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Sampling

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Sampling

  1. 1. Sampling
  2. 2. • Sampling • Population • Elements • Subject • Sampling unit • Sampling process • Sampling units
  3. 3. Sampling • The process of selecting right individual objects, or events as representative for the entire population is known as sampling. For example • Sensex-30 companies • Nifty- 50 companies
  4. 4. Population • The population refers to the entire group of people, events or things of interest that researcher wishes to investigate and wants to make inferences. For example • If researcher wants to know the investment pattern of Mumbai city then all residents of Mumbai will be the population.
  5. 5. Element • An element is a single member of the population. e.g. • Total students of GICED is 1800, then each student will be the element.
  6. 6. Sample • It is a subject of the population • It comprises some members selected from it • In other words, some, but not all elements of the population form sample. • By studying the sample, the researcher should be able to draw conclusion that are generalized to the population of interest E.g. • Sensex-BSE • Nifty-NSE
  7. 7. Sampling unit • It is the element or set of element that is available for set selection in some stage of the sampling process
  8. 8. Subject • It is a single member of the sample just as an element of the population.
  9. 9. Reasons for sampling • Practically impossible to collect data from, or examine every element • Even it is possible, it is prohibitive in terms of time, cost and human resources • Study of sample rather than population is also sometimes likely to produce more reliable data especially when a large number of elements is involved • E.g. election survey by media
  10. 10. Representative Sample - • σ 2 • σ • Population – µ • σ 2 • σ
  11. 11. Sampling process • Define population • Determine sample frame • Determine sample design • Determine appropriate sample size • Execute sampling process
  12. 12. Define population • It must be defined in terms of elements, geographical boundaries and time
  13. 13. Determine sample frame • It is a representation of all the elements in the population from which the sample is drawn E.g. • The payroll of an organization would serve as the sampling frame if its members are to be studied
  14. 14. Sampling design Two major types of sampling design • Probability • Non probability
  15. 15. Probability • The elements in the population have some known, non-zero chance or probability of being selected as a sample object • This design is used when the representative of the sample is of importance in the interest of wider generalizability
  16. 16. • Non probability • Elements in the population do not have a known or predetermined chance of being selected as a subject • When time and other factors, rather than generalizability, become critical, non probability sampling is generally used
  17. 17. Determine sample size • Is a large sample better than a small sample? • Decision about how large the sample size should be a very difficult one. • We can summarize the factors affecting factors affecting decision on sample size as • Research objective • Cost and time constraint • Amount of the variability in the population itself
  18. 18. Probability sampling 1. Unrestricted or simple random sampling 2. Restricted or complex probability sampling • Systematic sampling • Stratified random sampling • Cluster sampling • Double sampling or Area sampling
  19. 19. Unrestricted or simple random sampling • In this sampling every element in the population has known and equal chance of being selected as a subject • This sampling design, has the least bias and offers the most generalizability • However this sampling process could become cumbersome and expenive
  20. 20. Restricted or complex probability sampling • These probability sampling procedures offer a viable and sometimes more efficient design • More information can be obtained
  21. 21. Systematic sampling It involves drawing every nth element in the population starting with randomly chosen element between 1 & nth element
  22. 22. Stratified random sampling • It involves a process of segregation followed by random selection of subject from each stratum
  23. 23. Cluster sampling • In this target population is first divided into clusters • A specific cluster sampling is area sampling
  24. 24. • Double sampling • When further information is needed from subset of the first group from which some information has already been collected for the same study.
  25. 25. Non probability sampling • There are two types 1. Convenience 2. Purposive
  26. 26. Convenience • Collection of information from members of the population who are conveniently available to provide it.
  27. 27. Purposive 1. Judgment 2. Quota
  28. 28. 1. Judgment • Most advantageously placed • In the best position to provide information required
  29. 29. Quota • Certain group

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