interpretive
• Subjective
• Not scientific
• Qualitative
• Unrepresentative
Sampling

9. Probability Sampling Methods
• Random Sampling
• Systematic Random Sampling
• Stratified Random Sampling
• Cluster Random Sampling
• Quota Random Sampling
• Multi-Stage Sampling

10. Random Sampling
• This involves selecting anybody from the sample
frame entirely at random.
• Random means that each person within the
sample frame has an equal chance of being
selected.
• In order to be random, a full list of everyone
within a sample frame is required.
• Random number tables or a computer is then
used to select respondents at random from the
list.

11. Systematic Random Sampling
• This selection is like random sampling but
rather than use random tables or a computer
to select your respondents you select them in
a systematic way.
• E.g. every tenth
person on the college
list is selected.
k =
N
n
,
where:
n = sample size
N = population size
k = size of selection interval

12. Stratified Random Sampling
• An appropriate group is decided upon i.e.
female, male, 16 –18 year olds and the
participants are picked randomly from within
the strata

13. Cluster Random Sampling
• Similar to stratified sampling
but the groups are selected
for their geographical location
• i.e. school children within a
particular school.
• The school is the cluster with
the children being selected
randomly from within the
cluster

14. Quota Random Sampling
• Having decided on the characteristics of the
sample frame, a sample is selected to meet
these characteristics.
• E.g. if the sample frame is car drivers and
the car driving population is 55% male and
45% female then the quota would require
the same proportions.
• Participants would be selected to fill this
quota using the random method

15. Non-probability Sampling
• Convenience Sampling
• Snowball Sampling
• These non-probability methods can be used
in conjuncture with the cluster, quota or
stratified methods, however they will remain
non-probability samples

16. Convenience Sampling
• This involves selecting the nearest and
most convenient people to participate in
the research.
• This method of selection is not
representative and is considered a very
unsatisfactory way to conduct research.

17. Snowball Sampling
• This type of sampling is used when the research is
focused on participants with very specific
characteristics such as being members of a gang.
• Having identified and contacted one gang member
the researcher asks to be put in touch with any
friends or associates who are also gang members.
• This type of sampling is not representative
however is useful, especially where the groups in
the research are not socially organised i.e. they do
not have clubs or membership lists.

18. Quantitative Research - Sample
Size
• When conducting probability sampling it is important to use a
sample size that is appropriate to the aims and objectives of
the research.
• General rule the smaller the total sample frame the larger the
sample ratio needs to be.
• A common error is to assume that the sample should be a
certain percentage of the population, for example 10%. In
reality there is no such relationship and it only the size of the
sample that is important.
• A probability sample size of 100+ is considered a large enough
sample to conduct statistical analysis

19. Statistics and Samples
• When presenting your research you need to be able
to demonstrate, how representative of the whole
population the sample data you have collected is.
• There are two statistical test used to do this:
• Standard Error
• Confidence Levels

20. Standard Error
• Using the standard deviation of the population and
the sample size a statistical calculation can measure
the degree of error likely to occur between the
results of a sample and the results of a census, this is
call the standard error.
• The larger the sample the lower the standard error.
• When a probability sample of 100+ is undertaken
the distribution can usually be assumed to be
normal
• When the sample has normal distribution, we can
use the z score approach to obtain confidence limits
for the sample mean.

21. Confidence Levels
• Confidence levels are calculated using the Central
Limit Theorem (The central limit theorem (CLT) is a statistical
theory that states that given a sufficiently large sample size from a
population with a finite level of variance, the mean of all samples from
the same population will be approximately equal to the mean of the
population.)
• Using this and the sampling error we can then use
the area below the normal distribution curve to
make predictions about our sample.
• As well as making predictions we can use the
properties of the normal distribution curve to
provide us with confidence levels
• There are three confidence levels 68%, 95% and
99%

22. Confidence Levels
• The concept does not mean that we are 95% sure that
a single sample mean lies within these limits.
• The 95% confidence limits mean that if we drew many
samples, and find the mean for each, then we can
expect 95% of the sample means to lie within the
stated limits.
• 95% confidence is considered acceptable in social
research, medical research often requires 99%
confidence

23. There are several specific purposive sampling
techniques that are used in qualitative
research:
• Maximum variation sampling (i.e., you select a wide range of cases)
• Homogeneous sample selection (i.e., you select a small and homogeneous
case orset of cases for intensive study).
• Extreme case sampling (i.e., you select cases that represent the extremes on
some dimension).
• Typical-case sampling (i.e., you select typical or average cases).
• Critical-case sampling (i.e., you select cases that are known to be very
important).
• Negative-case sampling (i.e., you purposively select cases that disconfirm
your
generalizations, so that you can make sure that you are not just selectively
finding cases to support your personal theory).
• Opportunistic sampling (i.e., you select useful cases as the opportunity
arises).
• Mixed purposeful sampling (i.e., you can mix the sampling strategies we have
discussed into more complex designs tailored to your specific needs).

24. Review
• Can you explain what sampling means in
research?
• Can you list the different sampling methods
available?
• Have had an introduction to confidence levels
and sample error?

25. Further Reading
• Drummond, A. (1996) Research methods for
therapists. Cheltenham, Nelson Thornes
• Fielding J and Gilbert N (2000) Understanding social
statistics London: Sage
• Thomas J R and Nelson J K (2001) Research methods
in physical activity 4th Ed, Leeds, Human Kinetics