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sample, population , bias, proportional, cluster, extrapolate, cluster sampling, self selective
sampling, systematic
sampling, simple
random sampling, stratified random sampling, convenience sampling
Interactive Component Under Construction
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Learning Outcomes:
The student will:
Biases Affecting Information Processing
Sampling
population - eligible people for a data collection
investigation
sample - part of a population selected so as to give
information about the population as a whole
biased sample - sample is not representative of the
population from which it is taken because the method used to collect the data contains
unwanted influence(s).
unbiased sample - sample is representative of the
population from which it is taken
Biased Samples |
Unbiased Samples |
| convenience sampling - an easily
accessible group of people is chosen, and everyone in that group is surveyed. For
example, the Pizzatime owners might survey all of the people who go to a nearby grocery
store from 5PM to 6PM on September 6th.
Advantages
Disadvantages
- there is no guarantee that
the behaviors of these people represent behaviors of other groups.
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systematic sampling - every nth member
of the population is sampled. The list being sampled may be ordered (alphabetical,
seniority, street number, etc). Question : Is it equivalent to simple random sampling?
Strictly speaking the answer is No!, unless the list itself is in random order, which it
never is (alphabetical, seniority, street number, etc).
Advantages
- easier to draw, without mistakes (cards in file)
- more precise than simple random sampling as more evenly spread over population
Disadvantages
- if list has periodic arrangement then sample collected may not be an accurate
representation of the entire population
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| self-selective sampling - a population
provides information by volunteering their opinions. Advantages
Disadvantages
- there is no guarantee that
the behaviors of these people represent behaviors of other groups.
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simple random sampling - the sample is chosen randomly
from the population. Here each sample of size n from the population
of size N has an equal chance of selection. In practice "each unit in the
population is numbered 1 to N and n units are randomly drawn from the N''.
Advantages
- simple to apply
- analysis of data is reasonably easy and has a sound mathematical basis
Disadvantages
- if population heterogeneous estimates have large variance
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| cluster sampling - a particular segment of
the population is sampled using existing lists (Constituencies, Wards, Households, ...). Advantages
- reduced field costs
- applicable where no complete list of units is available (special lists only need be
formed for clusters).
Disadvantages
- clusters may not be representative of whole population but may be too alike
- analysis more complicated than for simple random sampling.
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stratified random sampling - the population is divided
into groups (strata) and the data is collected from the strata by simple random sampling.
Advantages
- If data of known precision is wanted for certain subdivisions of the population, then
each subdivision or strata can be
treated as a population.
- Administrative convenience may dictate its use, so that each field office can supervise
one strata.
- Sampling problems may differ markedly within a population (e.g. people in prisons and
people outside).
- Stratification will almost certainly produce a gain in precision in the estimates of the
whole population, because a heterogeneous population is split into fairly homogeneous
strata.
Disadvantages
- problems if strata not clearly defined.
- analysis is (or can be) quite complicated.
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