This document discusses various sampling methods used in research. It begins by defining key sampling terms like population, sample, sampling unit, and sampling frame. It then describes the main types of sampling: probability sampling methods which use random selection and allow statistical inference about the population, and non-probability sampling methods which do not use random selection. Specific probability methods discussed include simple random sampling, systematic random sampling, stratified random sampling, cluster sampling, and multistage sampling. Common non-probability methods mentioned are convenience sampling, purposive sampling, and snowball sampling. The document provides details on how to implement several of these sampling techniques and notes their relative advantages and limitations.

1. Dr. J. ANAIAPPAN M.D., D.C.H
Senior Assistant Professor
Department of Community medicine
Kilpauk Medical College

2.  Introduction
 Types of sampling methods
 Probability sampling methods
 Non-probability sampling methods
 Choice of sampling methods

5. Definition of sampling
What is the need for sampling?
What defines a proper sample?

7. Definition:
 Sampling is a process by which some persons
/objects / elements /events are selected from the
predetermined population for carrying out studies and
drawing inferences about the population as a whole.

8.  Sampling is a process of selecting a
required number of individuals from the
study population so as to make
observations on the sample instead of
whole population

9. Principle of sampling :
 To get maximum information about the
population with minimum effort and with
limited resources
Objectives of sampling :
 Estimation of population parameters
(proportion or mean) from the sample statistics
 To test the hypothesis about the population
from which the samples are drawn

10.  Studying the entire population is difficult
 It will be costly, time consuming and not feasible
 Studying the whole population is impossible and
unnecessary

11. If sampling is done properly :
 Accurate and reliable estimates can be made
 More characteristics or details can be collected
 Project management is easy
 Can get best possible results in least possible time

12. Sampling is inevitable when :
 Population is infinite
 Results are required in a short time
 Area is wide
 Resources are limited

13. What determines a proper sample?
 Representativeness
 Unbiased selection
 Adequacy of the sample

14. Representativeness:
 Sample has all the important characteristics and similar
distribution
 Requires knowledge of variables and their distribution
in the population
 Statistical sampling methods – gives reasonable
guarantee of representativeness

15. Bias occurs when :
 Wide difference between the estimate of the sample &
the true population value
 Some members are underrepresented or
overrepresented than others in the population
 Own bias or prejudice
 Laziness and sloppiness

16. Reasons for a biased sample :
 Faulty selection of sample
 Substitution
 Faulty demarcation of sampling units
 Non-response

17. Good sampling results in :
 Reduction of cost
 Saving of time
 Reduction in manpower requirement
Gives more accurate results than attempts to study
the entire population

19. Population : ( universe )
 The group of individuals or units possessing certain
predetermined characteristic intended for the study
 Population is an aggregate of elements (ie) persons,
objects, households or specified events

20. Representative sample :
 It has all the characteristics with similar distribution as
that of the population from which it is drawn
Sampling frame :
 It is the list of all elements – persons, households,
objects, specified events or units – in the population
eg. Voter’s list

21. Sampling unit :
 It is the constituent elements of a population which are
to be sampled from the population and cannot be
further subdivided for the purpose of sampling at a time
 It is the unit of selection in the sampling process (eg) a
person, a patient, a household, a village, a town, a
hospital or a district

22. Sampling Fraction :
 The proportion of population that is included in the
sample (eg) 20%
Sample :
 A finite subset of a population, a portion chosen from a
defined population
Sample size :
 The number of units in a sample

23. Sampling error is any type of bias that is attributable
to mistakes in either drawing a sample or determining
the sample size

24. Basics of Sampling TheoryBasics of Sampling Theory
Population
Element
Defined target
population
Sampling unit
Sampling frame

26.  Types of sampling :
 Probability sampling or Random sampling
 Non-Probability sampling or Non-Random sampling

28.  It uses some form of random selection
 All units in the study population have an equal chance
for being chosen for the study
 Best among all the methods
 Most powerful statistical analysis on the results can be
done subsequently

29. Random sampling methods are :
 Simple random sampling (unrestricted)
 Systematic random sampling (quasi-random)
 Stratified random sampling
 Cluster sampling (area sampling)
 Multistage sampling
 Multiphase sampling

31.  Difference between random and non-random sampling
is selection of sample unit does not ensure a known
chance to the units being selected
 May lead to unrepresentative samples
 It lacks accuracy in view of selection bias

32.  Does not involve random selection
 Subject to prejudice and bias of researcher
 May not represent the population well
 Used when there is no sample frame for the population
 Mostly used in qualitative research like exploratory
research, opinion surveys and marketing studies

33. Methods :
 Purposive sampling (judgemental sampling)
 Convenience sampling (oppurtunity sampling)
 Quota sampling
 Expert opinion sampling
 Snowball sampling (chain sampling, chain referral
sampling or referral sampling)

34.  Important and frequently used methods :
 Simple random sampling
 Systematic random sampling
 Stratified random sampling
 Cluster sampling
 Multi-stage sampling

36.  Define the study population ( N )
 Prepare a proper sampling frame (n)
 Determine the sample size
 Select the required number of samples

38. Selection of required number of samples by :
 Lottery method – small population
 Random number method – by using standard tables
( Tippet’s table, Fisher and Yate’s table and Kendall
and Smith’s table )
 Computer generated random numbers

39.  Advantages :
 Personal bias is eleminated
 Representative of a homogenous population
 No need for thorough knowledge of the units of
population
 Accuracy of the sample can be tested
 Used in other methods of sampling

40. Disadvantages :
 Cannot be used for large population
 When there is large difference between units
 Units of sample lie apart geographically
 Cost and time of collection of data are more
 Logistically more difficult in field conditions

42.  Simple & convenient way of selecting a sample
 Requires less time and cost
 Sample is spread evenly over entire reference
population
 Can be used in infinite population

43.  This method requires sampling frame
 Units are selected at an uniform interval
 Useful when information is collected from units which
are in serial order (ie) enteries in register, house in
blocks etc

44. Method :
 Identify the sample size (n)
 Put the population in sequential order & number them
serially – sampling frame
 Identify total no.of units in the population (N)

45.  Method :
 Divide N/n = sampling interval (k)
 Identify a random no.which is less than or equal to ‘k’
 Select every n’th item starting with a random one

48.  Dividing the population into subgroups or strata -
stratification
 Units within the stratum are homogenous and between
the strata are heterogeneous
 From each stratum a simple random sample is selected
and combined together to form the required sample
from the population

49.  Two types :
 Unequal size - Proportional stratified random sampling
 Equal size – Disproportionate stratified random
sampling

50.  Sample size in each stratum is
 Unequal size - proportionate to the no. of units in each
stratum
 Equal size - disproportionate to the no. of units in
each stratum

52.  Advantages :
 Every unit in the stratum has the same chance of being
selected
 More representative
 Ensures proportionate representation
 Greater accuracy
 Greater geographical concentration

53. Limitations:
 Division of population into strata needs more money,
time and statistical experience
 Improper stratification leads to bias – if there is
overlapping of strata

55.  The whole population is divided into groups called
clusters.
 Each cluster is representative of the population
 Clusters are selected randomly
 A random sample is then is taken from within each
cluster

56.  Lot of clusters are sampled so that the results can be
generalized for whole population
 Clusters should be as small a possible consistent with
the time & cost limitations
 No. of units in each cluster must be more or less equal
 Is a simple random sample of cluster of elements

57.  Examples :
 WHO 30 clusters for coverage evaluation survey
 Pulse polio immunization coverage evaluation survey

58. Eg: In a PHC estimate the proportion of infants with
age 6 months to 1 yr who are fully immunized .
1) Identification of total population and the
geographical area
2) Identification of age group to be included
3) Listing of all villages
4) Tabulation

59. village Population Cumulative
population
clusters
1.Adgaon 947 947 1
2.Asgaon 1208 2155 2
3.Borphal 712 2867 3
4.Bilaspur 3012 5879 4,5,6
5.Chitegaon 631 6510 7
6.Dhoregaon 1709 8219 8
7.Esapur 413 8632 9
8.Girnar 1203 9835 10
9.Goregaon 5153 14988 11,12,13,14,15
10.Himmatpur 3128 18116 16,17,18
11.Lalwadi 3689 21805 19,20,21
12.Puri 1529 23334 22
13.Solegaon 2604 25938 23,24,25
14.Tisgaon 3210 29148 26,27,28
15.Yeoti 2057 31205 29,30

60. 5) Sampling interval (S.I.):
Total cumulative
Population 31205
S.I. = ------------------- = 1040.
Number of clusters 30
6) Selection of a starting point
7) Selecting subsequent clusters
 C2 = random number + S.I.= 0196+1040= 1236
 C30= c29 + S.I.
8) Selecting first household in a cluster
9) Collection of information

61. Advantages Disadvantages
 Cuts down the cost of
preparing sampling frame and
cost of travelling between
selected units
 Eliminates the problem of
“packing”
 Sampling errors is usually
higher than for a simple
 Random sample of the same
size

63.  Used for large and diverse populations (eg) nation,
region or state
 Usually carried out in phases
 Involves more than one sampling methods
 Example : Estimating the problem of Iodine
deficiency disorders in India

64.  First stage : few states are randomly selected
 Second stage : few districts from above states
 Third stage : few blocks from above districts
 Fourth stage : few villages from above districts
 Fifth stage : few households from each village

69. ADVANTAGES :
 Sample frame for individual units not required
 Cuts down the cost of preparing sample frame
 DISADVANTAGES :
 final sample may not be representative of the total
population
 Sampling error is increased, when compared with
simple random sampling

70. Non-probability Sampling

71.  Does not involve random selection
 Subject to prejudice and bias of investigator
 May or may not represent the population well
 Used when there is no sampling frame
 Used in qualitative research
 If the investigator is experienced may yield valuable
results

72.  Convenience sampling
 Judgemental /purposive sampling
 Quota sampling
 Snow ball sampling

73.  Accidental, opportunity, accessibility or haphazard
sampling
 Use of readily available persons for the study-sample
of convenience
 Stopping people in a street corner, people select
themselves in response to public notices-risk of bias
is greater.
 Lack of representativeness
 Used for making pilot studies

74.  Judgmental sampling
 Researchers knowledge about the population can be
used to hand-pick sample members, knowledgeable
about the study
 Used in newly developed instruments can be
pretested and evaluated

75.  Researcher utilizes knowledge about the population –
representativeness into the sampling plan
 Population is divided into quotas – age,
socioeconomic status, religion etc.
 Number of units within each quota –personal
judgment of the investigator.
 Used by quantitative researchers
 Used in public opinion studies

76.  Network or chain/referral sampling
 Research population of specific traits-difficult to
identify
 Early sample members asked to refer other people
who meet eligibility criteria
 Sampling hidden populations-homeless or IV drug
users-respondent driven sampling (rds),variant of
snow ball sampling.

78. METHOD BEST WHEN
 Simple random whole population
sampling is available
 Stratified random when specific
sampling subgroups are to be
investigated

79. METHOD BEST WHEN
 Systematic random when a stream of
sampling representative
people are available
 Cluster sampling when population
groups are separated
& access to all is
difficult

81. THANK YOU