final-Sampling-techniques.ppt
- 1. Sampling methods & Sample Size Estimation Abdullah Asady MD, MSc 1
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- 4. Research IDs • Google Scholar: https://scholar.google.com/citations?user=bM2ThzUAAAAJ&hl=en • ORCiD: https://orcid.org/0000-0001-9775-739X • Web of Science: AAX-5132-2021 • Research Gate: https://www.researchgate.net/profile/Abdullah-Asady-2
- 5. Outline History of sampling Why sampling Sampling concepts and terminologies Types of sampling Sampling and non-sampling errors Sample size estimation 5
- 6. History of Sampling Dates back to 1920 and started by Literary Digest, a news magazine published in the U.S. between 1890 and 1938. Digest successfully predicted the presidential elections in 1920, 1924,1928, 1932 but; Failed in 1936… The Literary Digest poll in 1936 used a sample of 10 million, drawn from government lists of automobile and telephone owners. Predicted Alf Landon would beat Franklin Roosevelt by a wide margin. But instead Roosevelt won by a landslide. The reason was that the sampling frame did not match the population. Only the rich owned automobiles and telephones, and they were the ones who favored Landon. 6
- 7. What is sampling A sample is some part of a larger body specially selected to represent the whole Sampling is then is taking any portion of a population or universe as representative of that population or universe Sampling is the process by which this part is chosen 7
- 8. Key Definitions A population (universe) is the collection of things under consideration A sample is a portion of the population selected for analysis A parameter is a summary measure computed to describe a characteristic of the population A statistic is a summary measure computed to describe a characteristic of the sample 8
- 9. A Census A survey in which information is gathered about all members of a population Gallup poll is able to develop representative samples of any adult population with interviews of approximately 1500 respondents That sample size allows them to be 95% confident that the results they obtain are accurate within + or – 3% points 9
- 10. Sampling concepts and terminologies Population/Target population Sampling frame Sampling unit 10
- 11. Population/Target Population Target Population is the collection of all individuals, families, groups organizations or events that we are interested in finding out about. Is the population to which the researcher would like to generalize the results. For example, all adults population of Afghanistan aged 65 or older. 11
- 12. Sampling Frame The actual list of sampling units from which the sample, or some stage of the sample, is collected It is simply a list of a study population 12
- 13. Sampling unit/Element/ Unit of analysis Sampling unit is the unit about which information is collected. Unit of analysis is the unit that provides the basis of analysis. Each member of a population is an element. (e.g. a child under 5) Sometimes it is household, e.g. any injury in the household in the last three months. 13
- 14. Target population List of households (sampling frame) Each household (sampling unit) Children less than 5 years (sampling element) 14
- 15. Basic Principles • Law of Statistical Regularity – “moderately large number of the items chosen at random from the large group are almost sure on the average to possess the features of the large group.” • Law of Inertia of Large Numbers – the larger the size of the sample; the more accurate the results are likely to be. 15
- 16. Advantages of sampling Accurate. Economical in nature. Reliable. High suitability ratio towards the different surveys. Takes less time. In cases, when the universe is very large, then the sampling method is the only practical method for collecting the data. 16
- 17. Disadvantages of sampling Inadequacy of samples. Chances for bias. Problems of accuracy. Difficulty of getting the representative sample. Untrained manpower. Absence of the informants. Chances of committing errors in sampling. 17
- 18. Important Issues • Representation: The extent to which a sample is representative of the population • Generalization: The extent to which the results of a study can be reasonably extended from a sample to the population • Sampling error: The chance occurrence that a randomly selected sample is not representative of the population due to errors inherent in the sampling technique 18
- 20. Quota Sampling Non-Probability Samples Convenience Snow ball Probability Samples Simple Random Systematic Stratified Cluster Purposive 20
- 21. Simple Random Sampling Every individual or item from the frame has an equal chance of being selected Samples obtained from table of random numbers or computer random number generators Random samples are unbiased and, on average, representative of the population 21
- 22. Systematic random Sampling Randomly select one individual from the 1st group Select every k-th individual thereafter Number the houses first. Then a number is taken at random; say 3.Than every 10th number is selected from that point onward like 3, 13, 23, 33 etc. N = 500 n = 3 k = 10 First Group 22
- 23. Stratified Samples Procedure: Divide the population into strata (mutually exclusive classes), such as men and women. Then randomly sample within strata. Especially important when one group is so small (say, 3% of the population) 23
- 25. Cluster sampling Each unit selected is a group of persons (all persons in a city block, a family, etc.) rather than an individual. Used when (a) sampling frame not available or too expensive, and (b) cost of reaching an individual element is too high E.g., there is no list of automobile mechanics in Afghanistan. First define large clusters of people. Fairly similar to other clusters. For example, cities make good clusters. Once you've chosen the cities, might be able to get a reasonably accurate list of all the mechanics in each of those cities. Cluster sampling is less expensive than other methods, but less accurate. 25
- 26. Cluster Sampling • Population divided into several “clusters,” each representative of the population • Simple random sample selected from each • The samples are combined into one 26 Population divided into 4 clusters.
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- 29. Non- Probability Sampling / Non-Random This is where the probability of inclusion in the sample is unknown. Convenience sampling Purposive sampling Quota sampling Snow ball sampling 29
- 30. Convenience sampling Whoever happens to walk by your office; who's on the street when the camera crews come out If you have a choice, don't use this method. Often produces really wrong answers, because certain attributes tend to cluster with certain geographic and temporal variables. For example, at 8am, most of the people on the street are workers heading for their jobs. At 10am, there are many more people who don't work, and the proportion of women maybe much higher. At midnight, there are young people and muggers. 30
- 31. • The process whereby a researcher gathers data from individuals possessing identified characteristics and quotas • Is an improvement on convenience sampling, but still has problems. • The population is first segmented into mutually exclusive sub-groups, just as in stratified sampling. • Then judgment used to select subjects or units from each segment based on a specified proportion. Quota Sampling 31
- 32. Purposive/Judgment Selecting sample on the basis of knowledge of the research problem to allow selection of appropriate persons for inclusion in the sample Expert judgment picks useful cases for study Good for exploratory, qualitative work, and for pre- testing a questionnaire. 32
- 33. Snowball sampling Recruiting people based on recommendation of people you have just interviewed Useful for studying invisible/illegal populations, such as drug addicts 33
- 34. Friend Friend Friend Friend Friend Friend Friend Friend Main person 34 Friend Friend Friend Friend Friend Friend Friend Friend Friend Friend Friend
- 35. Sampling Errors Sampling errors are the representative errors due to selecting a sample of eligible units from the target population instead of including every eligible unit in the survey. Related to the sample size and the variability among the sampling units. Can be statistically evaluated after the survey. 35
- 36. Non-Sampling Errors Non-sampling errors result from problems during data collection and data processing, such as Failure to locate and interview the correct household, Misunderstanding of the questions on the part of either the interviewer or the respondent, and Data entry errors. An inadequate sampling frame (Non- coverage) Non-response from participants Response errors Coding and data entry errors The sampling design should be as simple and straightforward as possible. 36
- 37. Improving Response Rates 37 Prior Notification Motivating Respondents Incentives Questionnaire Design and Administration Follow-Up Other Facilitators Callbacks Methods of Improving Response Rates Reducing Refusals Reducing Not-at-Homes
- 39. Sample Size How many people to pick up for a study The question often asked is: How big a sample is necessary for a good survey? The main objective is to obtain both a desirable accuracy and a desirable confidence level with a minimum cost. 39
- 40. Determination of Sample Size Type of analysis to be employed The level of precision needed Population homogeneity /heterogeneity Available resources Sampling technique used 40
- 41. Sample Size Calculation n: the desired sample size z: the standard normal deviate usually set at 1.96 (which corresponds to the 95% confidence level) p: the proportion in the target population to have a specific characteristic. If no estimate available set at 50% (or 0.50) q:1-p d: absolute precision or accuracy, normally set at 0.05. 41
- 42. Sample Size Calculation… n = (1.96)2 (0.5) (0.5) (0.05) 2 n =384 42
- 43. Thank you 43