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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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Dr.A.P.Kulkarni
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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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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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
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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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
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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Dr.A.P.Kulkarni
When we consider methods of sampling, there are basically two kinds of sampling. Now under these two categories, there are many different methods. In today’s session we’ll concentrate on the more commonly used methods.
The biggest drawback of the systematic sampling method is that if there is some cycle in the way the population is arranged on a list and if that cycle coincides in some way with the sampling interval, the possible samples may not be representative of the population.
Suppose you run a large grocery store and have a list of the employees in each section. The grocery store is divided into the following 8 sections: bakery, pharmacy, and dry cleaning. Each section has 10 employees, including a manager (making 30 employees in total). Your list is ordered by section, with the manager listed first and then, the other employees by descending order of seniority.If you wanted to survey your employees about their thoughts on their work environment, you might choose a small sample to answer your questions. If you use a systematic sampling approach and your sampling interval is 10, then you could end up selecting only managers. This type of sample would not give you a complete or appropriate picture of your employees&apos; thoughts.
Sometimes it is too expensive to spread a sample across the population as a whole. Travel costs can become expensive if interviewers have to survey people from one end of the country to the other. To reduce costs, statisticians may choose a cluster sampling technique.
