1. Sampling Dr. Leela Community Medicine

2. 2 Learning objectives Define sampling Define Population & sample Purpose of studying a sample Different types of sampling Techniques of sampling procedures Bias in sampling Ethical consideration

3. Purpose of Statistics Assemble, Organize and Analyze Data Draw Conclusions about Data Form Predications

4. How Data is Obtained Observational Studies Descriptive Associations Case Control Cohort Experimental Controlled Two or more groups Randomized Laboratory Experiments

5. 5 What is Sampling? The process of selecting a number of study units from a defined study population

6. 6 Population & Samples  “ Population” in bio-statistics means “an aggregate of persons, events, or procedures with a common characteristics”  A subset of the population selected so as to represent the population is called sub unit e.g. for study of immunization coverage, population would be all children under one year of age in the defined area  one issue is to consider whether it would be necessary or feasible to study all the population. e.g. immunization coverage of Malaysia in 2009  to study the entire population; it would not only be expensive but also not improve the quality of the study

7. 7  It is usual to study a sample of the population on the basis that such a sample is truly representative of all the characteristics of the population  A representative sample can be drawn by using appropriate sampling technique  Study population has to be clearly defined.  Each study population consists of study units.  Data from population is called “parameter”  Data from the sample is called “statistic” Population & Samples (Cont:)

8. 8 Target Population –All Operated Cases –Universe Study Population –All Surgical Cases –Representative of Target Sample Population

9. 9 Problem Study Population Study Unit Malnutrition related to weaning in District X All children 6-24 months of age in District X One child between 6 and 24 months in District X Study population and study unit depend on the problem which we want to investigate.

10. 10 Parameter & Statistics A value calculated from a population, such as –Mean [µ] –Standard Deviation [σ] is called PARAMETER The value calculated from a sample, such as –Mean [x] –Standard Deviation [s] is called a STATISTICS

11. 11 Why is the sampling needed in the study? –Unable to study all members of a population –Save time and money –Measurements may be better and more accurate in sample than in entire population –Feasibility –Reduce bias

12. 12 Main Objectives of Sampling 1.Estimation of Population Parameters from the Sample Statistics [Mean, SD, SE, Proportion etc] 2. To test the Hypothesis about the Population from which the Sample or Samples are drawn

13. 13 Sampling Technique Process or technique of selecting a sample of appropriate characteristics and adequate size. The goal in sampling is to secure a sample that is representative of the population. Goal of sampling techniques

14. What is an adequate Sample?..... Usually the sample size is arrived at through statistical calculations These calculations depend upon 1.The assumed size [e.g. prevalence], in the population, of the characteristics that we want to study 2.The margin of error that we are prepared to allow ourselves

15. Sampling Variations In repeated samples of the same size from a population the population parameters would not be exactly the same in each sample However the estimates should all be close to the true value of the parameter in the population and the estimations should be similar to each other. Owing to chance, different samples give different results This is called Sample Variation 15

16. 16 Sampling Sampling  Probability sampling  Non-probability sampling Probability Sampling  involves using random selection procedures to ensure that each unit of the sample is chosen on the basis of chance.  All units of study population should have an equal or a known chance of being included in the sample. sampling frame  requires a listing of all study units exists, i.e. sampling frame

17. 17 1.Simple Random Sampling 2.Systematic Sampling 3.Stratified Sampling 4.Cluster Sampling 5.Multi-stage Sampling Probability Sampling

18. 18 Random Sample 4 Males 1 Female

19. 19 1.Simple Random Sampling Steps are; assign a serial number to each population element its value is unknown before the selection is made selection can be made by drawing serial numbers from a pool containing all serial numbers, e.g. lottery this is done until the sample size required is achieved alternative methods are table of random numbers and computer software

20. –Advantages Purest form of probability sampling Simple process and easy to understand Easy calculation of means and variance Ideal for Small sample selections –Disadvantages Not most efficient method, Requires knowledge of the complete sampling frame Cannot always be certain that there is an equal chance of selection Not good for very large populations Simple Random Sampling

21. 21 Systematic Sampling

22. 22 2. Systematic Sampling Steps are;  number of each population element is already arranged systematically in serial manner, e.g. case records using hospital admission number  determine population size (N) and sample size (n)  calculate the sampling interval (K) [K = N/ n]  randomly select the random start (r) among the first interval

23. 23 e.g. Batch 17 population size (N) = 230 Batch 17 sample size (n) = 10 K = N/ n = 230/ 10 = 23 select a random start (r) among serial numbers from the first interval (1,2,3,4,5,….23) if (r) = 3, continue to select as  (r + K) = 3 + 23= 26  (r + 2K)= 3+ 46 = 49  (r + 3K)= 3 + 69 = 72  until 10, sample size is obtained

24. –Advantages Sampling frame does not need to be defined in advance Easier to implement in the field If there are unrecognized trends in the sample frame, systematic sample ensure coverage of the spectrum of units As good as random sampling Simplicity in the technique Systematic sampling

25. 25  Random or systematic samples of predetermined size  Random or systematic samples of predetermined size will then have to be obtained from each stratum. e.g. survey of house water supply in a district of 20,000 households (20% urban, 80% rural). i.e. Urban= 4,000, Rural=16,000 Sample size should be 100 urban from 4,000 households and 200 rural from 16,000 sample unit for urban= 1 in 40 houses sample unit for rural = 1 in 80 houses

26. 26 Stratified Sample 3 Males 3 Females

27. 27 3. Stratified Sampling Steps are; strata)  Sampling frame is divided into groups (strata)  the strata may be based on a single criterion e.g. by sex = two strata of male & female by residence = two strata of urban & rural  may be also based on combination of two or more criteria e.g. by age and sex = female of reproductive age

28. Cluster Sampling

29. 29 4. Cluster sampling  Selection of groups of study units (clusters) instead of selection of study units individually  Population is divided into clusters and a subset of the clusters is randomly selected e.g. study of KAP for family planning in rural community of a region, list of villages is made and random sample of villages is chosen and all study units in selected villages are interviewed. Disadvantage: need a large sample size

30. 30 5.Multi-stage Sampling  Combination of two or more sampling methods are used as stage by stage for very large survey e.g.  simple random sampling for States  cluster sampling of Districts for each selected State  stratified sampling as part of cluster sampling (rural and urban)  systematic sampling for selection of clusters  simple random sampling from within each cluster

31. 31 e.g. study of utilization of pit latrines in a district, 150 houses are to be visited. The district is composed of 6 sub districts and each sub district has between 6-9 villages Steps are: Select 3 sub districts out of 6 by SRS Select 5 villages from each sub districts by SRS (15 villages) Select 10 houses from each village by systematic sampling or stratified sampling

32. 32 Non-probability Sampling Reasons for using Non-probability sampling Survey of hard to identify groups (e.g. IVDUs) Pilot surveys (evaluation of a health programme) Types of Non-probability sampling  Purposive sampling  Convenience sampling  Snow ball sampling

33. 33 1. Purposive sampling  used when to study the selected subjects for special reason, e.g. effect of new drug in volunteers 2.Convenience sampling  just only conveniently, e.g. at a bus stop, patients in waiting room of clinic 3.Snow ball or chain sampling a)used in studying population that are hard to reach and /or hidden b)observers enter to the specified area with a team containing only few members c)street to street or house to house sample selection is done d)become large group of people at the end of sample selection, e.g. survey of IVDUs

34. 34 Mr. Supandi and His Master

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37. Sampling Errors Two types of errors--sampling errors and non sampling errors Sampling Error is the unavoidable difference between the value of a sample statistics and the corresponding population parameter (the estimates from each sample would differ from the others) Non Sampling Error; These are systematic errors occurring during estimation (response differences, definitional difficulties, differing respondent interpretations, and respondent inability to recall information). To reduce SE: Increase sample size & unbiased probability sampling

38. Bias in Sampling  Common sources of bias are Non response Volunteers only Registered patients only Seasonal bias 38

39. 39 Ethical Consideration If the recommendation from the study will be implemented in entire population, one has an obligation to draw a sample from the population in a representative way.

40. 40 References Designing & Conduction Health System Research Projects (Vol:1), KIT Publishers (Amsterdam) by Corlien M. Varkevisser, Indra. Pathmanathan, Ann Brownlee BMJ Statistics at Square One, tenth edition by TDV Swinscow & MJ Campell Stat Trek Teach yourself statistics online StatTrek

41. 41 Thank You