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Chapter 12

# GGR271 Chapter 12.docx

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University of Toronto St. George

Geography

GGR271H1

Deborah Leslie

Winter

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Week 4 Reading Notes
Saturday, February 23, 2013
12:39 PM
Chapter 12 - Sampling
Sample: The segment or subset of the population selected for research. The selection
may be based on either probability or non-probability sampling.
Sampling error: difference between sample and population even though a random
process was used
Sampling frame: the listing of every unit from which the sample is being drawn
Non-Sampling Error: difference between population and sample caused by either
deficiencies in sampling approach, non-response, or poorly worded questions
Representative sample: a sample that represents the larger population
Probability sample: a sample selected through a random process so that each unit has
an equal chance of being included
Purposive sample/non-probability sample: A sample not selected using random
sampling method. This implies that some units in the population are more likely to be
selected than others
Problems of sampling in studies involving structured interviews or questionnaires:
Not using random method to pick sample - there is a risk that the selection process
will be affected by human judgment, so that some members of the population are
more likely to be selected than others.
The sampling frame or list of potential subjects is inadequate
Some people in the sample refuse to participate or cannot be contacted
Types of probability sample:
Simple Random Sample
Most basic form of sampling
Each unit of the population has an equal probability of inclusion in the sample
A sample in which each unit selected from the population and each combination
of units has an equal probability of being included.
There is almost no opportunity for bias since the selection process is entirely
random.
Systematic Sample sample is selected directly from the sampling frame
It is important to ensure that there is no inherent ordering or pattern in the
sampling frame, a feature called periodicity.
With systematic sampling, not every possible combination of cases has an equal
chance of being selected.
Stratified Random Sampling
A sample in which units are randomly sampled from a population that has been
previously divided into sub-groups (strata)
It is feasible only when it is relatively easy to identify and allocate units to sub-
groups
It ensures that the sample is distributed in the same way as the population in terms

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