Data sampling commonly used in health care statistics

1. Sampling Methods
A presentation on Sampling Methods
Prepared by MN Kiranmai, BSC RN, MSC
Psychology,
QI Officer
Department Of Quality And Patient Safety
KSA

2. WHAT IS SAMPLING
Sampling is a group of units taken from a larger
group

3. When to go for sampling
When a study or research involves a large
number of population you may wish to
evaluate using a smaller, carefully chosen
‘sample’ to represent the group as a whole.

4. Psychology of sampling

5. Founders of Statistics
Sir Ronald Aylmer Fisher, FRS (1890 - 1962)
was an English statistician, evolutionary
biologist, and geneticist
William Edwards Deming
An American engineer, statistician, professor,
author, lecturer, and management consultant.
Educated initially as an electrical
engineer and later specializing in
mathematical physics, he helped develop
the sampling techniques in quality

6. Why sampling?
• Sampling significantly helps in saving money,
resources, workload and time investment
• It also gives results with known accuracy that
can be calculated mathematically, that could
be generalized to the population meant to
study
• When there is no high response from target
population

7. What to keep on mind while selecting
a sample?
• The purpose of study
• The sample should represent the target
population or population of interest
• It should be unbiased
• It should be random
• It should be appropriate in size, method of
collection and to the purpose of study
• Who do you want the generalize the results

8. Determining Sample size

9. Ask the expert

10. Terminology of sampling
• Sample: Represents who is on our study ( Eg: PAE )
• Sample unit: Represents the participant ( PAE forms)
• Sample frame: Represents the access to sample or where the sample is drawn from( All PAE
forms from OR)
• Sample design: Represents the probability of sample being selected (method of sampling)
• Population: Represents the entire group or the total aggregation of data elements ( All
anaesthetists)
• Target population: Represents the participants chosen to do the actual study on
(Anaesthetists who did or did not do the PAE)
• Sample frame error: Represents the elements that could not be taken or accessed or
unavailable for study ( Missing PAE forms from OR)
• Parameter: Represents the statistic character of the sample(Mean or percentile or SD etc)
• Variable: Represents the measurable functions in the data both dependant and independent
variables ( OR check list independent variable, if PAE done or not even after implementing
check list is dependant variable)
• Sample error: Represents the difference of value between the sample and the population (
involves when the data is compared to a huge population)

11. How to choose a sample?
• List the research goals (usually some
combination of accuracy, precision, and/or
cost).
• Identify potential sampling methods
that might effectively achieve those goals.
• Test the ability of each method to achieve
each goal.
• Choose the method that does the best job of
achieving the goals.

12. Sample selection

13. Methods of sampling
• Probability sampling
• Non Probability sampling

14. Probability sampling
A method or set up that assures that the
different units or participants in your population
have equal probabilities or chance of being
Chosen through random selection

15. Types of probability sampling
• Simple Random
• Stratified Random
• Systematic Random
• Random Cluster
• Complex Multi-stage Random (various kinds)

16. Simple random
• Each element in the population has an equal probability of
selection AND each combination of elements has an equal
probability of selection
• Random numbers to select elements from an ordered list
• Easy and basic method in sampling
• Best for population based surveys
Eg: Names drawn out of a hat .

17. Stratified Random sampling
• Involves dividing your population into homogeneous subgroups and then taking a
simple random sample in each subgroup
• Stratified sampling works well for populations with a variety of attributes or
characteristics , but is otherwise ineffective, as subgroups cannot be formed.
• Eg: PAE among consultants, specialists and residents

18. Systematic Random Sampling
• Systematic random sampling is the random sampling method that requires
selecting samples based on a system of intervals in a numbered population.
• Systematic random= Total population/ number of participants included in study
Eg: PAE: Total number of PAE forms/10% of total number
If its 450 surgeries per month on an average, it is 450/45, which would be 10. So every
10
th
form would be selected from the set PAE forms

19. Random Cluster Sampling
• Cluster sampling is a sampling technique where the entire population is divided into groups,
or clusters, and a random sample of these clusters are selected. All observations in the
selected clusters are included in the sample
• Cluster sampling is typically used when the researcher cannot get a complete list of the
members of a population they wish to study but can get a complete list of groups or 'clusters'
of the population.
• It is also used when a random sample would produce a list of subjects so widely scattered
that surveying them would prove to be far too expensive
• More practical and/or economical than simple random sampling or stratified sampling.

20. Complex multistage sampling
• Multistage Sampling is a sampling strategy (e.g., gathering participants
for a study) used when conducting studies involving a very large
population. The entire population is divided into naturally-occurring
clusters and sub-clusters, from which the researcher randomly selects
the sample.

21. NON PROBABILTY SAMPLING
• A core characteristic of non-probability
sampling techniques is that samples are
selected based on the subjective judgement of
the researcher, rather than random
selection (i.e., probabilistic methods), which is
the cornerstone of probability sampling
techniques

22. Methods of non probability sampling
• Quota sampling
• Convenience sampling
• Purposive sampling
• Snowball sampling

23. Quota Sampling
A quota sample a type of non-probability
sample in which the researcher selects people
according to some fixed quota with same proportions of individuals as the entire
population with respect to known characteristics, traits or focused phenomenon.

24. Convenience Sampling
• Accidental sampling (sometimes known as grab, convenience or
opportunity sampling) is a type of non probability sampling which involves
the sample being drawn from that part of the population which is close to hand.
That is, a population is selected because it is readily available and convenient.
• Is quick and easy way
• Could be biased and may not represent population as whole

25. Purposive sampling
• Judgmental sampling or purposive sampling - The researcher chooses
the sample based on who they think would be appropriate for the study.
• This is used primarily when there is a limited number of people that have expertise
in the area being researched.
• A researcher may have a specific group in mind
• It may not be possible to specify the population and may not represent the
population as whole

26. Snowball sampling
• Snowball sampling (or chain sampling, chain-referral sampling, referral sampling)
is a non-probability sampling technique where existing study subjects recruit
future subjects from among their acquaintances
• Appropriate to use in research when the members of a population are difficult to
locate.
• The researcher collects data on the few members of the target population he or
she can locate, then asks those individuals to provide information needed to locate
other members of that population whom they know.
• The researcher has little control over the sampling method.
• Sampling bias might be expected with this technique.

27. Designing the process

28. YOU ARE THE EXPERTS NOW