Sampling is used to learn about a population by studying a subset of it. It allows researchers to gather information in a time and cost-effective manner. There are two main types of sampling: probability sampling, where every item has an equal chance of being selected, and non-probability sampling, which has no basis for estimating selection probabilities. Some common sampling designs include simple random sampling, systematic sampling, stratified sampling, cluster sampling, and quota sampling. Good sample design ensures representativeness, adequacy, independence, and homogeneity while accounting for resources and study goals.

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 Adequacy 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 - Method of sampling adopted - 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 Unplanned sampling is often misleading Requires experts At times complicated Not appropriate when every unit in the domain has to be studied.

16.    Thank you   