SAMPLING TECHNIQUES IN EPIDEMIOLOGICAL STUDIES.pptx

IN DEPTH VIEW OF SAMPLING TECHNIQUES IN EPIDEMIOLOGICAL STUDIES.

1. SAMPLING TECHNIQUES IN EPIDEMIOLOGICAL STUDIES BY: DR ASHISH PALIWAL (PG- Year) MODERATOR : DR SRISTHI KUKREJA (ASSISTANT PROFESSOR) DEPARTMENT OF COMMUNITY MEDICINE MGMC, JAIPUR  

2. CONTENTS Introduction Need for sampling Sampling Process Essentials of Sampling Methods of Sampling Non Probability Sampling Probability Sampling

3. INTRODUCTION Population/Universe : in statistics denotes the aggregate from which sample (items) is to be taken. A population can be defined as including all people or items with the characteristic one wishes to understand. Because there is very rarely enough time or money to gather information from everyone or everything in a population, the goal becomes finding a representative sample (or subset) of that population.

4. INTRODUCTION Sampling frame is the list from which the potential respondents are drawn . A sample is “a smaller (but hopefully representative) collection of units from a population used to determine truths about that population”

5. SAMPLING Sampling : the process of learning about population on the basis of sample drawn from it. Three elements in process of sampling: Selecting the sample Collecting the information Making inference about population Statistics: values obtained from study of a sample. Parameters: such values from study of population

6. METHODS Whole Population : Census method or complete enumeration method Few Individuals : Sample method

7. CENSUS (Complete Enumeration Survey) Merits: Data obtained from each and every unit of population. Results: more representative, accurate, reliable. Basis of various surveys. Demerits: More effort ,money , time. Big problem in underdeveloped countries.

8. ADVANTAGES OF SAMPLING Less resources (time, money) Less workload. Gives results with known accuracy that can be calculated mathematically.

9. SAMPLING PROCESS Defining the population of concern . Specifying a sampling frame , a set of items or events possible to measure. Specifying a sampling method for selecting items or events from the frame. Determining the sample size . Implementing the sampling plan. Sampling and data collection

10. NON PROBABILITY SAMPLING Principal: Selection of sampling units does not depend on probability. Non-random in nature.

11. TYPES OF NON PROBABILITY SAMPLING Judgmental /Purposive Sampling Convenience Sampling Self selection Sampling Snowball Sampling Criterion Sampling Quota Sampling

12. JUDGMENT /PURPOSIVE SAMPLING Judgment/Purposive/Deliberate sampling. Depends exclusively on the judgment of investigator . Sample selected which investigator thinks to be most typical of the universe. Merits: Small no. of sampling units Study unknown traits/case sampling Urgent public policy & business decisions Demerits: Personal prejudice & bias No objective way of evaluating reliability of results

13. CONVENIENCE SAMPLING Convenient sample units selected. Selected neither by probability nor by judgment. Merit – useful in pilot studies. Demerit – results usually biased and unsatisfactory .

14. SELF SELECTION SAMPLING Participants take part in the research on their own as volunteers. Commonly seen in online surveys.

15. SNOWBALL SAMPLING A special non probability method used when the desired sample characteristic is rare. It may be extremely difficult or cost prohibitive to locate respondents in these situations. ex: Drug addicts, sex workers etc. Snowball sampling relies on referrals from initial subjects to generate additional subjects. STEPS: Make contact with one or two cases in the population. Ask these cases to identify further cases. Ask these new cases to identify further new cases. Stop when either no new cases are given or the sample is as large as is manageable.

16. SNOWBALL SAMPLING Merit : Access to difficult to reach populations (other methods may not yield any results). Demerit : Not representative of the population and will result in a biased sample as it is self-selecting.

17. QUOTA SAMPLING Researchers are given quotas to fill from different strata of population. Keeping the proportions of quota same as observed in population. EXAMPLE : A village has a population where 60% are Hindus and 40% Muslims. So, the proportion is 6:4 Now, when selecting participants of investigators choice , the investigator has to select sample based on 6:4 proportion.

18. PROBABILITY SAMPLING Obeys law of probability. Each sampling unit has a known: non-zero probability of being selected. Superior to non-probability sampling technique. TYPES: Simple random sampling. Systematic random sampling. Stratified random sampling. Cluster sampling. Multistage sampling. Multiphase sampling. Sequential sampling.

19. SIMPLE RANDOM SAMPLING Most commonly used, simplest of sampling methods. Each unit has an equal opportunity of being selected. Chance determines which items shall be included. Applicable only when population is Small, homogenous and readily available. Complete population list must be available to build sampling frame. Methods: Random number table. Lottery method Computer generated random number.

20. Merits No personal bias. Sample more representative of population. Accuracy can be assessed as sampling errors follow principals of chance. Demerits Requires completely catalogued universe. Cases too widely dispersed - more time and cost.

21. LOTTERY METHOD Assign population with numbers. Numbers are written in piece of paper and placed in a bowl/container. Thoroughly mixed/ shuffled. Researcher picks up card/ piece of paper. Population members having the number drawn are selected. Repeat until sample size is reached.

22. SIMPLE RANDOM SAMPLING TYPES: With replacement: Probability each item: 1/N Without replacement: Probability 1st draw: 1/N Probability 2nd draw: 1/N-1 N total number of samples .

23. COMPUTER GENERATED RANDOM NUMBER Better used in large population. A program assigns number and randomly selects the sample. Pseudo random in nature as the program is itself run on a underlying formula/algorithm. Thus certain underlying set of instructions are always present.

24. SYSTEMATIC RANDOM SAMPLING Done in Large, scattered and heterogenous population. Method: A random starting point is chosen Remainder of the sample is selected by taking every n th unit. K= Sample interval Sample interval = Total population/desired sample size Example: A sample of 50 is to be taken of 500 population. Then K =500/50 i.e. 10. One random no. is selected less than or equal to sampling interval .ex. 5. Then every 10 th sample is selected following 5, i.e. 15,25,35,45… Until sample size is reached.

25. Merits Simple and convenient. Less time consuming. Demerits Population with hidden periodicities.

26. STRATIFIED RANDOM SAMPLING Done in heterogenous population. To assess the distribution of particular variable. Method: Entire heterogenous population is divided into small homogenous groups called strata. Then from each strata, required no. of study subjects are selected by S.R.S. The strata should be mutually exclusive The strata should be based on some known characteristics ex. Religion, occupation etc. Then, sampling frame of population in each strata is prepared. Finally , samples within each strata is done by probability sampling.

27. Merits More representative. Greater accuracy. Greater geographical concentration. Demerits Utmost care in dividing strata. Skilled sampling supervisors. Cost per observation may be high.

28. CLUSTER SAMPLING A sampling technique in which the entire population of interest is divided into groups, or clusters, and a random sample of these clusters is selected. Each cluster must be mutually exclusive and together the clusters must include the entire population . After clusters are selected, then all units within the clusters are selected No units from non-selected clusters are included in the sample. In cluster sampling, the clusters are the primary sampling unit (PSU’s) and the units within the clusters are the secondary sampling units (SSU’s)  

29. SELECTION OF CLUSTER SAMPLING Simple/ Systematic Random Sampling: clusters may be selected either by simple or systematic random sampling. Probability proportionate to sample size: by this method clusters with more number of subjects have more probability of selection.

30. TYPES OF CLUSTER SAMPLING “ 30 X 7” : Most commonly used. Developed by WHO for immunization coverage. Out of 30 clusters 7 are selected. “40 X 5” : From 40 clusters, 5 clusters are selected. “20 X 10”: From 20 clusters, 10 clusters are selected. “15 X 14” : From 15 clusters, 14 clusters are selected. As number of clusters decreases quality of sample decreases.

31. CLUSTER SAMPLING- STEPS Identification of clusters List all cities, towns, villages & wards of cities with their population falling in target area under study. Calculate cumulative population & divide by , this gives sampling interval. Select a random no. less than or equal to sampling interval having same no. of digits. This forms 1st cluster. Random start + (1 x Sampling Interval) = 2nd cluster. Random start + (2x Sampling Interval) = 3rd cluster. Random start + (29x Sampling Interval) = 30 th cluster  

32. Example: From the following table, 30 villages have to be chosen by cluster sampling out of 82 villages. At first all the villages are listed with respective population and simultaneously cumulative population for each village is also calculated.

33. Now, the final cumulative population i.e. 124356 is divided by 30, which come out to 4145.2 ≈ 4146. This is Sampling Interval (SI). one random number is selected for random start, which is less than or equal to sampling interval (4146). Suppose 1457. The village that has cumulative population equals or just exceeds the particular selected random number 1457 is the first selected cluster i.e. village AB. The second cluster is the village whose cumulative population equals or just exceeds the number (5603), which is calculated as = Random start + (1 x Sampling Interval) = [1457 + (1 x 4146) = 5603] i.e. village AD. The third cluster is the village whose cumulative population equals or just exceeds the number (9749), which is calculated as = Random start + (2x Sampling Interval) = [1457 + (2 × 4146) = 9749] i.e. village AF. This is how all the 30 clusters i.e. villages are selected.

34. SELECTION FROM EACH CLUSTER Simple -One stage Cluster : in first stage , clusters are selected and then all units whit in the cluster are selected. Simple Two stage Cluster : From the selected clusters, in first stage units are selected by simple or stratified random sampling. Multi-stage : When more than two stages are involved. At first stage clusters are as mentioned above. In stage two clusters are stratified, then in third stage units are selected by simple or stratified random sampling from each strata of individual clusters.

35. Merits Most economical form of sampling. Larger sample for a similar fixed cost. Less time for listing and implementation. Reduce travel and other administrative costs. Demerits May not reflect the diversity of the community. Standard errors of the estimates are high, compared to other sampling designs with same sample size .

36. STRATIFICATION V/S CLUSTERING

37. MULTISTAGE SAMPLING Sampling process carried out in various stages. An effective strategy because it banks on multiple randomizations. ( any type of probability sampling can be applied in each stage.) Used frequently when a complete list of all members of the population does not exist and is inappropriate.

38. Merits Introduces flexibility in the sampling method. Enables existing divisions and sub divisions of population to be used as units. Large area can be covered. Valuable in under developed areas. Demerits Less accurate than a sample chosen by a single stage process.

39. MULTIPHASE SAMPLING Part of information is collected from whole sample. Another set of information is obtained from subsample. Example: 20 fever cases Blood investigations done Raised ESR Normal ESR More blood test More blood test ( causing rise in ESR) ( another battery of test to evaluate further)

40. SEQUENTIAL SAMPLING Ultimate sample size is not fixed in advance. Determined on basis of information obtained as survey progresses by discussion rules.

41. THANK YOU