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Sampling. Population Well-defined set with specified properties –People –Animals –Events –Sport teams –Clinical units –Communities –Schools –Specimens.

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Presentation on theme: "Sampling. Population Well-defined set with specified properties –People –Animals –Events –Sport teams –Clinical units –Communities –Schools –Specimens."— Presentation transcript:

1 Sampling

2 Population Well-defined set with specified properties –People –Animals –Events –Sport teams –Clinical units –Communities –Schools –Specimens –Charts –Historical documents

3 Census Investigation of all individual elements that make up a population Sample

4 Why sample? Generally difficult to study entire population (Cost + Speed) Able to make generalizations to population from appropriately derived sample.

5 Sampling Procedure by which some members of the population are selected as representatives of the entire population

6 Sample Subset of a larger population Population Sample

7 Sampling Frame

8 Who do you want to generalize to? The Theoretical Population What population can you get access to? The Study Population How can you get access to them? The Sampling Frame Who is in your study? The Sample

9 Types of samples Nonprobabilistic –Nonrandom selection –Can not assure every element has an equal chance for being included Probabilistic –Uses some form of random selection –More likely to result in representative sample

10 Probability Sampling 1.Simple random sampling 2.Stratified random sampling 3.Systematic Sampling 4.Cluster sampling

11 Simple Random Sampling –the purest form of probability sampling. –Assures each element in the population has an equal chance of being included in the sample –Random number generators

12 List of Residents Simple Random Sampling

13 List of Residents Random Subsample Simple Random Sampling

14 STATISTICAL TABLES: Table A Random Digits

15 Example: Simple random sampling

16 SIMPLE RANDOM SAMPLING

17 Systematic Random Sampling 1265176 2275277 3285378 4295479 5305580 6315681 7325782 8335883 9345984 10356085 11366186 12376287 13386388 14396489 15406590 16416691 17426792 18436893 19446994 20457095 21467196 22477297 23487398 24497499 255075100 N = 100 want n = 20 N/n = 5 select a random number from 1-5: chose 4

18 Systematic Random Sampling 1265176 2275277 3285378 4295479 5305580 6315681 7325782 8335883 9345984 10356085 11366186 12376287 13386388 14396489 15406590 16416691 17426792 18436893 19446994 20457095 21467196 22477297 23487398 24497499 255075100 N = 100 want n = 20 N/n = 5 select a random number from 1-5: chose 4 start with #4 and take every 5th unit

19 Stratified Random Sampling List of Residents

20 Stratified Random Sampling List of Residents Strata surgicalNon-clinicalmedical

21 Stratified Random Sampling List of Residents Random Subsamples of n/N Strata surgicalNon-clinicalmedical

22 Cluster Sampling –The primary sampling unit is not the individual element, but a large cluster of elements. Either the cluster is randomly selected or the elements within are randomly selected –Why? – Frequently used when no list of population available or because of cost

23 Example: Cluster sampling Section 4 Section 5 Section 3 Section 2Section 1

24 Non-Probability Sampling 1.Convenience 2.Quota 3.Purposive 4.Snowball

25 Convenience Available subjects enter study until sample size reached Inexpensive, quick, easy Large risk of bias Questionable representativeness Examples: –First 30 patients who enter a clinic with arthritis –Parents of children in a shopping mall

26 Purposive sampling Handpick cases Conscious effort to include specific elements in sample May pick subjects with diverse views, specific characteristics Easy, bias present, limited representativeness Used in qualitative research

27 Purposive Sample Examples: Specific populations: –Victims of child abuse –Parents of children with rare illness Diverse views: –Those who support/don’t support a public policy (e.g., abortion)

28 Quota Sampling Include specific number of elements in pre-determined categories –Based on known pop. characteristics Relatively easy Bias present

29 Quota Sampling - Example Quota Sample

30 Snowball Sampling Networking sampling (snowballing) –Ask for referrals from identified case

31 Classification of Sampling Methods Sampling Methods Probability Samples Simple Random Cluster SystematicStratified Non- probability QuotaPurposive ConvenienceSnowball

32 Sample Size Should be determined by researcher before quantitative study is conducted Use the largest sample possible


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