This document discusses various sampling methods used in research. It defines sampling as selecting a subset of individuals from a larger population to gather information about that population. Probability sampling methods like simple random sampling, stratified random sampling, and systematic random sampling aim to provide an unbiased representation of the population. Non-probability methods like purposive sampling and snowball sampling are used when random selection is not feasible. Key factors that influence sampling like sample size, bias, and population characteristics are also reviewed. The document provides examples and compares advantages and disadvantages of different sampling techniques.

1. SAMPLING
Presented By
Mr. N. Sabari vel
Tutor, CON,
AIIMS, Jodhpur

2. INTRODUCTION
Sampling is a process of selecting a number of individuals
for a study in such a way that the individuals represent the larger
group from which they were selected

4. NEED FOR A SAMPLEING
• Studying the entire population for a given problem situation is
almost impossible.
• Sampling is important process for the following reasons:

5. CHARACTERISTICS OF A GOOD
SAMPLE
• Representative
• Free from bias and error
• No substitution and incompleteness
• Appropriate sample size

6. SCHEMATIC PRESENTATION OF
SAMPLING
population
Target
population
Accessible
population
Sample Subjects

7. SAMPLING PROCESS
Identifying and defining the target population
Describing the accessible population and ensure sampling frame
Specifying the sampling unit
Specifying sample selection methods
Determining the sample size
Specifying the sampling plan
Selecting the desired sample

8. FACTOR INFLUENCING SAMPLING PROCESS
• Nature of researcher
- Inexperience investigator
- lack of interest
- lack of honest
- Intensive workload
- Inadequate supervision
• Nature of the sample
- Inappropriate sampling technique
- Sample size
- Defective sample frame

9. FACTOR INFLUENCING SAMPLING PROCESS
• Circumstances
- Lack of time
- large geographical area
- lack of cooperation
- Natural calamities

10. TYPES OF SAMPLING TECHNIQUES
Probability sampling technique
• Simple random sampling
• Stratified random sampling
• Systematic random sampling
• Cluster and multistage
sampling
• Sequential sampling
Nonprobability sampling techniques
• Purposive sampling
• Convenience sampling
• Consecutive sampling
• Quota sampling
• Snowball sampling
• Volunteer sampling
• Genealogy sampling

11. FEATURES OF PROBABILITY SAMPLING
• It provide equal chances to all the individuals in the population of
getting selected. this is feasible only if the used randomization
• Probability sampling techniques the chances of sampling bias are
relatively less because subjects are randomly selected.

13. SIMPLE RANDOM SAMPLING
• This is a most pure and basic probability sampling design.
• The two important aspect need in simple random techniques first
population must be homogeneous and researcher have list of
members in accessible population
• The sampling frame can be used in following methods
- lottery method
- the use of table of random numbers
- the use of computer

15. TABLE OF RANDOM NUMBERS
• Table present rows and
columns
• Choose members list of
the population
• Blindfold chooses a
numbers from random
table
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
1 37 75 10 49 98 66 03 86 34 80 98 44 22 22 45 83 53 86 23 51
2 50 91 56 41 52 82 98 11 57 96 27 10 27 16 35 34 47 01 36 08
3 99 14 23 50 21 01 03 25 79 07 80 54 55 41 12 15 15 03 68 56
4 70 72 01 00 33 25 19 16 23 58 03 78 47 43 77 88 15 02 55 67
5 18 46 06 49 47 32 58 08 75 29 63 66 89 09 22 35 97 74 30 80
6 65 76 34 11 33 60 95 03 53 72 06 78 28 14 51 78 76 45 26 45
7 83 76 95 25 70 60 13 32 52 11 87 38 49 01 82 84 99 02 64 00
8 58 90 07 84 20 98 57 93 36 65 10 71 83 93 42 46 34 61 44 01
9 54 74 67 11 15 78 21 96 43 14 11 22 74 17 02 54 51 78 76 76
10 56 81 92 73 40 07 20 05 26 63 57 86 48 51 59 15 46 09 75 64
11 34 99 06 21 22 38 22 32 85 26 37 00 62 27 74 46 02 61 59 81
12 02 26 92 27 95 87 59 38 18 30 95 38 36 78 23 20 19 65 48 50
13 43 04 25 36 00 45 73 80 02 61 31 10 06 72 39 02 00 47 06 98
14 92 56 51 22 11 06 86 88 77 86 59 57 66 13 82 33 97 21 31 61
15 67 42 43 26 20 60 84 18 68 48 85 00 00 48 35 48 57 63 38 84
Need to use
Random
Number Table

16. SIMPLE RANDOM SAMPLING
Advantages
• Most reliable and unbiased methods
• Requires minimum knowledge of study population
• Sampling errors can be computed easily.
Disadvantages
• Need up to date complete list of all member of the population.
• It may be uneconomical and time consuming.

17. STRATIFIED RANDOM SAMPLING
• This method is used for heterogeneous population
• Researcher divides the entire population into different
homogeneous subgroups or strata.
• The strata are divided according to certain traits such as age,
gender, religion, socio economical status, diagnosis, education,
geographical region, types of institute, types of registered nurse.
• Stratified sampling is further divide in two categories
- Proportionate stratified random sampling
- Disproportionate stratified random sampling

18. STRATUM A B C
Population size 100 200 300
Sampling fraction 1/2 1/2 1/2
Final sample size 50 100 150

19. STRATUM A B C
Population size 100 200 300
Sampling fraction 1/2 1/4 1/6
Final sample size 50 50 50

20. STRATIFIED RANDOM SAMPLING
Advantages
• It is often more convenient to recruit a stratified sample than a
simple random sample.
• Comparison is possible in two groups.
• Ensure representative sample in heterogeneous population.
Disadvantages
• Require complete information of population
• Large population is required.
• Chance of faulty calculation strata.

21. SYSTEMATIC RANDOM SAMPLING
• This is a method of selecting subjects from a larger population in
which the first subject is selected randomly.
• The process of selecting individuals within the defined
population from a list by taking every Kth name.

22. SYSTEMATIC RANDOM SAMPLING

23. SYSTEMATIC RANDOM SAMPLING
Advantages
• Convenient and simple to carry out
• Time consuming and cheaper than simple random technique
Disadvantage
• If first Subject is not randomly selected, then it becomes non
random sampling techniques.
• Sometimes may result in biased sample.

25. CLUSTER OR MULTISTAGE SAMPLING
• Cluster sampling technique is chosen when the population is
too large and mostly using geographical unit. ( E.g.) To survey
the academic performance of Indian high school students.
• The population is divided into subgroups (clusters) like
families. Then each selected sampling unit, a sample of
population is drawn by either simple random selection or
stratified random sampling.

27. CLUSTER OR MULTISTAGE SAMPLING
Advantages
• This is a less expensive method.
• It is less time consuming.
• Easier to apply large geographical area.
Disadvantages
• The chance of error exists.
• This may be less accurate than a simple random sample.

28. SEQUENTIAL SAMPLING
• This method of sample selection is slightly different from other
methods. Here the sample size is not fixed.
• The investigator initially select small sample and tries out to
make inferences, if not able to draw results, he can adds more
subject until clear cut inferences can be drawn.

29. SEQUENTIAL SAMPLING
• ( E.g.) A researcher is studying association between smoking
and lung cancer.
Number of
subjects
Smokers
(A)
Non smokers
(B)
Having lung cancer
( A) ( B)
20 7 12 2 1
30 18 22 5 3
50 28 22 10 4

30. NON PROBABILITY SAMPLING
• Random sampling is not possible in all settings as most
researchers are bound by constraints such as time, money and
resources.
• Non-probability sampling refers to techniques where the
sample is assembled in a process that does not give all the
individuals in the population an equal chance of being selected
• This type of sampling can be used when it needed to show that
a particular trait is existent in population, qualitative, pilot or
exploratory study.

32. PURPOSIVE SAMPLING
• Purposive sampling is more commonly known as judgmental or
authoritative sampling.
• In this type of sampling subjects are chosen to be part of the
sample with a specific purpose in mind. Researcher believe that
some subjects are fit for research compared to other individuals.
• ( E.g.) A researcher wants to study the lived experiences of post
disaster depression among people living in earthquake affected
area of Gujarat

33. PURPOSIVE SAMPLING
Uses of purposive sampling
• It is usually used when a limited number of individual possess
the trait of interest.
Advantages
• Simple to draw sample and useful in explorative studies.
• Save resources, as it requires less field work.
Disadvantage
• Require considerable knowledge about the population.
• It is not always reliable sample, as conscious bias may exist

35. CONVENIENCE SAMPLING
• It is probably the most common of all sampling techniques used
by nurse researchers.
• Here, the subjects are selected as per the convenience of the
researcher or their easy accessibility to the researcher. Subjects
are chosen mostly because they are easy to recruit.
• ( E.g.) A researcher want to conduct a study on older people
residing in jodhpur and the researcher observe that he can meet
several older people coming for morning walk in park, he can
choose these people as his research subjects.

36. CONVENIENCE SAMPLING
Uses of convenience study
• In pilot study convenience sample is usually used because it allow
the researcher to obtain basic data and trends for his study without
the complication of using random sample selection methods
Advantages
• This technique is considering easiest, cheapest, and time consuming.
Disadvantages
• It may not be representative of the entire population so bias occurs.
• The results are less reliable, Generalisability of the study results is
limited.

37. VOLUNTEER SAMPLING
• Target subjects are informed through mass media to participate in
study and interested participants may voluntarily contact
researcher to participate in the study.
• ( E.g.) A nurse researcher is interested to assess the effectiveness
of selected yoga techniques on the reduction of blood pressure.

38. VOLUNTEER SAMPLING
Advantages
• Cost effect sampling techniques
• This technique help to collect large size data in limited period of
time
Disadvantages
• Sample bias occur Only interested people contact to participants
• Study result may lack of generalisability.

39. CONSECUTIVE SAMPLING
• Picks up all the available subject who are meeting the present
inclusive and exclusive criteria.
• This method is better one comparing to other non probability
sample techniques because it make a better representation of the
entire population.
• ( E.g.) researcher want to study the activity pattern of post kidney
transplant patients, he can select all post kidney transplant patient
who meet the designed inclusive and exclusive criteria.

40. CONSECUTIVE SAMPLING
Advantages
• Ensure more representative sample
• Convenient and less time consuming
Disadvantages
• Researcher has no set plan about sample schedule
• Result from the sampling techniques cannot be create conclusion
and interpretation pertaining to the entire population.

41. QUOTA SAMPLING

42. QUOTA SAMPLING
• This is a non-probability sampling technique where the
population is first divided into subgroups or quotas, just as the
population is divided into strata in stratified sampling.
• Subjects are selected conveniently (not randomly) from the
strata, either proportionally or disproportionally, depending on
the study requirements.
• ( E.g.) Researcher need 100 college students for study in quota
he must select 25 first year students, another 25 second year
students, 25 third year and 25 fourth year students.

43. SNOWBALL SAMPLING
• In this technique, the initial study participants are asked to
suggest someone else who can meet the study criteria and be
willing to participate in the study.
• This is usually done when the population size is very small and
the researcher is unable to locate study participants on her own.
• ( E.g.) A researcher want to conduct a study on prevalence of
HIV/AIDS among commercial sex workers.

44. TYPES OF SNOWBALL SAMPLING
• Linear snowball sampling: In this type, each selected subject
is asked to provide the reference of one person who is similar
to him.
• This process is like a linear chain hence it is termed as linear
snowball sampling. This method is appropriate when the
desired sample size is small.

45. TYPES OF SNOWBALL SAMPLING
• Exponential non-discriminative snowball sampling: Here,
the subject initially recruited is asked to provide a reference to
at least two similar subjects, and each of them further provides
references to two subjects. It is appropriate when the desired
sample size is large.

46. TYPES OF SNOWBALL SAMPLING
• Exponential discriminative snowball sampling: The subject
initially recruited is asked to refer two subjects. From these
two subjects, reference is sought from only one. It may
enhance the representativeness of the sample.

47. GENEALOGY SAMPLING
• In this method all the members of entire families are selected rather
than selecting the different households in the villages or area.
• The genealogy sampling begins with identifying a first participants
who is convinced to participate in the study and then further he
refer to close relatives of his family, who even may be living in
other areas of village.
• This technique is primarily used in rural population and frequently
used in genetics study.

48. SAMPLING IN QUALITATIVE STUDIES
• Convenience sampling technique
• Snowball sampling technique
• Purposive sampling techniques
• Maximum variation sampling
• Homogenous sampling
• Extreme case sampling
• Intensity sampling
• Reputational sampling

49. SAMPLE SIZE CALCULATION
Allowable Error Method in Descriptive study
• n = (4Pq/L2)
n = number of samples
P = Mean difference in previous study =2.4
q=100-p q=97.6
L= Allowable error
n= (4x2.4x97.6)/5x5
n=37
37 +4= 41 (considering10 percent dropout)
Sample size n=41

53. THANK YOU