This document discusses different types of sampling techniques, including non-probability and probability sampling. Non-probability sampling techniques include convenience, judgmental, snowball sampling, while probability sampling includes simple random sampling, systematic sampling, stratified sampling, and cluster sampling. The document provides details on each technique, including their objectives, processes, and uses.

1. Dr S S N Raju Indukoori
SAMPLING TECHNIQUES

2. TYPES OF SAMPLING TECHNIQUES
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Based on Restriction
1) Unrestricted
2) Restricted
Based on Probability
1) Non Probabilistic Sampling
2) Probabilistic Sampling

3. NON PROBABILITY SAMPLING
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• Unrestricted: It is convenience sampling, as a choice of sample
• Restricted
1) Purposive or Judgmental Sampling
a. Deviant Case: Avoid Problematic
b. Quota Sampling: Group of small size
c. Ad hoc Quotas: Limited size within group
2) Snowball Sampling: Referrals

4. NON PROBABILITY SAMPLING
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Reasons to use
• Procedure satisfactorily meets the sampling objectives
• Lower Cost
• Limited Time
• Not as much human error as selecting a completely
random sample.
• Total list population not available

5. PROBABILITY SAMPLING
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• Unrestricted : Simple random
• Restricted : Complex Probability Sampling
1. Systematic Sampling
2. Stratified Sampling
• Proportionate
• Disproportionate
3. Cluster Sampling.
4. Double / Multiphase Sampling.

6. SIMPLE RANDOM SAMPLING
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• Unrestricted and provide a known non zero probability.
• Purest form of probability Sampling
SizePopulation
SizeSample
SelectionofyProbabilit 

7. COMPLEX RANDOM SAMPLING
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1. Systematic Sampling
2. Stratified Sampling
• Proportionate
• Disproportionate
3. Cluster Sampling.
4. Double / Multiphase Sampling.

8. 1. Systematic Sampling
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• Every kth element is sampled.
• Skip interval is known as kth element.
• Simple and flexible
Process
• Identify, list and number the elements in the population skip
interval (k)
• Identify the random start
• Draw a sample by choosing every kth entry
SizeSample
SizePopulation
intervalskipk 

9. 2. Stratified Sampling
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• Population divided into sub categories (strata).
• Strata is selected based on some criteria
• Strata is homogeneous internally and
heterogeneous externally.
• Elements in strata are selected randomly

10. 2. Stratified Sampling
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Objective is to
1. increase sample’s statistical efficiency
2. Provide adequate data for analyzing strata
3. enable different research methods and procedures to be used in
different strata.
Types
• Proportional to population
• Disproportional to population

11. 3. CLUSTER SAMPLING
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• Population divided into sub groups (Clusters).
• Clusters are selected randomly
• Clusters are heterogeneous internally and homogeneous
externally.

12. 3. Cluster Sampling
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Objective is to increase economic efficiency.
Types
• Area Sampling for geographic area
• Double / Sequential / Multiphase Sampling

13. TYPES OF SAMPLING TECHNIQUES
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Element of
Selection
Non Probability Probability
Unrestricted Convenience Sampling Simple Random
Restricted Judgment Sampling
Deviant Case, Quota, Adhoc
Quota
Systematic Sampling
Snow Ball Sampling Cluster Sampling
Stratified Sampling
Double Sampling