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Sampling
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Steps in determining sampling
Identify unit of analysisAt what level the data needs to be gathered
May be multi level or single
Specify population and sample Representativeness
Populationgroup of individuals who have the samecharacteristics
Target populationgroup of individuals with somecommon defining characteristics
Samplesubgroup of the target population
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Samplingstrategies
Probability
sampling
Nonprobability
sampling
Simple
Random
sampling
Convenience
sampling
Multistage
Cluster
sampling
Stratified
sampling
Snowball
sampling
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Simple random sampling
Each element in the population has anequal probability of selection AND each
combination of elements has an equalprobability of selection
Names drawn out of a hat
Random numbers to select elements froman ordered list
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Stratified sampling
Divide population into groups that differ inimportant ways
Basis for grouping must be known beforesampling
Select random sample from within each
group
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Random Cluster Sampling
Done correctly, this is a form of randomsampling
Population is divided into groups, usuallygeographic or organizational
Some of the groups are randomly chosen In pure cluster sampling, whole cluster is
sampled. In simple multistage cluster, there is randomsampling within each randomly chosen cluster
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Random Cluster Sampling
Population is divided into groups Some of the groups are randomly selected
For given sample size, a cluster sample hasmore error than a simple random sample
Cost savings of clustering may permit largersample
Error is smaller if the clusters are similar toeach other
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Cluster sampling has very high error if theclusters are different from each other
Cluster sampling is NOT desirable if theclusters are different It IS random sampling: you randomly
choose the clusters
But you will tend to omit some kinds ofsubjects
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Stratified Cluster Sampling
Reduce the error in cluster sampling bycreating strata of clusters
Sample one cluster from each stratum The cost-savings of clustering with the
error reduction of stratification
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Stratification vs. Clustering
Stratification Divide population into
groups different fromeach other: sexes, races,ages
Sample randomly fromeach group
Less error compared tosimple random
More expensive to obtainstratification informationbefore sampling
Clustering Divide population into
comparable groups:schools, cities
Randomly sample someof the groups
More error compared tosimple random
Reduces costs to sampleonly some areas ororganizations
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Stratified Cluster Sampling
Combines elements of stratification andclustering First you define the clusters
Then you group the clusters into strata of clusters,putting similar clusters together in a stratum
Then you randomly pick one (or more) cluster fromeach of the strata of clusters
Then you sample the subjects within the sampledclusters (either all the subjects, or a simple randomsample of them)
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Multi-stage Probability Samples
Large national probability samples involveseveral stages of stratified cluster sampling
The whole country is divided into geographicclusters, metropolitan and rural
Some large metropolitan areas are selected withcertainty (certainty is a non-zero probability!)
Other areas are formed into strata of areas (e.g.middle-sized cities, rural counties); clusters areselected randomly from these strata
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Within each sampled area, the clusters aredefined, and the process is repeated, perhapsseveral times, until blocks or telephoneexchanges are selected
At the last step, households and individualswithin household are randomly selected
Random samples make multiple call-backs topeople not at home.
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The Problem of Non-Response
You can randomly pick elements from samplingframe and use them to randomly select people
But you cannot make people respond Non-response destroys the generalizeability ofthe sample. You are generalizing to people whoare willing to respond to surveys
If response is 90% or so, not so bad. But if it is50%, this is a serious problem
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Multiple call-backs are essential for trying to reduce non-response bias
Samples without call-backs have high bias: cannot really
be considered random samples Response rates have been falling It is very difficult to get above a 60% response rate You do the best you can, and try to estimate the effect
of the error by getting as much information as possibleabout the predictors of non-response.
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Non-probability Samples
Convenience Purposive
Quota
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Convenience Sample
Subjects selected because it is easy toaccess them.
No reason tied to purposes of research. Students in your class, people on State,
Street, friends
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Purposive Samples
Subjects selected for a good reason tied topurposes of research
Small samples < 30, not large enough for powerof probability sampling. Nature of research requires small sample
Choose subjects with appropriate variability in what
you are studying Hard-to-get populations that cannot be found
through screening general population
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Quota Sampling
Pre-plan number of subjects in specifiedcategories (e.g. 100 men, 100 women)
In uncontrolled quota sampling, the subjects
chosen for those categories are a conveniencesample, selected any way the interviewerchooses
In controlled quota sampling, restrictions are
imposed to limit interviewers choice No call-backs or other features to eliminateconvenience factors in sample selection
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Sample Size
Heterogeneity: need larger sample to studymore diverse population
Desired precision: need larger sample to get
smaller error Sampling design: smaller if stratified, larger if
cluster Nature of analysis: complex multivariate
statistics need larger samples Accuracy of sample depends upon sample size,not ratio of sample to population
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Sampling in Practice
Often a non-random selection of basic samplingframe (city, organization etc.)
Fit between sampling frame and research goals
must be evaluated Sampling frame as a concept is relevant to all
kinds of research (including nonprobability) Nonprobability sampling means you cannot
generalize beyond the sample Probability sampling means you can generalizeto the population defined by the sampling frame