Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our User Agreement and Privacy Policy.

Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our Privacy Policy and User Agreement for details.

Successfully reported this slideshow.

Like this presentation? Why not share!

- 51923545 res320-res-320-individual-... by amazen12 5730 views
- Problem definition and research pro... by university of pes... 20344 views
- Bus 309 business ethics week 10 quiz by mariajackson2018 162 views
- T5 sampling by kompellark 4466 views
- Business Research Methods. problem ... by Ahsan Khan Eco (S... 32884 views

No Downloads

Total views

909

On SlideShare

0

From Embeds

0

Number of Embeds

2

Shares

0

Downloads

35

Comments

0

Likes

1

No embeds

No notes for slide

- 1. Sampling
- 2. • Sampling • Population • Elements • Subject • Sampling unit • Sampling process • Sampling units
- 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. 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. 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. 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. Sampling unit • It is the element or set of element that is available for set selection in some stage of the sampling process
- 8. Subject • It is a single member of the sample just as an element of the population.
- 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. Representative Sample - • σ 2 • σ • Population – µ • σ 2 • σ
- 11. Sampling process • Define population • Determine sample frame • Determine sample design • Determine appropriate sample size • Execute sampling process
- 12. Define population • It must be defined in terms of elements, geographical boundaries and time
- 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. Sampling design Two major types of sampling design • Probability • Non probability
- 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. • 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. 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. 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. 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. Restricted or complex probability sampling • These probability sampling procedures offer a viable and sometimes more efficient design • More information can be obtained
- 21. Systematic sampling It involves drawing every nth element in the population starting with randomly chosen element between 1 & nth element
- 22. Stratified random sampling • It involves a process of segregation followed by random selection of subject from each stratum
- 23. Cluster sampling • In this target population is first divided into clusters • A specific cluster sampling is area sampling
- 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. Non probability sampling • There are two types 1. Convenience 2. Purposive
- 26. Convenience • Collection of information from members of the population who are conveniently available to provide it.
- 27. Purposive 1. Judgment 2. Quota
- 28. 1. Judgment • Most advantageously placed • In the best position to provide information required
- 29. Quota • Certain group

No public clipboards found for this slide

Be the first to comment