Sampling

1. Sampling Ms. Nithyashree B V Lecturer YNC

2. Terminology Population: The total set of units. • Population is the aggregation of all the units in which a researcher is interested. • Population is a set of people or entities to which the results of a research are to be generalized. Census: A count of (or collection of information from) the entire population.

3. Target population: the entire population in which the researchers are interested and to which they would like to generalize the research findings. • Accessible population: the aggregate of cases that conform to designated inclusion or exclusion criteria and that are accessible as subjects of the study.

4. Sampling: Sampling is the process of selecting a representative segment of the population under study. • Sample: sample may be defined as representative unit of a target population which is to be worked upon by researchers during their study. subset of the population that is selected for a study

6. Elements: the individual entities that comprise the samples and population are known as elements and an element is the most basic unit about whom/which the information is collected. An element is also called as subject in research.

7. Sampling frame: it is a list of all the elements or subjects in the population from which the sample is drown • Sampling error: there may be fluctuation in the values of the statistics of characteristics from one sample to another or even those drawn from the same population

8. Sampling bias: distortion that arises when a sample is not representative of the population from which it was drawn. • Sampling plan: the formal plan specifying a sampling method, sample size, and the procedure of selecting the subjects.

9. The need to sample • Budget constraints prevent you from surveying the entire population • Time constraints prevent you from surveying the entire population • Impracticable to survey the entire population • You have collected all the data but need the results quickly

10. Purposes of sampling • Economical • Improved quality of data • Quick study results • Precision and accuracy of data

11. Contd..... • To draw conclusions about populations from samples • To determine population characteristics by directly observing only a portion (or sample) of the population. • Population is so large and scattered. • It offers high degree of accuracy. • Results can be obtained shortly. • Needs small portions. • Economical one.

12. Principles of sampling: - • Sampling should be • Based on the objectives • Systematic • Clearly defined and easily identifiable • Used throughout the study • Based on sound criteria and avoid errors and bias.

13. Characteristic of good sample • Representative • Free from bias and errors • No substitution and incompleteness • Appropriate sample size

14. Sampling process

15. Factors affect sampling process • Nature of the researcher: • Inexperienced investigator • Lack of interest • Lack of honesty • Intensive workload • Inadequate supervision

16. 2. Nature of the sample: • Inappropriate sampling technique • Sample size • Defective sampling frame

17. 3. Circumstances • Lack of time • Lack of geographic area • Lack of cooperation • Natural calamities

18. Advantage and disadvantages of sampling

19. Types of sampling

20. Basic sampling classifications Non-probability samples Instances in which the chances (probability) of selecting members from the population are unknown Probability samples One in which members of the population have a known chance (probability) of being selected

21. Sampling techniques -types • Probability • Non probability

22. Probability S T • Simple random sampling • Stratified R S • Systematic R S • Cluster R S • Sequential R S

23. Non Probability S T • Purposive Sampling • Convenient ‘’ • Consecutive ‘’ • Quota ‘’ • Snow ball ‘’

24. Probability sampling technique • Based on the theory of probability • Random selection of elements from the population • Used to enhance representativeness of the selected sample for the study • Chance of systematic bias is relatively less because subjects are randomly selected

25. Features of Probability Sampling • Equal chance to all individuals in the population • Researcher must guarantee that every individual has an equal opportunity for selection • Absence of systematic and sampling bias

26. Simple random sampling • Most pure and basic probability sampling design • Whole process of sampling is carried out in a single step, with each subject chosen independently of the other members of the population.

27. Requisites of Simple random sampling technique • Population must be homogeneous • Researcher must have list of elements or members of accessible population

28. Steps of Simple random sampling technique • Identify accessible population • Prepare a list of all the elements/members of the population • Draw sample using sample frame by methods like • Lottery method • Use of table of random numbers • The use of computer

30. Merits of Simple random sampling technique • Easy to assemble sample • Fair way as equal opportunity provided to each member • Require minimum knowledge about sample • Unbiased method • Free from sampling errors • Sample errors can be easily computed and accuracy of estimate easily assessed

31. Demerits • Require up to date list of accessible population • Doesn’t make use of knowledge about population which researcher may already have • Lots of procedure to be done before sampling to be accomplished • Expensive and time consuming

32. Stratified random sampling technique • Used for heterogeneous population • Division into homogeneous group /strata • Randomly select the final subject from different strata • Strata are devided according to selected traits of the population i.e. age, gender, religion, type of institution, type of care etc

33. Example • If the researcher is studying attitude of women towards menopause, the women can be divided into two strata working women and non working women • Here stratification is done on the basis of work • Researcher can select required sample from each of this group say 50 from each women 500 • Working women Non working women

34. Categories of Stratified random sampling technique According weightage of sample and proportion it is two: • Proportionate stratified sampling technique • Disproportionate Stratified random sampling technique

35. Proportionate stratified sampling technique

36. Disproportionate stratified sampling technique

38. Merits of stratified sampling technique • Ensures representation of all the group • Researcher also employ when they want to observe existing r/s between two or more subgroups, thus helps in comparisonrepresnt the inaccessible population and even the smallest poplation • Higher statistical precision • As Higher statistical precision it needs less sample size which save much time, money and effort

39. Demerits of stratified sampling technique • Proportionate stratification requires accurate information on proportion of population • Large population to be available to select subjects • Chance of faulty classification

40. Systematic random sampling technique • It involves selection of every Kth case from list of group • A desired sample size is established at some number(n) and size of population must be known or estimated(N). K=N/n

41. Number of subjects in target population K= ------------------------------------------------------ size of the sample

42. Merits of Systematic random sampling technique • Researchers find it convenient and easy to use • Distribution of sample is spread evenly over the entire population • Less time consuming and cheaperthan sampling technique • Statistically more efficient and better representative sample elements are randomly distributed

43. Demerits of Systematic random sampling technique • If first subject is not selected randomly then it becomes a nonrandom sampling technique • Sometime may be biased • If sampling frame is nonrandomly distributed subjects this sampling technique may not be appropriate to select representative sample

44. Cluster or multistage sampling • Cluster sampling means random selection of sampling unit consisting of population elements. Then from each selected sampling unit a sample of population elements is drawn by either simple random sampling technique / stratified sampling technique

45. It can be used whene population elements are scattered over a wide areaand when it is impossible to obtain list of all elements. • Important is to note that give equal chances of being selected for each clusters

46. Cluster or multistage sampling • One stage cluster sample • Two stage cluster sample • Multistage cluster sample • Probability proportion to size cluster sampling

47. Merits of Cluster or multistage sampling • Cheap, quick, easy for large population • Large population can be studies only list of members to be obtained • Enables investigators to use existing division like districts, villages.. Etc. • Same cluster can be used again for the study

48. Demerits of Cluster or multistage sampling • Least representative • Possibility of High sampling error • If small population under study this technique not at all useful

49. Sequential sampling • It’s a different method • Sample size is not fixed • Investigator select small sample and tries out make inferences; if not able to draw results he or she then adds more subject until clear cut inferences can be drawn

50. Ex: sequential sampling