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Lecture 12

SOC-1101 Lecture Notes - Lecture 12: Stratified Sampling, Systematic Sampling, Cluster Sampling

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Anna Borisenkova

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Research Method - INTERVIEWS
Sample is a small proportion of the overall group. One can usually be confident
that results from a population sample, as long as it was properly chosen, can
be generalized to the total population.
All members of a population may not be available
Less time consuming
Random sampling - an example of random sampling would be picking a name
out of a hat. In random sampling everyone in the population has the same
chance of getting picked. This is easy because it is quick and can even be
performed by a computer. However, because it is down to chance you could
end up with a unrepresentative sample, perhaps with one demographic being
missed out
Systematic sampling - an example of systematic sample would be picking every
10th person on a list or register. This carries the same risk of being
unrepresentative as random sampling, for example, every 10th person could
be a girl.
Stratified sampling - this method attempts to make the sample as
representative as possible, avoiding the problems that could be caused by
using a completely random sample. To do this the sample frame will be divided
into a number of smaller groups, such as social class, age, gender, ethnicity,
etc. individuals are then drawn at random from these groups.
Cluster sampling - this is taking a random sample at various stages of the
sampling process.
Haphazard - get any cases in any matter that is convenient.
Quota - get a present number of cases in each of several predetermined
categories that will reflect the diversity of the population, using haphazard
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