population well-defined set with specified properties –people –animals –events –sport teams...
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Population
• Well-defined set with specified properties– People– Animals– Events– Sport teams– Clinical units
– Communities– Schools– Specimens– Charts– Historical
documents
Census
• Investigation of all individual elements that make up a population
Sample
Why sample?
• Generally difficult to study entire population
(Cost + Speed)• Able to make
generalizations to population from appropriately derived sample.
Sampling
Procedure by which some members of the population are selected as representatives of the entire population
Sample
• Subset of a larger population
Population
Sample
Sampling Frame
Who do you want Who do you want to generalize to?to generalize to?Who do you want Who do you want to generalize to?to generalize to?
The Theoretical The Theoretical PopulationPopulation
The Theoretical The Theoretical PopulationPopulation
What population can What population can you get access to?you get access to?
What population can What population can you get access to?you get access to?
The Study The Study PopulationPopulationThe Study The Study PopulationPopulation
How can you get How can you get access to them?access to them?How can you get How can you get access to them?access to them?
The Sampling The Sampling FrameFrame
The Sampling The Sampling FrameFrame
Who is in your study?Who is in your study?Who is in your study?Who is in your study? The SampleThe SampleThe SampleThe Sample
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
Probability Sampling
1. Simple random sampling
2. Stratified random sampling
3. Systematic Sampling
4. Cluster sampling
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
List of ResidentsList of ResidentsList of ResidentsList of Residents
Simple Random Sampling
List of ResidentsList of ResidentsList of ResidentsList of Residents
Random SubsampleRandom SubsampleRandom SubsampleRandom Subsample
Simple Random Sampling
STATISTICAL TABLES: Table A Random Digits
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.
SIMPLE RANDOM SAMPLING
Systematic Random SamplingSystematic Random Sampling1 26 51 762 27 52 773 28 53 784 29 54 795 30 55 806 31 56 817 32 57 828 33 58 839 34 59 8410 35 60 8511 36 61 8612 37 62 8713 38 63 8814 39 64 8915 40 65 9016 41 66 9117 42 67 9218 43 68 9319 44 69 9420 45 70 9521 46 71 9622 47 72 9723 48 73 9824 49 74 9925 50 75 100
N = 100N = 100N = 100N = 100
want n = 20want n = 20want n = 20want n = 20
N/n = 5N/n = 5N/n = 5N/n = 5
select a random number from 1-5: select a random number from 1-5: chose 4chose 4
select a random number from 1-5: select a random number from 1-5: chose 4chose 4
Systematic Random SamplingSystematic Random Sampling1 26 51 762 27 52 773 28 53 784 29 54 795 30 55 806 31 56 817 32 57 828 33 58 839 34 59 8410 35 60 8511 36 61 8612 37 62 8713 38 63 8814 39 64 8915 40 65 9016 41 66 9117 42 67 9218 43 68 9319 44 69 9420 45 70 9521 46 71 9622 47 72 9723 48 73 9824 49 74 9925 50 75 100
N = 100N = 100N = 100N = 100
want n = 20want n = 20want n = 20want n = 20
N/n = 5N/n = 5N/n = 5N/n = 5
select a random number from 1-5: select a random number from 1-5: chose 4chose 4
select a random number from 1-5: select a random number from 1-5: chose 4chose 4
start with #4 and take every 5th unitstart with #4 and take every 5th unitstart with #4 and take every 5th unitstart with #4 and take every 5th unit
Stratified Random SamplingStratified Random Sampling
List of ResidentsList of ResidentsList of ResidentsList of Residents
Stratified Random SamplingStratified Random Sampling
List of ResidentsList of ResidentsList of ResidentsList of Residents
StrataStrataStrataStrata
surgicalsurgical Non-clinicalNon-clinicalmedicalmedical
Stratified Random SamplingStratified Random Sampling
List of ResidentsList of ResidentsList of ResidentsList of Residents
Random Subsamples of n/NRandom Subsamples of n/NRandom Subsamples of n/NRandom Subsamples of n/N
StrataStrataStrataStrata
surgicalsurgical Non-clinicalNon-clinicalmedicalmedical
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
Example: Cluster sampling
Section 4
Section 5
Section 3
Section 2Section 1
Non-Probability Sampling
1. Convenience
2. Quota
3. Purposive
4. Snowball
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
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
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)
Quota Sampling
• Include specific number of elements in pre-determined categories– Based on known pop. characteristics
• Relatively easy• Bias present
Quota Sampling - Example
1 0 0 fem a les
1 0 00 fe m a les
5 0 m a les
5 0 0 m a les
P op u la tion1 50 0 e lder ly liv in g in a res ide n tia l se tt ing
Quota Sample
Snowball Sampling
• Networking sampling (snowballing)– Ask for referrals
from identified case
Classification of Sampling Methods
SamplingMethods
ProbabilitySamples
SimpleRandom
Cluster
Systematic Stratified
Non-probability
QuotaPurposive
Convenience Snowball
Sample Size
• Should be determined by researcher before quantitative study is conducted
• Use the largest sample possible
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