This document provides an overview of sampling methods used in research. It defines key terms like population, sample, and sampling frame. It discusses the purposes of sampling like being economical and improving data quality. Good sample characteristics include being representative and free of bias. Probability sampling techniques like simple random, stratified, systematic, and cluster sampling are described. Non-probability methods like convenience, purposive, quota, and snowball sampling are also outlined. Factors that affect sampling like sample size, sampling errors, and selection bias are covered. The document concludes that adopting appropriate sampling methods can enhance research quality while saving time and budget.

1. 1
Sampling
Dr.Rajathi.S R.NM., M.A(Psy).,M.Phil., Ph.D
Vice- principal
Hindu Mission College of Nursing
West Tambaram, Chennai.

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Director/Principal
Dr. Smriti Arora
Amity University
Nightingale Institute of Nursing (NIN), Noida

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

4. 4
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
Sampling

5. 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 - Aggregate of cases
confirm the designated criteria
and are accessible as subjects for
a study.
All DM - Delhi
All DM with
co-morbidities –Delhi
All DM with
co-morbidities in
selected hospitals - DelhiEx - Study to assess the prevalence of co
morbidities of Diabetic patients in Delhi

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6
6
2.TERMINOLOGIES …..
DM/HT
DM/HT -Settings
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

7. 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

8. Representative
Free from bias and errors
No substitution & Incompleteness
Appropriate sample size
4.Characteristics of good sample
1
2
3
4

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

10. 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
Representative Unit
Probability/Non Probability
Calculation based on formula
Inclusion/Exclusion
Chart of Plan
Subjects

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Sampling
techniques
Used
Quantitative
Design

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

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

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a7. . TYPES OF SAMPLING….. PRObAbILITY…..
Probability / Random Sampling - ‘4S’C
Simple
Random
Stratified
Random
Systematic Cluster Sequential
Proportionate Disproportionate
One - Stage
Two - Stage
Multi - Stage

15. 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
a.7. 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
inside hat or bowl, blended
through manner
❑ Number chosen by the
researcher become the
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.
a.7. 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 / Non
replacement method
possible
a.7. TYPES OF SAMPLING - PRObAbILITY…

19.  Heterogenous population
 Divide two or more groups of
homogenous population called
subgroups/strata based on
criterion randomly selects subjects
 Weightage sample/proportion
◦ Proportionate
◦ Disproportionate
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7.a.2. Stratified Random Sampling
a.7. TYPES OF SAMPLING - PRObAbILITY…
Ex- Assess the relationship between the Obesity and Diabetes in selected community

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

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

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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……
7.a.3. Systematic Random Sampling…..
a.7. TYPES OF SAMPLING - PRObAbILITY…

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

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7.a.4. Cluster / Multi stage Sampling…..
a.7. TYPES OF SAMPLING - PRObAbILITY…..
 One stage - all the elements within
cluster are selected as final sample
& all individual units as subjects.
 Two stage - randomly select some
clusters first from the given
population, then use simple 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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7.a.5. Sequential Sampling
No.of.
Subject
s
Smoker
(A)
Non
Smoker
(B)
Having
Corona
A B
20 7 12 2 1
30 18 22 5 3
60 28 23 10 4
110 53 57 17 8
❑ Sample size not fixed
❑ Start with small sample
and tries to get inferences
❑ If not able to draw and add
more subjects until
inferences drawn
a.7. TYPES OF SAMPLING - PRObAbILITY…
Ex- Assess the risk factor of acquiring of corona in COVID 19 clients

26. 7. TYPES OF SAMPLING….. 7. NON PRObAbILITY
Nonprobability
samplingtechniques
Convenient
Purposive /
Judgmental
Quota sampling
Snow ball
Consecutive
Population - not give
all individuals equal
chances of being
selected.
Chosen by choice not by chance
b.

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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..

28. 7.b.2.Purposive Sampling
 Recruits subjects with
“purpose” in 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

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

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

31. ❑ Initial potential sample members
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
7.b.5.Snowball Sampling
Ex – Assess the QOL among transgenders

32. 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

35. 8.STRENGTHS & WEAKNESSES
PROBABILITY SAMPLING NON 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
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. Need sample size estimation
9.Sample size
▪ 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

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

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9.SAMPLE SIzE dETERMINATION …..
9. Quantitative studies – 1.Using Table.

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

40.  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. Quantitative studies – 3.POWER ANALYSIS
9.SAMPLE SIzE dETERMINATION …..

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Estimation of Sample size – 3.Power Analysis
9.Sample size determination
Requirements for calculating sample size

42.  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
(1- n / N) X t2 ( p X q)
d2
n =Descriptive study
8.Sample size determination
8. Quantitative studies – 3.POWER ANALYSIS
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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❑ Thumb Rule
✓ Data saturation
✓ Numbers of factors
✓Scope of research
9. Qualitative study
10 to less
20 to 3025 to 50
9.SAMPLE SIzE dETERMINATION …..
1 to 3
based on
theme
10-50

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

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

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11. Factors Affecting Sampling process

50. ❑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

51. REFERENCES
➢ Creswell, J., W. (2012) Educational research:
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:
Principles, Process and Issues Basingstock ,
Palgrave Macmillan
➢ Polit D, Hungler B (1991 ) 4th ed. Nursing
Research London Lippincott 6/11/2020 51

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