sampling techniques used in research
- 2. Population is defined as the entire mass of observation, which is the parent group from which a sample is to be formed. Sample is defined as the aggregate of objects, person or elements, selected from the universe.
- 3. Sampling- The method of taking the sample is known as sampling.
- 4. Economical Importance of Sampling Accuracy Save time and efforts Easily approachable Practical Errors can be controlled
- 5. Sampling Probability Non Probability
- 6. Probability sampling • Every unit of the population has an equal chance of being selected for the sample. Non probability sampling • Sampling techniques one cannot estimate beforehand the chance of each element being included in the sample.
- 7. Simple random sampling Stratified random sampling Systematic sampling Cluster sampling Multi-stage sampling
- 8. Random sampling is applied when the method of selection assures each individual element in the universe an equal chance of being chosen.
- 9. Conjunction with other methods Advantages unbiased Easy to find errors Equal chance
- 10. Consumes time and energy More chances of misleading sample Difficult for comparison study
- 11. Lottery Method Tippet’s Number Grid method
- 12. Stratified sampling - When the population is divided into different strata then samples are selected from each stratum by simple random sampling or by regular interval method we call it as stratified random sampling method.
- 13. Advantages •Comparing sub-categories •Save time, money and energy •Can represent various group
- 14. Requires more efforts Needs a larger sample size Strata are overlapping, chances of bias
- 15. Stratified sampling Disproportionate sampling Proportionate sampling
- 16. Systemic sampling - This sampling is obtaining a collection of elements by drawing every nth person after that; n is a number termed as sampling interval.
- 17. Advantages •Easy to use Disadvantages •Over representation of several groups is greater.
- 18. Cluster Sampling- The whole population is surveyed and such areas are located wherein elements are seen clustering themselves and sample is selected from such clusters and they reflect all characteristics of the Universe.
- 19. Advantages Easier to apply larger Geographical area Save time of travelling
- 20. Disadvantages Not good representative of the population Sampling error Same individual can belong to two clusters and studied twice
- 21. Multi stage sampling sample is selected in various stages but only last sample is studied.
- 22. Advantages •Good representative of population •Improvement of other sampling methods Disadvantages •Difficult and complex method
- 23. Non probability Sampling- One cannot estimate beforehand the probability of each element being included in the sample. It also does not assure that every element has a chance of being included.
- 24. Incidental/ Accidental sampling Convenience sampling Purposive sampling Quota sampling
- 25. Incidental or Accidental sampling means selecting the units on basis of easy approaches.
- 26. Advantages • Easy and quick results • Saves time, money and energy Disadvantages • Not representative of population • Cannot produce reliable results
- 27. In Convenience method, the investigator selects certain items are to his convenience. No pre planning is necessary for the selection of items.
- 28. disadvantages • Biased data • Not representative population Advantages • Easy method • Economical
- 29. Purposive sampling- The selection of elements is based upon the judgement of the researcher, the purposive sampling is called judgement sample
- 30. Advantages •Control on variable Disadvantages •Reliability of criterion is questionable
- 31. Quota sampling:-In the quote sampling the interviewers are instructed to interview a specified number of persons from each category.
- 32. • Practical Advantages • Economical • Not true representative • Not free from error disadvantages
- 33. Errors Sampling Errors Biased errors Unbiased errors Non Sampling Errors
- 34. Technique Strength Weakness Probability Simple Random Sampling Easily understood, results projectable Expensive, assurance of representative Stratified sampling Include all important sub populations Expensive, Difficult to select relevant stratification variables Systemic sampling Increase representativeness Can decrease representative Cluster sampling Easy to implement, cost effective Difficult to interpret results Non probability Convenience sampling Least expensive, least time consuming. Quota sampling Sample can be controlled for certain characteristics Bias, no assurance of representative
- 35. Choosing non Probability vs. Probability sampling Conditions favouring the use of Factors Non probability sampling Probability sampling Nature of research Exploratory Conclusive Relative magnitude of Non sampling errors sampling and non are larger sampling errors Sampling errors are larger Variability in the population Homogeneous Heterogeneous Statistical consideration Unfavourable Favourable Operational considerations Favourable Unfavourable