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Sampling

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**Population Well-defined set with specified properties People Animals**

Events Sport teams Clinical units Communities Schools Specimens Charts Historical documents

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

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**Why sample? Generally difficult to study entire population**

(Cost + Speed) Able to make generalizations to population from appropriately derived sample.

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

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Sample Subset of a larger population Population Sample

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Sampling Frame

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**Who do you want to generalize to? The Theoretical Population**

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

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**Types of samples Nonprobabilistic Probabilistic 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

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**Probability Sampling Simple random sampling Stratified random sampling**

Systematic Sampling Cluster sampling Need a sampling frame Every element has an equal chance of being in the sample

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**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

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**Simple Random Sampling**

List of Residents

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**Simple Random Sampling**

List of Residents Random Subsample

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**STATISTICAL TABLES: Table A Random Digits**

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**Example: Simple random sampling**

1 Albert D. 2 Richard D. 3 Belle H. 4 Raymond L. 5 Stéphane B. 6 Albert T. 7 Jean William V. 8 André D. 9 Denis C. 10 Anthony Q. 11 James B. 12 Denis G. 13 Amanda L. 14 Jennifer L. 15 Philippe K. 16 Eve F. 17 Priscilla O. 18 Thomas G. 19 Brian F. 20 Hellène H. 21 Isabelle R. 22 Jean T. 23 Samanta D. 24 Berthe L. 25 Monique Q. 26 Régine D. 27 Lucille L. 28 Jérémy W. 29 Gilles D. 30 Renaud S. 31 Pierre K. 32 Mike R. 33 Marie M. 34 Gaétan Z. 35 Fidèle D. 36 Maria P. 37 Anne-Marie G. 38 Michel K. 39 Gaston C. 40 Alain M. 41 Olivier P. 42 Geneviève M. 43 Berthe D. 44 Jean Pierre P. 45 Jacques B. 46 François P. 47 Dominique M. 48 Antoine C.

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**SIMPLE RANDOM SAMPLING**

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**Systematic Random Sampling**

N = 100 want n = 20 N/n = 5 select a random number from 1-5: chose 4

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**Systematic Random Sampling**

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

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**Stratified Random Sampling**

List of Residents

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**Stratified Random Sampling**

List of Residents surgical medical Non-clinical Strata

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**Stratified Random Sampling**

List of Residents surgical medical Non-clinical Strata Random Subsamples of n/N

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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

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**Example: Cluster sampling**

Section 1 Section 2 Section 3 Section 5 Section 4

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**Non-Probability Sampling**

Convenience Quota Purposive Snowball Every element does not have an equal chance of being in the sample

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**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

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**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

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**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)

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

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**Quota Sampling - Example**

Quota Sample

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**Snowball Sampling Networking sampling (snowballing)**

Ask for referrals from identified case

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**Classification of Sampling Methods**

Probability Samples Non- probability Systematic Stratified Convenience Snowball Cluster Simple Random Purposive Quota

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

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Ch. 11 SAMPLING. Sampling Sampling is the process of selecting a sufficient number of elements from the population.

Ch. 11 SAMPLING. Sampling Sampling is the process of selecting a sufficient number of elements from the population.

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