This document discusses various sampling methods used in research. It defines key terms like population and sample. It describes the need for sampling due to limited resources. Different probability sampling methods are covered like simple random sampling, systematic sampling with random start, and stratified sampling. Simple random sampling selects units with equal probability from a sampling frame. Systematic sampling selects units at regular intervals but can result in bias. Stratified sampling divides the population into homogeneous subgroups before sampling. Finally, multistage sampling is used for large, scattered populations and involves multiple stages of sampling.

1. Sampling MethodsSampling Methods

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IntroductionIntroduction
Population (Universe): All the persons,
objects or events about which information
is sought for.
Sample: Part of the population drawn by
scientific method, for detailed study.
Objective could be making estimate about
the parameter

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Need of SamplingNeed of Sampling
Shortage of resources: Personnel,
equipment, time
Detailed examination of smaller units
Population may be infinite.
Reasonable estimates of parameter
required in short time.

4. Sampling FrameSampling Frame
Defining who enters sample and who does not
Eligibility : Inclusion & Exclusion criteria
Defining sampling frame makes sampling easier
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5. Types of Sampling MethodsTypes of Sampling Methods
Cluster
Sampling
Non-Probability
Sampling
Convenience
Probability Sampling
Simple
Random
Systematic
Stratified
Quota
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Sampling MethodsSampling Methods

7. Simple Random SamplingSimple Random Sampling
 Principle
– Equal chance of drawing each unit.
 Merits
– Easy to implement if list frame available or small
population
– Approximately satisfies the sampling model on which
conventional statistics is based, so we can carry out
complex analyses
 Demerits
– Need complete list of units
– Units may be scattered
– Large sample size
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Selection of Random NumbersSelection of Random Numbers
Random Number Table
Lottery
Computer Generation

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10. StepsSteps
1. Enumerate all units
2. Decide sample size
3. Selection of row and
column
4. Selection of digits
5. Selection of direction
6. Selection of number if
eligible
1. 500: Say 001 to 500
2. Say 50
3. Say R-2, C-3 (to be
done randomly)
4. Last three (or first 3?)
5. Down words then to
Right & up
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01465 Eligible 1
93054 Eligible 2
55270 Eligible 3
32504 Not Eligible --
78323 Eligible 4
20466 Eligible 5
25276 Eligible 6
98411 Eligible 7
04796 Not eligible --

12. Simple Random Sampling
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Lottery MethodLottery Method
For small, finite populations
Step-1: Take small papers and write numbers
1 through maximum in population (say
1000)
Step-2: Mix and select papers = sample size
(say 50)
Students selected would enter sample

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Search word
in Google
search box
Click this option
Down load file &
Install file
You may require
“Unzipping”

15. Random number: by computerRandom number: by computer
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1.
Input entered in
the input box
2.
Press
“Calculate button”

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Ten numbers in
the range of 1-
500 generated
Generated
numbers saved
to a file

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Systematic sampling with Random startSystematic sampling with Random start
(Two examples)(Two examples)
1. Enumerate & enlist all units in population
(N) Families: 1000 Students:
400
2. Decide sample size (n)
Families : 50 Students:
40
3. Decide sampling interval (SI) = N /n
Families: 1000 /50 = 20
Students : 400 / 40 = 10

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5. Decide starting point (SP) randomly in range
of 1 to SI :
Families : 14 (say) Students: 8
(say)
6. Select units for sample:
Unit-No Family Student
1 SP = 14 SP = 8
2 14 +SI =34 8+SI =18
3 34 +SI =54 28 + SI =28
4 74 38
5 94 48
and so on….. 994 398

21. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
31 32 33 34 35 36 37 38 39 40 41 42 43 44 45
16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
46 47 48 49 50 51 52 53 54 55 ……..
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22. DrawbacksDrawbacks
Periodic effect or cyclic effect
Eg. In a large grocery store divided into the following
8 sections: bakery, pharmacy, dry cleaning etc. Each
section has 10 employees, including a manager. In the
list manager is listed first and then, the other
employees by descending order of seniority.
What happens if starting point is 1 and SI is 10?
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Only
Managers are
Selected !!!

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Stratified samplingStratified sampling
Indication: Finite
population with
Heterogeneous
groups for
Characteristic
being studied.
There is
within group
homogeneity.
Characteristic Hb %
Group Mean
Male 13.5
Female 9.0
Characteristic
Immunization coverage
Rural : 50%
Urban : 80%

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Stratified Sampling: Step-1Stratified Sampling: Step-1
Contribution of each strata to population
A) Hb %:
Total Population : 1000
Males : 600 (60%), Females : 400 (40%)
B) Immunization:
Total Population: 1,00,000
Rural : 70,000 (70%) , Urban : 30,000
(30%)

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Stratified sampling: Step -2 & 3Stratified sampling: Step -2 & 3
Step -2: Sample size from total population
A) Hb% : 200 B ) Immunization : 1000
Step-3: Allocation to strata : Sample size * proportion
of strata in population
A) Hb% : Males : 200 x 0.60 = 120
Females : 200 x 0.40 = 080
B) Immunization: Rural : 1000 x 0.70 = 700
Urban : 1000 x 0.30 = 300

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Stratified sampling: Step-4Stratified sampling: Step-4
Each strata is considered as
separate population and required
number of units are drawn from it
by:
Random sampling
Systematic sampling

28. Multistage SamplingMultistage Sampling
When population is large, scattered and not
homogenous.
Used for large scale surveys.
Sampling is done in different stages and each
stage being selected by some random procedure.
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29. Multistage SamplingMultistage Sampling
Eg.
– Immunisation status of children <5yrs of age in
Maharashtra.
– First stage. A sample of district is selected.
– Second stage. A sample of taluka is selected from the
selected district.
– Third Stage. A sample of village is selected from the
selected taluka.
– Fourth Stage. Sample of children <5yrs of age are
selected from the selected village.
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30. Multistage SamplingMultistage Sampling
Advantages
– Sample is spread over the entire population.
– Sampling frame is not required so cuts down the cost.
– Every unit has equal chance to be selected.
– Saves time and cost.
Disadvantages
– Sampling error is higher as village population may differ,
culture and religion differ etc
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31. Factors Affecting Choice of SamplingFactors Affecting Choice of Sampling
DesignDesign
–Sampling Frame: Existence and Size
–Costs
–Precision Desired
–Sub-Population Comparisons
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