Sampling Techniques, Sampling Concepts, Sampling tools.

1. SAMPLING Tools / Techniques /
Concepts
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2. IMPORTANT STATISTICAL TERMS
Population:
A set which includes all
measurements of interest to the
researcher (The collection of all
responses, measurements, or
counts that are of interest)
Sample:
A subset of the population
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3. USES OF SAMPLING
Get information about large populations
 Less costs
 Less field time
 More accuracy i.e. Can Do A Better Job of Data Collection
 When it’s impossible to study the whole population
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4. TYPES OF SAMPLING
Convenience samples (ease of access)
sample is selected from elements of a population that are easily
accessible
Snowball sampling (friend of friend….etc.)
Purposive sampling (judgemental)
You chose who you think should be in the study
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5. TYPES OF SAMPLING
Random sampling
 Each subject has a known probability of being selected
 Advantages:
 Can be used with large sample populations
 Avoids bias
 Disadvantages:
 Can lead to poor representation of the overall parent population or area if large areas are not hit by the
random numbers generated. This is made worse if the study area is very large
 There may be practical constraints in terms of time available and access to certain parts of the study area
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7. TYPES OF SAMPLING
Systematic sampling
Samples are chosen in a systematic, or regular way.
 They are evenly/regularly distributed in a spatial context, for example every two metres
along a transect line
 They can be regularly numbered, for example every 10th house or person
Advantages:
 It is more straight-forward than random sampling
 A grid doesn't necessarily have to be used, sampling just has to be at uniform intervals
 A good coverage of the study area can be more easily achieved than using random
sampling
Disadvantages:
 It is more biased, as not all members or points have an equal chance of being selected
 It may therefore lead to over or under representation of a particular pattern
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