SAMPLING TECHNIQUES

1. SAMPLING TECHNIQUES Mrs.S. Valarmathi. M.sc., Mphil., Research Officer, Department of Epidemiology The Tamil Nadu Dr. MGR Medical University

3. Is it possible to taste the whole sambar and add salt NO Is it possible to work out what 50 million people think by asking only 1000? YES

4. What exactly IS a Population ? The entire group under study as defined by research objectives. Sometimes called the “universe.” The totality or aggregate of all individuals with the specified characteristics is a population

5. TYPES OF POPULATION • Finite • Infinite • Hypothetical

6. What exactly IS a “sample”?

7. What exactly IS a “sample”? A subset of the population.

8. What exactly IS a “sampling”?

9. What exactly IS a “sampling”? Selecting and studying a small number of subjects from a specified population in order to draw inferences about the whole population

10. Sampling Terminology Who do you want to generalize to THEORETICAL POPULATION

11. Sampling Terminology What population can you get access to ? STUDYPOPULATION

12. Sampling Terminology Who do you want to generalize to What population can you get access to ? THEORETICAL POPULATION STUDYPOPULATION

13. Sampling Terminology How Can you get access to them? SAMPLING FRAME

14. Sampling Terminology Who do you want to generalize to What population can you get access to ? How Can you get access to them? THEORETICAL POPULATION THEORETICAL POPULATION STUDYPOPULATION SAMPLING FRAME

15. Sampling Terminology Who is in your study? THE SAMPLE

16. Sampling Terminology Who do you want to generalize to What population can you get access to ? How Can you get access to them? Who is in your study? THE SAMPLE THEORETICAL POPULATION SAMPLING FRAME STUDY POPULATION

17. Sampling and representative ness Sample Study Population Theoretical Population Theoretical Population  Study Population  Sample

18. Sampling Fraction n N

19. Sample • Representativeness express the degree to which the sample data precisely characterize the population. • Sample should reflect the study character of the population . • Strength of statistical inference also depends on representativeness. • Confidence level 95%, 99% for population

20. Errors Survey Errors Systematic / Non-sampling Errors Random / Sampling Errors

21. S P S S Why Sampling Errors ? Sampling error can be reduced simply by increasing the sample size! When you take a sample from a population, you only have a subset of the population - a piece of what you’re trying to understand.

22. Standard Error IV Mean II Mean V Mean I Mean Population Mean III Mean

23. The sampling distribution • The distribution of an infinite number of samples of the same size as the sample in your study is known as the sampling distribution.

24. Standard Error • The standard deviation of the sampling distribution. • It tells us something about how different samples would be distributed • A measure of sampling variability

25. Systematic / Non-sampling Errors • Occurs whether a census or a sample is being used. • Results solely from the manner in which the observations are made. • Cannot be measured.

26. Bad Question! Types of Non-sampling Errors • Coverage error • Non response error • Measurement error Excluded from frame. Follow up on non responses.

28. Sampling Probability Sampling Non-Probability Sampling TYPES OF SAMPLING

29. TYPES OF SAMPLING Non-Probability Sampling Probability Sampling

30. Sampling Probability Sampling Non-Probability Sampling Simple Random Stratified Convenience Quota Cluster Systematic TYPES OF SAMPLING Judgement Snowball

31. Convenience Sampling The sample is identified primarily by convenience. Examples: “Man on the street” Medical student in the library Volunteer samples Patient coming to OP Problem : No evidence for representativeness. HAPZHARD SAMPLE

32. Judgment Sampling The sampling procedure in which an experienced research selects the sample based on some appropriate characteristic of sample members… to serve a purpose (Purposive sampling, Deliberate sampling)

33. Quota Sampling Attempt to be representative by selecting sample elements in proportion to their known incidence in the population

34. Snowball sampling Typically used in qualitative research When members of a population are difficult to locate, hidden activity groups, non-cooperative groups Recruit one respondent, who identifies others, who identify others,…. Primarily used for exploratory purposes

35. Respondent Driven Sampling • Applicable for Hidden, Hard to reach populations – MSM, IDU • A systematic form of snowball sampling with unique identification procedure. • Depends social network of target population • Under certain assumptions may be treated as a Random sample

36. Steps involved in RDS • Begin with a small set of identified seeds. • Seeds recruit peers, who recruit their peers, etc., continued till required sample size is achieved. • Recruits are linked by coupons with unique identifying numbers. • Incentives provided for participation and each successful recruit.

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43. RDS: Advantages • No need of sampling frame / mapping • Ease of field operations - Target members recruit samples for you. • Reach less visible segment of population

44. HIV – ve HIV +ve CCPUR IDU Network

45. Convenience sampling relies upon convenience and access Non Probability Sampling Methods Judgment sampling relies upon belief that participants fit characteristics Quota sampling emphasizes representation of specific characteristics Snowball sampling relies upon respondent referrals of others with like characteristics

46. Probability samples A sampling that selects subjects with a known, non zero, probability. Removes possibility of bias in selection of subjects. Allows application of statistical theory to results. Important when one wishes to generalize the findings of the sample to the larger population from which samples are selected.

47. Simple random sampling Applicable when population is small, homogeneous & readily available Required number of units are selected randomly. Each unit of the frame has an equal non zero probability of selection.

48. Simple random sampling Merits Easy to implement if list frame available or small population Approximately satisfies the sampling model on which conventional statistics is based, so we can carry out complex analyses Demerits Need complete list of units Units may be scattered

49. SRS METHODS LOTTERY METHOD RANDOM NUMBERS TABLE Computer Generated Random numbers

50. Simple random sampling Example: evaluate the prevalence of hypertension among the 1200 children attending schoolin the age group 14 to 17 years. List of children attending the school Children numerated from 1 to 1200 Sample size = 100 children Random sampling of 100 numbers between 1 and 1200