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Chapter 7
Sampling
(Reminder: Don’t forget to utilize the concept maps and study questions as you study this
and the other chapters.)
The purpose of Chapter 7 it to help you to learn about sampling in quantitative and
qualitative research. In other words, you will learn how participants are selected to be
part of empirical research studies.
Sampling
refers to drawing a sample (a subset) from a population (the full set).
•
The usual goal in sampling is to produce a representative sample
(i.e., a sample
that is similar to the population on all characteristics, except that it includes fewer
people because it is a sample rather than the complete population).
•
Metaphorically, a perfect representative sample would be a "mirror image" of the
population from which it was selected (again, except that it would include fewer
people).
Terminology Used in Sampling
Here are some important terms used in sampling:
•
A sample
is a set of elements taken from a larger population.
•
The sample is a subset of the population
which is the full set of elements or
people or whatever you are sampling.
•
A statistic
is a numerical characteristic of a sample, but a parameter
is a numerical
characteristic of population.
•
Sampling error
refers to the difference between the value of a sample statistic,
such as the sample mean, and the true value of the population parameter, such as
the population mean. Note: some error is always present in sampling. With
random sampling methods, the error is random rather than systematic.
•
The response rate
is the percentage of people in the sample selected for the study
who actually participate in the study.
•
A sampling frame
is just a list of all the people that are in the population. Here is
an example of a sampling frame (a list of all the names in my population, and they
are numbered). Note that the following sampling frame also has information on
age and gender included in case you want to draw some samples and do some
calculations.
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The two major types of sampling in quantitative research are random sampling and
nonrandom sampling.
•
The former produces representative samples.
•
The latter does not
produce representative samples.
Simple Random Sampling
The first type of random sampling is called simple random sampling
.
•
It's the most basic type of random sampling.
•
It is an equal probability sampling method (which is abbreviated by EPSEM
).
•
Remember that EPSEM means "everyone in the sampling frame has an equal
chance of being in the final sample."
•
You should understand that using an EPSEM is important because that is what
produces "representative" samples (i.e., samples that represent the populations
from which they were selected)!
You will see below that, simple random samples are not
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 Spring '11
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