17 june, 2003sampling two-stage cluster sampling (with quota sampling at second stage)
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17 June, 2003 Sampling
TWO-STAGE CLUSTER SAMPLING (WITH QUOTA SAMPLING AT SECOND STAGE)
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STATISTICAL TABLES: Table A Random Digits
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SIMPLE RANDOM SAMPLING
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STRATIFIED RANDOM SAMPLINGGrouped by characteristic
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SYSTEMATIC SAMPLING
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CLUSTER SAMPLING
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TWO STAGE CLUSTER SAMPLING
(WITH RANDOM SAMPLING AT SECOND STAGE)
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FLOWCHART
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TABLE 1
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TABLE 2
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POPULATIONPopulation units
e.g.; children or adults
Population observations, characteristics or attributes
e.g.; immunization history
Time and resources are limited so that only sample units and sample observations can be selected from the population.
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Total Count versus sampling:
National census is conducted every 10-15 years:
Less accurate over time.
Less accurate in dynamic (shifting) populations.
Very expensive.
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Sample surveys allows obtaining more extensive information (smaller number of persons)
Need to train a limited numberof interviewer
More in-depth questions or detailed data
Can quickly provide useful information
Relatively low cost
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"Less is more" Mies Van der Rohe
A sample should be representative to the population of interest.
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Simple Random Sampling:
Need a list of all eligible persons in the populationEvery person has equal chance (equal probability) to be selected in the sampleBasic method, important for comparison with other sampling methodsProvides an unbiased estimate of a variable in a population
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Simple Random Sampling: (continued)
Permits quantitative assessment of sampling error
Rarely used in actual surveys• Difficult• Expensive• Excessive travel time (different location of subjects)• Excessive local introduction and organization
time
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Sampling with replacement:
Individuals from a population of observations may appear more than
once in a sample of population
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Sampling without replacement:
Individuals from a population of observations can appear only once in a sample of population.
This is the usual case.
Number of possible samples = N!/n!(N-n)! (if order is not important):
Equal probability selection Method (EPSEM):
Use of random tables, or computers
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Systematic Sampling:
Similar Procedure:List all persons in the population
Define selection interval:
= (Sampled population)/(Sample size)
= N/n
= An integer for ease of field use
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Systematic Sampling:(continued)
Select a random starting point (first person in the sample)
Next selection = the random start + the random interval
And so on and so forth…
Data should not be ordered in a special way.
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Stratified random sample:
The population is divided into multiple strata based on common characteristics
e.g.;
Residence (Urban or rural) Tribe, ethnicity or race Family income (poor, moderate, or wealthy)
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Stratified random sample:(continued)
A random sample is selected from each stratum
The samples from each stratum are combined for a single estimate of the population mean and variance.
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One-Stage Cluster Sampling:
The population is listed as groups (termed clusters), not individuals e.g.;
• Area of residence (village, town, .. etc.)• School or classroom within a school
All clusters are listed and a sample of clusters is selected.All persons in the selected clusters are examined.The samples from each of the clusters are combined into a single estimate of the population mean and variance.
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Two-Stage Cluster Sampling with Simple Random Sampling at the
Second Stage:
Stage I: A random sample of clusters
Stage II: A sample from selected clusters
The samples from each of the selected clusters are combined into a single
estimate of the population mean and variance.
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Two-stage Cluster Sampling with Quota
Sampling in the Second Stage: The population is divided into multiple clusters.
Stage I: A random sample of clustersStage II: A random start Interviewer continues within
a cluster until the quota (constant number) is filled.
The samples from each cluster are combined into a single estimate of the population mean and variance.
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Two-stage Proportionate to size (PPS) Cluster Sample with Quota Sampling in the
Second Stage:The population is divided into multiple clusters.
Stage I: A random sample of clusters with probability proportionate to their size (PPS)
"Size" means the number of eligible persons residing in the cluster.
Stage II: A random start Interviewer continues within a cluster until the quota (constant number) is filled.
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Two-stage Proportionate to size (PPS) Cluster Sample with Quota Sampling in the
Second Stage: (continued)
The samples from each cluster are combined into a single estimate of the population mean and variance.
This method is favored by Expanded program on Immunization (EPI).
Note: No random selection in the second stage.
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Probability sample versus Non-probability sample:
Every person has equal chance (equal probability) to be selected in the sample.
• No bias• Generalization of the results
On average, the characteristics of people in probability samples are similar to those of the population from which they were selected, particularly if a larger number are chosen.
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Probability sample versus Non-probability sample:
Sampling in clinical trials are usually highly selected and biased samples of all patients with the condition of interest. (Internal validity)
[1] Use of inclusion/ exclusion criteria:Restricts the heterogeneity of patientsExcludes atypical forms of the diseaseImproves chances of patients completing the assigned treatment used in the studyExcludes presence of other diseasesExcludes an unusually poor prognosisExcludes patients with contra-indication for the treatment
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Probability sample versus Non-probability sample (continued)
[2] Refusal of patients to participate in the study: Tend to be systematically
different from those who agree to enter in the trial:
– Socio-economic class– Severity of disease
[3] Patients who are thought to be unreliable (would not follow the groundrules of the trial are usually not enrolled.
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Determine the desired level of precision i.e. amount of error in parameter estimates that can be tolerated by the decision-maker.
Definitions:Precision is the size of deviations from the average value of some parameters of interest obtained by repeated application of sampling procedures.
Accuracy is the size of deviations from the true mean of some parameter in a population.
In surveys, we cannot measure accuracy but can measure precision
PRECISION
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MATCHING Is stratified sampling in which
numbers selected in each stratum are determined by the numbers in that stratum in some other sample.
Main stay in epidemiology.1:1 is the best.Can have up to 5:1.