sampling technique

1. Sampling Techniques
Dr. Vicky Kasundra
(MPT Neuro)

2. Population
• Population refers to an entire group of people or study elements. From which
intervention has been taken at a particular time.
• Also known as the universe or study population.
• Denoted by N.
• The study of whole population k/a “census study”.
• e.g. total number of stroke patients in the universe.
Sampling unit
• Each number of population.
• E.g. each stroke patient in the population.

3. Sampling Frame
• It is a complete, non-overlapping list of all the sampling units in the
population, from which the sample is to be drawn.
• E.g. stroke patients in Navsari.
Sample
• A sample is a part or portion of the population that represents the entire
population, which we select for the purpose of investigation.
• E.g. specific stroke patient MCA.

4. Parameter
Value calculated from population k/a parameter.
e.g. mean, SD, proportion (P)
Statistic
Value calculated from sample k/a statistic.
e.g. mean, SD (s), proportion (p)

5. Need For Sampling
• Save time & money.
• Results obtained more quickly.
• Ethically acceptable.
• Better accuracy of collected data.
• Only feasible method for collecting data when population is too large.

6. Types of Sampling/Sampling
Design/Methods of Sampling
Probability / random sampling Non-probability / non-random sampling
1. Simple random sampling
2. Systematic sampling
3. Stratified sampling
4. Cluster sampling
5. Multistage sampling
6. Multiphase sampling
1. Purposive / judgmental sampling
2. Convenience/availability sampling
3. Snowball sampling
4. Quota sampling

7. Probability sampling
Non-Probability sampling
Here, all the subjects have an equal chance of
being selected
Here, all the subjects do not have an equal
chance of being selected
Also k/a random sampling
Also k/a non-random sampling
Result: unbiased
Result: biased
Basis of selection: randomly
Basis of selection: arbitrarily
Hypothesis: tested
Hypothesis: generated
inferential or parametric statistics Non-inferential or non-parametric statistics
Method: objective Method: subjective
Opportunity of selection: not fixed &
unknown
Opportunity of selection: specified & known
Research: conclusive Research: explorator
Sampling process:
Population sampling determine
size
Sampling process:
Frame sampling method executes
the sampling process

8. Probability sampling
techniques

9. Simple random sampling
• This method is applicable when the population is small, homogenous, and easily
available.
• E.g. patient coming to the hospital.
• Here, the samples are chosen randomly so that each unit of the population has an
equal chance of being selected.
• So, sometimes k/a “ unrestricted random sampling”.

10. Two types
With replacement without replacement
Allow to be part of Not allow to be part of
sample more than once sample more than once

11. • Method of drawing a random sample
i. Lottery method
ii. Table of random number method

12. Lottery method
• Each member of population assigning a number.
• Each number placed in a card of same size and shape.
• All cards put in a bowl & thoroughly mixed.
• Blindfolded person selects a card until desired number of sample is not
achieved.
• E.g., lottery ticket

13. Table of random sample method
• Most common and most adequate method for simple random sampling
• Researcher prepare a numbered list of the all number of population in rows
and column
• Then blindfolded person chooses a number from the random table and this
process is repeated until the desired sample size is not achieved.
• If repeatedly same numbers are occurs, then the number ignored and next
number will be chosen.

14. Merits of simple random sampling
• Each unit has an equal chance of being selected
• Simple to conduct
• No personal bias
• Easy to assess the accuracy

15. Demerits of simple random sampling
• Need complete list of individual in the population is required.
• Large sample size
• Widely dispersed

16. Systematic sampling
• Applicable when population is large and homogenous.
• Samples were selected in a systematic mathematical way
• E.g. every k unit in the population is selected.
• First sample interval will be calculated.
• Sample interval k = total population/desired sample size
• So first number will be selected by simple random method followed by every
k is selected from the population.

17. • E.g. population size is 1000 and you need 100 sample so k = 1000/100 so
the k = 10
• So every 10th person is selected i.e. 10,20,30 etc. till the desired sample size is
not achieved.

18. Merits of systematic random sampling
• Systematic design is simple & convenient to adopt
• Easier than simple random
• Time and labor cost is very less for collecting data
• If population is large, homogenous and numbers were given than it is the
most easier method

19. Demerits of systematic random sampling
• Need complete list of individual in population
• Less representation
• periodicity

20. Stratified random sampling
• Applicable when population is large and heterogenous
• First population is divided into group known as strata e.g. gender, religious,
location
• Now from strata the sample is drawn randomly in proportion to its size.
• E.g. 100 sample of boys and girls. Out of 60 boys and 40 is girls
• So for example you need 10 sample so you select 6 boys and 4 boys in a same
proportion but in random manner.

21. Merits of stratified random sampling
• More representative
• Greater accuracy

22. Demerits of stratified random sampling
• Careful stratification
• Time consuming & high cost

23. Cluster random sampling
• Applicable when population area is too large
• So, the first population is divided into smaller non overlapping units or
groups known as clusters.
• Than some of this clusters are randomly selected in the study
• E.g. geographical area (villages, city, town etc.)

24. Merits of cluster random sampling
• Cheap, quick and easy method
• Save travelling cost
• Useful when sampling frame is not available

25. Demerits of cluster random sampling
• Chances of over representation or under representation of same clusters.
• Larger sampling error than simple random sampling

26. Multistage sampling
• Applicable only in large surveys
• As the name implies, sampling procedure carried out in several stages using simple
random sampling technique.
• At each stage any one of the different type of random sampling technique may be
used.
• Eg. Country survey Stage 1- Districts choose
Stage 2- Taluka
Stage 3- village, etc.

27. Merits
• Most flexible type of Sampling
• Cut down the cost of preparing the sample frame.

28. Demerits
• Sampling error higher
• Sampling units may vary in size at different stages. So ultimate unit size
different universe

29. Multiphase sampling
• In this type of sampling, part of information is collected from the whole
sample and part from the subsample in a subsequent survey regarding some
relevant variable under study.
• At each stage the sample size become successively smaller & smaller.
• E.g. study of TB patient

30. • Phase 1 : all montux test +ve patient
• Phase 2 : chest x-ray reveals abnormality
• Phase 3 : sputum analysis +ve
• Phase 4 : final sample that included in study

31. Merits of multiphase sampling
• save cost because those who are found to be positive on the 1st survey will
included
• more accurate
• time consuming

32. Demerits of multiphase sampling
• time consuming

33. Non probability
sampling technique

34. Quota sampling
• In this method, the population is divided into different subgroups based on
relevant characteristics .
• Then from subgroups the individual were selected.(according to researcher
convenience)
• It is similar to stratified but the subjects were chosen randomly in stratified
but here the subject were chosen non-randomly from subgroups.

35. Purposive and judgmental sampling
• In this method, sampling units selected on the basis of personal judgement.
• Here, instead of random selection, population of interest are choose based
on judgement or prior knowledge of thee units for getting good result .
• E.g. cricket match , so cotch will select good player only for getting good
result .

36. Convenience or availability sampling
• In this type of sampling, the subject were selected that is easily available &
easy to recruit .
• Research does not consider selecting subjects are representative of the entire
population or not.
• The only aim is to complete the study at easy way.
• E.g. researcher want to study on stroke patients so he take all type of stroke
[ACA, MCA, PCA]

37. Snowball sampling
• In this type of sampling, a few individuals who meet the inclusion criteria
were selected.
• Then asked to that all participants to recruited other participants with the
same characteristics.
• The process of repeated till desired sample size reached.
• E.g. one facial palsy patient selected then that participants selected other
facial palsy patients .

38. Merits of non-probability sampling
• No sampling frame required
• Very convenient
• Time saving & cost saving
• Done rapidly

39. Demerits of non-probability sampling
• Sample may be biased.
• Results may be biased.
• Restriction of generalization or controlled .
• Bias cannot be measured or controlled.

40. Reference
• Methods in biostatistics for medical students and research workers by b k
mahajan 7th edition

41. Thank you