sampling 1 biostatistics
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SamplingSampling
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PopulationPopulation
All the inhabitants of a given country or area
considered together; the number of inhabitants of a
given country or area The population is all elements (individuals, objective,
or substance) that meet certain criteria for inclusions
in a study (Kerlinger, 1986).
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PopulationPopulation
Target Population
The group from which the study population is selected
Study Population The group selected for investigation
Elements of a population
The subject on which the measurement is collected
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SamplingSampling
Sample
A sample is a subset of the population that is
selected for a particular study, and the members of
a sample are the subjects.
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SamplingSampling
Sampling
The process of selecting a number from all the subjects
is a process of selecting subjects who are representative of thepopulation being studied
Sampling frameList of Participants
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Probability SamplingProbability Sampling
Is a method of sampling that utilizes
some form ofrandom selection. In order
to have a random selection method, youmust set up some process or procedure
that assures that the different units in
your population have equal probabilities
of being chosen.
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Stratified Random SampleStratified Random Sample
A stratified random sample is one obtained
by separating the population elementsinto non-overlapping groups, calledstrata, and then selecting a simplerandom sample from each stratum.
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Systematic Random SamplingSystematic Random Sampling
Number the units in the population from 1 to
N decide on the n (sample size) that you
want or need k = N/n = the interval sizerandomly select an integer between 1 to k
then take every kth unit
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Systematic Random SamplingSystematic Random Sampling
All of this will be much clearer with an
example. Let's assume that we have a
population that only has N=100 people in itand that you want to take a sample of n=20.
To use systematic sampling, the population
must be listed in a random order. The
sampling fraction would be f = 20/100 = 20%
in this case, the interval size, k, is equal to
N/n = 100/20 = 5.
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Systematic Random SamplingSystematic Random Sampling
Now, select a random integer from 1 to 5. In
our example, imagine that you chose 4.Now, to select the sample, start with the 4th
unit in the list and take every k-th unit
(every 5th, because k=5). You would be
sampling units 4, 9, 14, 19, and so on to
100 and you would wind up with 20 units
in your sample.
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Cluster SamplingCluster Sampling
Divide population into clusters (usually along
geographic boundaries)
Randomly sample clusters
Measure all units within sampled clusters
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Cluster SamplingCluster Sampling
is a probability samplein which eachsample unitisa collection, or cluster, of
elements. Thefirsttaskin clustersampling istospecify appropriate clusters.
Elements withina clusterareoftenphysically closetogetherand hencetenttohavesimilar characteristics.
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Non Probability samplingNon Probability sampling
Convenience sampling
Quota Sampling
Purposive sampling
Network Sampling
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Convenience samplingConvenience sampling
is used in exploratory research where the
researcher is interested in getting an inexpensive
approximation of the truth. As the name implies,the sample is selected because they are
convenient. This non-probability method is often
used during preliminary research efforts to get agross estimate of the results, without incurring
the cost or time required to select a random
sample.
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Quota SamplingQuota Sampling
It uses a convenience sampling technique
with added feature - a strategy to ensure the
inclusion of subjects types who are likely tobe underrepresented in the convenience
sample e.g. ethnicity , Hindu religion in
Pakistan
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Quota samplingQuota sampling
is the non-probability equivalent of stratifiedsampling. Like stratified sampling, the
researcher first identifies the stratums and theirproportions as they are represented in thepopulation. Then convenience or judgmentsampling is used to select the required number of
subjects from each stratum. This differs fromstratified sampling, where the stratums are filledby random sampling.
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Purposive /JudgmentPurposive /Judgment
SamplingSampling
is a common non-probability method. Theresearcher selects the sample based on judgment.
This is usually and extension of conveniencesampling. For example, a researcher may decideto draw the entire sample from one"representative" city, even though the population
includes all cities. When using this method, theresearcher must be confident that the chosensample is truly representative of the entirepopulation.
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Network / Snowball SamplingNetwork / Snowball Sampling
is a special non-probability method used when thedesired sample characteristic is rare. It may beextremely difficult or cost prohibitive to locate
respondents in these situations. Snowball samplingrelies on referrals from initial subjects to generateadditional subjects. While this technique candramatically lower search costs, it comes at the
expense of introducing bias because the techniqueitself reduces the likelihood that the sample willrepresent a good cross section from the population.