stat masteral report
TRANSCRIPT
8/18/2019 Stat Masteral Report
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Random and Non-RandomSampling Techniques
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Random Sampling
• Random sampling is designed to select
sampling units from the population with known
probabilities.
• This means that the sampling properties of
estimators of population quantities can be
determined, such as whether or not the
estimator is unbiased (i.e., does it on averagegive the right answer?) and what is its precision
(i.e., how do we calculate its variance or its
standard error).
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Random Sampling
Techniques• Simple random sampling , which samples
randoml without replacement from the
sampling frame so that at ever stage
ever sampling unit not alread selected
from the sampling frame is equall likel to
be chosen. This results in all samples of a
given si!e being equall likel to beselected.
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Random Sampling
Techniques
• Systematic sampling is sometimes used
as a simple alternative to simple random
sampling and works at least as well in
situations where the population sampled
from is "randoml ordered" with respect to
the value of a quantit being measured or
recorded, or is ordered in order of si!e ofsuch a quantit. #t does not alwas require
a sampling frame.
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Random Sampling
Techniques• Stratified sampling splits the population
into subgroups or strata, using
stratification factors such as geographical
area or degree of e$perience of the
beekeeper, or beekeepers%bee farmers,
which are &udged to be important in terms
of coverage of the population and whichare likel to be related to the response
variable(s) or interest.
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Random Sampling
Techniques• Cluster sampling is the other main method of
probabilit sampling. #f the population can be
divided into convenient groups of population
elements rather than strata thought to differ inwas relevant to the response(s) of interest, then
randoml selecting a few of the groups and
including everone in those selected groups as
part of the sample will provide a representativesample population from the whole population if
the groups or clusters are representative of the
population.
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Non-Random Sampling
• 'onrandom sampling, is an other kind of
sampling. uch methods are often used for
speed and convenience, and also the do
not require a sampling frame.
• Their big disadvantage is that sampling
error cannot reliabl be quantified, as the
sampling properties of an estimators usedare not known (since the probabilit of
choosing an one individual or sample
cannot be determined).
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Non-Random Sampling
Techniques• Convenience or accessibility
sampling involves asking a sample of
people to respond to a surve.
• *n e$ample is distributing surve
questionnaires at a meeting of a local
beekeeping association or at a
beekeepers+ convention. owever thesepeople ma not be representative of the
whole target population of beekeepers.
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Non-Random Sampling
Techniques• Quota sampling is like stratified sampling
in that stratification factors are identified
which are thought to be relevant to the
surve, but instead of sampling randoml
the participants to come from each
stratum, the surve samplers themselves
choose the people sub&ectivel from eachstratum until sufficient people have been
chosen and have responded.