This document provides an overview of sampling techniques used in research. It discusses key terms like population and sample. The main types of sampling covered are probability sampling methods like simple random sampling, stratified random sampling and cluster sampling as well as non-probability methods like convenience sampling and purposive sampling. Factors that influence sampling like sample size, sampling errors and strengths and limitations of different approaches are also outlined. The goal of sampling is to select a subset of a population that accurately represents the whole to allow researchers to make generalizations.

1. 1
SAMPLING
Prepared by;
Mr. vivek bhatt M.Sc. (N.)

2. 3
HIGHLIGHTS
1. Introduction
2. Terminologies
3. Purposes
4. Characteristics
5. Principles
6. Sampling process
7. Types of sampling
1. Quantitative
2. Qualitative
8.Strengths & weaknesses
9.Sample size
10.Sampling Error
11.Factors
12.Conclusion

3. 1. INTRODUCTION
 Sampling process of selecting
portion of population to represent
entire population
 Representative Unit of entire
population
 Reflect study character of
population.
 Significant of statistical
inferences
Ex- Study to assess the prevalence of co morbidities of Diabetic patients
4
Sampling

4. 5
2.TERMINOLOGIES
POPULATION
Set of people, totality or Entire
aggregation of cases/subjects
 Target - Aggregate of cases had
designated criteria about which the
researchers would like to generalize
 Accessible - of cases
Aggregate
confirm the designated criteria
and are accessible as subjects for
a study.
All DM - Rajkot
All DM with
co-morbidities –Rajkot
All DM with
co-morbidities in
Christ hospitals - rajkot
Ex - Study to assess the prevalence of co
morbidities of Diabetic patients in rajkot

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6
2.TERMINOLOGIES …..
SAMPLING FRAME
Subjects in the population from
which the sample is drawn
Ex: Source material – Registers
SAMPLING PLAN
Formal Plan includes methods,
size and procedure for selecting the
subjects.
SAMPLE
Subset /representative unit of the
accessible population.
Ex - Study to assess the prevalence of co morbidities of Diabetic patients
Set of people
Sub Set of
people

6. Purposes
of
sampling
Economical
Quick study
result
Precision
and
Accuracy
Improved
quality of
data
To gather data
from population in
order to make an
inferences that
can be
generalized
3.PURPOSES

7. Representative
Free from bias and errors
No substitution & Incompleteness
Appropriate sample size
4.CHARACTERISTICS OF GOOD SAMPLE
1
2
3
4

8. 5.PRINCIPLES
PRINCIPLES
OF
SAMPLING
Law of
Statistical
Regularity
Law of
Inertia of
Large
Numbers
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More
Accurate
results
9
Select
Sample
Random

9. Sample
Sampling
Plan
Sample size
Selection methods
Sampling unit
Describe Accessible population
& Sampling Frame
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Define Population
(Identify & Target)
Constructing list
6.SAMPLING
PROCESS
Probability/Non Probability
Inclusion/Exclusion
Representative Unit
Chart of Plan
Calculation based on formula
Subjects

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SAMPLING
TECHNIQUES
USED
QUANTITATIVE
DESIGN

11. 7. TYPES OF SAMPLING TECHNIQUES
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7.a
Probability /
Random
sampling
• Each sample unit
in a group has an
equal chance of
being selected.
7.b
Non-probability/
Non-Random
sampling
• Choice of
sample group
by researcher.

12. Probability
sampling
techniques
Simple Random
Stratified Random
Systematic
Sequential
Cluster / Multi stage
Each
group
sample unit in
has equal
being
chance of
selected & probability
accurately determined
Absence of systematic Bias & more representativeness
7. a.TYPES OF SAMPLING….. PROBABILITY

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7. a. TYPES OF SAMPLING….. PROBABILITY…..
Probability / Random Sampling - ‘4S’C
Simple
Random
Stratified
Random
Systematic Cluster Sequential
Proportionate Disproportionate
One - Stage
Two - Stage
Multi - Stage

14. 7.a.1.Simple Random Sampling
Basic design, identify accessible
population & prepare sampling
frame.
Each member in frame equal
probability of selection.
 Techniques
 Lottery method,
 Table of random numbers
 Use of computers
Equal chance for drawing
each unit
7.a.TYPES OF SAMPLING -
PROBABILITY…

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7.a. TYPES OF SAMPLING….. PROBABILITY
7.a.1.Simple Random Sampling - LOTTERY METHOD
 Each member attributed to
Unique number.
 All Unique number placed
through
inside hat or bowl, blended
manner
chosen by the
become the
Number
researcher
subjects.

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7.a.1.Simple Random Sampling - RANDOM NUMBERS
 Include each sample number
/ Name list
Each sample number/name
placed in table
Number chosen - become
subjects.
Replacement/ Non
replacement method possible.
7.a.TYPES OF SAMPLING - PROBABILITY…

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7.a.1.Simple Random Sampling – Use of computers
 Large samples - Computer
aided random selection
 Software - MINITAB and
Excel, SPSS.
 Replacement
replacement
possible
/ Non
method
7.a.TYPES OF SAMPLING - PROBABILITY…

18. ⚫ Heterogenous population
more groups of
⚫ Divide two or
homogenous population
subgroups/strata based
called
on
criterion randomly selects subjects
⚫ Weightage sample/proportion
◦ Proportionate
◦ Disproportionate
7.a.2. Stratified Random Sampling
7.a.TYPES OF SAMPLING - PROBABILITY…
Ex- Assess the relationship between the Obesity and D
6/1
i1
a
/2
b
02
e
0tes in selected community 19

19. to all
size of all population.
 Fraction Not equal
subgroups
Example of Proportionate & Dis Proportionate
stratified random sampling
Proportionate Dis-
Proportionat
e
STARTUM A B C A B C
Population
size
100 200 300 100 200 300
Samplin
g
fraction
1/2 1/2 1/2 1/2 1/4 1/6
Final
sampl
e size
50 100 150 50 50 50
7.a.2. Stratified Random Sampling…..
a. PROPORTIONATE
 Subjects in proportion to size
of equal to all population.
 Fraction equal to all
subgroups
b. DISPROPORTIONATE
 Subjects Not proportion to
7.a.TYPES OF SAMPLING - PROBABILITY…
Ex- Assess the relationship between the Obesity and Diabetes in selected community 20

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7.a.TYPES OF SAMPLING - PROBABILITY…
7.a.3. Systematic Random Sampling
Sample members selected by
random in starting point and fixed
as per Sampling interval
K= Number of subjects in target population (N)
Size of Sample (n)
 Selection every kth (case) subject
from list are selected as samples .

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7.a.3. Systematic Random Sampling…..
K=N / n
Example:
N = 1200 and n = 60
Interval = 1200/60 = 20
Randomly select a number between 1 and 20
 1st person selected = the 8th on the list
2nd person = 8 + 20 = the 28th
3nd person = 28 + 20 = the 48th
 4th person = 48 + 20 = the 68th etc……
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7.a.TYPES OF SAMPLING - PROBABILITY…

22. ⚫Select subjects ,
probability technique
based on
such as
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7.a.4. Cluster / Multi stage Sampling
⚫ Large population - states, cities,
districts.
⚫Target population – divide to
subpopulations / clusters
7.a.TYPES OF SAMPLING - PROBABILITY…
Simple/ Stratified random sampling.
Ex- Assess the level of stress among school going adolescents in selected schools .

23. 7.a.TYPES OF SAMPLING - PROBABILITY…..
7.a.4. Cluster / Multi stage Sampling…..
⚫One stage - all the elements within
cluster are selected as final sample
& all individual units as subjects.
⚫Two stage - randomly select some
clusters
population,
first from the
then use simple
given
and
stratified random sampling to select
subjects as per inclusion.
⚫Multi stage - more than two levels
- Ex –Nation ,Cites & districts
Ex- Assess the level of stress among school going adolescents in selected schools, Tamilnadu
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No.of.
Subjec
t s
Smoke
r
(A)
Non
Smoke
r (B)
Havin
g
Coron
a A B
20 7 12 2 1
30 18 22 5 3
60 28 23 10 4
110 53 57 17 8
7.a.5. Sequential Sampling
 Sample size not fixed
 Start with small sample
and tries to get inferences
 If not able to draw and add
more subjects until
7.a.TYPES OF SAMPLING - PROBABILITY…
inferences drawn
Ex- Assess the risk factor of acquiring of corona in COVID 19 clients
6/11/2020

25. Non
probability
sampling
techniques
Convenient
Purposive /
Judgmental
Quota sampling
Snow ball
Consecutive
Population - not give
all individuals equal
being
chances of
selected.
Chosen by choice not by chance
7. TYPES OF SAMPLING….. 7. b. NONPROBABILITY

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7.b.1. Convenience Sampling
Researcher accessible /
Proximity.
Accidental sampling- Subjects
are chosen simple way easy to
recruit.
Fast, Inexpensive & less time
consuming.
Ex - Assess the attitude of mental illness among geriatric people..
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27. 7.b.2.Purposive Sampling
⚫ Recruits
“purpose” in
subjects with
mind who fit
their criteria.
⚫ Selection based on
experience or knowledge of
group to be sampled…
⚫Judgmental / Authoritative
sampling”.
Ex – Assess level of depression among COVID 19 patients in chennai

28. . Depends on trait considered
basis of quota
Ex-age, gender, education, religion
and socio economic status.
equal or
representation
Identifies
proportionate
of subjects
7.b.3.Quota Sampling
Ex – Assess level of self esteem among B.Sc Nursing College students

29. 7.b.4. Consecutive Sampling
⚫Small size population
⚫All available subjects who are
meeting the preset inclusion
and exclusion criteria.
⚫ Total Enumerative sampling
Example: Assess QOL of post kidney transplant patients

30.  Initial potential sample members
7.b.5.Snowball Sampling
and they are asked to refer other
members who meet the eligibility
criteria.
Study participants continues
participant referrals otherwise it
difficult to identify.
 Network / Chain referral sampling
Ex – Assess the QOL among transgenders

31. 7.b.5.Snowball Sampling -Types
 Linear
Subject refers only one other subject
 Exponential Non-Discriminative
Subject gives multiple referrals and
each referral gives some more until
required sample size is reached.
⚫Exponential discriminative
Subject refers multiple people but only
one is chosen as sample

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SAMPLING
TECHNIQUES
USED IN
QUALITATIVE
DESIGN

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7. SAMPLING – QUALITATIVE METHOD
Ex – Assess lived experiences of COVID 19 patients in selected settings

34. 8.STRENGTHS &
W
EAKNESSES
PROBABILITY SAMPLING
Strengths
⚫ Representative samples
⚫ Estimate the level of sampling
error
⚫ Reduce selection bias
⚫ Stronger design
Weaknesses
⚫ Difficult to construct sampling
frame
⚫ Expensive
⚫ Inconvenience and complexity
⚫ Time consuming
NON PROBABILITY SAMPLING
Strengths
⚫ Low cost
⚫ Convenient
⚫ Not time consuming
Weaknesses
⚫ Selection bias
⚫ Sample not representative
⚫ Does not allow
generalization
⚫ Subjective
⚫ Weaker design
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9.SAMPLE SIZE
9. Need sample size estimation
 Mathematical estimation of the subjects /units.
 Small sample - fail to detect significant inferences
 Large sample - wastage resources.
 Optimum size is required for
 Appropriate analysis.
 Accuracy
 Validity of significance test
 Generalization

36. 9.SAMPLE SIZE DETERMINATION
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Formula/
Power
Analysis
Nomo
grams
(Chart)
Computer
software
Ex: Epi-info,
Raosoft
9.Quantitative studies – No Thumb Rule
SAMPLE SIZE
Ready Made
Table

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9.SAMPLE SIZE DETERMINATION …..
9. Quantitative studies – 1.Using Table.

38. 9.SAMPLE SIZE DETERMINATION …..
9. Quantitative studies – 2.Nomograms
• Nomogram – Nomograph or
alignment chart,, is a graphical
calculating
dimensional
device, a two-
diagram designed
for experimental study.
• The research should decide the
sample size based on effect
that is clinically important to
detect.

39. 9. Quantitative studies – 3.POWER ANALYSIS
⚫Determine effects of the study to
detect differences or
relationships that actually exist
in the population
⚫ Measure capacity to accept or
reject a null hypothesis
⚫ Minimum acceptable power - 80
9.SAMPLE SIZE DETERMINATION …..

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9.SAMPLE SIZE DETERMINATION
Estimation of Sample size – 3.Power Analysis
Requirements for calculating sample size

41. ⚫n = sample size
⚫N = size of the eligible population
⚫t2 = square value of the standard
deviation score
⚫P = % population which we computing
the sample size
⚫q = 1-p (remaining % of Population)
⚫d2 = confidence interval
n = (1- n / N) X t2 ( p X q)
Descriptive study
8.SAMPLE SIZE DETERMINATION
8. Quantitative studies – 3.POWER ANALYSIS
d2
Confidence Interval calculation

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9. Quantitative studies – 3.Formula
9.SAMPLE SIZE DETERMINATION …..

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9. Quantitative studies – 4. Using Computer
• Rao soft
9.SAMPLE SIZE DETERMINATION …..

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9.SAMPLE SIZE DETERMINATION
8. Quantitative studies – Using Computer….
Automated
software program
Calculate required
sample size

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 Data saturation
 Numbers of factors
Scope of research
9. Qualitative study
 Thumb Rule
10 to less
20 to 30
25 to 50
9.SAMPLE SIZE DETERMINATION …..
1 to 3
based on
theme
10-50

46. 10. SAMPLING
ERRORS
Sampling error
selected sample
deviation of
from true
characteristics, traits,
figures of
behaviors,
entire
qualities or
population
⚫ Non-sampling Error
Biases and mistakes in selection of
sample.
⚫ Sampling Error:
Difference
and population
between
values
sample
considered
as sampling error.,
Subset - Individual differences, random
and systematic error

47. 10. M
INIMIZE OF SAMPLING ERRORS …..
 Prepare updated sampling frame
 Use appropriate probability sampling
Technique.
 Minimizes the stages sampling.
 Appropriate sample size
Small – Increase sampling error
Large – decrease sampling error
 Reduction Attrition rates

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11. FACTORS AFFECTING SAMPLING PROCESS

49. Sampling is the part of every day life
 Adopt the requirement of
 Use probability sample methods
 Appropriate sample size
 Saves budget & time Saves
budget & time.
 Reduce sampling errors & enhance
quality of research
10.
CONCLUSION

50. • Planning Conducting, and Evaluating
Quantitative and Qualitative Research, 4th ed.
 Patton, M.Q. (2002). Qualitative
Research and Evaluation Methods.
Thousand Oaks, CA: Sage.
• Suresh K sharma (2016) .Nursing research
and staistics, 2nd edition, Elseivers Publications
• Parahoo K (2006) 2nd ed. Nursing
Research:
 Creswell, J., W. (2012) Educational research:


51

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