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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.