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# Sampling

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• 1. SAMPLING
• 2. Sampling
Sampling is simply the process of learning about the population on the basis of a sample drawn from it.
Sampling is a tool which helps to know the characteristics of the universe or population by examining only a small part of it.
• 3. Terminology
Population - Population is the group which is used to generalize the study.
Sample - Sample is the group of people that is to be selected for study.
Census- A census is the process of obtaining information about every member of a population
Sampling Design- It is a technique or procedure for selecting item for the sample.
• 4. Sampling frame- The listing of the accessible population from which the sample is drawn is called sampling frame.
Response- Response is a specific measurement value that a sampling unit supplies.
Population Parameter – When mean or average is calculated on entire population, this is not referred as statistics, it is a population parameter.
• 5. Why Sampling???
Time constraints
Cost constraints
Easy and convenient Processing
Reuse of population
Lesser resources required
Helpful in studying the hypothetical data.
• 6. SAMPLING MODEL
• 7. Characteristic of sampling
Representativeness
Independence
Homogeneity
• 8. Steps in sample design
Type of Universe
Sampling Unit
Source List
Size of Sample
Parameters of Interest
Budgetary Constraints
Sampling Procedure
• 9. Types of Sampling Design
NON PROBABILITY SAMPLING
PROBABILITY SAMPLING
JUDGEMENT SAMPLING
SIMPLE RANDOM SAMPLING
SYSTEMATIC
SAMPLING
QUOTA SAMPLING
CLUSTER SAMPLING
CONVENIENCE SAMPLING
STRATIFIED SAMPLING
• 10. NON PROBABILIITY SAMPLING
It is a sampling procedure which does not afford any basis for estimating the probability that each item in the population has of being included in the sample.
Also known as deliberate sampling, purposive sampling.
• 11. PROBABILITY SAMPLING
Probability sampling is a sampling procedure where every item in the universe has an equal chances of inclusion in the sample.
Also known as ‘random sampling’ or ‘chance sampling’.
• 12. CHARATERSTICS OF GOOD SAMPLE DESIGN
Must result in a truly representative sample
Must result in small sampling errors
Must be viable in context of funds available for the research study.
Must be such so that systematic bias can be controlled in a better way.
• 13. Techniques of selecting arandom sample
Size of sample
- Size of universe
- Resources available
- Degree of accuracy
- Homogeneity
- Nature of study
- Nature of respondents
• 14. Merits of sampling
Less time consuming
Less cost
More reliable results
More detailed information
Practical method
To judge the accuracy of the information obtained on a census basis.
• 15. Limitations of sampling